Big Data Science and Analytics for Smart Sustainable Urbanism: Unprecedented Paradigmatic Shifts and Practical Advancements [1st ed.] 978-3-030-17311-1;978-3-030-17312-8

Table of contents :
Front Matter ....Pages i-xxii
The Evolving Data-Driven Approach to Smart Sustainable Urbanism for Tackling the Conundrums of Sustainability and Urbanization (Simon Elias Bibri)....Pages 1-10
The Leading Smart Sustainable Paradigm of Urbanism and Big Data Computing: A Topical Literature Review (Simon Elias Bibri)....Pages 11-30
The Theoretical and Disciplinary Underpinnings of Data–Driven Smart Sustainable Urbanism: An Interdisciplinary and Transdisciplinary Perspective (Simon Elias Bibri)....Pages 31-68
Sustainable, Smart, and Data-Driven Approaches to Urbanism and their Integrative Aspects: A Qualitative Analysis of Long-Lasting Trends (Simon Elias Bibri)....Pages 69-93
The Underlying Technological, Scientific, and Structural Dimensions of Data-Driven Smart Sustainable Cities and Their Socio-Political Shaping Factors and Issues (Simon Elias Bibri)....Pages 95-129
Smart Sustainable Urbanism: Paradigmatic, Scientific, Scholarly, Epistemic, and Discursive Shifts in Light of Big Data Science and Analytics (Simon Elias Bibri)....Pages 131-181
On the Sustainability and Unsustainability of Smart and Smarter Urbanism and Related Big Data Technology, Analytics, and Application (Simon Elias Bibri)....Pages 183-220
Advancing Sustainable Urbanism Processes: The Key Practical and Analytical Applications of Big Data for Urban Systems and Domains (Simon Elias Bibri)....Pages 221-252
The Unfolding and Soaring Data Deluge for Transforming Smart Sustainable Urbanism: Data-Driven Urban Studies and Analytics (Simon Elias Bibri)....Pages 253-272
Novel Intelligence Functions for Data–driven Smart Sustainable Urbanism: Utilizing Complexity Sciences in Fashioning Powerful Forms of Simulations Models (Simon Elias Bibri)....Pages 273-313
Toward the Integration of the Data-Driven City, the Eco-city and the Compact City: Constructing a Future Vision of the Smart Sustainable City (Simon Elias Bibri)....Pages 315-337

Citation preview

Advances in Science, Technology & Innovation IEREK Interdisciplinary Series for Sustainable Development

Simon Elias Bibri

Big Data Science and Analytics for Smart Sustainable Urbanism Unprecedented Paradigmatic Shifts and Practical Advancements

Advances in Science, Technology & Innovation IEREK Interdisciplinary Series for Sustainable Development Editorial Board Members Anna Laura Pisello, Department of Engineering, University of Perugia, Italy Dean Hawkes, Cardiff University, UK Hocine Bougdah, University for the Creative Arts, Farnham, UK Federica Rosso, Sapienza University of Rome, Rome, Italy Hassan Abdalla, University of East London, London, UK Sofia-Natalia Boemi, Aristotle University of Thessaloniki, Greece Nabil Mohareb, Beirut Arab University, Beirut, Lebanon Saleh Mesbah Elkaffas, Arab Academy for Science, Technology, Egypt Emmanuel Bozonnet, University of la Rochelle, La Rochelle, France Gloria Pignatta, University of Perugia, Italy Yasser Mahgoub, Qatar University, Qatar Luciano De Bonis, University of Molise, Italy Stella Kostopoulou, Regional and Tourism Development, University of Thessaloniki, Thessaloniki, Greece Biswajeet Pradhan, Faculty of Engineering and IT, University of Technology Sydney, Sydney, Australia Md. Abdul Mannan, Universiti Malaysia Sarawak, Malaysia Chaham Alalouch, Sultan Qaboos University, Muscat, Oman Iman O. Gawad, Helwan University, Egypt Series Editor Mourad Amer, International Experts for Research Enrichment and Knowledge Exchange (IEREK), Cairo, Egypt

Advances in Science, Technology & Innovation (ASTI) is a series of peer-reviewed books based on the best studies on emerging research that redefines existing disciplinary boundaries in science, technology and innovation (STI) in order to develop integrated concepts for sustainable development. The series is mainly based on the best research papers from various IEREK and other international conferences, and is intended to promote the creation and development of viable solutions for a sustainable future and a positive societal transformation with the help of integrated and innovative science-based approaches. Offering interdisciplinary coverage, the series presents innovative approaches and highlights how they can best support both the economic and sustainable development for the welfare of all societies. In particular, the series includes conceptual and empirical contributions from different interrelated fields of science, technology and innovation that focus on providing practical solutions to ensure food, water and energy security. It also presents new case studies offering concrete examples of how to resolve sustainable urbanization and environmental issues. The series is addressed to professionals in research and teaching, consultancies and industry, and government and international organizations. Published in collaboration with IEREK, the ASTI series will acquaint readers with essential new studies in STI for sustainable development.

More information about this series at http://www.springer.com/series/15883

Simon Elias Bibri

Big Data Science and Analytics for Smart Sustainable Urbanism Unprecedented Paradigmatic Shifts and Practical Advancements

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Simon Elias Bibri Department of Computer Science and Department of Urban Planning and Design Norwegian University of Science and Technology (NTNU) Trondheim, Norway

ISSN 2522-8714 ISSN 2522-8722 (electronic) Advances in Science, Technology & Innovation IEREK Interdisciplinary Series for Sustainable Development ISBN 978-3-030-17311-1 ISBN 978-3-030-17312-8 (eBook) https://doi.org/10.1007/978-3-030-17312-8 © Springer Nature Switzerland AG 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

To Fatima Zahrae Gouttaya for her generosity and good nature, knowing all my story and living it with me, making the good times unforgettable, and becoming my family. What sheer bliss to have someone to lean on and to share daily experiences, intellectual passions, and life aspirations with. I admire her for her integrity, moral fiber, intellectual curiosity, native wit, and sense of humor, as well as for her distinct combination of optimism, passion, perseverance, and determination as to pursuing long–term goals in life.

Preface

Key Aims and Themes This timely and multifaceted book is concerned with the complex interplay of the scientific, technological, and social dimensions of the city, and what it entails in terms of the ensuing systemic outcomes pertaining to sustainability as informed and underpinned by data-driven smart urbanism. In concrete terms, it explores the interdisciplinary and transdisciplinary field of smart sustainable urbanism and the unprecedented paradigmatic shifts and practical advances it is undergoing in light of big data science and analytics and the underlying advanced technologies. The scholarly, practical, and futuristic strands of this rapidly burgeoning field are currently at the center of debate due to the emerging paradigmatic shift in science development and epistemic shift in knowledge production brought about by big data science and analytics, coupled with their salience to the fundamental change in the way the city is operated, managed, planned, designed, developed, and governed. In this respect, big data science and analytics as a new area of science and technology is seen as a major factor that determines the way the city will tackle the kind of special conundrums, wicked problems, intractable issues, and complex challenges it embodies through a multitudinous array of alternative solutions in the form of novel applications and sophisticated methods informed by advanced scientific and scholarly research. This consequently determines how the city will evolve in the future under the multiple processes of, and pathways towards achieving, smart sustainable urban development. In a nutshell, developments in science and technology fundamentally alter the way people live, with profound effects on all spheres of society in terms of advancements and innovations. This book aims to help view the challenges of sustainability and urbanization as well as the alternative approaches to tackling them from the perspective of big data science and analytics through the lens of smart sustainable cities as a leading paradigm of urbanism and a manifestation of social evolution. It also intends to facilitate the understanding of the fundamental principles of big data computing with respect to the automated extraction of useful knowledge from large masses of data for enhanced decision-making and deep insights pertaining to urban operational functioning, management, planning, design, and development for the primary purpose of addressing those challenges. Indeed, this book is about data-driven smart sustainable urbanism in the sense of exploiting, harnessing, and leveraging the unfolding and soaring deluge of urban data through advanced analytics to discover new knowledge in the form of applied intelligence intended for enhancing and optimizing urban operations, functions, services, designs, strategies, and policies across multiple urban domains in line with the goals of sustainable development in a rapidly urbanizing world. This book involves innovative, up-to-date big data science and analytics research related to smart sustainable urbanism, that is, theoretical, technological, and interdisciplinary and transdisciplinary studies that make up the field of data-driven smart sustainable urbanism in terms of practice. Accordingly, it provides theoretical and applied contributions fostering a better understanding of this approach to urbanism and the synergistic relationship between the related practices. With respect to the latter, at the core of smart sustainable urbanism is the vii

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synergy between urban operational functioning, planning, design, and development in terms of their interaction or cooperation to produce a combined effect greater than the sum of their separate effects. This entails using big data computing and the underpinning technologies as an enabler for such synergy and a determinant of its outcomes. Further, this book offers contributions pertaining to the ongoing development of urban intelligence functions and related simulation models and optimization and prediction methods as innovative solutions for how smart sustainable cities can be monitored, understood, and analyzed so as to be effectively operated, managed, planned, designed, developed, and governed in line with the goals of sustainable development. Applying urban intelligence functions as new conceptions of the way such cities function and utilize complexity science, data science, and urban science in fashioning new powerful forms of urban simulation models and optimization and prediction methods that generate urban forms and structures that improve sustainability, efficiency, resilience, and the quality of life is crucial to dealing with such cities as complex systems and dynamically changing environments. In short, it provides in-depth coverage of the latest advances in the field of smart sustainable urbanism in the wake of the big data revolution. To facilitate embarking on exploring the field of smart sustainable urbanism and the unprecedented shifts and advances it is undergoing, I have designed this book around three related aims: to help readers gain essential underpinning knowledge about the topic of smart sustainable urbanism, especially in terms of its scientific, scholarly, and practical dimensions; to enable them develop a broader understanding of this flourishing field as they make connections between their own understandings of the current urban challenges and the ongoing urban transformations, on the one hand, and the emerging shifts instigated by big data science and analytics, on the other hand; and, more importantly, to encourage them to take part in the ongoing debate about smart sustainable urbanism in the big data era and the ensuing datafication of the city. The data avalanche is here.

Uniqueness and Subject Treatment This book is the first of its kind with respect to the topicality of the issues it addresses, the contemporaneous phenomena it is concerned with, and the unprecedented shifts and advances it covers in the context of smart sustainable urbanism in the era of big data science and analytics. This unique amalgam is indeed deemed relevant and salient in light of the changes taking place in the urban world. We are currently in the midst of a new wave of enthusiasm for scientific urbanism of a historically unparalleled kind inspired by the big data revolution and carrying wide-ranging implications for the practice of smart sustainable urban planning, design, and development. This is manifested in us experiencing the accelerated datafication of the city in a rapidly urbanizing world and witnessing the dawn of the big data era not out of the window, but in everyday life. Our urban everydayness is entangled with data sensing, data processing, and communication networking, and our wired world generates and analyzes overwhelming and incredible amounts of data. This allows for, over sufficiently long periods of time, extracting changes to the structure and form of the city and the way people behave in the form of useful knowledge and valuable insights associated with applied intelligence. The modern city is turning into constellations of instruments and computers across many scales and morphing into a haze of software instructions, which are becoming essential to the operational functioning of the city. The datafication of spatiotemporal citywide events has become a salient factor for smart sustainable urban planning, design, and development. This book is also unique in regard to the approach to studying the field of smart sustainable urbanism—based on a uniquely holistic perspective. Accordingly, it approaches the topic of smart sustainable urbanism from an interdisciplinary and transdisciplinary perspective while adopting a compelling approach to cross-disciplinary integration and fusion involving diverse scientific and academic fields, notably data science, urban science, urban informatics,

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complexity science, environmental engineering, sustainability science, and systems science, as well as urban planning and design, sustainable development, philosophy, and the social sciences. This is meant to achieve a broader and more inclusive understanding of the phenomenon of smart sustainable urbanism by facilitating collaboration among and between an array of disciplines for the primary purpose of generating the kind of interactional and unifiable knowledge necessary for such understanding. This is a core contribution that supports the foundational ethos of interdisciplinarity and transdisciplinarity characterizing the research field of smart sustainable urbanism. Interdisciplinarity and transdisciplinarity have become a widespread mantra for research within diverse fields, accompanied by a growing body of scientific and scholarly publications. On the whole, this book offers a novel, fresh, all-encompassing approach to the exploration of smart sustainable urbanism as a holistic and integrated paradigm of urban planning, design, and development. In doing so, it combines scientific, academic, and practical relevance with philosophical, social, ethical, and environmental analyzes, supported with critical and reflective thinking.

Originality and Value Up till now, no multifaceted book has, to the best of one’s knowledge, been produced elsewhere—as to exploring smart sustainable urbanism and examining the historically unprecedented shifts and advances this blossoming field is undergoing as a result of the uptake and diffusion of big data science and analytics and underpinning technologies. Nor has any book approached the topic from the perspective of integrating and fusing these scientific fields: data science, urban science, complexity science, systems science, sustainability science, and environmental engineering—with a result that both yields new ideas by thinking across disciplinary boundaries as well as exceeds the simple sum of each discipline. This can be accomplished by combining different analyzes, using insights and methods in parallel and conjunction, and spilling over and blurring boundaries. Indeed, there is a growing need to fill the shortage urban research is facing nowadays in key scientific respects, and to advance urban sustainability science as to tackling the dilemma of the wicked problems associated with urbanism in terms of planning, design, and development. In particular, urban sustainability science requires a decisive, radical change in the way science is undertaken and developed. Such change is, in fact, what data-intensive science is about. Moreover, beyond the need for a stronger interdisciplinary and interdisciplinary lens, urban research needs to be adequately directed to real-world problem applications pertaining mainly to sustainability and urbanization. Urban research is still segmented by disciplinary boundaries when urban transformations demand truly holistic urban research, and whereas solutions to global problems require integrated (cross-disciplinary) knowledge. This seminal work provides the necessary material to inform the research communities concerned with the unprecedented shifts and advances that smart sustainable urbanism is going through and with the state-of-the-art research and the latest development in this area in light of big data science and analytics. It also provides a valuable reference for scholars and practitioners who are seeking to contribute to, or already working towards, the development and implementation of smart sustainable cities as a leading paradigm of urbanism based on big data computing and the underpinning technologies. In this respect, the upshot of this book enables researchers to focus their work on the extreme fragmentation and weak connection between sustainable cities and smart cities as landscapes and approaches, respectively, while embracing the emerging shifts pertaining to smart sustainable urbanism to mitigate or overcome such issues by realizing smart sustainable/sustainable smart cities. Practitioners can use the outcome of this book to identify common weaknesses, flaws, and drawbacks in smart sustainable urbanism projects and initiatives and then deal with them through devising alternative solutions on the basis of what big data computing and the underpinning

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technologies have to offer as novel applications and sophisticated approaches. These pertain to new ways of optimizing and enhancing urban operational functioning, planning, design, and development in response to the challenges, or in line with the goals, of sustainable development. While this book can best be seen as being aimed at those with a background in both urban science and sustainable urbanism, it is primarily from an urban science angle. That is to say, it would be more appropriate for giving urban scientists a vantage on sustainable urbanism than giving sustainable urbanists a vantage on urban science. Nonetheless, it contains value-laden knowledge and technology of high relevance to sustainable urbanists.

Intended Readership Big Data Science and Analytics for Smart Sustainable Urbanism is intended for several classes of readers, including students, researchers, academics, data scientists, urban scientists, urban informaticians, philosophers of science, social scientists, futurists, technologists, ICT experts, urbanists, planners, engineers, architectural designers, built and natural environment specialists, and policy analysts and makers, whether they are new to or already involved in smart sustainable urbanism as a field for research and practice. It is also intended for all of those interested in an overview covering an extensive range of topics pertaining to the role of big data science and analytics in catalyzing the emerging shifts and advances that are of an unprecedented kind as related to both this field as well as the other fields or disciplines concerned with data-intensive science. Specifically, I have written this book with two kinds of readers in mind. I am writing to students taking graduate and postgraduate courses or pursuing Master’s and PhD programs in the areas of sustainable cities, smart cities, smart sustainable cities, urban planning and design, sustainable urban development, environmental engineering, urban informatics, urban science, sustainability science, and so forth. Those readers already familiar with sustainable cities and smart cities as leading paradigms of urbanism and their relationship as both landscapes and approaches in the context of sustainability and with the growing role of big data computing and the underpinning technologies in improving and advancing their contribution to the goals of sustainable development will certainly get much more out of this book and find much more that appeals to them in it than those lacking that grounding. Nevertheless, those readers with limited or without knowledge in this particular area are provided and supported with a detailed explanation and discussion of the relevant conceptual, theoretical, disciplinary, discursive, and practical foundations with reference to the integrated field of smart sustainable urbanism and the underlying scientific and technological components. This is meant to appease the uninitiated readers. Second, I believe that this book will be a very useful resource for all of those involved or with interest in smart sustainable urbanism (including scholars, scientists, practitioners, intellectuals, technology forecasters, decision makers, etc.) that are looking for an accessible and essential reference with respect to the interplay between big data science and analytics as a new area of science and technology and smart sustainable urbanism as a rapidly emerging field. Overall, people in many scientific and academic disciplines and professional fields will find the unique coverage of the scientific and epistemic shifts and scholarly and practical advances related to this flourishing field (as well as other fields and disciplines) as brought about by the materialization of big data science and analytics and the increasing adoption and use of the underlying core enabling technologies to be of great value and usefulness. My hope is that this book will also be of interest to people of other countries than ecologically and technologically advanced nations.

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Perspectives and Prospects This book benefits indirectly from the work of many people working within the field of smart urbanism, sustainable urbanism, smart sustainable urbanism, sustainable smart urbanism, or at the intersection of urban science and urban sustainability, and focusing on the transformational effects of big data science and analytics on urbanism. Thus, I am indebted to other scientific and scholarly writings in the sense of inspiring me into a quest for the great opportunities enabled by endeavoring to explore the emerging field of smart sustainable/sustainable smart urbanism along with the emerging shifts it is undergoing because of big data science and analytics. This has led me to espouse an intellectually distinctive approach into writing this book so that it can offer a tremendous value with auspicious effects in the field and be differentiated from other books on the topic of smart sustainable/sustainable smart urbanism, if any, with regard to their focus and scope of scholarship, as well as to their approach to exploration. While this book has an ambitious goal, clearly it is not possible to deal with every aspect of and shift in such urbanism in a single book, nor can it cover all of the chosen topics in equal depth. Nevertheless, it will be a great asset to the relevant scientific and scholarly communities, as well as to those who are simply interested in urban transformations enabled by technological innovations. This book highlights the increasing urgency to merge big data computing and the underpinning technologies as recent discoveries and innovations as part of urban science with urban sustainability and sustainability science in the research and applied domain of smart sustainable urbanism. The strength of this integrated approach lies in using the most advanced strategies and methods for decoupling the overall wellbeing and health of the city and the quality of life of citizens from the energy use and concomitant environmental risks associated with urban operations, functions, services, designs, and policies. Indeed, the current and future investments in big data computing and the underpinning technologies ought to be justified by environmental concerns and socioeconomic needs, enabling livable and healthy human environments in conjunction with minimal demand on resources and minimal environmental impacts—rather than by sheer technical advancement and unjustified industrial competitiveness. What is mostly needed nowadays are urbanism approaches and innovations that are not driven by distant and overblown ICT of pervasive computing research agendas focused mainly on technological superiority motivated by short-term profits, narrow outlooks, and unsustained disruptive effects—but rather by the pursuit of the persistent delivery of robust solutions for improving and advancing urban sustainability and stimulating research opportunities in this direction. Especially, big data science and analytics is a means for science and society to control uncertainty and to make discoveries in relation to sustainability, among others. As long as there is uncertainty and intractability in the world, there is a need for big data science and analytics, In addition, this book expects to elicit novel insights and spark new perspectives as a result of integrating and harnessing the emerging shifts associated with smart sustainable urbanism. The primary intent is to bring scholars and practitioners closer together from different disciplines and professional fields, or who are working on cross connections of data science, urban science, sustainability science, and urbanism, to develop, concretize, and disseminate new ideas for advancing the field of smart sustainable urbanism as well as promoting related projects, programs, and initiatives based on big data computing and the underpinning technologies. Furthermore, I consider that this book represents a basis for further discussions to debate the point that big data science and analytics and the underlying core enabling technologies have disruptive, substantive, and synergetic effects, particularly on forms of urban planning, organization, design, and development that are required for future forms of smart sustainable urbanism. In the meantime, this book seeks to encourage in-depth research, thorough qualitative analyzes, and empirical investigations focused on establishing, substantiating, or

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challenging the assumptions and claims made by the advocates of big data science and analytics with regard to advancing sustainability. Finally, I believe that I have achieved an important goal with this book—by creating a valuable, strategic resource for the scientific and scholarly communities and the industries involved in the field of smart sustainable urbanism. Especially, there is an urgent need for a multifaceted book on smart sustainable urbanism given that the field is remarkably heterogeneous with a large number and wide variety of unaddressed and unsettled questions and unexplored and promising opportunities. I will be pleased if this book contributes to a better understanding of the topic under investigation, and, more importantly, stimulates the development and implementation of smart sustainable cities on the basis of big data computing and the underpinning technologies and thereby mitigates or overcomes the extreme fragmentation of and the weak connection between sustainable cities and smart cities as landscapes and approaches, respectively. All in all, I hope that this book will be enlightening, thought-provoking, and making good reading for the target audience. And ultimately, the first edition will be well received. Trondheim, Norway October 2018

Simon Elias Bibri

Acknowledgements

This second scholarly book as an integral part of my ongoing Ph.D. research is the fruit of rich learning experiences and enduring intellectual pursuits as part of my academic journey in Sweden and Norway. I am more indebted than I can possibly acknowledge to the people that have contributed directly, indirectly, or unknowingly to my intellectual development and knowledge enrichment. Also, I am greatly thankful to those who have supported me throughout my academic journey, encouraged me to pursue the path of research, believed in my intellectual abilities, and inspired me to become an academic author. I wish next to offer my most heartfelt thanks to those who have contributed to this book. I am deeply grateful to my main supervisor, Professor and Head of the Department of Computer Science John Krogstie, for giving me the opportunity to carry out my Ph.D. research in the area of data-driven smart sustainable cities of the future, allowing me to choose the topic that I am truly passionate about, and providing me with support through various means. I also owe a great deal of gratitude to the administrative and technical staff at the Department of Computer Science for their constant support and immense help. They have contributed tangibly to providing a working environment conducive to knowledge production and intellectual advancement. The atmosphere or vibe at NTNU, coupled with flexible working hours, has been instrumental in boosting my academic productivity and performance. Finally, and most importantly, I would like to express my deepest gratitude and appreciation to my dearest sister Amina for her wholehearted love and immeasurable moral support. I owe my life to her for the sacrifice she has made for me and for her willingness and determination to sacrifice a lot more so that I can continue to thrive in my academic endeavors and to nourish my passion for the pursuit of knowledge. Thank you for always being there beside me through thick and thin. You have long been a constantly restorative counterbalance to my life. You have made this piece of work possible and this intellectual journey delightful in more ways than one. Nothing compares to the bliss of having a sister like you.

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Contents

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The Evolving Data-Driven Approach to Smart Sustainable Urbanism for Tackling the Conundrums of Sustainability and Urbanization . . . . 1 Introduction and Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 The Aim of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 The Structure and Content of the Book . . . . . . . . . . . . . . . . . . . . . . 4 The Organization and Design Purposes of the Book . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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The Leading Smart Sustainable Paradigm of Urbanism and Big Data Computing: A Topical Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Foundational Components and Assumptions . . . . . . . . . . . . . . . . . . . . . . 2.1 Smart Sustainable Cities: Characterization, Leading Position, and Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Big Data Computing in the Ambit of Smart Sustainable Urbanism . 3 On the Research and Its Status of Big Data Analytics and Smart Sustainable Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 A State-of-the-Art Review of Smart Sustainable Cities and Related Big Data Analytics and Its Application . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Smart Sustainable Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Big Data Analytics and Its Application . . . . . . . . . . . . . . . . . . . . . 5 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Theoretical and Disciplinary Underpinnings of Data–Driven Smart Sustainable Urbanism: An Interdisciplinary and Transdisciplinary Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Concepts, Theories, and Academic Discourses . . . . . . . . . . . . . . . . . . . 2.1 Big Data Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Big Data Concept, Analytics, Technology, and Application . . . . . 2.3 Urban Sustainability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Sustainable Urban Development . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Urbanism and Sustainable Urbanism . . . . . . . . . . . . . . . . . . . . . 2.6 Ecological Urbanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Strategic Smart Sustainable Urbanism . . . . . . . . . . . . . . . . . . . . . 2.8 Smart Sustainable/Sustainable Smart Cities: A Leading Paradigm of Urbanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Academic and Scientific Disciplines . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Urban Planning and Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Computer Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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3.3 Data Science . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Urban Informatics . . . . . . . . . . . . . . . . . . . . . . 3.5 Urban Science . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Systems Thinking . . . . . . . . . . . . . . . . . . . . . . 3.7 Complexity Science and Complex Systems . . . . 3.8 Systems Science and Theory . . . . . . . . . . . . . . 3.9 Sustainability Science . . . . . . . . . . . . . . . . . . . 3.10 Scientifically Oriented Sustainable Development 4 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

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Sustainable, Smart, and Data-Driven Approaches to Urbanism and their Integrative Aspects: A Qualitative Analysis of Long-Lasting Trends . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Conceptual Definition and Analytical Approach . . . . . . . . . . . . . . . . . . . 3 On Futures Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Backcasting Approaches to Future Studies and Urban Sustainability . . . . 5 Key Prevailing and Emerging Trends and Relevant Expected Developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Sustainable Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Smarter Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Sustainable Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Smart Sustainable Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 Big Data Computing/Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Data-Intensive Scientific Development and Smart Sustainable Urbanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.8 The Key External Forces Affecting the Combination of the Trends: The Role of Political Action in Smart Sustainable/Sustainable Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Underlying Technological, Scientific, and Structural Dimensions of Data-Driven Smart Sustainable Cities and Their Socio-Political Shaping Factors and Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Conceptual Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Data-Driven Smart Sustainable Cities . . . . . . . . . . . . . . . . . . . . . . 2.2 Datafication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Big Data Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 A Survey of Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 What Lies at the Heart of the Data-Driven Smart Sustainable City . . . . . . 4.1 On the Evolving Integration of Data-Driven Smart Cities and Sustainable Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Digital Instrumentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Big Data Ecosystem and Its Components . . . . . . . . . . . . . . . . . . . 4.4 Cloud Computing for Big Data Analytics . . . . . . . . . . . . . . . . . . . 4.5 Urban Operating Centers and Strategic Planning and Policy Offices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Living Laboratories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7 Innovations Laboratories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8 Urban Intelligence Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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4.9 Public, Private, and Open Data and Their Analysis . . . . . . . . . . 4.10 Data-Driven Urbanism, Urban Science, and Data-Intensive Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Key Practical and Analytical Applications of Big Data Technology for Urban Systems and Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 A Novel Architecture and Typology of Data-Driven Smart Sustainable Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Specialized Constituents for Making up a Whole . . . . . . . . . . . 6.2 Typological Dimensions and Functions . . . . . . . . . . . . . . . . . . . 7 Socio-Political Shaping Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Recasting Urban Science and Big Data Computing Technology . . . . . . 9 Challenges and Concerns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

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Smart Sustainable Urbanism: Paradigmatic, Scientific, Scholarly, Epistemic, and Discursive Shifts in Light of Big Data Science and Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Conceptual and Theoretical Background . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Science and Philosophy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 The Scientific Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Hypothesis and Hypothesis Testing . . . . . . . . . . . . . . . . . . . . . . . 2.4 Scientific Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Scientific Theories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Scientific Laws . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Theoretical Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8 The Philosophy of Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.9 Paradigm and Paradigm Shift . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.10 Discourse: Concepts and Theories . . . . . . . . . . . . . . . . . . . . . . . . 2.11 Epistemology, Episteme, Historical a Priori, and Their Interrelationships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Michel Foucault and Thomas Kuhn’s Contribution to the Philosophy of Scientific Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Scientific, Paradigmatic, and Scholarly Shifts . . . . . . . . . . . . . . . . . . . . . 4.1 On the Old and New Way of Doing Science . . . . . . . . . . . . . . . . 4.2 Data-Intensive Science as a Paradigmatic/Epistemological Shift and Its Underpinnings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 The Data–Intensive Scientific Approach to Urban Sustainability Science and Related Wicked Problems . . . . . . . . . . . . . . . . . . . . . 4.4 Building the New Urban Science and Establishing the Related Research Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Urban Knowledge Discovery/Data Mining and Big Data Studies and Related Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Discursive, Epistemic, Historical a Priori, Institutional, Non-paradigmatic, Preparadigmatic, and Postparadigmatic Dimensions . . . . . . . . . . . . . . . . . 5.1 Discursive Dimensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Historical a Priori, Epistemic, and Institutional Dimensions . . . . . . 5.3 Non-paradigmatic Aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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5.4 Preparadigmatic and Postparadigmatic Aspects . . . . 5.5 Paradigm and Paradigm Shift in the Social Sciences 6 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

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On the Sustainability and Unsustainability of Smart and Smarter Urbanism and Related Big Data Technology, Analytics, and Application . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Methodical–Topical Literature Review Methodology . . . . . . . . . . . . . . . . 2.1 Interdisciplinary and Transdisciplinary Approach . . . . . . . . . . . . . 2.2 Hierarchical Search Strategy and Scholarly Sources . . . . . . . . . . . . 2.3 Selection Criteria: Inclusion and Exclusion . . . . . . . . . . . . . . . . . . 2.4 Combining Three Organizational Approaches . . . . . . . . . . . . . . . . 2.5 Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Conceptual, Theoretical, and Discursive Foundations and Assumptions . . 3.1 Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Smarter Cities and Other Single and Hybrid Faces . . . . . . . . . . . . 4 A Detailed Survey of Relevant Work: Issues, Debates, Gaps, Challenges, Opportunities, and Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Smart and Smarter Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Big Data Analytics and Its Application in Smart and Smarter Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 The Main Scientific and Intellectual Challenges and Common Open Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Advancing Sustainable Urbanism Processes: The Key Practical and Analytical Applications of Big Data for Urban Systems and Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Conceptual and Theoretical Background . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Urban Planning and Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Sustainable Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Sustainable Urban Forms: Compact City and Eco-city Models . . . . 3 Sustainable Cities—Compact City and Eco-city Models of Sustainable Urban Form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 The Key Benefits of Sustainable Cities . . . . . . . . . . . . . . . . . . . . . 3.2 Design Concepts and Typologies of Compact Cities and Eco-cities: Characteristic Features and Sustainability Effects . . . . . . . . . . . . . . 3.3 The Built Environment and Sustainable Urbanism: Issues and Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Limitations, Inadequacies, Fallacies, Uncertainties, Challenges, and Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Toward Smartening up Sustainable Cities: Driving Factors and Conceptual and Analytical Frameworks . . . . . . . . . . . . . . . . . . . . . . 5 The Key Practical and Analytical Applications of Big Data Technology for the Multiple Systems and Domains of Smart Sustainable Cities . . . . . 6 Discussion of Relevant Policy and Technology Issues . . . . . . . . . . . . . . . 7 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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The Unfolding and Soaring Data Deluge for Transforming Smart Sustainable Urbanism: Data-Driven Urban Studies and Analytics . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Data Mining as a Concept and Process . . . . . . . . . . . . . . . . . . . . . . . . . . 3 A State-of-the-Art Review: ‘Small Data’ and ‘Big Data’ Studies and City Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Supervised Versus Unsupervised Methods and Their Application: Predictive and Descriptive Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . 5 From Urban Sustainability Problems to Data Mining Tasks . . . . . . . . . . . 6 A Data Mining Framework for Urban Analytics: Data-Analytic Solutions to Urban Sustainability Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Understanding and Specifying Urban Sustainability Problems . . . . 6.2 Understanding Urban Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Preparing and Combining Urban Data from Diverse Sources . . . . . 6.4 Building Models and Generating Patterns as True Regularities . . . . 6.5 Evaluating and Interpreting the Obtained Results . . . . . . . . . . . . . 6.6 Deploying the Results for Urban Operations, Functions, Services, Strategies, and Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 On the Emerging Applications of Data Mining for Urban (Sustainability) Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 The Unfolding and Soaring Deluge of Urban Data for Big Data Studies . 9 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10 Novel Intelligence Functions for Data–driven Smart Sustainable Urbanism: Utilizing Complexity Sciences in Fashioning Powerful Forms of Simulations Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Theoretical Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Smart Sustainable Urbanism: Planning, Design, and Development . 2.2 Complexity Science and Complex Systems . . . . . . . . . . . . . . . . . . 2.3 Modeling and Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 A Survey of Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 The Data-Driven Components of Smart Sustainable Urbanism and Related Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Decision Support System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Decision-Making Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Big Data Analytics for Enhancing Decision-Making . . . . . . . . . . . 4.4 The Process of Knowledge Discovery/Data Mining: Advanced Decision Support as Urban Intelligence . . . . . . . . . . . . . . . . . . . . 4.5 Expected Advancements and Opportunities . . . . . . . . . . . . . . . . . . 5 Urban Planning and Design and Related Issues: New Approaches and Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 New Simulation Models and Prediction Methods for Urban Planning and Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 The Dilemma of Wicked Problems and the Potential of Big Data Analytics for Tackling It . . . . . . . . . . . . . . . . . . . . . . 6 New Urban Intelligence Functions and Related Simulation Models and Optimization and Prediction Methods . . . . . . . . . . . . . . . . . . . . . . . 7 Advanced Urban Simulation Models and Related Methods . . . . . . . . . . . 8 Smart Sustainable/Sustainable Smart Cities as Complex Systems . . . . . . .

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Complexity Aspects and Complexity Science Relevance and Usefulness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Some Essential Tensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Complex Systems Simulation Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 Challenges and Driving Forces . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 New Opportunities and Future Prospects . . . . . . . . . . . . . . . . . . . 9.3 Toward Novel Urban Simulation Models: Incorporating Dynamical Properties of Complex Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Toward the Integration of the Data-Driven City, the Eco-city and the Compact City: Constructing a Future Vision of the Smart Sustainable City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Background of the Futures Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Backcasting as a Scholarly and Planning Approach . . . . . . . . . . . . . . . . . 4 A Summary of the Previous Backcasting Study: Step 1 and 2 . . . . . . . . . 4.1 The Outcome of Step 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 The Outcome of Step 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Step 3 of the Applied Backcasting Approach: Future Vision Generation . 5.1 On the Visionary Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Combining Urban and Technological Visions . . . . . . . . . . . . . . . . 5.3 The Future Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 The Three Strands of the Novel Model for Smart Sustainable City of the Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 The Rationale Behind Developing the Future Vision: The Novel Model for Smart Sustainable City of the Future . . . . . . . . . . . . . . 5.6 An Applied Theoretical Approach to Addressing Problems, Issues, and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Big Data Technologies and Their Novel Analytical and Practical Applications for the Future Vision . . . . . . . . . . . . . . . . . . . . . . . . 6 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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About the Author

Simon Elias Bibri is a Ph.D. scholar in the area of data-driven smart sustainable cities of the future and Assistant Professor at the Norwegian University of Science and Technology (NTNU), Department of Computer Science and Department of Urban Planning and Design, Trondheim, Norway. His true passion for academic and lifelong learning, coupled with his natural thirst for interdisciplinary and transdisciplinary knowledge, has led him to wittingly pursue an unusual academic journey by embarking on studying a diverse range of subject areas—at the intersection of computer science, data science, environmental science, and the social and human sciences. His intellectual pursuits and endeavors have resulted in an educational background encompassing knowledge from, and meta-knowledge about, different academic and scientific disciplines. He holds the following degrees: 1. Bachelor of Science in computer engineering with a major in software development and computer networks 2. Master of Science-research focused-in computer science with a major in Ambient Intelligence 3. Master of Science in computer science with a major in informatics 4. Master of Science in computer and systems sciences with a major in decision support and risk analysis 5. Master of Science in entrepreneurship and innovation with a major in new venture creation 6. Master of Science in strategic leadership toward sustainability 7. Master of Science in sustainable urban development 8. Master of Science in environmental science with a major in ecotechnology and sustainable development 9. Master of Social Science with a major in business administration (MBA) 10. Master of Arts in communication and media for social change 11. Postgraduate degree (one year of Master courses) in management and economics 12. Ph.D. in computer science and urban planning with a major in data-driven smart sustainable cities of the future Bibri has earned all his Master’s degrees from different Swedish universities, namely Lund University, West University, Blekinge Institute of Technology, Malmö University, Stockholm University, and Mid-Sweden University. Before embarking on his long academic journey, Bibri had served as a sustainability and ICT strategist, business engineer, project manager, researcher, and consultant. Over the past years and in parallel with his academic studies, he has been involved in a number of research and consulting projects pertaining to smart sustainable cities, smart cities, sustainable cities, environmental engineering, green innovation, sustainable business model innovation, and green ICT strategies. Bibri’s current research interests include smart sustainable cities, sustainable cities, urban science, sustainability science, data-intensive science, data-driven and scientific urbanism, as well as big data computing and its core enabling and driving technologies, namely sensor xxi

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About the Author

technologies, data processing platforms, big data applications, cloud and fog computing infrastructures, and wireless communication networks. Bibri has a genuine interest in interdisciplinary and transdisciplinary research. Given his multidisciplinary academic background, his general research interests fall within the following areas: • • • • • • • • • • • •

ICT of ubiquitous computing (i.e., Ambient Intelligence, the IoT, and Sentient Computing) Big data analytics and context-aware computing Data-driven smart sustainable urbanism Sustainable cities (e.g., compact city, eco-city, sustainable urbanism, green urbanism, etc.) Smart cities (e.g., real-time city, data-driven city, ambient city, ubiquitous city, sentient city, etc.) Sustainability transitions and socio-technical shifts Environmental innovations Philosophy and sociology of science Social shaping of science-based technology Technological innovation systems Sustainable business models innovation Technology, innovation, and environmental policies Bibri has authored four academic books whose titles are as follows:

1. The Human Face of Ambient Intelligence: Cognitive, Emotional, Affective, Behavioral and Conversational Aspects (525 pages), Springer, 07/2015. 2. The Shaping of Ambient Intelligence and the Internet of Things: Historico-epistemic, Socio-cultural, Politico-institutional and Eco-environmental Dimensions (301 pages), Springer, 11/2015. 3. Smart Sustainable Cities of the Future: The Untapped Potential of Big Data Analytics and Context-Aware Computing for Advancing Sustainability (660 pages), Springer, 03/2018. 4. Big Data Science and Analytics for Smart Sustainable Urbanism: Unprecedented Paradigmatic Shifts and Practical Advancements (505 pages), Springer, 05/2019.

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The Evolving Data-Driven Approach to Smart Sustainable Urbanism for Tackling the Conundrums of Sustainability and Urbanization

Abstract

Opening the book as a scene-setting chapter, this chapter covers introduction and background as well as the aim, structure and content, and organization and design purposes of the book. The main topics, concepts and theories, research issues, knowledge gaps, opportunities, and prospects pointing to a need for elaboration or investigation in relevance to the focus and scope of the book are introduced in this chapter and then will be developed further or addressed and discussed in more details in the subsequent chapters as part of the systematic exploration of the field of smart sustainable/sustainable smart urbanism and the examination of the unprecedented paradigmatic shifts and practical advances it is undergoing in light of big data science and analytics and the underlying advanced technologies. Keywords




Smart sustainable/sustainable smart urbanism Big data computing/analytics Sustainability

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Smart sustainable/sustainable smart cities Sustainable development Urbanization



ICT

Introduction and Background

Smart sustainable/sustainable smart cities as a defining context of ICT for sustainability and urbanization have recently become the leading global paradigm of urbanism (urban planning and development). With this leading position, they are increasingly gaining traction and prevalence worldwide as a promising response to the mounting challenges of sustainability and the potential effects of urbanization. The rapidly unfolding body of work, the countless research endeavors going on, and the multitudinous unexplored opportunities within the domain of smart sustainable/sustainable smart urbanism reflect the characteristic spirit and prevailing tendency of the ICT–sustainability–urbanization era as manifested in its aspirations for increasingly directing the advances in ICT of pervasive computing toward addressing and overcoming the challenges of sustainability and containing the potential effects of urbanization within the ambit of smart sustainable/sustainable smart cities. Furthermore, the subject of ‘smart sustainable/sustainable smart cities’ is endlessly enticing and magnetizing, whether from an intellectual or practical perspectives, as there are numerous actors involved in the academic and practical aspects of the endeavor, including engineers and architects, green and energy efficiency technologists, built and natural environment specialists, environmental and social scientists, ICT experts, computer and data scientists, and applied urban scientists. All these actors are undertaking research and developing strategies, approaches, and programs to tackle the challenging elements of smart sustainable/sustainable smart urbanism. This adds to the work of policymakers and political decision-makers in terms of formulating and implementing regulatory policies and devising and applying political mechanisms and governance arrangements to promote and spur innovation and monitor and maintain progress within such urbanism. Indeed, modern cities have a central and defining role in strategic sustainable development; therefore, they have gained a central position in operationalizing and applying it. This is clearly reflected in the Sustainable Development Goals (SGDs) of the United Nations’ 2030 Agenda for Sustainable Development, which entails, among other things, making cities more sustainable, resilient, and safe (UN 2015a), as well as well documented by European Commission (2011). This is anchored in the recognition that cities as the engines of, and the hubs of innovation that drive economic development are the world’s © Springer Nature Switzerland AG 2019 S. E. Bibri, Big Data Science and Analytics for Smart Sustainable Urbanism, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-17312-8_1

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major consumers of energy resources and significant contributors to GHG emissions. It is estimated that they consume about 67% of the global energy demand and generate up to 70% of the harmful GHG emissions. Accordingly, they represent the key generators of environmental pollutants and the main hot spots of vulnerability to climatic hazards and related upheavals, in addition to social inequality, disparity, vulnerability, and insecurity (Bibri 2018a, 2019a). In view of that, they are seen as the most important arena for instigating major sustainability transitions and thus making a significant contribution to sustainable urban transformations by linking the agenda of sustainable development with that of ICT of pervasive computing research and development. In addition, they constitute the key sites of economic, environmental, and social dynamism and innovation, thereby holding great potential for bringing about societal transformation and cultural enhancement (Bibri 2018a, c). Their prominence in this regard emanates from that they encompass different, yet related, innovation systems, including national, regional, sectoral, technological, and quadruple helix of university–industry–government–citizen relations. As such, they provide ideal testing grounds and operating environments for innovative ICT solutions pertaining to diverse urban systems and domains in the context of sustainability. In this respect, the United Nations’s 2030 Agenda regards ICT as a means to promote socio-economic development and protect the environment, increase resource efficiency, achieve human progress and knowledge in societies, upgrade legacy infrastructure, and retrofit industries based on sustainable design principles (UN 2015a, b). Hence, the multifaceted potential of the smart city approach as enabled by ICT has been under investigation by the UN (2015c) through their study on ‘Big Data and the 2030 Agenda for Sustainable Development’. In particular, there is an urgent need for developing and applying innovative solutions and sophisticated methods to overcome the environmental challenges of urbanization (UN 2016) and sustainability (Batty et al. 2012; Bibri 2018a, 2019a; Bibri and Krogstie 2017b). Undoubtedly, the main strength of the big data technology is the high influence; it will have on many facets of smart sustainable/sustainable smart cities and their citizens’ lives (see, e.g., Al Nuaimi et al. 2015; Angelidou et al. 2017; Batty et al. 2012; Bettencourt 2014; Bibri 2018a, b, 2019a; Bibri and Krogstie 2017a, b; Pantelis and Aija 2013; Townsend 2013). Manifestly, the unfolding and soaring urban data deluge with its extensive and new sources hides in itself the answers to the most challenging analytical questions as well as the solutions to the most complex challenges pertaining to urbanization and sustainability, in addition to playing, a key role in understanding urban constituents as data agents. Likewise, sustainable urban planning and development represent a process of change that promotes the health of citizens, communities, and natural ecosystems and fosters economic development while conserving resources in the face of urbanization. The way forward for cities to better cope with the restructuring and changing conditions is to adopt the long-term approach that emphasizes sustainability (Bulkeley and Betsill 2005). While the concept of sustainable development has enhanced cities with the planning principles and ecological design of sustainability, smart development as being predominately driven by big data computing is striving to enhance sustainable cities by smartening up their sustainability performance. It has become of high pertinence and importance to augment sustainable cities with big data technology and its novel applications so as to boost this performance (Bibri and Krogstie 2017b). Overall, modern cities are well positioned to evolve in ways that address and overcome environmental concerns and respond to socio-economic needs in an increasingly technologized, computerized, and urbanized world. They are the incubators, generators, and transmitters of creative ideas and innovative solutions for solving many complex problems and pressing issues (Bibri and Krogstie 2017a). The world is fast moving to cities and for the long term as manifested in the rapid and continuous urbanization taking place since the beginning of the last century. With more than half of the world’s population living in urban areas, and by 2050 more than two-thirds (66%) is expected to be urbanized (UN 2015d), coupled with the rising concerns over the environment and social inequality and vulnerability, it is time to turn our attention to cities and to unlock their creativity and innovation potential in order to tackle these enormous challenges. Unprecedented in their magnitude and influence in history, the spread of urbanization and the rise of ICT are among the most important global shifts at play across the world today and will undoubtedly change urbanism in a drastic and irreversible way. As widely estimated, the urban world will become largely technologized, computerized, and urbanized within just a few decades, and ICT as an enabling, integrative, and constitutive technology of the twenty-first century will accordingly be instrumental, if not determining, in solving many of the conundrums posed, the issues raised, and the challenges presented by urbanization (Bibri 2019a). It is therefore of strategic value to start directing the use of emerging ICT into understanding and proactively mitigating the potential effects of urbanization, with the primary aim of tackling the many intractable and wicked problems involved in urban operational functioning, management, planning, and development, especially in the context of sustainability, which is another macro-shift at play across the world today. Indeed, the anticipated urbanization of the world poses significant and unprecedented challenges associated with sustainability (e.g., David 2017; Han et al. 2016; Estevez et al. 2016) due to the issues engendered by urban growth in terms of resource depletion, environmental degradation, intensive energy usage, air and water pollution, toxic waste disposal, endemic traffic congestion, ineffective decision-making processes, inefficient

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Introduction and Background

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planning systems, mismanagement of urban infrastructures and facilities, poor housing and working conditions, public health and safety decrease, social vulnerability and inequality, and so on (Bibri 2018a), in spite of urbanization epitomizing an emblem of social evolution. These accordingly affect the quality of life and well-being of citizens as well as the efficiency of urban operations and functions (Degbelo et al. 2016). In short, the multidimensional effects of unsustainability in modern and future cities are most likely to exacerbate with urbanization (Bibri 2018a). Indeed, urbanization as a dynamic clustering of people, buildings, infrastructures, and resources strains and puts pressure on the limited urban and natural resources and affects the resilience to the growing demands on them, and urban functioning, management, planning, and governance face ever-mounting challenges. The underlying argument is that urban growth will jeopardize the sustainability of cities as to its environmental, economic, and social dimensions. Again, to disentangle this kind of intractable problems requires evidently major shifts in urban thinking and planning—i.e., newfangled ways founded on more innovative solutions and sophisticated approaches with respect to how cities can be understood, operated, managed, planned, designed, developed, and governed (Bibri 2018a; Bibri and Krogstie 2017a). In this regard, advanced ICT can provide integrated information intelligence for enhancing urban functioning, socio-economic forecasting, and policy design on the basis of participatory, poly-centric, and digital models and processes of governance. Therefore, ICT has come to the fore and become of crucial importance for winning the battle of sustainability and containing the potential effects of urbanization. ICT becoming part of mainstream debate in this regard stems from the increasing ubiquity presence of, and new discoveries in, computing, coupled with the massive use of its technological applications across various urban systems and domains. In fact, advanced technologies and novel approaches are now more needed than ever to address and overcome the challenges and issues facing modern and future cities. This pertains to the way such cities should be monitored, understood, analyzed, and, hence, operated, managed, organized, and planned to improve, advance, and maintain their contribution to the goals of sustainable development. It is therefore an unsurpassed opportunity to use advanced ICT to rise to the challenges of sustainability and urbanization in new ways and to resolve the many intractable and wicked problems involved in urban management, planning, and development. There is an increasing recognition that emerging and future ICT constitutes a promising response to the challenges of urban sustainability due to its tremendous, yet untapped, potential to catalyze and boost sustainable development processes. Many urban development approaches reference the role of ICT in achieving the goals of sustainable development (Bibri 2019a). As pointed out by Bibri (2019a), the use of advanced ICT in both sustainable cities and smart cities constitutes an effective approach to decoupling the health of the city and the quality of life of citizens from the energy and material consumption and concomitant environmental risks associated with urban operations, functions, services, designs, strategies, and policies. In this respect, it is important to consider urgent urban needs, emerging computing and societal trends, urban readiness for the upcoming change, available resources, and technological capabilities, cutting-edge innovations, and smart initiatives and solutions directed for sustainable transformations. These entail developing visionary approaches, comprehensive frameworks, and roadmaps for organizing and launching concrete projects, supported by long-term strategic and operational objectives and immediate policy measures for guiding and sustaining the needed transformative processes. Against the backdrop of the unprecedented rate of urbanization and the complex problems of sustainability, an array of alternative ways of understanding, operating, managing, planning, designing, developing, and governing cities based on advanced ICT is materializing and evolving in terms of how smart cities can transition toward the needed sustainable development and sustainable cities can enhance their sustainability performance. This can be attained through adopting a set of integrated frameworks, strategies, and policies to foster advancement and innovation in urban systems and domains in line with the goals of sustainability. An increasing urgency to find and apply innovative solutions is motivated by the increasing urban growth and the diffusion of sustainable development in terms of seeking out ways to circumvent the associated effects and challenges, respectively. In particular, Townsend (2013) portrays urban growth and ICT development as a form of symbiosis. This entails an interaction that is of advantage to, or a mutually beneficial relationship between, ICT and urbanization (Bibri and Krogstie 2017a). One way of looking at this form of tie-in is that urbanization can open entirely new windows of opportunity, or simply provide a fertile environment, for cities to act as vibrant hubs of technological innovations in a bid to solve a wide variety of environmental, social, and economic problems and challenges, thereby containing the potential effects of urbanization. Indeed, a large number and variety of sophisticated technologies and their novel applications, especially those enabled by big data computing, are being developed and applied in response to the need for finding more effective ways to deal with the complexity of the knowledge necessary for understanding, operating, managing, and planning modern cities as complex systems and dynamically changing environments. This entails developing and implementing novel decision-making processes based on new urban intelligence functions and powerful new forms of simulation models and optimization and prediction methods, which are enabled by big data analytics. Modern cities must take greater advantages of emerging and future technologies to be able to move with the times as to handling their

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operations, functions, designs, services, strategies, and policies. We are at a critical point in this evolution as new technological and societal forces are merging to create new approaches to smart sustainable/sustainable smart urbanism. While the two main urbanism approaches: sustainable cities and smart cities, as a set of practices have been evolving for quite some time: since the diffusion of sustainable development around the late 1980s and the prevalence of ICT around the mid-1990s, what is presently new is that the emerging urban initiatives and endeavors are rather shifting from merely focusing on the application of sustainability knowledge to the operational functioning, planning, design, and development of cities or the development and deployment of smart technologies to optimize these urbanism practices in cities to connecting the sustainable city and smart city as both landscapes and approaches, in addition to creating integrated frameworks for mitigating the potential effects of urbanization. In more detail, ‘since the late 1980s, the discourse on sustainable cities has focused on the environmental, social, and economic sustainability of cities, and since the 1990s, the focus has turned to the discourse on smart cities emphasizing ICT as a lens to see the future form of urbanism. ICT has since gained recognition that it can contribute to transforming cities into spaces that can adapt to environmental, societal, and economic shocks. Compared to smart cities and sustainable cities, which have been around for quite sometime, smart sustainable/sustainable smart cities have come to light over the past few years … While there is a growing interest in this flourishing, interdisciplinary field of research, the academic discourse on smart sustainable urban planning and development within the relevant literature is still scant-yet rapidly burgeoning … The case is evidently different from smart cities and sustainable cities as urban planning and development approaches, which have witnessed a proliferation of academic publications and thus varied emphases of research and a large body of successful practices. However, the speed at which the field of smart sustainable/sustainable smart cities is gaining momentum and attracting attention gives a clear indication of its developmental path, blossoming nature, and future direction. In fact, this field of research comes as a natural pursuit within urban planning and development in light of the unsolved issues and intellectual challenges pertaining to existing sustainable city models in terms of their contribution to the goals of sustainable development, as well as in terms of the deficiencies and conundrums associated with existing smart city approaches in terms of their incorporation of the goals of sustainable development’ (Bibri 2018a, p. 7) and their risks to environmental and social sustainability (Bibri 2019a). In light of the above, a recent research wave has started to focus on smartening up sustainable cities in ways that can improve, advance, and maintain their contribution to the goals of sustainable development, as well as on incorporating these goals into smart cities in a bid to enhance their sustainability performance (Bibri 2018a, b, c, 2019a, b). Explicitly, this wave centers around amalgamating the landscapes of, and the approaches to, sustainable cities and smart cities in a variety of ways in the hopes of reaping the numerous benefits of sustainability through enhancing and optimizing urban operational functioning, management, planning, and governance in line with the goals of sustainable development under what is labeled ‘smart sustainable cities’ or ‘sustainable smart cities’, respectively. These two integrated approaches tend to take multiple forms in terms of combining the strengths of sustainable cities and smart cities based on how the concept of smart sustainable/sustainable smart cities can be conceptualized and operationalized. As a corollary of this, there is a multitudinous array of unexplored opportunities toward new approaches to smart sustainable/sustainable smart urbanism in order to mitigate or overcome the extreme fragmentation and weak connection between the landscapes of, and the approaches to, sustainable cities and smart cities, respectively. In view of that, there has recently been a conscious push for sustainable cities and smart cities across the globe to be smarter and thus more sustainable by particularly implementing big data technology and its novel applications in the hopes of reaching the optimal and required levels of sustainability, respectively. This is due to the kind of well-informed decision-making and enhanced insights enabled by big data analytics in the form of applied intelligence and planning functions. As an advanced form of ICT of pervasive computing, big data technology and its applications are increasingly becoming of crucial importance to new approaches to smart sustainable/sustainable smart urban planning and development, gaining traction and foothold among urban scholars, scientists, practitioners, and policymakers over the past few years (Bibri 2018a, 2019a). Urban big data computing and the underpinning technologies have become essential to the operational functioning of cities, and consequently, urban planning, development, governance, and services are becoming highly responsive to a form of data-driven urbanism (Kitchin 2014, 2015, 2016), especially within the sphere of smart sustainable/sustainable smart cities (Bibri 2018a, 2019a, b). One of the salient driving factors for urbanism embracing the wave of data-driven smartness and sustainability as combined lies in the fertile environment and immense opportunity being created through the utilization of the innovative solutions and sophisticated approaches (i.e., intelligence and planning functions, simulation models, prediction and optimization methods, intelligent decision support systems, etc.). These are increasingly enabled and afforded by big data technologies for data acquisition, storage, management, processing, and analysis that are primarily intended and applied for supporting the goals of sustainable development and thus advancing sustainability (Bibri 2018a). This is manifested in the rapid evolvement of smart sustainable/sustainable smart cities as a new approach to and a new leading paradigm of urbanism into becoming more and

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Introduction and Background

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more computationally augmented and digitally instrumented and hence technologically advanced and data-analytically driven in relation to operational functioning, planning, design, and development practices with respect to sustainability and the integration of its dimensions. In several information societies, national urban projects are investing heavily in, and focusing on strengthening the role of, big data technologies in smart sustainable/sustainable smart urbanism (Bibri 2018a). If this approach is understood as what smart cities are doing to incorporate the goals of sustainable development, on the one hand, and sustainable cities are doing to smarten up their sustainability performance and how they do it, on the other hand, then the scholarly enterprise of big data analytics and the role of its uses in catalyzing and boosting the process of sustainable development and, thus, in facilitating the contribution of both smart cities and sustainable cities to those goals is most likely to represent an important changing dynamic in the transition toward smart sustainable/sustainable smart cities based on data-driven urbanism. In a nutshell, data-driven approach is of paramount importance to smart sustainable/sustainable smart urbanism. Indeed, aligning the functioning and performance of urban systems as well as the coordination and integration of urban domains with the agenda of sustainable development and the vision of sustainability require sophisticated technologies and profound data analytics capabilities to leverage the underlying complex interdisciplinary and transdisciplinary knowledge in the enhancement of decision-making in urban planning, design, and development. Currently, smart sustainable/sustainable smart urbanism as an approach entails harnessing ideas about how advanced big data technologies can optimize efficiency, boost resilience, improve equity, and enhance the quality of life by developing and implementing novel solutions and sophisticated methods for addressing and overcoming environmental and socio-economic problems on the basis of the automated extraction of useful knowledge and valuable insights from the unfolding and soaring deluge of urban data for well-informed, fact-based, strategic decision-making in the realm of smart sustainable/sustainable smart cities. In short, the value of big data computing lies in finding more effective ways of how data can be applied and how new data-driven innovations can be facilitated and diffused throughout the systems and domains of such cities for instigating and stimulating the sought-after, drastic transformations. One key facet of such transformations is how to improve the different aspects of sustainability by translating it into the built, spatial, infrastructural, operational, functional, and serviceable forms of such cities. On the whole, the phenomenon of smart sustainable/sustainable smart cities has emerged as a result of an amalgam of dominating and long-lasting trends, including global shifts (urbanization, sustainability, and ICT), intellectual discourses (e.g., sustainable development, sustainable urbanism, and smart urbanism), academic discourses (e.g., sustainable cities, smart cities, and smarter cities), computing paradigms (ubiquitous computing, big data computing, and the IoT), and technological innovations (e.g., big data analytics, big data technologies, big data applications, and fog/edge computing models). The dynamic interplay between these varied forms of trends, which will undoubtedly continue to evolve simultaneously and affect one another in a mutual process for many years yet to come, is the backcloth against which many recent innovations and transition endeavors or undertakings have emerged and materialized, and thus, countless opportunities have been created and exploited within the sphere of data-driven smart sustainable urbanism within both ecologically and technologically advanced nations. In particular, as a paradigmatic shift in societal thinking of a kind that is unprecedented, sustainability has been determining in instigating and engendering drastic changes in the core practices, primary operations, and central institutions of ecologically advanced nations in response to the goals and challenges of sustainable development over the past three decades or so (Bibri 2018a). Many technologically advanced nations have also started to exhibit shifting patterns as to responding to these goals and challenges, as well as in response to the global calls for tackling the pressing issues of urbanization by developing and implementing the most innovative solutions and sophisticated approaches being offered by big data computing and the underpinning technologies. This is increasingly being fueled by the recent advances in ICT and its ever-growing embeddedness into the very fabric of contemporary cities. Indeed, in both smart sustainable cities and sustainable smart cities, urban systems are becoming complexly and intricately interconnected and integrated and urban domains increasingly heavily favorably networked and coordinated, thereby giving rise to new urban environments that must rely on the use of more sophisticated technologies to realize their full potential as to responding to the environmental and socio-economic challenges of sustainability in an increasingly urbanized world (Bibri 2018a).

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The Aim of the Book

Integrating and fusing theoretical and practical perspectives from a number of city-related disciplinary fields and combining them with the recent computational innovations and technological advancements, this book explores the field of smart sustainable urbanism and the unprecedented paradigmatic shifts and practical advances it is undergoing in light of big data science and analytics, as well as highlights and discusses how these shifts and advances intertwine with and affect one

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another. This exploration also includes a comprehensive, state-of-the-art review of the field of data-driven smart sustainable urbanism in terms of data-driven cities, smart cities, smarter cities, sustainable cities, and smart sustainable/sustainable smart cities approaches, as well as big data analytics and application. Moreover, this book involves the various strands of urbanism: operational functioning, planning, design, and development, and how these interrelate synergistically with respect to sustainability and related big data technologies and their novel applications and sophisticated approaches in the ambit of smart sustainable cities. Indeed, at the core of smart sustainable urbanism is the synergy between urban operational functioning, planning, design, and development as regards their interaction or cooperation to produce a combined effect greater than the sum of their separate effects. This entails using big data computing and the underpinning technologies as an enabler for such synergy and a determinant of its outcomes. In light of the above, this book brings together the sciences underlying smart sustainable urbanism, which underpin the understanding, development, and application of technologies, to improve, advance, and maintain the contribution of both sustainable cities and smart cities to sustainability over the long run. In doing so, it highlights the need to consider the science and technology for environmental and social benefits in the context of smart sustainable/sustainable smart cities, as well as the environmental and social evidence for the uptake and success of the technologies underlying big data science and analytics. In a nutshell, this book brings together scholars, researchers, scientists, and practitioners to promote collaborations, enhance practices, discuss new opportunities, analyze emerging trends, and investigate advanced analytics and related frameworks along with their applications to real-life situations pertaining to sustainability and urbanization.

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The Structure and Content of the Book

The book is divided into (11) chapters. Opening the book as a scene-setting chapter, this chapter covers introduction and background as well as the aim, structure and content, and organization and design purposes of the book. The main topics, concepts and theories, research issues, knowledge gaps, opportunities, and prospects pointing to a need for elaboration or investigation in relevance to the focus and scope of the book are introduced in this chapter and then will be developed further or addressed and discussed in more details in the subsequent chapters as part of the systematic exploration of the field of smart sustainable/sustainable smart urbanism and the examination of the unprecedented paradigmatic shifts and practical advances it is undergoing in light of big data science and analytics and the underlying advanced technologies. Chapter 2 provides a comprehensive, state-of-the-art review of smart sustainable/sustainable smart cities as a leading paradigm of urbanism in terms of the underlying foundational components and assumptions, research status, issues and debates, research opportunities and challenges, future practices and horizons, and technological trends and developments. As to the findings, this chapter shows that smart sustainable urbanism involves numerous issues that are of unsolved, largely ignored, or underexplored from an applied theoretical perspective. And a large part of research in this area focuses on exploiting the potentials of big data technologies and their novel applications as an effective way to mitigate or overcome the issue of sustainable cities and smart cities being extremely fragmented as landscapes and weakly connected as approaches. The comprehensive overview of and critique on existing work on smart sustainable urbanism provides a valuable reference for researchers and practitioners in related research communities, and the necessary material to inform these communities of the latest developments in the area of smart sustainable urban planning and development. The outcome of this topical review will help strategic city stakeholders understand what they can do more to advance sustainability based on big data technology and its novel applications and also give policymakers an opportunity to identify areas for further improvement while leveraging areas of strength with regard to the future form of sustainable smart urbanism in the era of big data. Chapter 3 endeavors to systematize the complex field of smart sustainable/sustainable smart urbanism by identifying, distilling, mixing, fusing, and thematically analytically organizing the core dimensions of a foundational approach consisting of a set of relevant concepts, theories, discourses, and academic and scientific disciplines that underpin this field for research and practice. The primary intention of setting such approach is to conceptually and analytically relate urban planning and development, sustainable development, and urban science while emphasizing why and the extent to which sustainability and big data computing have particularly become influential in urbanism in modern society. Being interdisciplinary and transdisciplinary in nature, such approach is meant to further highlight that this scholarly character epitomizes the orientation and essence of the research field of smart sustainable/sustainable smart urbanism in terms of its pursuit and practice. Moreover, its value lies in fulfilling one primary purpose: to explain the nature, meaning, implications, and challenges pertaining to the multifaceted phenomenon of smart sustainable/sustainable smart urbanism. This chapter provides an important lens through which to understand a set of theories that is of high integration, fusion, applicability, and influence potential in relation to smart sustainable/sustainable smart urbanism. In this subject, in particular, theories from academic

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and scientific disciplines constitute a foundation for action—data-driven smart sustainable urbanism and related urban big data development as informed by data science practiced within the fields of urban science and urban informatics as well as sustainability science and sustainable development. Chapter 4 intends to provide a detailed qualitative analysis of the key forms of trends shaping and driving the emergence, materialization, and evolvement of the phenomenon of smart sustainable cities as a leading paradigm of urbanism, as well as to identify the relevant expected developments related to smart sustainable urbanism. It is more likely that these forms of trends reflect a congeries of long-lasting forces behind the continuation of smart sustainable cities as a set of multiple approaches to, and multiple pathways to achieving, smart sustainable urbanism. As part of the futures studies related to smart sustainable city planning and development using a backcasting methodology, both the trends and expected developments are key ingredients of, and crucial inputs for, analyzing different alternative scenarios for the future or long-term visions pertaining to desirable sustainable futures in terms of their opportunities, potentials, environmental and social benefits, and other effects. This study serves to provide a necessary material for scholars, researchers, and academics, as well as other futurists, who are in the process of conducting or planning to carry out, futures research projects or scholarly backcasting endeavors related to the field of smart sustainable urbanism. Chapter 5 has a threefold aim. First, it examines how data-driven smart sustainable cities are being instrumented, datafied, and computerized so as to improve, advance, and maintain their contribution to the goals of sustainable development through enhanced practices. Second, it highlights and substantiates the real potential of big data technology for enabling such contribution by identifying, synthesizing, distilling, and enumerating the key practical and analytical applications of this advanced technology in relation to multiple urban systems and domains with respect to operations, functions, services, designs, strategies, and policies. Third, it proposes, illustrates, and describes a novel architecture and typology of data-driven smart sustainable cities. This chapter intervenes in the existing scholarly conversation by calling attention to a relevant object of study that previous scholarship has neglected and whose significance for the field of urbanism is well elucidated, as well as by bringing new insights into and informing the ongoing debate on smart sustainable urbanism in light of big data science and analytics. This work serves to bring data-analytic thinking and practice to smart sustainable urbanism and seeks to promote and mainstream its adoption, in addition to drawing special attention to the crucial role and enormous benefits of big data technology and its novel applications as to transforming the future form of such urbanism. Chapter 6 examines the unprecedented paradigmatic, scientific, scholarly, epistemic, and discursive shifts the field of smart sustainable urbanism is undergoing in light of big data science and analytics and the underlying advanced technologies, as well as discusses how these shifts intertwine with and affect one another, and their sociocultural specificity and historical situatedness. I argue that data–intensive science as a new paradigmatic or epistemological shift is fundamentally changing the scientific and practical foundations of urban sustainability. In specific terms, the new urban science—as underpinned by sustainability science—is increasingly making cities more sustainable, resilient, efficient, livable, and equitable by rendering them more measurable, knowable, and tractable in terms of their operational functioning, management, planning, design, and development. Chapter 7 provides a comprehensive, state-of-the-art review and synthesis of the field of smart and smarter cities in relation to sustainability and related big data applications in terms of the underlying foundations and assumptions, research issues and debates, opportunities and benefits, technological developments, emerging trends, future practices, and challenges and open issues. This study shows that smart and smarter cities are associated with misunderstanding and deficiencies as regards their incorporation of, and contribution to, sustainability, respectively. Nevertheless, as also revealed by this study, tremendous opportunities are available for utilizing big data applications in smart cities of the future or smarter cities to improve their contribution to the goals of sustainable development through optimizing and enhancing urban operations, functions, services, designs, strategies, and policies, as well as finding answers to challenging analytical questions and advancing knowledge forms. However, just as there are immense opportunities ahead to embrace and exploit, there are enormous challenges ahead to address and overcome in order to achieve a successful implementation of big data applications in such cities. These findings will help strategic city stakeholders understand what they can do more to advance sustainability based on big data applications and also give policymakers an opportunity to identify areas for further improvement while leveraging areas of strength with regard to the future form of sustainable smart urban development. Chapter 8 has a twofold aim. First, it provides a comprehensive, state-of-the-art review of the domain of sustainable urbanism, with a focus on compact cities and eco-cities as models of sustainable urban forms and thus instances of sustainable cities, in terms of research issues and debates, knowledge gaps, challenges, opportunities, benefits, and emerging practices. Second, it highlights and substantiates the real, yet untapped, potential of big data technology and its novel applications for advancing sustainable cities. In so doing, it identifies, synthesizes, distills, and enumerates the key practical and analytical applications of big data technology in relation to multiple urban domains. This study shows that sustainable

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urban forms involve limitations, inadequacies, difficulties, fallacies, and uncertainties in the context of sustainability, in spite of what has been realized over the past three decades or so within sustainable urbanism. Nevertheless, as also revealed by this study, tremendous opportunities are available for exploiting big data technology and its novel applications to smarten up sustainable urban forms in ways that can improve, advance, and sustain their contribution to the goals of sustainable development by optimizing and enhancing their operations, functions, services, designs, strategies, and policies across multiple urban domains, as well as by finding answers to challenging analytical questions and transforming the way knowledge can be developed and applied. These findings will help strategic stakeholders understand what they can do more to advance sustainable urbanism based on big data computing and also give policymakers an opportunity to identify areas for further improvement while leveraging areas of strength with regard to the future form of smart sustainable urbanism. Chapter 9 develops, illustrates, and discusses a systematic framework for city analytics and ‘big data’ studies in relation to the domain of smart sustainable/sustainable smart urbanism based on cross-industry standard process for data mining. This endeavor is in response to the emerging paradigm of big data computing and the increasing role of underpinning technologies in operating, organizing, planning, and designing smart sustainable cities as a leading paradigm of urbanism. The intention is to utilize and apply well-informed, knowledge-driven decision-making and enhanced insights to improve and optimize urban operations, functions, services, designs, strategies, and policies in line with the long-term goals of sustainability. I argue that there is tremendous potential for advancing smart sustainable urbanism or transforming the knowledge of smart sustainable cities through creating a data deluge that can, through analytics, provide much more sophisticated, finer-grained, wider-scale, real-time understanding and control of various aspects of urbanity in the undoubtedly upcoming Exabyte/Zettabyte Age. Chapter 10 examines and discusses the approach to data-driven smart sustainable/sustainable smart urbanism in terms of computerized decision support and making, intelligence functions, simulation models, and optimization and prediction methods. It also documents and highlights the potential of the integration of these advanced technologies for facilitating the synergy between the operational functioning, planning, design, and development of smart sustainable/sustainable smart cities for the primary purpose of improving, advancing, and maintaining their contribution to the goals of sustainable development. I conclude that the upcoming developments and innovations in big data computing and the underpinning technologies, coupled with the unfolding and soaring deluge of urban data, hold great potential for enhancing and advancing the different practices of smart sustainable/sustainable smart urbanism. This work serves to contribute to bringing data-analytic thinking and practice to smart sustainable/sustainable smart urbanism, as well as seeks to promote and mainstream its adoption across the urban world, in addition to drawing special attention to the crucial role and enormous benefits of the emerging paradigm of big data computing as to transforming the future form of such urbanism. Chapter 11 is an integral part of an ongoing futures study whose aim is to analyze, investigate, and develop a novel model for smart sustainable city of the future using backcasting as a scholarly and planning methodology. In doing so, it endeavors to integrate the physical landscape of sustainable cities with the informational landscape of smart cities at the technical level, as well as to merge the two strategies on several scales, all in the context of sustainability. This chapter is concerned with Step 3 of the backcasting approach being used to achieve the overall aim of the futures study. In this respect, it aims to report the outcome of Step 3 by answering 6 guiding questions. Visionary images of a long-term future can stimulate an accelerated movement toward achieving the long-term goals of sustainability. The proposed model is the first of its kind and thus has not been, to the best of one’s knowledge, produced, nor is it being currently investigated, elsewhere. Chapters 2–11 have a standardized scholarly research structure, which makes them easy to navigate and read. Specifically, these chapters are presented and organized in the form of journal articles consisting of abstract, introduction, analysis, and discussion and conclusion. Some of them include research approaches as well. As to the conceptual, theoretical, and disciplinary background underpinning these chapters, it is covered separately in Chap. 3. By and large, this book is about data-driven smart sustainable/sustainable smart urbanism in the sense of exploiting and leveraging the unfolding and soaring deluge of urban data through advanced data analytics techniques to extract useful knowledge and valuable insights into the form of applied intelligence intended for enhancing decision-making pertaining to urban operational functioning, management, planning, development, and governance in the context of sustainability. As the massive collection of data has spread through just about every domain of both sustainable cities and smart cities, so have the opportunities for big data science and analytics as related to smart sustainable cities and sustainable smart cities. Underlying the extensive body of big data analytics is a much smaller set of fundamental concepts comprising data science as practiced within the field of urban science in relevance to the topic of this book. These concepts are general and encapsulate much of the essence of big data analytics in relation to urban analytics and big data studies as underpinning urban functions, operations, services, designs, strategies, and policies. Success in today’s data-driven smart sustainable/sustainable smart urbanism entails the ability to think about how these fundamental concepts apply to particular sustainability problems in short, to think data-analytically about such problems. The premise is that big data should be thought of as a strategic asset in

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the realm of smart sustainable/sustainable smart cities, a direction of thinking that brings the fundamental question of how much such cities should invest in big data analytics. Thus, an understanding of these fundamental concepts is especially important for anyone using such analytics in any urban domain that is associated with sustainability. There is growing evidence that data-driven decision-making and related advanced technologies can substantially improve sustainability performance through enhancing and optimizing urban operations, functions, services, designs, strategies, and policies in relation to diverse of urban domain. Furthermore, in 15 years’ time, the predominant computational and analytical algorithms, data-intensive techniques, mathematical models, data processing platforms, and cloud and fog computing infrastructures as the core enabling and underpinning technologies of big data computing will most likely have advanced enough that a detailed discussion in this book would be obsolete. Whereas, the general principles are the same as they were 20 or so years ago, and will likely change little over the coming decades. Besides, there are so many books out there that cover such technologies in more detail with illustrative examples for those reader interested in exploring further the field of big data computing/analytics, whether in relation to the city or other venues.

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The Organization and Design Purposes of the Book

This book is organized in a way to achieve two main outcomes. Firstly, it is written so that the readers can read it easily from end to end. Whether the readers read it in several sessions or go through a little every now and then, they will find it interesting to read and accessible–especially those with passionate interest in, and prior knowledge about, smart sustainable urbanism, or with deep curiosity about big data computing as a disruptive technology and its far-reaching implications for every domain of urban life. Secondly, it is written so that the readers can call upon specific parts of its content in an easy to do manner. Indeed, each of its chapters can be read on its own or in sequence. It is difficult to assign a priority rating to the chapters given that the book is intended for readers with different backgrounds and interests, but the readers will get the best out of it from reading the whole book in the order it is written so that they can gain a deep understanding of smart sustainable/sustainable smart urbanism as driven by big data science and analytics. However, if the readers are short of time and must prioritize, they can start with those chapters they find of highest relevance and importance based on their needs or interests. Therefore, as to how relevant and important the topics of the book are, the choice is yours based on your own assessment and subjective interpretation. Overall, this book has been carefully designed to provide the tools, material, and repository required to explore the realm of smart sustainable/sustainable smart urbanism, which is an extremely complex, dynamic, and challenging area of thinking and practice. Hence, it is well worth exploring in some depth and from multiple perspectives. The best way to enable the readers to embark on such exploration is to seamlessly amalgamate multiple perspectives and to harness this amalgamation in relevance to sustainability, in a multifaceted, unified analysis, synthesis, and evaluation. Achieving this kind of amalgamation in a form of a systematic examination is the main strength and major merit of this book. And succeeding in doing so is meant to provide the readers with valuable insights into the emerging scientific and technological innovations and their anticipated role in, and their implications for, enabling smart sustainable/sustainable smart cities as a leading paradigm of urbanism and making living in them an attainable reality, as well as into the more effective ways of addressing and overcoming the challenges of sustainability in the face of urbanization. Adding to this is to offer the people of ecologically and technologically advanced nations the resources with which to evaluate the opportunities for such cities to win the battle of sustainability and tackle the pressure of urbanization in the years ahead in the upcoming age of big data. This is believed to be an important achievement in its own right and certainly makes this book a rewarding reading and learning experience for those who feel they could benefit from and deepen their understanding of the domain of smart sustainable/sustainable smart urban planning and development. I encourage the readers to make the most of this opportunity to explore smart sustainable/sustainable smart cities as an inspiring vision in the upcoming Exabyte/Zettabyte Age. While some of us might shy away from foreseeing what the future urban world will look like with the imminent advancements and disruptive innovations in big data computing and the underpinning technologies, it is certain that it will be a very different world from what has hitherto been experienced on many scales. I wish you well on the exploration journey.

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References Al Nuaimi, E., Al Neyadi, H., Nader, M., Al-Jaroodi, J. (2015). Applications of big data to smart cities. Journal of Internet Services and Application, 6(25), 1–15. Angelidou, M., Psaltoglou, A., Komninos, N., Kakderi, C., Tsarchopoulos, P., & Panori, A. (2017). Enhancing sustainable urban development through smart city applications. Journal of Science and Technology Policy Management, 1–25. Batty, M., Axhausen, K. W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., et al. (2012). Smart cities of the future. The European Physical Journal Special Topics, 214, 481–518. Bettencourt, L. M. A. (2014). The uses of big data in cities. Santa Fe, New Mexico: Santa Fe Institute. Bibri, S. E. (2018a). Smart sustainable cities of the future: The untapped potential of big data analytics and context aware computing for advancing sustainability. Germany, Berlin: Springer. Bibri, S. E. (2018b). The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability. Sustainable Cities and Society, 38, 230–253. Bibri, S. E. (2018c). A foundational framework for smart sustainable city development: Theoretical, disciplinary, and discursive dimensions and their synergies. Sustainable Cities and Society, 38, 758–794. Bibri, S. E. (2019a). On the sustainability of smart cities of the future and related big data applications: An interdisciplinary and transdisciplinary review and synthesis. Journal of Big Data (in press). Bibri, S. E. (2019b). A novel model for smart sustainable city of the future: A scholarly backcasting approach to its analysis, investigation, and development. Journal of CITA (in press). Bibri, S. E., & Krogstie, J. (2017a). Smart sustainable cities of the future: An extensive interdisciplinary literature review. Sustainable Cities and Society, 31, 183–212. Bibri, S. E., & Krogstie, J. (2017b). ICT of the new wave of computing for sustainable urban forms: Their big data and context-aware augmented typologies and design concepts. Sustainable Cities and Soceity, 32, 449–474. Bulkeley, H., & Betsill, M. (2005). Rethinking sustainable cities: multilevel governance and the “urban” politics of climate change. Environmental Politics, 14(1), 42–63. David, D. (2017). Environment and urbanization. The International Encyclopedia of Geography, 24(1), 31–46. https://doi.org/10.1002/ 9781118786352.wbieg0623. Degbelo, A., Granell Granell, C., Trilles Oliver, S., Bhattacharya, D., Casteleyn, S., & Kray, C. (2016). Opening up smart cities: citizen-centric challenges and opportunities from GIscience. ISPRS International Journal of Geo-Information, 5(2), 16. https://doi.org/10.3390/ijgi5020016. Estevez, E., Lopes, N. V., & Janowski, T. (2016). Smart sustainable cities. Reconnaissance Study, 330. European Commission. (2011). Cities of tomorrow. Challenges, visions, ways forward. Brussels: Publications Office of the European Union. Han, J., Meng, X., Zhou, X., Yi, B., Liu, M., & Xiang, W.-N. (2016). A long-term analysis of urbanization process, landscape change, and carbon sources and sinks: A case study in China’s Yangtze River Delta region. Journal of Cleaner Production, 141, 1040–1050. https://doi.org/10. 1016/j.jclepro.2016.09.177. Kitchin, R. (2014). The real-time city? Big data and smart urbanism. Geo Journal, 79, 1–14. Kitchin, R. (2015). Data-driven, networked urbanism. https://doi.org/10.2139/ssrn.2641802. Kitchin, R. (2016). The ethics of smart cities and urban science. Philosophical Transactions of the Royal Society A, 374, 20160115. Pantelis, K., & Aija, L. (2013). Understanding the value of (big) data. In Big Data 2013 IEEE International Conference on IEEE, pp. 38–42. Townsend, A. (2013). Smart Cities–Big data, civic hackers and the quest for a new Utopia. New York: Norton & Company. United Nations. (2015a). Transforming our world: The 2030 agenda for sustainable development. New York, NY. Available at: https:// sustainabledevelopment.un.org/post2015/transformingourworld. United Nations. (2015b). Habitat III issue papers, 21—Smart cities (V2.0), New York, NY. Available at: https://collaboration.worldbank.org/docs/ DOC-20778. Accessed May 2, 2017. United Nations. (2015c). Big data and the 2030 agenda for sustainable development. Prepared by A. Maaroof. Available at: www.unescap.org/ events/call-participants-big-data-and-2030-agendasustainable-development-achieving-development. United Nations. (2015d). World urbanization prospects. The 2014 revision. New York: Department of Economic and Social Affairs. http://esa.un. org/unpd/wup/Publications/Files/WUP2014-Report.pdf. Accessed January 22, 2017. United Nations. (2016). Paris agreement. United Nations treaty collection, reference C.N. 63.2016. TREATIES–XXVII.7.d. Agreement adopted at the twenty-first session of the Conference of the Parties to the United Nations Framework Convention on Climate Change, in Paris from 30 November to 13 December 2015. https://treaties.un.org/doc/Publication/CN/2016/CN.63.2016-Eng.pdf. Accessed January 22, 2017.

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The Leading Smart Sustainable Paradigm of Urbanism and Big Data Computing: A Topical Literature Review

Abstract

The big data revolution is set to erupt in both smart cities and sustainable cities throughout the world. This is manifested in bits meeting bricks on a vast scale as instrumentation, datafication, and computation are routinely pervading urban environments. As a result, smart sustainable urbanism is becoming more and more data-driven. Explicitly, big data computing and the underpinning technologies are drastically changing the way both smart cities and sustainable cities are understood, operated, managed, planned, designed, developed, and governed in relation to sustainability in the face of urbanization. This implies that urban systems are becoming much more tightly integrated and urban domains much more highly coordinated while more holistic views and synoptic city intelligence can now be provided thanks to the possibility of drawing together and interlinking urban big data as well as reducing urban life to a form of logic and calculative procedures on the basis of powerful computational algorithms. These data-driven transformations are in turn being directed for improving, advancing, and maintaining the contribution of smart sustainable/sustainable smart cities to the goals of sustainable development. This chapter provides a comprehensive, state-of-the-art review of smart sustainable/sustainable smart cities as a leading paradigm of urbanism in terms of the underlying foundational components and assumptions, research status, issues and debates, research opportunities and challenges, future practices and horizons, and technological trends and developments. As to the findings, this chapter shows that smart sustainable urbanism involves numerous issues that are unsolved, largely ignored, or underexplored from an applied theoretical perspective. And, a large part of research in this area focuses on exploiting the potentials of big data technologies and their novel applications as an effective way to mitigate or overcome the issue of sustainable cities and smart cities being extremely fragmented as landscapes and weakly connected as approaches. The comprehensive overview of and critique on existing work on smart sustainable urbanism provides a valuable reference for researchers and practitioners in related research communities and the necessary material to inform these communities of the latest developments in the area of smart sustainable urban planning and development. The outcome of this topical review will help strategic city stakeholders to understand what they can do more to advance sustainability based on big data technology and its novel applications, and also give policymakers an opportunity to identify areas for further improvement while leveraging areas of strength with regard to the future form of sustainable smart urbanism in the era of big data. Keywords











Smart sustainable urbanism Smart sustainable cities Big data computing data applications Sustainability Sustainable development

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Big data analytics




Data deluge




Big

Introduction

Against the backdrop of the unprecedented rate of urbanization and the complex problems of sustainability, an array of alternative ways of understanding, operating, managing, planning, designing, developing, and governing cities based on advanced ICT is materializing and evolving, providing the raw material for how smart cities can transition toward the needed sustainable development and sustainable cities can enhance their sustainability performance The two main urbanism © Springer Nature Switzerland AG 2019 S. E. Bibri, Big Data Science and Analytics for Smart Sustainable Urbanism, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-17312-8_2

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approaches: sustainable cities and smart cities, as a set of practices have been evolving for quite some time: since the diffusion of sustainable development around the early 1990s and the prevalence of ICT around the mid-1990s. But what is presently new is that the emerging urban initiatives and endeavors are rather shifting from merely focusing on the application of sustainability knowledge to the operational functioning, planning, design, development, and governance of cities or the development and deployment of smart technologies to optimize these urbanism practices in cities to connecting the sustainable city and the smart city as both landscapes and approaches. In view of the above, the concept of smart sustainable/sustainable smart cities has emerged as a result of three important global shifts at play across the world, namely the rise of ICT, the diffusion of sustainability, and the spread of urbanization. As an integrated framework and holistic approach, such cities amalgamate the strengths of sustainable cities in terms of the design concepts and principles and planning practices of sustainability and those of smart cities in terms of the innovative solutions and sophisticated approaches being developed for sustainability as mainly offered by big data technology (Bibri 2018a; Bibri and Krogstie 2017b, c). Therefore, they are increasingly gaining traction and prevalence worldwide as a response to the imminent challenges of sustainability and urbanization. They are, moreover, being embraced as an academic pursuit, societal strategy, and, thus, evolving into a scholarly and realist enterprise around the world, not least within ecologically and technologically advanced nations (Bibri and Krogstie 2016; Bibri 2018a, c). In a nutshell, the concept and development of smart sustainable/sustainable smart cities are gaining increased attention worldwide among research institutes, universities, governments, policymakers, and ICT companies. Furthermore, there has recently been a conscious push for cities across the globe to be smarter and thus more sustainable by developing and implementing big data applications across various urban domains to enhance and optimize their operations, functions, services, designs, strategies, and policies (Bibri 2018a). This is justified by the kind of well-informed, knowledge-driven decision-making enabled by the process of big data analytics through the automated extraction of useful and valuable insights in the form of applied intelligence. Underneath advanced ICT solutions, there indeed is a vast deluge of big data that is being harnessed, analyzed, and put to work for the benefit and health of cities in terms of sustainability, efficiency, resilience, and the quality of life. Thus, big data are the new natural resources that, when mined well, provide the foundation for tomorrow’s smarter, more sustainable societies. As a research wave and direction, big data analytics and its application have recently attracted urban scholars and scientists from diverse disciplines as well as urban practitioners from different professional fields due to their importance and influence within urbanism, in addition to being a major intellectual, scientific, and practical challenge (e.g., Batty 2013; Batty et al. 2012; Bibri 2018a, b, 2019; Bibri and Krogstie 2017a, b; Bettencourt 2014; Kitchin 2014, 2016). Big data analytics is increasingly seen to provide unsurpassed and innovative ways to address a range of complex environmental challenges and rising socio-economic concerns facing the contemporary city. Therefore, urban planners, strategists, and policymakers are faced with unique opportunities in this direction. Big data analytics is enriching and reshaping our experiences of how the city can be operated, planned, designed, developed, and governed due to the kind of the underlying well-informed, knowledge-driven decision-making associated with the knowledge of how effectively, fast, and best to advance urban sustainability (Bibri 2018a, 2019). Unsurprisingly, a number of advanced infrastructures, platforms, systems, methods, techniques, and algorithms pertaining to big data computing are being developed and implemented in response to the urgent need for handling the availability of the vast troves of urban data being generated and using them to manage, control, regulate, and plan the city by extracting the kind of knowledge needed for integrating urban systems, coordinating urban domains, and coupling urban networks in ways that enhance sustainability performance in the realm of smart sustainable and sustainable smart cities (Bibri 2018a, 2019). This chapter provides a comprehensive, state-of-the-art review of smart sustainable/sustainable smart cities as a leading paradigm of urbanism in terms of the underlying foundational components and assumptions, research status, issues and debates, research opportunities and challenges, future practices and horizons, and technological trends and developments. The outcome of this topical review will help strategic city stakeholders to understand what they can do more to advance sustainability based on big data technology and its novel applications, and also give policymakers an opportunity to identify areas for further improvement while leveraging areas of strength with regard to the future form of sustainable smart urbanism in the age of big data. The remainder of this chapter proceeds as follows. Section 2 introduces and discusses the foundational components and assumptions of smart sustainable urbanism in relation to big data computing and the underpinning technologies. Section 3 provides an account of research and its status as to big data analytics and smart sustainable cities. Section 4 provides a state-of-the-art review of smart sustainable cities and related big data analytics and its application. This chapter ends, in Sect. 5, with concluding remarks, findings, and some reflections.

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Foundational Components and Assumptions

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Foundational Components and Assumptions

2.1 Smart Sustainable Cities: Characterization, Leading Position, and Prospects The concept of smart sustainable/sustainable smart cities has emerged as a result of three important global shifts at play across the world, namely the rise of ICT, the diffusion of sustainability, and the spread of urbanization, and such cities as a new techno-urban phenomenon materialized or became widespread around the mid-2010s (Bibri 2018a, b, c, d, 2019). Accordingly, not long ago, they took the leading position as a global paradigm of urban planning and development. Their subject is endlessly enticing and magnetizing, whether from an intellectual or practical perspective, as there are numerous actors involved in the academic and practical aspects of the endeavor, including engineers and architects, green and energy efficiency technologists, built and natural environment specialists, environmental and social scientists, ICT experts, computer and data scientists, and applied urban scientists. All these actors are undertaking research and developing strategies, approaches, and programs to tackle the challenging elements of smart sustainable/sustainable smart urbanism. This adds to the work of policymakers and political decision-makers in terms of formulating and implementing regulatory policies and devising and applying political mechanisms and governance arrangements to promote and spur innovation and monitor and maintain progress within such urbanism across the globe. In light of the above, it is not until very recently that smart sustainable/sustainable smart urban planning and development as an intellectual discourse did elicit and attract attention among urban scholars, practitioners, and policymakers, as well as ICT experts and computer scientists working within the area of applied urban science or urban informatics, especially in the subfield of big data and its relation to urban analytics, planning, and development. Smart sustainable/sustainable smart cities evolving subsequently into a more powerful and established techno-urban discourse and realist enterprise emanates from the fact that many strategic urban actors are increasingly relating to such discourse and enterprise in a structured way in different contexts of their practices—socially anchored and culturally institutionalized actions (Bibri and Krogstie 2016; Bibri 2018b, 2019). The accordingly increasing insertion, functioning, and dissemination of such discourse, in particular, is increasingly shaped and influenced by the emerging sophisticated technologies and their future generation being under vigorous investigation and scrutiny by the ICT industry consortia, collaborative research institutes, policy networks, and Quadruple Helix of University–industry–government–citizen relations in terms of research, development, and innovation within ecologically technologically or technologically advanced nations (Bibri 2018a). In consideration of the above, just like the concept of smart sustainable cities (Bibri and Krogstie 2016), the concept of sustainable smart cities (Bibri 2018b, 2019) has gained momentum as both a holistic approach to urban planning and development as well as an academic and societal pursuit, not least in technologically advanced nations. That is to say, it has become important not only in urban planning and policymaking, but also in urban research and practice, generating worldwide attention as a powerful framework for strategic sustainable urban planning and development (Bibri 2019). Therefore, the development of sustainable smart cities, just like that of smart sustainable cities, is increasingly gaining traction and pre-eminence worldwide, surpassing all other urban development approaches, especially in the world’s major cities, supported by policymakers, governments, research institutions, universities, and industries. Given the apparent relevance and usefulness of the findings produced in the field of smart sustainable urbanism, the related research and development have been embraced and advocated by the United Nations (UN), the European Union (UN), and the Organization for Economic Cooperation and Development (OECD) (Bibri 2018b). In addition, many governments have recently set ambitious targets to transition their cities to being smart sustainable/sustainable smart using a variety of initiatives and programs, or have adopted the concept of smart/sustainable city and implemented big data applications to reach the required or optimal level of sustainability and to improve the living standards. Accordingly, it has become of crucial importance to develop and utilize new methods for measuring the performance of smart sustainability or sustainable smartness. This is due to the growing realization of the untapped potential of emerging advanced technologies, especially big data analytics and its application, for addressing the challenges of sustainability and containing the potential effects of urbanization.

2.2 Big Data Computing in the Ambit of Smart Sustainable Urbanism 2.2.1 Characterization and Prospects Advances in ICT and its widespread development, diffusion, and integration into many spheres of society and hence numerous domains of all kinds of specializations, including urban, scientific, medical, technological, engineering, economic,

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environmental, ecological, social, and political are resulting in data explosion as manifested in the huge data deluge flooding from new and extensive sources, rapidly unfolding, and endlessly soaring. Data mining, knowledge discovery, and decision-making from voluminous, varied, real-time, exhaustive, fine-grained, indexical, dynamic, flexible, evolvable, relational data are a daunting challenge/task in terms of storage, management, organization, processing, analysis, evaluation, interpretation, modeling, and simulation, as well as in terms of the visualization and deployment of the obtained results for enhancing and optimizing operations, functions, services, designs, strategies, and policies. This is an emerging trend known as big data computing, which is influential, formative, groundbreaking, pioneering, innovative, and long-lasting. As a new paradigm, it amalgamates, as underpinning technologies, large-scale computation as well as new data-intensive techniques and algorithms and advanced mathematical models to build and perform data analytics. This paradigm has materialized as a result of the rise, advance, and prevalence of ICT, as well as of the maturity and evolvement of the dominant ICT visions into achievable and deployable computing paradigms, especially UbiComp and the IoT as a form of it. The expansion and success of this computing paradigm is increasingly stimulating smart sustainable/sustainable smart city projects and initiatives as well as research opportunities to an increasing extent, within both ecologically technologically and technologically advanced nations (Bibri 2018b, 2019). Big data in this context are referred to with respect to their humongous size and wide variety, with a particular focus on the deluge of urban data (i.e., datasets collected and coalesced through data warehousing for wide-city uses) that are directed toward advancing smart sustainable urbanism or transforming the knowledge of smart sustainable/sustainable smart cities, in particular, in relation to sustainability and the integration of its dimensions. Such data enable real-time analysis of city systems in terms of the operating and organizing processes of city life, new modes of urban planning and governance, and advanced approaches to interconnecting and fusing data across urban domains to provide detailed views of the relationships between urban data, as well as provides the raw material and favorable conditions for enacting and envisioning more sustainable, efficient, resilient, equitable, open, and transparent cities. The notion of big data analytics and its application in sustainable urban planning and development have gained traction and foothold among urban scholars, academics, scientists, practitioners, and policymakers over the past few years. Manifestly, big data computing is fundamentally changing the way modern cities can sustainably be operated, managed, planned, developed, and governed, shaping and driving decision-making processes within many urban domains (Bibri 2018a, b), especially with regard to optimizing resource utilization, mitigating environmental risks, responding to socio-economic needs, and enhancing the quality of life and well-being of citizens in an increasingly urbanized world (Bibri 2019). This paradigm is clearly on a penetrative path across all the systems and domains of smart, smarter, sustainable, and sustainable smart/smart sustainable cities that rely on sophisticated technologies in relation to their operational functioning, management, planning, and development. Thus, it is manifested in the proliferation and increasing utilization of the core enabling technologies of big data analytics across those cities badging or regenerating themselves as one of such cities for storing, managing, processing, analyzing, and sharing colossal amounts of urban data for the primary purpose of extracting useful knowledge in the form of applied intelligence functions and simulation models directed for multiple purposes, especially sustainability. Big data are regarded as the most scalable and synergic asset and resource for modern cities to enhance their performance on many scales. Indeed, they have become the fundamental ingredient for the next wave of urban analytics and planning (Bibri 2018a, b; 2019). As a result, many governments have started to exploit urban data and their numerous benefits to support the development of their cities with regard to sustainability, efficiency, resilience, equity, and the quality of life.

2.2.2 Data Growth Projection and Related Driving Technologies The deluge of urban data is, and will continue to be, unfolding and soaring, amounting to hundreds of exabytes every year and covering so many aspects of urbanity in its complexity, breadth, depth, and heterogeneity as demonstrated in, among others, the nature of urban systems and their continuous integration, that of urban domains and their coordination, and that of urban networks and their coupling. This urban data growth will undoubtedly continue in this direction, and expectedly, the resulting datasets are set to proliferate and be coalesced, integrated, and coordinated. The explosive data growth is due to a number of the core enabling and driving technologies of ICT of various forms of pervasive computing, including data sensing devices and sensor networks, data processing platforms, data analytics techniques and processes, cloud and fog computing infrastructures, and wireless networking technologies. These are being fast embedded into the very fabric of contemporary cities, everyday practices and spaces, whether badging or regenerating themselves as smart sustainable/sustainable smart to pave the way for utilizing and adopting the upcoming innovative solutions to overcome the challenges of sustainability and urbanization in the years ahead. In the meantime, the increasing convergence, advance, and ubiquity of ICT are giving rise to new computationally augmented urban environments that are enabling sophisticated operating and organizing processes of urban life. This is in response to the event of cities becoming more and more complex

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and dynamically changing systems together with their domains getting more and more coordinated, their systems integrated, and their networks coupled (Bibri 2018b, 2019). This concerns, particularly, those domains, systems, and networks that rely heavily on complex technologies to realize their full potential for responding to the challenges of sustainability and urbanization.

2.2.3 Datafication: Urban Data Deluge and Its Sources and Enabling Capabilities There has been a marked intensification of datafication in recent years, manifested in an unprecedented, radical expansion in the volume, range, variety, and granularity of the data being generated about urban environments and citizens. We are currently experiencing the accelerated datafication of the city in a rapidly urbanizing world and witnessing the dawn of the big data era not out of the window, but in everyday life. Our urban everydayness is entangled with data sensing, data processing, and communication networking, and our wired world generates and analyzes overwhelming and incredible amounts of data. This allows for, over sufficiently long periods of time, extracting changes to the structure and form of the city and the way people behave. The modern city is turning into constellations of instruments and computers across many scales and morphing into a haze of software instructions, which are becoming essential to the operation and functioning of the city. The datafication of spatiotemporal citywide events has become a salient factor for the practice of smart sustainable urbanism. Consequently, there has been much enthusiasm in the domain of smart sustainable/sustainable smart urbanism about the immense possibilities and fascinating opportunities created by the data deluge and its extensive sources with regard to improving urban operational functioning, management, planning, design, and development in line with the long-term goals of sustainability. This results from thinking about and understanding sustainability and urbanization and their relationships in a data-analytic fashion for the purpose of generating and applying knowledge-driven, fact-based, strategic decisions in relation to such urban domains as transport, traffic, mobility, energy, environment, education, health care, public safety, public services, governance, economy, and science and innovation (Bibri 2018a, b, 2019). The exponentially growing amount of the data being constantly produced across many urban systems, domains, and networks is at such a high value that it has become of astuteness and strategic value for urban planners, strategists, and policymakers in collaboration with urban and data scientists and ICT experts to exploit, harness, and analyze these data for advancing urban sustainability (Bibri 2018a). Within such cities, citizens, activities, movements, processes, physical structures, urban infrastructure, distribution systems and networks, natural ecosystems, spatial organizations, scale stabilizations, socio-economic networks, facilities, services, spaces, and citizen objects all contribute to the generation of the huge amounts of data from heterogeneous and distributed sources. Basically, virtually every aspect of urbanity has become open to, and instrumented for, data collection, processing, and analysis. As a result, vast troves of information have become widely available on numerous aspects of urbanity, including social trends, global shifts, environmental dynamics, socio-economic needs, spatial patterns, land use patterns, travel and mobility patterns, traffic patterns, energy consumption patterns, life quality levels, and citizens’ lifestyles and participation levels (Bibri 2018a, 2019; Bibri and Krogstie 2017c). The data from these sources and on these aspects cascade into urban data deluge, which calls for prudent big data applications that can churn out useful knowledge and valuable insights from this huge deluge. The sustainability of smart cities and the smartness of sustainable cities are being digitally fueled and driven by the data being generated for processing, analysis, and deployment for enhanced decision-making purposes and innovative solutions development. The unfolding and soaring deluge of urban data is increasingly stimulating wide-scale attempts to extract value from and make sense of such data, which is driven primarily by the desire to translate contextual and actionable data and data analytics into data-driven operational functioning, planning, design, development, and governance focused more and more on advancing smart sustainable urbanism. In more detail, the value of the useful knowledge resulting from data analytics lies in enhancing physical forms, infrastructures, resources, networks, facilities, and services by developing urban intelligence functions for automating and supporting decisions pertaining to control, automation, optimization, management, and prediction for the purpose of improving, advancing, and maintaining the contribution of smart sustainable/sustainable smart cities to the goals of sustainable development (Bibri 2018a, b, 2019). However, while there is no doubt that colossal amounts of urban data are being collected and generated on a daily basis and in continuous streams within and across urban systems, domains, networks, and related processes, there is less clarity on how such data can be applied, used, and thus leveraged to address the many wicked problems and intractable issues embodied in smart sustainable/sustainable smart urbanism or involved in smart sustainable/sustainable smart cities (Bibri 2018a).

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2.2.4 The Core Enabling Technologies of Big Data Analytics and ICT of Pervasive Computing ICT is becoming increasingly spatially all pervasive, located anywhere and everywhere across urban environments, thereby providing the necessary basic infrastructure backbone for cities to realize their full potential as to interconnecting sustainability goals with smart targets under what is labeled ‘smart sustainable cities’ or ‘sustainable smart cities.’ As a result, data sensing devices and data processing platforms are being fast embedded into the very fabric of such cities while wireless networking technologies are proliferating on a hard-to-imagine scale, with all being predominately hosted by large-scale cloud and fog computing infrastructure. In light of this, the prospect of such cities is becoming the new reality with the increasing combination of, the evolving advancements in, and the massive proliferation of, the core enabling technologies underlying ICT of various forms of pervasive computing. Such cities typically rely on the fulfillment of the most prevalent ICT visions of pervasive computing (especially UbiComp and the IoT, where everyday objects communicate with each other and collaborate across heterogeneous and distributed computing environments to provide relevant information and efficient services to urbanites and urban entities (Bibri 2018a). These two socially disruptive technologies are projected to yield a drastic transformation of the techno-urban ecosystem in all its complexity and variety. This entails altering the way ICT can be used and applied in all urban spheres with far-reaching implications. Indeed, it has been widely suggested that as ICT permeates urban infrastructures, architectures, natural ecosystems, ecosystem services, public and social services, planning processes, governance models, and citizens’ objects, we can speak of cities getting smarter as to tackling environmental, social, and economic problems, as well as providing an array of multitudinous services to citizens aimed at improving the quality of their life and well-being (Bibri 2018a, 2019). Therefore, smart sustainable/sustainable smart cities are opening entirely new windows of opportunity for grandiose plans to achieve sustainability thanks to the recent advances in several scientific and technological areas in the realm of ICT of pervasive computing for sustainability (Bibri and Krogstie 2016), especially those associated with big data computing. However, the need to understand what constitutes the informational landscape of smart sustainable/sustainable smart cities in terms of big data computing as a set of related technologies presents an important topic and new direction of research in the domain of smart sustainable/sustainable smart urbanism. The prominence lies in identifying the core enabling technologies and related key techniques and processes required to design, develop, deploy, and implement big data applications for enhancing and advancing the different aspects of sustainability. The underlying argument is that the research on big data analytics and its core enabling technologies in relation to the domain of sustainable urban planning and development remains scant (Bibri 2018a). 2.2.5 Big Data Ecosystem and Its Components Big data trends are associated with pervasive and ubiquitous computing, which involves myriads of sensors pervading urban environments on a massive scale. Therefore, the volume of the data generated is huge and thus the processes, systems, platforms, infrastructures, and networks involved in handling these data are complex. Mechanisms to store, integrate, manage, process, analyze, and visualize the generated data through scalable applications remain a major scientific and technological challenge in the ambit of data science, urban science, and computer science. Like many areas to which big data computing can be applied, smart sustainable cities require the big data ecosystem and its components to be put in place as part of their ICT infrastructure prior to designing, developing, deploying, implementing, and maintaining the diverse applications that support sustainability and reduce the negative effects of urbanization. As a scientific and technological area, the core enabling technological components underlying the big data ecosystem are under vigorous investigation in both academic circles as well as the ICT industry toward the development of computationally augmented urban environments as part of the informational landscape of such cities (Bibri 2019). Big data ecosystems are for capturing data to generate useful knowledge and deep insights. In the sphere of smart sustainable cities, the big data landscape is daunting, and there is no one ‘big data ecosystem’ or single go-to solution when it comes to building big data architecture. The big data ecosystem involves multivarious technologies in terms of quality and form, which allow to store, manage, process, analyze, visualize data, and deploy the obtained results. It consists of infrastructure and tools for storing, managing, processing, and analyzing data; specialized analytics techniques; and applications. Bibri and Krogstie (2017c) provide a comprehensive, state-of-the-art review of the core enabling technologies of big data analytics in relation to smart sustainable cities, including a synthesis and illustration of the key computational and analytical techniques, processes, and models associated with the functioning and application of big data analytics. The components addressed by the authors in rather more detail include, but are not limited to, the following:

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Foundational Components and Assumptions

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• Pervasive sensing in terms of collecting and measuring urban big data; the IoT and related RFID tags; sensor-based urban reality mining; and sensor technologies, types, and areas in big data computing. • Wireless communication network technologies and smart network infrastructures; • Data processing platforms; • Cloud and fog/edge computing; • Advanced techniques and algorithms; • Conceptual and analytical frameworks. Generally, big data ecosystems entail a number of permutations of the underlying core enabling technologies as shaped by the scale, complexity, and extension of the city projects and initiatives to be developed and implemented. In this respect, it is necessary to take into account flexible design, quick deployment, extensible implementation, comprehensive interconnections, and advanced intelligence. Regardless, while there are some permutations that may well apply to most urban systems and domains, there are some technical aspects and details that remain specific to smart sustainable cities, more specifically to the requirements, objectives, and resources of related projects and initiatives, which are usually determined by and embedded in a given context (Bibri 2019; Bibri and Krogstie 2017c). Yet, most of, if not all, the possible permutations involve sensing technologies and networks, data processing platforms, cloud computing and/or fog computing infrastructures, and wireless communication and networking technologies. These are intended to provide a full analytic system of big data and related functional applications based on advanced decision support systems and strategies—urban intelligence functions and related simulations models and optimization and prediction methods (Bibri 2018b). On this note, Batty et al. (2012) state that much of the focus on sustainable smart cities of the future, ‘will be in evolving new models of the city in its various sectors that pertain to new kinds of data and movements and actions that are largely operated over digital networks while at the same time, relating these to traditional movements and locational activity. Very clear conceptions of how these models might be used to inform planning at different scales and very different time periods are critical to this focus… Quite new forms of integrated and coordinated decision support systems will be forthcoming from research on smart cities of the future.’

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On the Research and Its Status of Big Data Analytics and Smart Sustainable Cities

Having recently, as a research wave and direction, permeated and dominated academic circles and industries, coupled with its research status being consolidated as one of the most appealing and fertile as well as fanciest areas of investigation beyond the realm of sustainable smart/smart sustainable urbanism, big data analytics has attracted researchers, scholars, academics, scientists, experts, and practitioners from diverse disciplines and professional fields—given its importance and relevance for generating well-informed decisions and deep insights of highly useful value to many sectors of society. Therefore, big data analytics is a rapidly expanding research area merging computer science, data science, and complexity sciences (Bibri 2018a, b, 2019), and becoming a ubiquitous term in understanding and solving complex challenges and problems in many fields. The big data movement has been propelled by the intensive R&D activities taking place in academic and research institutions, as well as in industries and businesses—with huge expectations being placed on the upcoming innovations and advancements in the field. A large part of ICT investment is being directed by giant technology companies, such as Google, IBM, Oracle, Microsoft, SAP, and CISCO, toward creating novel computing models and enhancing existing practices pertaining to the storage, processing, analysis, management, modeling, simulation, and evaluation of big data, as well as to the visualization and deployment of the analytical outcome for different purposes (Bibri 2018a, b; Bibri and Krogstie 2017a). Adding to this is the active, ongoing research within so many universities across the globe, especially in relation to smart sustainable/sustainable smart cities. Big data analytics is considered as a prerequisite technology for realizing the novel applications and services offered and promised by the ICT visions of pervasive computing, which is a determinant enabler and powerful driver for smart sustainable/sustainable smart cities of the future. The notion of smart sustainable/sustainable smart cities has come to the fore in recent years as a result of three global trends at play across the world today, namely the recent patterns of change associated with ICT, sustainability, and urbanization. With this conspicuous position, it is increasingly gaining traction and foothold within smart sustainable/sustainable smart urbanism as a promising response to the mounting challenges of sustainability and urbanization. In the meantime, the research on smart sustainable/sustainable smart cities is garnering growing attention and rapidly burgeoning, and its status is consolidating as one of the most enticing and fanciest areas of investigation today, making the relevance and rationale behind the smart sustainable/sustainable smart city debate of high significance and value with respect

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to the future form of urbanism (Bibri 2018b). This area is typically concerned with addressing a large number and variety of issues related to both sustainable cities and smart cities in terms of their planning and development in the context of sustainability, as well as to the amalgamation of these two classes of cities as landscapes and approaches in the context thereof (Bibri 2018a, b; Bibri and Krogstie 2017a). A large part of research in this area focuses on exploiting the potentials and opportunities of advanced technologies and their novel applications, especially big data computing/analytics, as an effective way to mitigate or overcome the issue of sustainable cities and smart cities being extremely fragmented as landscapes and weakly connected as approaches, particularly at the technical and policy levels (Bibri 2018b, 2019). Furthermore, the quest for, and the challenge of, finding more effective ways to merge the physical and informational landscapes of the emerging smart sustainable/sustainable smart cities in ways that can continuously improve, advance, and maintain their contribution to the goals of sustainable development and thus advance their sustainability is currently motivating, inducing, and inspiring many researchers, scholars, academics, and practitioners within the domain of smart sustainable/sustainable smart urbanism, as well as real-world cities. The field of smart sustainable/sustainable smart cities is still in the early stages of its development. Therefore, there is a multitudinous array of research problems to investigate and countless research opportunities to explore based on conceptual, theoretical, applied theoretical, empirical, analytical, exploratory, and discursive inquiry approaches. This typically covers a range of research strands, including, but not limited to, technological, computational, urban, environmental, economic, cultural, social, political, ethical, socio-technical, regulatory, institutional, and practical. Moreover, the wave of research on such cities has mainly been fueled by the most prevalent ICT visions of pervasive computing becoming deployable and achievable computing paradigms and thus the new reality in different parts of the world, especially Europe, Asia, and the USA (Bibri 2019). This new paradigmatic shift in computing as heralding a drastic change in ICT in its various forms and thereby giving rise to sophisticated approaches and novel applications increasingly pervading urban environments have made the vision of building and living in such cities an attainable reality. Other driving factors for, or global shifts triggering, this wave of research, in addition to the rise, advance, and prevalence of ICT, is the unprecedented urbanization of the world’s population and the rising concerns over its multidimensional effects, coupled with the mounting challenges of urban sustainability (Bibri 2018a, 2019). In particular, what has brought the two disciplines of smart urban growth and sustainable urban development closer than ever before, despite their different trajectories followed until recently, is the growing realization of the role of technological advancements in monitoring, understanding, analyzing, and planning urban environments and making well-informed technical and policy decisions and gaining deep insights into major trends and shifts, as well as in reducing resource consumption, lowering pollution and waste, and improving the quality of life and well-being. Smart sustainable/sustainable smart cities as a holistic urban planning and development approach aim primarily at substantiating and strengthening the growing potential and role of advanced ICT, especially big data technology and its novel applications, in enabling sustainable cities to improve, advance, and maintain their contribution to the goals of sustainable development, smart cities to incorporate these goals and thus enhance their sustainability performance, and both classes of cities to eventually rise to the challenges of urbanization and contain its potential effects. With that in mind, the way forward for developing and realizing smart sustainable/sustainable smart cities is through amalgamating the sustainable city and smart city landscapes and approaches, a process which typically takes multiple forms depending on several factors, including objectives, requirements, resources, and interpretations, in addition to the social, cultural, national, and local contexts in which these factors arise and are embedded as associated with particular urban projects, initiatives, and programs.

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A State-of-the-Art Review of Smart Sustainable Cities and Related Big Data Analytics and Its Application

In addition to being an intellectual discourse that has emerged in more recent years and is increasing becoming powerful, more established, and prevailing worldwide (see Chap. 4 for further details), smart sustainable/sustainable smart urban planning and development can be viewed as a strategic approach or pathway to achieving the long-term goals of urban sustainability in an increasingly technologized, computerized, and urbanized world. Important to note, while there is a growing interest in the flourishing field of sustainable smart city research, the academic discourse on sustainable smart urban planning and development within the relevant literature is still scant and also heavily weak on empirical grounding—yet rapidly burgeoning (Bibri 2018b, 2019). Indeed, a few studies exploring the subject of sustainable smart cities have been published in mainstream journals. The case is evidently different from smart cities as an urban planning and development approach that has been around for more than two decades or so, thereby witnessing a proliferation of academic publications and scientific writings and thus demonstrating a large body of successful practices.

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However, the extent to which the field of sustainable smart cities is blossoming gives a clear indication of its future developmental path and research direction. In fact, this field of research has materialized in response to the need for overcoming the numerous challenges and issues pertaining to the existing approaches to smart cities with regard to sustainability and urbanization. Worth pointing out is that there are several differences between sustainable smart cities and smart sustainable cities. One obvious distinction to highlight is that the former involves those cities that badge themselves as smart and are striving to become sustainable, and this class of cities often relates to technologically advanced nations. The latter involves those cities that badge themselves as sustainable and are striving to improve, advance, and maintain their contribution to sustainability using the advanced forms of ICT, and this class of cities pertains to ecologically (technologically) advanced nations. Regardless, the whole idea of both classes of cities revolves around leveraging the convergence, ubiquity, advance, and potential of ICT of pervasive computing and its prerequisite enabling technologies, especially big data computing and the underpinning technologies, in the transition toward the needed, or the advisement of, sustainable development in an increasingly urbanized world (Bibri 2018a, b, 2019). Much of what can be said on sustainable smart cities does apply to smart sustainable cities due to the relatively parallel emergence of these two urbanism approaches and the many overlapping technical aspects between them, coupled with their prominence and significance as research areas today in terms of urban analytics, planning, development, and governance (see Bibri 2018b for a qualitative review).

4.1 Smart Sustainable Cities The notion of smart sustainable/sustainable smart cities has a positive connotation in academic and urban circles. The idea of such cities as one of the key-defining and enabling contexts for ICT of pervasive computing for sustainability (Bibri and Krogstie 2016) is seen as a promising approach into decoupling the health of the city and the quality of life of its citizens from the energy and material consumption and concomitant environmental risks that are associated with urban operations, functions, services, designs, strategies, and policies. Such cities as an emerging holistic urban development approach open new windows of opportunity for doing a lot more to advance sustainability with the support of emerging and future ICT, and offer the types of insights and practical ideas that scholars, practitioners, and policymakers need in order to bring about sustainability transitions. At the technical level, they rely on the fulfillment of the most prevalent ICT visions of pervasive computing, especially UbiComp and the IoT (Bibri 2018a; Bibri and Krogstie 2017b, c), which are essentially enabled by big data technology. In other words, big data analytics and its application are prerequisites for realizing ICT of pervasive computing, which is a critical enabler for building such cities. The ability of computerizing urban systems and domains and hence thinking data analytically about how to enhance their contribution to the different aspects of sustainability constitutes an indication of the reach of the gravitational field of big data computing as to enabling a host of innovative solutions and sophisticated approaches from the ground up for operating, managing, designing, planning, developing, and governing smart sustainable/sustainable smart cities of the future (Bibri 2018a, b, 2019; Bibri and Krogstie 2016).

4.1.1 The Key Underlying and Driving Forces for Smart Sustainable City Development We live in a world where ICT has become deeply embedded and interwoven into the very fabric of contemporary cities, i.e., the operating and organizing processes of urban life and thus urban systems and domains are dominated by data and pervaded with information intelligence and high levels of automation and computation (Bibri 2018a). It follows that it is high time for sustainable cities to smarten up and smart cities to get smarter in ways that enable them to meet the required level and optimal level of sustainability, respectively. In particular, for sustainable cities to improve, advance, and maintain their contribution to the goals of sustainable development, they need to leverage their informational landscape by embracing what emerging and future ICT has to offer to make urban living more intelligently sustainable and attractive over the long run in an increasingly computerized and urbanized world (Bibri and Krogstie 2017b). This is predicated on the assumption that emerging and future ICT offers tremendous potential for, and unsurpassed ways of, monitoring, understanding, analyzing, and planning the two classes of cities (Batty et al. 2012; Bibri 2018a, 2019), which can be beneficially directed toward addressing and overcoming the complex challenges of sustainability and the multidimensional effects of urbanization within. There is a considerable body of literature offering a detailed account of the uses and applications of highly sophisticated ICT in a variety of urban contexts (Angelidou et al. 2017; Batty et al. 2012; Bibri 2018a, b, 2019; Bibri and Krogstie 2017a; Lytras et al. 2015a, b, 2017). The research field of smart sustainable/sustainable smart cities has materialized in response to the mounting challenges of sustainability and the potential effects of urbanization in an increasingly technologized and computerized world, coupled

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with the unsettled, largely ignored, or intractable issues pertaining to both sustainable cities and smart cities in terms of their planning and development in the context of sustainability. Such issues involve limitations, difficulties, inadequacies, fallacies, and uncertainties as to sustainable cities (especially compact cities and eco-cities) (e.g., Bibri 2018b, 2019; Bibri and Krogstie 2017b; Bulkeley and Betsill 2005; Hofstad 2012; Jabareen 2006; Jenks et al. 1996a, b; Joss 2011; Kärrholm 2011; van Bueren et al. 2011; Neuman 2005; Rapoport and Vernay 2011; Williams 2009). Likewise, they involve misunderstandings, deficiencies, inconsistencies, and paradoxes as to smart cities (e.g., Bibri 2018a, b, 2019; Bibri and Krogstie 2017a; Höjer and Wangel 2015; Kramers et al. 2014; Marsal-Llacuna 2016). Concerning sustainable cities, as an example of related issues, the main question to raise is how compact cities and eco-cities as the most prevalent models of sustainable urban form can be monitored, understood, analyzed, and even integrated, so as to be effectively operated, managed, planned, developed, and governed in ways that strategically enhance, advance, and maintain their contribution to the goals of sustainable development (Bibri 2018a, b). The underlying argument is that more innovative solutions and sophisticated approaches are needed to overcome the kind of wicked problems associated with existing sustainable urban forms. In fact, there is an extreme fragmentation of sustainable cities and smart cities as landscapes, especially on the policy and technical levels, as well as a weak connection between these two classes of cities as approaches on several scales (e.g., Angelidou et al. 2017; Bibri 2018a; Bibri and Krogstie 2017a; Bifulco et al. 2016; Kramers et al. 2014), despite the huge potential of advanced ICT for, and also its proven role in, supporting both sustainable cities in enhancing their performance as well as smart cities in their transition toward the needed sustainable development (e.g., see, Batty et al. 2012; Bettencourt 2014; Bibri 2018a, b, 2019; Kramers et al. 2014; Shahrokni et al. 2015; Yigitcanlar and Lee 2013). In this regard, there is a host of unexplored opportunities toward new approaches to smart sustainable/sustainable smart urban planning and development. In particular, tremendous opportunities are available for utilizing big data applications in smart sustainable/sustainable smart cities to achieve sustainable development objectives. See Chap. 8 for a comprehensive, state-of-the-art review of the field of sustainable cities, with a focus on compact cities and eco-cities as models of sustainable urban forms, in terms of research issues and debates, knowledge gaps, challenges, opportunities, benefits, and emerging practices. And see Chap. 7 for a comprehensive, state-of-the-art review of the field of smart and smarter cities in relation to sustainability and related big data applications in terms of research issues and debates, opportunities and benefits, technological developments, emerging trends, future practices, and challenges and open issues. In light of the above, however, a recent research wave has started to focus on smartening up sustainable cities in ways that can improve, advance, and maintain their contribution to the goals of sustainable development, as well as on incorporating these goals in smart cities in a bid to enhance their sustainability performance (e.g., Al Nuaimi et al. 2015; Batty et al. 2012; Bibri and Krogstie 2017b; Kramers et al. 2014; Neirotti et al. 2014; Shahrokni et al. 2015). The concept and development of smart sustainable/sustainable smart cities seek to enable both sustainable cities and smart cities to realize their potential by getting smarter in their endeavor to rise to and overcome the challenges of sustainability and urbanization. In view of that, the way forward for realizing this goal as crystallized not long time ago in academic circles is through integrating these two urban planning and development approaches. This is to achieve the required level of sustainability with respect to such urban aspects as operations, functions, services, designs, strategies, and policies as manifestations of the processual outcomes of urbanization across diverse urban domains, irrespective of the ambition of the projects and initiatives considered to be smart sustainable/sustainable smart cities, which there will indeed be multiple ways to achieve, based on the requirements, objectives, and resources involved, to reiterate. On the whole, the two integrated approaches hold great potential to provide the kind of solutions and methods needed for advancing sustainability and mitigating the effects of urbanization, especially smart sustainable city approach given the underlying advanced sustainability knowledge and green technology. Yet, smart cities provide numerous benefits in terms of sustainability that should be exploited in the development and implementation of sustainable cities. Bibri and Krogstie (2017a) summarize the main advantages of smart cities for sustainable cities (see Table 1), which are reframed within the research need for advancing sustainable urban forms. The purpose is to provide insights into the relevance and usefulness of combining the strengths of both smart cities and sustainable cities into an integrated holistic approach (Bibri 2018a). Remaining on the topic of the integrated approach, however, Al-Nasrawi et al. (2015) point out that there exists a competition on how to interpret and operationalize the concept of smart sustainable cities. As a corollary of it, there is a great deal of diversity among projects and initiatives considered to be smart sustainable/sustainable smart cities in the form of ideas, arguments, or facts. The diversity underneath the various uses of the term smart sustainable/sustainable smart cities implies that there are both convergences and divergences on the way projects and initiatives conceive of what a smart sustainable/sustainable smart city should be in terms of which integrative perspective should be adopted or how it should be conceptualized and operationalized. This can, though, translate into numerous opportunities toward new approaches to smart sustainable/sustainable smart urban development in order to mitigate or overcome the current fragmentation of the

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Table 1 Benefits of smart cities for sustainable cities Benefits of smart cities for sustainable cities • Data-driven applications for enhancing the performance of the typologies and design concepts of sustainable cities in terms of operational functioning • Sophisticated simulation models for evaluating and optimizing the performance of these typologies and design concepts • Advanced simulation models for enabling the design scalability and planning flexibility of sustainable cities that are necessary for responding to urban growth, environmental pressures, changes in socio-economic needs, unprecedented shifts, global trends, discontinuities, and societal transitions • Intelligence functions for monitoring, operating, and planning sustainable city systems • Innovative frameworks for smartening up the urban metabolism of sustainable cities to maintain their levels of sustainability • Data-centric solutions for integrating urban systems, coordinating urban domains, and coupling urban networks • Data-centric applications for enhancing participation, equity, fairness, safety, and accessibility, as well as service delivery and efficiency in relation to the quality of life • Data-centric solutions for identifying risks, uncertainties, and hazards

landscapes of sustainable cities and smart cities. Already, several topical studies (e.g., Angelidou et al. 2017; Bibri 2018a; Bibri and Krogstie 2017b; Kramers et al. 2014; Kramers et al. 2016; Rivera et al. 2015; Shahrokni et al. 2015; Yigitcanlar and Lee 2013) have addressed the merger of these two landscapes from a variety of perspectives on how the different forms of advanced ICT can improve the different aspects of sustainability, namely using ubiquitous computing, big data computing, and/or context-aware computing to advance urban metabolism, urban form (design and planning), urban public and ecosystem services, urban operations and functions, urban strategies and policies, urban governance and citizen participation, or using simply ICT to optimize energy efficiency and provide solutions for everyday life practices. As an example with more detail concerning the conceptualization of the idea of smart sustainable city, Yigitcanlar and Lee (2013) focus on ‘ubiquitous-eco-city: a smart sustainable urban form’ whose principal premise is to provide a high quality of life and place to residents with low-to-no negative impacts on the natural environment with support of the state-of-the-art technologies in terms of management, planning, and development. The authors intend to put this premise into a test and address whether u-eco-city is a dazzling smart sustainable urban form that constitutes an ideal twenty-first-century city model. In doing so, they place Korean u-eco-city initiatives under the microscope, as well as critically discuss their prospects in forming a smart sustainable urban form and becoming an ideal city model. A conceptualization of u-eco-city as an integrated approach is illustrated in Fig. 1. U-eco-city is an ICT and eco-technology-(EcoT)-embedded smart and sustainable city, where people can access both digital and eco-services based on the technology convergence between ICTs and EcoTs (Lee 2009). Furthermore, worth pointing out is that the divergences on the way to conceptualize and operationalize the concept of smart sustainable cities is due to several factors, or can be explained in various ways. One of which is that this urban phenomenon is relatively new, the term only became widespread during the mid-2010s, and the field is in the very early stages of its development (Bibri 2018a; Bibri and Krogstie 2017a, b). From a conceptually different angle, the integrated approach to smart sustainable cities taking many forms has to do with the commonly held view that there is a conceptualization and operationalization of multiple processes of, and pathways toward, smart sustainable urbanism based on a variety of contexts. In fact, the understanding of the multiplicity of socially constructed visions of smart sustainable cities is at the heart of advancing research and practice, as long as it is driven by some coherence of purpose. In this respect, many socio-culturally specific ideas of smart sustainable urbanism could be replicated in different locations across the world, with little consideration or investigation of their appropriateness. The role of research and practice within such urbanism is to keep alive a multiplicity of pathways by opening a wider discourse about the types of future that are likely to be created and eventually attained. As complex and dynamic systems, smart sustainable cities involve special conundrums, intractable problems, and complex challenges pertaining to sustainability and urbanization (Bibri 2018a). It follows that to tackle such systems requires newfangled ways founded on sophisticated methods as to how they can be monitored, understood, and analyzed so as to be effectively operated, managed, designed, planned, developed, and governed in line with the long-term goals of sustainability. These methods must be, among others, based on complexity sciences and complex systems for explaining and dealing with smart sustainable cities so as to enable more effective actions necessary for enhancing their operational functioning, planning, and adaptation in ways that guide their development toward sustainability (Bibri 2019). The underlying assumption is that complexity sciences are integral to the understanding of smart sustainable cities, which is a

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Fig. 1 Relation between ubiquitous-city and eco-city in the context of u-eco-city. Source Yigitcanlar and Lee (2013)

moving target in that they are becoming more complex through the very technologies being used to understand them (see, e.g., Batty et al. 2012; Bibri 2018a, c). The intent of using complex scientific knowledge in city analysis is to ‘provoke thought, foster deeper understanding, and create fertile insights, with the primary purpose of making visible possible places for actions that can improve the contribution of smart sustainable cities to the goals of sustainable development. This can be accomplished by…devising new urban intelligence functions…[that utilize the complexity sciences in fashioning new powerful forms of urban simulation models] for strategic decision-making based on big data analytics in conjunction with the established design concepts and principles and planning practices of sustainability.’ (Bibri 2018a, p. 297) Our world continues to change rapidly and become more and more complex, and thus, systemic ways of thinking are necessary for managing, adapting, and seeing the wide range of choices we have before us (see Meadows and Wright 2012). This way of thinking provides the opportunity to identify the root causes of sustainability problems and see new opportunities in an increasingly urbanized world. ‘Especially, some of our solutions have created further problems, and many complex problems have been solved by focusing on external factors because they are embedded in larger systems. As real messes, the problems rooted in the internal structure of complex systems as well as their interaction with their environment (e.g., pollution, environmental degradation, toxic waste, economic instability, social inequality, unemployment, and chronic disease) have been difficult to deal with and refused to go away. They persist despite the analytical ability, technical intelligence, and human brilliance that have been directed toward circumventing or eradicating them. They persist because they constitute intrinsically system problems—undesirable patterns of behavior characteristic of the system structures and reciprocal relationships resulting from the profound interactions that produce those patterns. They will yield only as we reclaim our holistic thinking as well as intuition and thereby see the whole system as the source of its own problems, and find the astuteness and wisdom to restructure it and reshape its interaction in ways that control or predict the cycling of reciprocal relationships to yield positive patterns of behavior’ (Bibri 2018a, p. 300).

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Moreover, several ecologically advanced nations aim at or strive for being associated with the concept of smart sustainable cities as a sign of societal development. While some countries claim to have evolved toward smart sustainable cities, and others to have developed the technical infrastructure needed for sustainable smart cities and focused on sustainable development policies, there is no hard evidence to confirm these claims, as there is still no assessment models or advanced frameworks to measure the performance of such cities (Al-Nasrawi et al. 2015; Bibri and Krogstie 2017b). In this respect, Al-Nasrawi et al. (2015) suggest a multidimensional methodological model that assists in evaluating the smartness level of a city while being sensitive to its context and provide further contribution by combining sustainable and smart dimensions or attributes of a city. In addition, the European Union supports the movement of its cities to being smart (and) sustainable; hence, its conscious efforts to drive this by investing in various city initiatives. In relation to the European Innovation Partnership on Smart Cities and Communities website, there are 34 EU projects in different cities concerned with mitigating the various pressures that arise from urban growth and sustainable development. This led to the meeting of the Environment Agency Austria (EAA), the International Telecommunication Union (ITU), EU member states, and other stakeholders in Geneva to come up with and discuss a set of standard indicators to assess a city’s path to being smart and sustainable (UNECE 2015a, b). The Europe 2020 targets serve as a challenge for European cities to improve their competitiveness in terms of how smart, sustainable, and inclusive they are (European Commission 2010). There have been several efforts toward measuring the systematic progress of cities in achieving these targets, as well as comparing progress made with other cities. One of these efforts is city rankings, which serves as a benchmark that cities can use to measure their overall progress toward well-defined targets, as well as to define goals and strategies for future development (Debnath et al. 2014). The indicators jointly proposed by the United Nations Economic Commission for Europe (UNECE) and the International Telecommunications Union (ITU) to rank European capital cities are being used to gauge how smart and sustainable these and other cities are.

4.1.2 Research Gaps There is a wide range of research problems that are worthy for exploration or investigation, since smart sustainable cities are an emerging area and flourishing field. In an extensive interdisciplinary literature review, Bibri and Krogstie (2017a) list a number of existing knowledge gaps within the field of smart sustainable cities in a series of bullet points. The most prominent among these gaps are presented in Table 2. The field of smart sustainable cities is a fertile area of interdisciplinary and transdisciplinary research, entailing clearly a wide spectrum of explorable horizons with many intriguing questions awaiting scholars and practitioners within different disciplines and fields (Bibri and Krogstie 2017a). This is underpinned by the recognition that it provides a unique opportunity to take stock and harness the plethora of lessons learned from almost three decades or so of research and planning devoted to seeking, developing, and implementing sustainable cities, and almost one decade for developing and applying advanced technologies in smart cities to advance sustainability. Therefore, it is high time to leverage the theoretical and substantive knowledge accumulated hitherto on smart sustainable urban development from all kinds of research endeavors as well as projects and initiatives that have contributed to making urban living sustainable and smart (Bibri 2018a).

Table 2 Research gaps Key research gaps within the field of smart sustainable cities • There is a need for investigating the use of smart methods to evaluate the practicality of the typologies and design concepts of sustainable urban forms as to their contribution to sustainability in the context of smart sustainable cities • There is a need for exploring better ways of augmenting these typologies and design concepts with smart applications to enhance their sustainability performance • There is a need for theory for comparing the evolving models of smart sustainable city according to their contribution to sustainability goals and smart targets and the extent to which these are an integrated • There is a need for theory for evaluating the extent to which a given model of smart sustainable city contributes to sustainability • There is a lacuna in analytical studies for investigating propositions about what makes a city smart sustainable • There is a need for developing integrated models or frameworks for spurring the development and deployment of smart sustainable cities • There is no comprehensive framework for merging the informational and physical landscapes of smart sustainable cities to advance sustainability • There is a need for a shared model of smart sustainable city given the normative nature and universal character of sustainability

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4.1.3 Evaluation/Assessment Gaps and Challenges Smart sustainable cities as urban development processes and projects are highly complex and multifaceted in nature—as they involve wicked and intractable problems. Hence, when evaluating them, it is necessary to adopt methods that enable to handle the underlying complexities and to capture their diverse values and multiple purposes. Especially, evaluation methods provide unique windows of opportunity for understanding the outcomes and gauging the impacts of the implemented projects and programs of smart sustainable cities, as well as for learning from experiences in ways that allow urban planners and applied urban scientists to improve the future forms of smart sustainable urban development (Bibri 2018a). As to the practical use of the concept of smart sustainable cities, it has become crucial to develop and implement robust assessment methods and practices (metrics and tools for their continuous evaluation) to ensure that such cities are in fact smart sustainable (Bibri 2019; Bibri and Krogstie 2017a). This is one of the key challenges in the realm of smart sustainable cities (Höjer and Wangel 2015), just as it has always been in the field of sustainable cities: developing and implementing ‘methods for identifying which kinds of solutions (i.e., combining design and planning concepts, infrastructural systems, environmental and urban management systems, and environmental technologies, etc.) are needed, and also evaluating the effects of these solutions in terms of their contribution to the goals of sustainable development based on a systemic perspective… Any assessment framework should entail a holistic approach into evaluating the effects of ICT solutions on the different dimensions of sustainability.’ (Bibri 2018a, pp. 12–13) Bibri (2018a) distills (see Table 3) the main assessment gaps related to smart sustainable cities. 4.1.4 Key Scientific and Intellectual Challenges Smart sustainable cities involve a number of scientific and intellectual challenges concerning the use and application of emerging and future technologies and their novel applications, and also the integration of these with the established design concepts and principles and planning practices of urban sustainability. According to Bibri (2018a), such challenges include, but are not limited to, the following: • Monitoring smart sustainable cities and relating their strategies, design concepts and principles, spatial organizations, infrastructures, and ecosystem and human services to their operational functioning and planning through control, automation, optimization, management, and prediction in the form of intelligence functions. This entails using advanced analysis, modeling, and simulation methods based on big data analytics and underpinning technologies to enhance decision-making processes pertaining to such functions. In this respect, major efforts should be directed toward showing how developments in big data technology and its novel applications can be integrated so that cities can become truly smart sustainable in the way urban planners and citizenry can use such technology and its capabilities to improve the different aspects of sustainability and the quality of life. This form of multimodality constitutes a real challenge. • Exploring the notion of smart sustainable/sustainable smart cities as innovation labs in terms of developing and applying urban intelligence functions covering many different urban domains and sub-domains. As an advanced form of decision support, such functions integrate, synthesize, and analyze data flows for the purpose of improving the sustainability, efficiency, resilience, equity, and quality of life in smart sustainable/sustainable smart cities. Accordingly, the kind of urban intelligence functions that such cities should evolve in the form of laboratories that enable their monitoring, planning, design, and development include, but are not limited to, the following. • The efficiency of energy systems. • The improvement of transportation and communication systems. • The improvement of water, power, and sewage systems. • The enhancement of urban metabolism.

Table 3 Assessment gaps Key assessment gaps pertaining to smart sustainable cities • There is a lack of models that can be used as a classification system or ranking instrument against which smart sustainable cities can be evaluated in terms of their smart contribution to sustainability • There is no assessment framework for measuring the extent to which smart targets enhance sustainability goals • There are deficiencies in the few available models for measuring the performance of smart sustainable cities • There is a need for benchmarking tools for measuring the overall progress of smart sustainable cities toward well-defined targets, as well as for defining goals and strategies for future development

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• The effectiveness of distribution systems. • The robustness and resilience of urban infrastructures in terms of their ability to withstand adverse conditions and to quickly recover from difficulties. • The efficiency and scalability of urban design in terms of forms, structures, and spatial organizations. • The optimal use and accessibility of facilities. • The efficiency of human services delivery. • The optimization of ecosystem services provision. • The dynamic, continuous, and short-term forms of planning. Such functions represent new conceptions of how smart sustainable/sustainable smart cities function and utilize and combine complexity science and urban science in fashioning new powerful forms of urban simulations models and optimization and prediction methods that can generate urban structures and forms as well as spatial organizations and scale stabilizations that improve sustainability, efficiency, resilience, and the quality of life. Especially, building models of such cities operating and functioning in real time from routinely and automatically sensed data have become a clear prospect (see, e.g., Batty et al. 2012; Kitchin 2014). • Constructing and aggregating many different urban simulation models related to various urban systems, domains, and networks in terms of their integration, coordination, and coupling, respectively, as well as to human mobility in terms of its link to spatial organizations, scale stabilizations, typological arrangements, transport systems, socio-economic performance, environmental performance, and land use. The aim of providing portfolios of such models is to inform the future design of smart sustainable cities on the basis of predictive insights and forecasting capabilities. This is becoming increasingly achievable due to the recent advances in, and the pervasiveness of, sensor technologies and their ability to provide information about medium- and long-term changes in the realm of real-time cities (Bibri 2018a, b). Therefore, there clearly is an immediacy in the construction of urban simulation models. Adding to this is to explore and diversify the approach to the construction and evolution of such models based on the science of complexity. Indeed, it is important to build many different models of the same situation in the belief that a pluralistic approach is central to enhancing the understanding of this complexity (Batty et al. 2012). • Developing effective technologies and platforms that ensure equity and fairness, facilitate widespread participation, enable shared knowledge for democratic governance, as well as realize a better quality of city life. Here, the role of big data technologies is instrumental in providing effective and useful tools. However, new technologies have a tendency to create new, and worsen existing, digital divides at many levels. This relates to the risks of ICT being posed to social sustainability. Of particular importance is to address the digital divides pertaining to education, age, social status, culture, ethnicity, gender, and disability (Bibri 2019). For example, as revealed by Gebresselassie and Sanchez (2018), urban transport apps have the potential to address or respond to the equity and inclusion challenges of social sustainability by employing universal design in general-use apps, including cost-conscious features, providing language options, as well as by developing smartphone apps for persons with disabilities. Overall, smart sustainable city strategies should accommodate the needs of their citizens and incorporate bottom-up approaches. In a recent work, Carrasco-Sáez et al. (2017) propose a new pyramid of needs for digital citizens as a way of transitioning toward socially sustainable smart cities. Regardless, socio-economic factors affect the use of smart technologies, and to fully optimize their potential, such factors need to be addressed so that smart sustainable city technologies can contribute to social sustainability. As stated by Batty et al. (2012, p. 481), ‘We need to explore how new forms of regulation at the level of urban transport and planning, and economic and community development can be improved using future and emerging technologies.’ In relation to citizen participation and knowledge sharing, ‘new ICT is essentially network-based and enables extensive interactions across many domains and scales. Part of the process of coordination and integration using state-of-the-art data systems and distributed computing must involve ways in which the citizenry is able to participate and to blend their personal knowledge with that of experts who are developing these technologies.’ (Batty et al. 2012, p. 481) • Promoting virtual mobility for reducing environmental impacts and improving physical mobility for enhancing spatial and non-spatial accessibility to opportunities, services, and facilities for citizens, thereby enabling them to improve their levels of life satisfaction. The latter form of mobility is at the core of such typologies as density, diversity, compactness, and mixed-land use, supported by sustainable and efficient transport systems as well as advanced smartphone apps.

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In light of the above, the challenges substantiate the need for integrating smart cities and sustainable cities as urban development strategies. However, it is important to address and overcome these challenges in order for smart sustainable cities to corroborate the quest for achieving the long-term goals of sustainability in an increasingly technologized and urbanized world.

4.2 Big Data Analytics and Its Application We stand at a threshold in beginning to make sense of big data analytics and its core objective: data-driven decision-making that will be of massive use in, and interwoven into the very fabric of, smart sustainable/sustainable smart cities of the future as complex systems and dynamically changing environments within the next decades. The ultimate aim is to advance the different aspects of urban sustainability by smartening up the way smart sustainable/sustainable smart cities of the future can be monitored, understood, and analyzed so as to be more intelligently operated, managed, organized, designed, planned, developed, and governed in terms of improving and maintaining their contribution to sustainability. We are certainly entering a new era, an increasingly computerized and data-dominated urban world where new and extensive data sources, coupled with big data computing and the underpinning technologies, will be instrumental, if not determining, in discovering useful knowledge in large masses of data that no one has hitherto been able to discover for making a wide range of well-informed, strategic, and fact-based decisions across many urban domains in the context of sustainability (Bibri 2018a).

4.2.1 Big Data Applications for Multiple Urban Domains or Sub-Domains In smart sustainable/sustainable smart cities, big data analytics and its application are associated with such diverse intelligence functions as control, automation, optimization, management, prediction, and enhancement, which are involved in the operational functioning and planning of urban systems as spanning urban domains. Hence, big data applications are well positioned to enhance the sustainability, efficiency, and resiliency performance of such cities, as well as the life quality, well-being, and equity of their citizens (Bibri 2019). Indeed, they are increasingly permeating the systems and domains of such cities due to their potential for enabling and realizing the needed transition to sustainable development in an increasingly urbanized world. The range of the emerging big data applications as novel analytical and practical solutions that can be utilized for advancing urban sustainability is potentially huge, as many as the case situations where big data analytics may be of relevance to enhance some sort of decision or insight in connection with the existing urban domains or sub-domains. In a recent interdisciplinary and transdisciplinary review and synthesis, Bibri (2019) lists the most common big data applications, both analytical and practical, in a series of bullet points in relation to the key existing urban domains or sub-domains, and moreover, and their sustainability effects are elucidated, which are associated with the underlying functionalities of such applications as pertaining to urban operations, functions, services, designs, strategies, and policies. This review indeed reveals that tremendous opportunities are available for utilizing big data applications in smart sustainable/sustainable smart cities of the future to improve, advance, and maintain their contribution to the goals of sustainable development through optimizing and enhancing the underlying processes and activities, as well as finding answers to challenging analytical questions and advancing different forms of knowledge. The data-centric applications addressed in this review pertain to transport and traffic, mobility, energy, power grid, environment, buildings, infrastructures, urban planning, urban design, academic and scientific research, governance, health care, education, and public safety. In addition, big data analytics capabilities hold tremendous potential to revolutionize city analytics in relation to sustainable planning and development with regard to the analytical applications enabled by data mining techniques as innovative solutions for sustainability problems (see Chap. 8 for further details). In the spirit of the evolving paradigm shift in city planning and development, manifested in the rise of smart sustainable/sustainable smart cities, advances in such capabilities, especially large-scale data mining computation, will make it possible to find answers to challenging analytical questions and hence address complex challenges pertaining to sustainability and urbanization in ways that were, in many cases, not conceivable, even a decade ago (Bibri 2018a). Such cities are primarily aimed at a set of transformative, innovative urban processes and approaches that amalgamate technological capabilities and strategic decision-making in planning and development to advance the operation, functioning, organization, management, and governance of urban systems and domains on the basis of a quest for promoting the health of individual citizens, communities, and natural ecosystems; conserving resources; and fostering economic development.

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4.2.2 Research Problems, Challenges, and Open Issues The past few years have witnessed extensive investments in the ICT infrastructure of smart sustainable/sustainable smart cities in terms of large-scale deployments across the globe, especially in big data analytics and its core enabling technologies. This is making it increasingly feasible to collect, store, manage, and analyze large amounts of data throughout urban domains and to deploy the analytical outcome to serve many purposes, despite the limited capacities of the prevailing analytic systems or data processing platforms in use (Bibri 2019). This development is opening new windows of opportunity for invigorating the application demand for the urban sustainability solutions that big data analytics can offer. Concurrently, the application of big data analytics has been expanded beyond the realm of business intelligence (in light of this development) to include the domain of smart sustainable/sustainable smart urban development (e.g., Al Nuaimi et al. 2015; Batty et al. 2012; Bibri 2018a, b, 2019; Bibri and Krogstie 2017b, c; Bettencourt 2014; Kumar and Prakash 2014). However, research on big data analytics and its application tends to deal largely with economic development (i.e., management, optimization, effectiveness, innovation, productivity, etc.) and the quality of life in terms of service efficiency and betterment in the context of smart and smarter cities (e.g., Batty 2013; Khan et al. 2015; Khanac et al. 2017; Kitchin 2014, 2016; Hashem et al. 2016; Rathore et al. 2018) while overlooking and barely exploring the rather more urgent or pressing issues related to the different dimensions of sustainability. This paucity of research pertains particularly to the untapped potential of big data technologies and their novel applications for enhancing the environmental and social aspects of sustainability in the context of smart sustainable/sustainable smart cities (Bibri 2018a, 2019). Indeed, many of the emerging smart solutions are not aligned with sustainability goals (Ahvenniemi et al. 2017). This relates to the deficiencies of smart and smarter cities in this regard. As discussed above, such cities have, irrespective of which ICT visions they tend to instantiate in relation to their operational functioning, management, planning, and development, been subject to much debate, generating a growing level of criticism that essentially questions their added value to sustainability due to the lack of incorporating the fundamental goals of sustainable development, as well as falling short in considering the environmental and social indicators of sustainability (e.g., Ahvenniemi et al. 2017; Bibri 2018a, b, 2019; Bibri and Krogstie 2017a; Höjer and Wangel 2015; Kramers et al. 2014; Marsal-Llacuna 2016). Consequently, a recent research wave has started to focus on enhancing smart and smarter city approaches to achieve the required level of sustainability through aligning urban operations, functions, designs, strategies, services, and policies with the goals of sustainable development using big data applications under what is labeled ‘sustainable smart cities’ (e.g., Al Nuaimi et al. 2015; Batty et al. 2012; Bibri 2018a, b, 2019; Bettencourt 2014). In this regard, there are only a few topical studies that address the uses of big data applications but mainly in relation to some aspects of environmental sustainability, or that only pass reference on the role of such applications in improving sustainability (Bibri 2019). Similarly, there are only a few studies that have recently started to focus on the uses of big data applications in relation to the different aspects of sustainability in the context of smart sustainable cities (see, e.g., Bibri 2018a, b; Bibri and Krogstie 2017b, c). This paucity of research can be, nevertheless, explained by the fact that smart sustainable cities is a new urban phenomenon, and the concept only became widespread during the mid-2010s, as mentioned above. For those who are interested in gaining further insights, Bibri (2019) provides a comprehensive, state-of-the-art review and synthesis addressing the sustainability of smart and smarter cities and related big data applications in terms of research issues and debates as well as challenges and open issues. With respect to the latter, the author identifies significant scientific and intellectual challenges and common open issues that need to be addressed and overcome prior to achieving a more effective utilization of big data analytics and related applications in the realm of sustainable smart and smarter cities. They do apply to smart sustainable cities as well (Bibri 2018a). These challenges are mostly of computational, analytical, technical, and logistic kinds. While most of the challenges and open issues are currently under investigation and scrutiny by the relevant research and industry communities, supported by technology and innovation policies, deploying big data applications in smart sustainable/sustainable smart cities of the future requires overcoming other organizational, institutional, political, social, ethical, and regulatory challenges. These are likely to hinder the development and implementation of big data applications in such cities. Nevertheless, with all success factors in place, coupled with a deep understanding of the emerging phenomenon of smart sustainable/sustainable smart cities and an acknowledgment of the potential of big data computing, making such cities smarter in achieving sustainability becomes an attainable goal in an increasingly urbanized world.

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Discussion and Conclusion

This chapter provides a comprehensive, state-of-the-art review of smart sustainable/sustainable smart cities as a leading paradigm of urbanism. Different topical subjects or categories were identified and addressed, including foundational components and assumptions, research status, issues and debates, research opportunities and challenges, future practices and horizons, and technological trends and developments. This interdisciplinary and transdisciplinary review in nature is primarily meant to facilitate collaboration among different disciplinary fields and technological areas for the sheer purpose of generating the kind of interactional and unifiable knowledge that is necessary for a more integrated and deeper understanding of the topic of smart sustainable urbanism in the age of big data. The outcome of this topical review allowed to establish the status of the current knowledge about the field, as well as to highlight the potential of big data computing for advancing urban sustainability in the future. As to the findings, this chapter shows that smart sustainable urbanism involves numerous issues that are unsolved, largely ignored, or underexplored from an applied theoretical perspective. And a large part of research in this area focuses on exploiting the potentials of big data technologies and their novel applications as an effective way to mitigate or overcome the issue of sustainable cities and smart cities being extremely fragmented as landscapes and weakly connected as approaches, especially at the technical level. We are living at the dawn of what has been termed as ‘the fourth paradigm of science,’ a scientific revolution that is marked by the recent emergence of big data science and analytics as well as the increasing adoption of the underlying technologies in scientific and scholarly research practices. Big data science and analytics possesses the unparalleled potential to revolutionize society in a way that no one is able to predict in terms of the dramatic change that it will have on our lives. Indeed, it embodies an unprecedentedly transformative and constitutive power—manifested not only in the form of revolutionizing science and transforming knowledge, but also in advancing research and social practices, producing new discourses (e.g., data-driven smart sustainable urbanism), creating and catalyzing major shifts, and fostering societal transitions. The prospect of smart sustainable/sustainable smart cities is becoming the new reality, in particular within ecologically and technologically advanced nations (Bibri 2019; Bibri and Krogstie 2016), owing to the underlying global driving factors and prevailing and emerging trends. This development will undoubtedly continue, as it is supported by strong external forces and societal structures affecting the phenomenon of smart sustainable/sustainable smart cities. Moreover, it constitutes part of rather larger societal shifts (i.e., sustainability transitions as enabled by emerging and future ICT) with far-reaching and long-term implications. This is anchored in the recognition that there are fascinating possibilities and immense opportunities to exploit and realize from deploying and implementing the innovative solutions and sophisticated approaches being offered by big data computing and the underpinning technologies. Concerning the value of this topical review, the outcome enables researchers and scholars to focus their work on the identified real-world challenges and issues and the existing knowledge gaps pertaining to smart sustainable/sustainable smart cities as an approach to urbanism from the perspective of integrating technology and sustainability and harnessing their clear synergy in improving, advancing, and maintaining the contribution of such cities to sustainability. Practitioners and experts can make use of this outcome to identify common weaknesses and potential ways to solve them as part of the ongoing and future endeavors of sustainable smart urban planning and development. In view of that, this interdisciplinary and transdisciplinary review provides a valuable reference for researchers and practitioners in related research communities and the necessary material to inform these communities of the latest developments in the field. Lastly, this chapter provides a form of foundation for further discussion to debate over the disruptive, substantive, synergetic, and transformational effects of big data computing on forms of the operational functioning, management, planning, and development of smart sustainable/sustainable smart cities in terms of sustainability practices in the future. Also, it presents a sort of basis for stimulating more in-depth research on such cities and big data computing in the form of both qualitative analyses and quantitative investigations focused on establishing, uncovering, substantiating, and/or challenging the assumptions underlying the relevance and meaningfulness of big data analytics and related applications as technological advancements with regard to advancing sustainability.

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Kramers, A., Wangel, J., & Höjer, M. (2016). Governing the smart sustainable city: The case of the Stockholm Royal Seaport. In: Proceedings of ICT for Sustainability 2016 (Vol. 46, pp. 99–108). Amsterdam: Atlantis Press. Kumar, A., & Prakash, A. (2014). The role of big data and analytics in smart cities. International Journal of Scientific Research (IJSR), 6(14), 12–23. Lee, S. (2009). Introduction to ubiquitous city. In S. Lee (Ed.), Ubiquitous city: Future of city, city of future. Daejeon, Korea: Hanbat National University Press. Lytras, M. D., Mathkour, H. I., Abdalla, H., Al-Halabi, W., Yanez-Marquez, C., & Siqueira, S. W. M. (2015). Enabling technologies and business infrastructures for next generation social media: Big data, cloud computing, internet of things and virtual reality. Journal of Universal Computer Science, 21, 1379–1384. Lytras, M. D., Mathkour, H. I., Abdalla, H., Al-Halabi, W., Yanez-Marquez, C., & Siqueira, S. W. M. (2015). An emerging—Social and emerging computing enabled philosophical paradigm for collaborative learning systems: Toward high effective next generation learning systems for the knowledge society. Computers in Human Behavior, 5, 557–561. Lytras, M. D., Raghavan, V., & Damiani, E. (2017). Big data and data analytics research: From metaphors to value space for collective wisdom in human decision making and smart machines. International Journal on Semantic Web and Information Systems, 13, 1–10. Marsal-Llacuna, M.-L. (2016). City indicators on social sustainability as standardization technologies for smarter (citizen-centered) governance of cities. Social Indicators Research, 128(3), 1193–1216. https://doi.org/10.1007/s11205-015-1075-6. Meadows, D., & Wright, D. (2012). Thinking in systems: A primer. Taylor and Francis. Neirotti, P., De Marco, A., Cagliano, A. C., Mangano, G., & Scorrano, F. (2014). Current trends in smart city initiatives—Some stylized facts. Cities, 38, 25–36. Neuman, M. (2005). The compact city fallacy. Journal of Planning Education and Research, 25, 11–26. Rapoport, E., & Vernay, A. L. (2011). Defining the eco-city: A discursive approach. Paper presented at the management and innovation for a sustainable built environment conference, international eco-cities initiative (pp. 1–15). Amsterdam: The Netherlands. Rathore, M. M., Won-HwaHong, A. P., Seo, H. C., Awan, I., & Saeed, S. (2018). Exploiting IoT and big data analytics: Defining smart digital city using real-time urban data. Journal of SSC, 40, 600–610. Rivera, M. B., Eriksson, E., & Wangel, J. (2015). ICT practices in smart sustainable cities—In the intersection of technological solutions and practices of everyday life. In: 29th International Conference on Informatics for Environmental Protection (EnviroInfo 2015), Third International Conference on ICT for Sustainability (ICT4S 2015) (pp. 317–324). Atlantis Press. Shahrokni, H., Årman, L., Lazarevic, D., Nilsson, A., & Brandt, N. (2015). Implementing smart urban metabolism in the Stockholm Royal Seaport: Smart city SRS. Journal of Industrial Ecology, 19(5), 917–929. UNECE. (2015a). Key performance indicators for smart sustainable cities to assess the achievement of sustainable development goals (Vol. 1603). https://doi.org/ITU-TL.1603. UNECE. (October 2015b). The UNECE–ITU smart sustainable cities indicators. Van Bueren, E., van Bohemen, H., Itard, L., & Visscher, H. (2011). Sustainable urban environments: An ecosystem approach. Springer International Publishing. Williams, K. (2009). Sustainable cities: Research and practice challenges. International Journal of Urban Sustainable Development, 1(1), 128–132. Yigitcanlar, T., & Lee, S. H. (2013). Korean ubiquitous-eco-city: A smart-sustainable urban form or a branding hoax? Journal of Technological Forecasting and Social Change, 89, 100–114.

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The Theoretical and Disciplinary Underpinnings of Data–Driven Smart Sustainable Urbanism: An Interdisciplinary and Transdisciplinary Perspective

Abstract

Interdisciplinarity and transdisciplinarity have become a widespread mantra for research within diverse fields, accompanied by a growing body of academic and scientific publications. The research field of smart sustainable/sustainable smart urbanism is profoundly interdisciplinary and transdisciplinary in nature. It operates out of the understanding that advances in knowledge necessitate pursuing multifaceted questions that can only be resolved from the vantage point of interdisciplinarity and transdisciplinarity. Indeed, related research problems are inherently too complex and dynamic to be addressed by single disciplines. In addition, this field does not have a unitary approach in terms of a uniform set of concepts, theories, and disciplines, as it does not represent a specific direction of research but rather multiple directions. These are analytically quite diverse. Regardless, interdisciplinarity and transdisciplinarity as scholarly perspectives apply, by extension, to any conceptual, theoretical, and/or disciplinary foundations underpinning this field. Such perspectives in this chapter represent a rather topical and organizational approach as justified and determined by the interdisciplinary aid transdisciplinary nature of the research field of smart sustainable urbanism. In this subject, additionally, theories from academic and scientific disciplines constitute a foundation for action—data–driven smart sustainable urbanism and related urban big data development as informed by data science practiced within the fields of urban science and urban informatics, as well as by sustainability science and sustainable development. In light of this, it is of relevance and importance to develop a foundational approach consisting of the relevant concepts, theories, discourses, and academic and scientific disciplines that underpin smart sustainable urbanism as a field for research and practice. With that in regard, this chapter endeavors to systematize this complex field by identifying, distilling, mixing, fusing, and thematically analytically organizing the core dimensions of this foundational approach. The primary intention of setting such approach is to conceptually and analytically relate urban planning and development, sustainable development, and urban science while emphasizing why and the extent to which sustainability and big data computing have particularly become influential in urbanism in modern society. Being interdisciplinary and transdisciplinary in nature, such approach is meant to further highlight that this scholarly character epitomizes the orientation and essence of the research field of smart sustainable urbanism in terms of its pursuit and practice. Moreover, its value lies in fulfilling one primary purpose: to explain the nature, meaning, implications, and challenges pertaining to the multifaceted phenomenon of smart sustainable urbanism. This chapter provides an important lens through which to understand a set of theories that is of high integration, fusion, applicability, and influence potential in relation to smart sustainable urbanism. Keywords


















Smart sustainable urbanism Sustainable urbanism Interdisciplinary Transdisciplinary Sustainability Sustainable development Urban planning and development Big data computing Scientific disciplines Academic disciplines




© Springer Nature Switzerland AG 2019 S. E. Bibri, Big Data Science and Analytics for Smart Sustainable Urbanism, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-17312-8_3




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Introduction

Interdisciplinarity and transdisciplinarity have become a widespread mantra for research within diverse fields, accompanied by a growing body of academic and scientific publications. The research field of smart sustainable/sustainable smart urbanism (urban planning and development) is profoundly interdisciplinary and transdisciplinary in nature. This applies, by extension, to any approach to its conceptual, theoretical, and/or disciplinary foundations, which is accordingly multidisciplinary as well in the sense of using insights from several disciplines, or involving several disciplines in an approach to a problem or topic. However, multidisciplinary efforts remain limited in impact on theory building for coping with the changing human condition (Morinière 2012). Clearly, smart sustainable/sustainable smart urbanism research naturally lends itself to multidisciplinary, interdisciplinary, and transdisciplinary approaches and strategies (see, e.g., Bibri 2018a, 2019a; Warleigh-Lack 2011). They all require conceptual precision in order for research outcomes to be valid and usable (e.g., Lytras and Visvizi 2018). In the subject of smart sustainable/sustainable smart urbanism, the underlying theories from academic and scientific disciplines constitute a foundation for action—data–driven smart sustainable urbanism and related urban big data development as informed by data science practiced within the fields of urban science and urban informatics, as well as by sustainability and sustainable development. The theories of sustainability science, data science, urban science, urban informatics, urban computing, and ICT have become influential within urbanism with respect to urban forms, urban systems, urban domains, urban networks, urban ecosystems, urban services, and so on, as well as to related processes. To put it differently, as an advanced form and area of ICT of pervasive computing, big data computing and its technological applications are increasingly becoming of crucial importance to smart sustainable/sustainable smart urbanism in terms of operational functioning, planning, design, and development, gaining traction and foothold among urban scholars, researchers, scientists, practitioners, and policymakers over the past few years (Bibri 2018a, 2019a). Urban big data technologies have become essential to the operational functioning of cities, and consequently, urban planning, governance, and services are becoming highly responsive to a form of data–driven urbanism (Kitchin 2016), especially in relation to its smartness and sustainability dimensions (Bibri 2018a, 2019a). The main premise is that smart sustainable/sustainable smart urban planning and development as a form of practice is underpinned by complex interdisciplinary and transdisciplinary knowledge. In more detail, such planning and development as an intellectual discourse being applied or practiced in smart sustainable/sustainable smart cities as a holistic approach to urbanism involves a range of theoretical perspectives and scientific and technological foundations drawn from a variety of academic and scientific disciplines, which converge on a common vision of the future and the immense opportunities and fascinating possibilities such future will bring that can be created by amalgamating the innovative solutions and sophisticated approaches enabled by ICT of pervasive computing with urban sustainability (Bibri 2018a; Bibri and Krogstie 2016, 2017b). The research field of smart sustainable/sustainable smart urbanism operates out of the understanding that advances in knowledge necessitate pursuing multifaceted questions that can only be resolved from the vantage point of not only multidisciplinarity, but also interdisciplinarity and trandisciplinarity (Bibri 2018a, c). Related problems are inherently too complex and dynamic to be addressed by single disciplines. Seeking to provide a holistic understanding of the topic of smart sustainable/sustainable smart urbanism for the common purpose of policy or in the pursuit of normative actions, the interdisciplinary approach to research insists on mixing disciplines. This approach crosses boundaries between different disciplines to create new perspectives and insights on the basis of interactional knowledge beyond these disciplines (Bibri 2018a, c, 2019a). Its strength lies in the ability of interlinking different analyzes, using insights and methods from different disciplines in parallel—not in conjunction, and spilling over disciplinary boundaries. The interdisciplinary perspective of the foundational approach this chapter is concerned with is a topical and organizational unit that is driven and determined by the interdisciplinary nature of the research field of smart sustainable/sustainable smart urbanism. This relates to the world’s most pressing and complex problems with long-term wide-area impacts (Max-Neef 2005). Indeed, such urbanism requires understanding diverse academic and scientific disciplines and how these may interrelate to solve similar problems, and accordingly, interdisciplinarity can be best applied to complex subjects that can only be understood by interrelating the perspectives of several disciplines. Whereas the transdisciplinary approach to research insists on fusing, rather than mixing, different disciplines and hence using insights and methods from them in conjunction—with a result that exceeds the simple sum of each (Bibri 2018a, c, 2019a). Transdiciplinarity lends itself readily to the exploration of complex topics or problems. It concerns that which is at once between, across, and beyond single disciplines, and its aim is to understand the present phenomena in the world, of which a key imperative is the overarching unity of knowledge. Thus, understanding the tenets, and setting side-by-side

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elements, of pertinent theories that have clear implications for the nation of smart sustainable/sustainable smart urbanism permits a complete understanding of its topic (Bibri 2019a). These theories are to be drawn, in the context of this chapter, from urban planning, urban development, sustainability science, sustainable development, urban informatics, urban science, data science, complexity science, and ICT. Moreover, within such urbanism, the tension is between the development and performance of urban systems, domains, and networks as well as advanced technologies and their applications, on the one hand, and scientific, environmental, social, institutional, and political practices, on the other hand. From a general perspective, transdisciplinary research entails efforts made in light of different academic and scientific disciplines in a joint endeavor to innovate as to creating new conceptual, theoretical, and methodological approaches that integrate and move beyond discipline–specific ones to address and overcome a common problem. An example of such problem would be to use big data technology and its novel applications to achieve the required or optimal level of sustainability in the context of sustainable cities and smart cities. The transdisciplinary perspective of the foundational approach this chapter is concerned with is, in addition to being a topical and organizational unit driven and determined by the transdisciplinary nature of the research field of smart sustainable/sustainable smart urbanism, an opportunity to situate the researcher in an ecology of ideas, a process which can be approached from the perspective of complexity. In this respect, the key dimensions that can be considered include: integrating rather than eliminating the researcher from the research, meta–paradigmatic rather than intra–paradigmatic, research–grounded rather than discipline–grounded, and applying systems and complexity thinking rather than reductionism (Bibri 2019a). In light of the above, it is of relevance and importance to develop a foundational approach consisting of the relevant concepts, theories, discourses, and academic and scientific disciplines that underpin smart sustainable/sustainable smart urbanism as a field for research and practice. With that in regard, this chapter endeavors to systematize this complex field by identifying, distilling, mixing, fusing, and thematically analytically organizing the core dimensions of this foundational approach. The primary intention of setting such approach is to conceptually and analytically relate urban planning and development, sustainable development, and urban science while emphasizing why and the extent to which sustainability and big data computing have particularly become influential in urbanism in modern society. Being interdisciplinary and transdisciplinary in nature, such approach is meant to further highlight that this scholarly character epitomizes the orientation and essence of the research field of smart sustainable/sustainable smart urbanism in terms of its pursuit and practice. The remainder of this chapter is organized as follows. Section 2 addresses the conceptual and theoretical dimensions of the foundational approach to smart sustainable/sustainable smart urbanism. Section 3 looks at its disciplinary dimension in terms of academic and scientific disciplines and relevant cross–disciplinary aspects and issues. This chapter ends, in Sect. 4, with concluding remarks along with varied discussions.

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Concepts, Theories, and Academic Discourses

In this section, the focus is on identifying, describing, and discussing the key concepts, theories, and academic discourses in relevance to the topic of data–driven smart sustainable/sustainable smart urbanism, as well as on highlighting and elucidating the pertinent linkages between them to facilitate the understanding of this interdisciplinary and transdisciplinary topic. Before delving into this, it might be useful to explain what an academic discourse is and entails. An academic discourse refers to the various ways of thinking, using language, and producing meaning in academic institutions, more succinctly, the specific styles of communication used in the academic world (Bibri 2018a). It represents a privileged form of argument in modern society, offering a model of rationality and detached reasoning (Hyland and Bondi 2006). As such, it provides a somewhat objective description of what the human and social world is actually like, and this, in turn, serves to distinguish it from the socially contingent form of description (Bibri 2018a). This pattern of persuasion, which involves the use of language to relate independent beliefs to shared experience, is seen as a guarantee of reliable knowledge, and we invest it with cultural authority, free of the cynicism with which we view other discourses such as politics (see Hyland 2000). Overall, an academic discourse depends on the demonstration of absolute truth or empirical evidence, representing what Lemke (1995) refers to as the discourse of truth. Next, the relevant concept, theories, and academic/intellectual discourses identified are addressed.

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2.1 Big Data Computing Big data computing is an emerging paradigm of data science, which is of multidimensional data mining for scientific discovery over large scale infrastructure. Advances in ICT in various forms and its widespread development, diffusion, and integration into many spheres of society and hence numerous domains of all kinds of specializations, including urban, scientific, medical, technological, engineering, economic, environmental, ecological, social, and political, are resulting in data explosion as manifested in the huge data deluge flooding from new and extensive sources, rapidly unfolding, and endlessly soaring. Data mining/knowledge discovery and decision–making from voluminous, varied, real-time, exhaustive, fine-grained, indexical, dynamic, flexible, evolvable, relational data is a daunting challenge/task in terms of storage, management, organization, processing, analysis, evaluation, interpretation, modeling, and simulation, as well as in terms of the visualization and deployment of the obtained results for enhancing and optimizing operations, functions, services, designs, strategies, and policies. This emerging trend of big data computing is influential, formative, groundbreaking, innovative, and long-lasting. As a new paradigm, it amalgamates, as underpinning technologies, large-scale computation as well as new data–intensive techniques and algorithms and advanced mathematical models to build and perform data analytics. As such, big data computing demands a huge storage and computing power for data curation and processing for the purpose of discovering new or extracting useful knowledge typically intended for immediate use in an array of multitudinous decision– making processes to achieve different purposes. In more detail, it entails the following components: • Advanced techniques based on data science fundamental concepts, including data mining, machine learning, statistical analysis, explanatory and predictive modeling, database querying, data warehousing, and so on. Computer science methods such as artificial intelligence, mathematical modeling, database management, pattern recognition, data manipulation, and data visualization. • Data mining models to perform data processing and analysis functions, including distributed data mining, multilayer data mining, multi-technology integration-oriented data mining, and grid-based data mining. • Computational mechanisms such as selection, preprocessing, transformation, analysis, evaluation, interpretation, deployment, as well as search, sharing, transfer, querying, updating, modeling, and simulation, just to name a few of them. They involve such sophisticated and dedicated software applications and database management systems. • Advanced data mining tasks such as classification, clustering, regression, probability estimation, causal modeling, link prediction, data reduction, co-occurrence grouping, and profiling. • Advanced algorithms such as classifiers (e.g., decision tree induction, Support Vector Machine, Bayesian network, K-nearest neighbor, rough set approach, fuzzy set approach, case-based reasoning, and back propagation); regression algorithms (e.g., multivariate linear, multivariate nonlinear and nonlinear methods); and clustering algorithms (e.g., hierarchical (divisive and agglomerative), partitioning (relocation, probabilistic), density-based (connectivity and function), model-based (high dimensional data, projection, subspace, and co-clustering), grid-based (artificial neural networks, co-occurrence of categorical data, gradient descent, constraint-based, and evolutionary), K-medoids, and K-means). • Modeling and simulation involves digital, computational, mathematical, and/or logical representations of systems or processes as a basis for simulations for implementing related models to develop data as a basis for decision support and thus decision-making pertaining to, for example, experimentation, operation, management, planning, and design. In this regard, engineering knowledge is needed for the conceptualization and implementation of modeling and simulation methods. One of the existing taxonomies of modeling and simulation that is of relevance to smart sustainable/sustainable smart city planning, design, and operation entails the following: – Analyzes support is conducted in support of urban planning (see, e.g., Batty et al. 2012; Bibri 2018a; Bibri and Krogstie 2017b). Very often, the search for an optimal solution (e.g., integration of design concepts and principles and planning practices with big data technology and its novel applications to advance sustainability) that shall be implemented is driving these efforts. What–if analyzes of alternatives fall into this category as well. This sort of work is often accomplished by simulysts. A special use of analyzes support is applied to urban operations. Simulation methods improve the functionality of decision support systems by adding the dynamic element, as well as allow to compute estimates and predictions, including optimization and what–if analyzes. – Systems engineering support is applied for the design, development, and testing of systems (energy, transport, traffic, etc.). It can start in early phases and include topics like executable system architectures. And it can support testing by providing a virtual environment in which tests can be carried out. This sort of work is often accomplished by engineers and architects.

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• Data processing platforms such as Hadoop MapReduce, Spark, Stratosphere, and NoSQL–database system management, with Hadoop MapReduce being the most robust and widely used tool for very large-scale batch data processing. It has various extensions, including Co–Hadoop, Hadoop++, HadoopDB, Cheetahand, and Dare. And numerous technologies (e.g., Apache PIG, Apache Hive, Apache Tez, Apache Giraph, Apache Cassandra, Apache Spark, Apache Scoop, Apache Zookeepe, Apache HBase, Apache Flume, and Scribe) can, together with HDFS, be built on the top of the Hadoop system to form a Hadoop ecosystem to enhance efficiency and functionality (Bibri 2018a, 2019a). • Computing models such as cloud computing and fog/edge computing as platforms and infrastructures. In the first model, standardized, scalable, flexible ICT–enabled capabilities and functions are delivered in real–time via the Internet in the form of three types of services: (1) Software–as–a–Service (SaaS), (2) Platform–as–a–Service (PaaS), and (3) Infrastructure–as–a–Service (IaaS). Fog computing, also known as fogging or edge computing ‘is an architecture that uses one or more collaborative near–user edge devices to carry out a substantial amount of storage (rather than stored primarily in cloud data centers), communication (rather than routed over the Internet backbone), control, configuration, measurement, and management (rather than controlled primarily by network gateways)… Although both fog computing and cloud computing provide storage, applications, and data to end–users, fog/edge computing has a bigger proximity to end–users and bigger geographical distribution…. In the context of smart sustainable cities, fog computing can be seen in big data structures as well as in large cloud systems, making reference to the growing difficulties in accessing information objectively. Fog/edge computing in smart sustainable city applications are network and system architectures that attempt to collect, analyze, and process data from physical assets closer to the requester and in a more efficient way than traditional cloud architecture.’ (Bibri 2018a, pp. 155–157) In their article ‘The Anatomy of Big Data Computing,’ Konugurthi et al. (2016) discuss the following issues: • • • • • •

the evolution of big data computing; differences between traditional data warehousing and big data; taxonomy of big data computing and the underpinning technologies; integrated platform of big data and clouds known as big data clouds; layered architecture and components of big data cloud; and open technical challenges and future directions. Overall, big data computing involves a range of scientific and technological areas, including the following:

• • • • • •

Computer science Data science Information technology Information systems Computer engineering Software engineering.

Big data computing in the context of smart sustainable/sustainable smart urbanism is concerned with the study, design, development, implementation, and maintenance of big data technologies and their novel applications in urban areas, i.e., across urban systems and domains. Specifically, it is concerned with: • designing and constructing urban-oriented big data systems and applications and making them behave intelligently as to decision support to achieve multiple urban goals; • representing, modeling, processing, and managing various kinds of urban data; • collecting information and discovering new, or extracting useful, knowledge for various purposes; and • designing and applying evaluation methods for improving and maintaining the operation of big data systems and applications.

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2.2 Big Data Concept, Analytics, Technology, and Application There is no agreed academic or industry definition of big data. Therefore, many definitions have been suggested and are available in the literature, with each tending to offer a particular or different view of the concept based on the context of use and hence serving as, as one way of looking at it, a constituting or complementary aspect of the full picture of the concept. For example, a survey of the emerging literature conducted by Kitchin (2014, p. 3) denotes a number of key characteristic features. Big data are: • • • •

huge in volume, consisting of terabytes or petabytes of data; high in velocity, being created in or near real–time; diverse in variety, being structured and unstructured in nature, and often temporally and spatially referenced; exhaustive in scope, striving to capture entire populations or systems (n = all), or at least much larger sample sizes than would be employed in traditional, small data studies; • fine-grained in resolution, aiming to be as detailed as possible, and uniquely indexical in identification; • relational in nature, containing common fields that enable the conjoining of different data sets; • flexible, holding the traits of extensionality (can add new fields easily) and scaleability (can expand in size rapidly). A great deal of the existing definitions tend to converge on three main attributes of big data: the huge volume of data, the wide variety of data types, and the velocity at which the data can be collected and analyzed. These are identified as the most agreed upon Vs (e.g., Laney 2001). Yet, big data tend to be characterized by a number of other Vs than these three, including inveracity, validity, value, and volatility (e.g., Khan et al. 2014). See Bibri (2018a) for a descriptive account of all these Vs. Konugurthi et al. (2016) chacterize big data into four dimensions: volume, variety, velocity, and veracity, as illustrated in Fig. 1. In the context of this chapter, the term ‘big data’ is essentially used to mean collections of datasets whose volume, velocity, variety, exhaustivity, relationality, and flexibility make it so difficult to manage, process, and analyze the data using the traditional database systems and software techniques. In other words, big data refer to humongous volumes of both structured and unstructured data that cannot be processed and analyzed with conventional applications, or that exceed their computational and analytical capabilities. Figure 2 depicts big data and the traditional/relational data layers. However, as a common thread running through most of the definitions of big data, the associated information assets are, to reiterate, of high–volume, high–variety, and high–velocity, and thus require cost–effective, innovative forms of data processing, analysis, and management. In the context of smart sustainable/sustainable smart cities, the term can be used to describe a colossal amount of urban data, typically to the extent that their manipulation, analysis, management, and communication present significant computational, analytical, logistical, integrative, and coordinative challenges. Such data are invariably tagged with spatial and temporal labels, commonly streamed from a large number and variety of sources, and mostly generated automatically and routinely; hence, it is near on impossible to make sense of, or decipher, the big data generated in smart sustainable/sustainable smart cities based on computing technology being in use (Bibri 2018a). Therefore, the big data deluge flooding within such cities requires rather the use of novel technologies and their integration in terms of algorithms that are based on supervised and unsupervised learning methods (e.g., classification, clustering, regression, causal

Fig. 1 Big data dimensions 4Vs. Source Konugurthi et al. (2016)

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Fig. 2 Big data versus Traditional data abstraction. Source Konugurthi et al. (2016)

modeling, etc.), techniques (e.g., data mining, machine learning, statistical analysis, predictive modeling, database querying, etc.), and data processing platforms (Hadoop, Spark, HBase, MongoDB, etc.) that could work beyond the limits of the existing analytic systems employed to extract useful knowledge from large masses of data for timely and accurate decision– making and enhanced insights. The common types of big data analytics being used in the domain of smart sustainable/sustainable smart urbanism are: descriptive, predictive, diagnostic, and prescriptive (Bibri 2018a, 2019a). They are to be applied to extract useful knowledge of different forms of intelligence from large datasets, which can in turn be used to serve various purposes depending on the urban application domain. As far as the complexity of big data analytics is concerned, it is commonly characterized by four Is, namely (1) In-situ analytics which directly operates on the data where it sits without requiring an expensive process of Extract, Transform, Load (ETL), (2) interactive analysis where the analysts work interactively with data and the subsequent questions are formulated depending on the results of the previous ones, (3) incremental analysis which requires maintaining models under high data arrival rates and datasets be interactively analyzed based on the previous results, and (4) iterative analysis which iterates over the data several times in order to build and train a model of the data (e.g., predictive data mining) rather than just extract data summaries or make grouping (e.g., descriptive data mining). The term ‘big data analytics’ denotes ‘any vast amount of data that has the potential to be collected, stored, retrieved, integrated, selected, preprocessed, transformed, analyzed, and interpreted for discovering new or extracting useful knowledge. Prior to this, the analytical outcome (the obtained results) can be evaluated and visualized in an understandable format before their deployment for decision-making purposes (e.g., an enhancement of, or a change in, an operation, function, service, design, strategy, or policy). Other computational mechanisms involved in big data analytics include search, sharing, transfer, querying, updating, modeling, and simulation. In the context of sustainable smart/smart sustainable cities, big data analytics refers to a collection of sophisticated and dedicated software applications and database management systems run by machines with very high processing power, which can turn a large amount of urban data into useful knowledge for enhanced, well-informed decision-making and deep insights in relation to various urban domains, such as transport, mobility, traffic, environment, energy, land use, waste management, education, healthcare, public safety, planning and design, and governance’ (Bibri 2018b, p. 234). There exist many data processing platforms that can be used to perform big data analytics in terms of storage, manipulation, management, analysis, and evaluation of large masses of data to extract useful knowledge deployable in the form of intelligence in relation to various urban domains as to operations, functions, strategies, designs, and policies. The use and

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implementation of such platforms depend, or vary based, on several factors pertaining to the computational, analytical, logistic, integrative, and coordinative requirements of big data projects as well as their objectives (e.g., environmental sustainability, social sustainability, public services, etc.). Among the existing data processing platforms being used in smart sustainable/sustainable smart cities based on cloud computing and fog/edge computing models (see Bibri 2018b for a detailed account and comparison of these two models) include Hadoop MapReduce, Spark, Stratosphere, and NoSQL– database system management (e.g., Al Nuaimi et al. 2015; Bibri 2018a,b, 2019a; Fan and Bifet 2013; Karun and Chitharanjan 2013; Khan et al. 2015; Singh and Singla 2015). These platforms ‘perform big data analytics related to a wide variety of large–scale applications intended for different uses associated with the process of sustainable urban development, such as management, control, optimization, assessment, and improvement, thereby spanning a variety of urban domains and sub– domains… Thus, they are prerequisite for data–centric applications in the context of smart sustainable/sustainable smart cities.’ (Bibri 2018b, p. 203) All in all, big data analytics has become a key component of the ICT infrastructure of smart sustainable/sustainable smart cities due to its role in improving sustainability, resilience, efficiency, and the quality of life (see, e.g., Al Nuaimi et al. 2015; Angelidou et al. 2017; Bibri 2018a, b, c, d, Bibri 2019a, b; Bibri and Krogstie 2017a, b; Batty et al. 2012; Hashem et al. 2016; Kumar and Prakash 2014) through effective decision–making processes and thus desired outcomes. In this context, it targets intelligent decision support and optimization and simulation associated with the operational functioning, planning, design, and development of urban systems as operating and organizing processes of urban life in terms of control, automation, management, efficiency, enhancement, and prediction as urban intelligence functions. One example of such functions concerns the provision of ecosystem services and the delivery of human services, as well as the effectiveness of strategies and policies based on emerging trends and shifts, in line with the long–term goals of sustainability (Bibri 2018a). As put by Kitchin (2016), various kinds of data–informed urbanism have been occurring for inasmuch data have been generated about cities; that is, data have been used as the evidence base for formulating urban policies, programmes, and plans to track their effectiveness and to model and simulate future development, Further, however, the targets big data analytics pursues entails the implementation of decision–taking processes, optimization strategies, and simulation models. Overall, the analytical outcome serves to optimize resources utilization, reduce environmental risks, enhance the quality of life and well–being of citizens, and so on.

2.3 Urban Sustainability The concept of sustainability has been applied to urban planning since the early 1990s, a few years after the widespread diffusion of the concept of sustainability. There is no canonical definition of urban sustainability in the literature, as it is a too complex and sweeping concept to delineate or pin down given its normative, multifaceted, and sometimes contested nature. In general terms, urban sustainability refers to a state of change in which the city doesn’t undermine the natural and social systems, which can occur through resource depletion and intensive consumption, pollution, environmental degradation, and hazardous substances, as well as through public health decrease, social instability/insecurity, social inequality, and social hazard, and these can make citizens subject to conditions that inhibit their ability to satisfy their needs and pursue their aspirations, to draw on Bibri and Krogstie (2017a). To put it differently, urban sustainability means a desired state in which the city strives to achieve a balance between environmental protection and integration, economic development and regeneration, social equity and stability, and resilient physical structure and its efficient operation as long–term goals through the strategic process of sustainable development as a desired trajectory (Bibri 2018a). While the concept of urban sustainability has developed as a proposal to overcome the environmental and socio–economic problems associated particularly with the rapid urbanization of the world, there are a number of interdisciplinary frameworks that have attempted to conceptualize it in various, and sometimes distinct, ways and thus offer different access roads to the topic of urban sustainability. However, the ultimate goal of urban sustainability is to develop human settlements and environments that are healthy, livable, and equitable together with minimal demand on natural resources and thus minimal impacts on the environment. Urban sustainability is often cast in terms of four dimensions: environmental, economic, social, and physical which should together—as interdependent and synergic pillars—be enhanced over the long run in line with the goals of sustainability. It articulates how the city values the environment, the equity, the economy, and the built environment. Urban environmental sustainability means understanding, adapting to, and living within the carrying capacity of the urban ecosystem as part of the built environment in terms of natural resources and systemic limits by making decisions and taking actions that restore and maintain the quality of the environment and conserve it so as to allow all citizens to live well on the

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long–term basis. In modern urban world, finding ‘ways of consciously living with the grain of nature” could possibly be the core idea of environmental concern; sustaining “human continuance through permanent living self–adjustment to systemic constraint thus grows naturally from the metaphorical root of environmental concern’ (Foster 2001). Furthermore, the environment and the economy are closely interlinked systems (Dasgupta 2007), especially in relation to cities which indeed are the engines of economic growth and thus major consumers of energy resources and significant contributors to GHG emissions. Urban economic sustainability entails in the context of economic productivity ‘identifying and implementing various strategies for utilizing available resources optimally or that make it conceivable to make the best use of their availability. The basic premise is to uphold the amount of consumption of these resources over the longer term in an efficient and responsible way, thereby shunning degrading capital stocks and providing long–term environmental benefits and economic gains’ (Bibri 2018a, p. 102) within cities. Urban economic sustainability has also been associated with the quality of life of citizens, efficient mobility, and effective accessibility to job opportunities and services by investing in modern ICT and physical infrastructure as well as in human and social capital, thereby fostering economic development. As far as urban social sustainability is concerned, it entails maintaining social conditions that support healthy and livable communities or human settlements, and that enable citizens to fulfill their social, cultural, and technological needs in a continuous way, including justice, equity, stability, safety, well–being, diversity, inclusion, and cultural enhancement. In this sense, all systems, structures, models, and networks in the city should be directed towards actively supporting the ability of all citizens to benefit from available and new technologies as effective means to achieve sustainable communities. Regarding urban physical sustainability, achieving it has to do with urban design concepts and planning principles pertaining to sustainable urban forms. Lynch (1981, p. 47) defines urban form as ‘the spatial pattern of the large, inert, permanent physical objects in a city.’ In more detail, urban form as aggregations of repetitive elements denotes combined characteristic features involving land use patterns, spatial organizations, and other urban design features, as well as transportation systems, environmental management systems, and planning and governance models (Handy 1996; Williams et al. 2000). In short, urban form results from bringing together many urban patterns ‘made up largely of a limited number of relatively undifferentiated types of elements that repeat and combine’ (Jabareen 2006, p. 39), and encompass densities, street patterns, block sizes and shapes, spatial scales, area configurations, street designs, park layouts, and public space arrangements (Jabareen 2006; van Assche et al. 2013). Urban morphology studies the spatial organizations and characters of cities. Further, however, sustainable urban form refers to urban form for human settlements that seeks to meet the required level of sustainability by enabling urban systems and domains to function in a constructive and efficient way (Bibri and Krogstie 2017b). It is about understanding the relationships between the built form, the environment, and the dwellers and their activities and needs, which is key to achieving sustainability. Jabareen (2006) provides a four tier taxonomy of sustainable urban forms: (1) compact city, (2) eco–city, (3) new urbanism, and (4) urban containment. The main design concepts and planning principles associated with such forms, combined, include, but are not limited to, the following (Bibri 2018a): • • • • • • • • • • • •

Compactness Mixed–land use Density Diversity Ecological and cultural diversity Passive solar design Renewable resources Greening or ecological design Environmental management Environmentally sound policies Sustainable transportation Design coding.

The effects of sustainable urban form involve energy conservation, pollution reduction, sustainable transport provision, traffic congestion mitigation, sustainable travel behavior, mobility effectiveness, economic viability, life quality, and social equity, which are compatible with the fundamental foals of sustainable development (Bibri and Krogstie 2017b). The spatial distribution of activities and the effective accessibility of different services as enabled by the design concepts and planning principles of sustainability—especially urban forms and functions and their connections—are crucial aspects of sustainable urban development that emphasise the efficient use of resources (Bourdic, Salat and Nowacki 2012; Salat and Bourdic 2012).

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Furthermore, urban physical sustainability involves urban planning and design, which is associated with the physical, spatial, architectural, geographical, ecological, technical, economic, social, cultural, and political aspects of the built form and environment. Urban design constitutes part of urban planning, or the latter overlaps with the former. As an academic field, urban design is concerned with urban planning, landscape architecture, and civil engineering (van Assche et al. 2013), in addition to sustainable design, ecological design, sustainable urbanism, ecological urbanism, and strategic urban design (Bibri 2018a; Bibri and Krogstie 2017a). As an academic discipline, urban planning is concerned with research and analysis, strategic thinking, sustainable development, transportation planning, environmental planning, land–use planning, policy recommendations, public administration, urban design, landscape architecture, and civil engineering (Bibri 2018a; Nigel 2007).

2.4 Sustainable Urban Development The concept of sustainable development has been applied to urban development since the early 1990s, a few years after the widespread diffusion of the concept of sustainable development. This resulted from the then realisation that the predominant paradigm of urban development was oblivious to both the risks of, and triggering, environmental upheavals and crises as well as the effects of, and worsening, social vulnerability and injustice, causing environmental and social deprivation within cities. Achieving the goals of urban sustainability requires finding and fostering linkages between scientific and social research, technological developments and innovations, institutional structures and practices, regulatory policy design and planning, and governance and citizen participation by means of devising and bolstering urban strategies ‘that facilitate and contribute to the design, development, implementation, evaluation, and improvement of urban systems and other practical interventions within various urban domains that promote sustainability in terms of replenishing resources, lowering energy use, lessening pollution and waste levels, as well as improving social justice, stability, and safety.’ (Bibri 2018a, p. 104). The urban strategies developed with these underlying purposes epitomise an instance of sustainable development as a strategic approach to city design, operation, management, planning, and governance. Accordingly, sustainable urban development refers to a process of change in the built environment, the development or redevelopment of urban areas, that seek to foster economic development and enhance the quality of life while wisely managing and conserving natural resources as well as promoting the health of citizens, communities, and ecosystems. Hiremath et al. (2013) describe it as ‘achieving a balance between the development of the urban areas and protection of the environment with an eye to equity in income, employment, shelter, basic services, social infrastructure and transportation in the urban areas.’ In the context of this chapter where the focus is on smart sustainable/sustainable smart cities, achieving the goals of urban sustainability through sustainable development as a strategic approach occurs through or with support of the advanced forms of ICT such as big data analytics and its application to various urban domains in connection with operations, functions, services, designs, strategies, and policies. This entails unlocking the untapped potential and transformational power of ICT in terms of its innovative solutions and sophisticated approaches given its disruptive, substantive and synergetic effects enabled by big data applications, as well as its integrative and constitutive features. The way forward is to direct the research and innovation agenda of ICT of pervasive computing with the agenda of sustainable development while aligning and mobilizing strategic urban planning and development actors, thereby justifying ICT investment and its orientation by addressing physical infrastructure inefficiencies, environmental concerns, and socio–economic needs, in addition to guiding and sustaining this momentum through effective policy frameworks and measures and relevant institutional structures and practices (Bibri 2018a). However, achieving the long–term goals of urban sustainability as a desirable state is of an enormous challenge due to the conflicts that exist among and between the fundamental goals of sustainable urban development as a desirable trajectory pertaining to the aforementioned four dimensions. These conflicts in turn are challenging to deal with and daunting to overcome, as experiences have shown since the inception of sustainability and sustainable development and their application to urban planning, design, and development (Bibri and Krogstie 2017b). That is to say, tackling these conflicts has been, and continues to be, one of the toughest challenges facing urban planners and scholars as to decision–making and action–taking in the context of sustainable cities, smart cities, and smarter cities (Bibri 2018a; Bibri and Krogstie 2017a) and thus in the realm of smart sustainable urban development. Despite the appealing and holistic character of sustainable urban development approach into evading or mitigating those conflicts, they ‘cannot be shaken off so easily’, as they ‘go to the historic core of planning and are a leitmotif in the contemporary battles in our cities’, rather than being ‘merely conceptual, among the abstract notions of ecological, economic, and political logic’ (Campbell 1996, p. 296). Yet, sustainable development as a

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long–range objective for achieving the goals of sustainability, whether in the context of smart sustainable cities or sustainable smart cities of the future, constitutes a worthy strategic approach for planners, scholars, and policymakers to reach the required or optimal level of sustainability within such cities with support of advanced technology and its novel applications towards spurring and mainstreaming their development and implementation. Remaining on the topic of sustainable development, Campbell (1996, p. 9) contends that urban planners and developers, in particular, will in the upcoming years ‘confront deep–seated conflicts among economic, social, [physical] and environmental interests that cannot be wished away through admittedly appealing images of a community in harmony with nature. Nevertheless, one can diffuse the conflict, and find ways to avert its more destructive fall–out.’ As put differently by Bibri (2018a, p. 105), ‘sustainable urban development advocates can—and ought to—seek ways to make the most of all four value–sets at once. This is in contrast to keeping on playing them off against one another. With that in mind, the synergistic and substantive effects of sustainable development on forms of urban management, planning, and development require cooperative effort, collaborative work, and concerted action from diverse urban stakeholders in order to take a holistic view of the complex challenges and pressing issues facing contemporary cities, whether smart or sustainable.’

2.5 Urbanism and Sustainable Urbanism As a research field and practice, urbanism covers the study of urban phenomena in terms of the urbanization and organization of cities, as well as the practice of urban planning, design, and development. Urbanism in English language revolves around the social. ‘Urbanism describes the distinctive features of the experience of everyday life in cities’ (Bridge 2009, p. 106). It is defined as a way of living in cities (Gregory et al. 2009), sets of social relationships in cities (Harvey 2009) or studies of urban ways of living (Gregory et al. 2009). Its root is in Louis Wirth’s article ‘Urbanism as a way of life’ from 1938. In the context of this chapter, it refers to the planning and development of cities. Sustainable urbanism is an approach to the study of urbanism focusing on strategies that promote the long–term resilience and viability of cities, which implies adopting sustainable development as a strategic approach to achieving the long–term goals of sustainability. In more detail, rooted in the study of sustainability and urban planning and design in a rapidly urbanizing world, sustainable urbanism is concerned with the study of cities and the practices to plan and develop them that focus on reducing material use, lowering energy consumption, mitigating pollution, and minimizing waste, as well as improving equity, inclusion, and the quality of life, thereby enhancing the overall health and well–being of both citizens and places. This can be attained through a process of change aiming at fostering innovation and advancement in built environment, urban infrastructure, operational functioning, management, planning, and governance, as well as human and ecosystem service provisioning, while continuously optimizing efficiency gains, all in line with the vision of sustainability (Bibri 2018a, 2019b). As a leading paradigm of sustainable urbanism and an approach to sustainable urban planning and development, sustainable cities entail practically applying the knowledge about sustainability to the operational functioning and thus planning and design of existing and new cities. The notion and academic discourse of sustainable urbanism (or sustainable urban planning and development) has become more established as a result of the widespread diffusion of sustainability as a major global shift, which is still at play across the world today. In more detail, while this notion has been around for more than three decades or so, it did gain strong foothold and become powerful a few years after the inception and dissemination of the concept of sustainable development by the World Commission on Environment and Development (WCED 1987). Indeed, this concept has been applied to, or adopted within, urban planning and development since the very early 1990s (e.g., Bibri 2018a, 2019a, b; Bibri and Krogstie 2017a; Wheeler and Beatley 2010; Williams 2009). This adoption was accordingly marked by the emergence of the notion of sustainable urban planning and development/sustainable urbanism. There are a range of institutions and organizations researching and promoting related practices, including research institutes, universities, governmental agencies, non-governmental organizations, and professional associations and enterprises around the globe. The aims of the sustainable urbanism approach are to eliminate environmental impacts of urban development by providing all resources locally and evaluating the full life cycle of public and ecosystem services from production to consumption with the intent of minimizing or eliminating waste and environmental externalities. This involves designing communities that are walkable and transit–served so that people will prefer to meet their daily needs by walking. Farr’s definition of sustainable urbanism is based on bringing everything closer together, being more efficient, using higher quality goods, having everything within walking distance, and closing the loop (Sharifi 2016). Indeed, used interchangeably and being synonymous with sustainable urban planning and development, sustainable urbanism encompasses most of the concepts, ideas, and visions of both compact cities and eco–cities as instances of sustainable cities. These two models of sustainable urban forms are advocated as the most sustainably, and particularly environmentally sound, forms for human

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settlements (e.g., Bibri 2018a, 2019b; Bibri and Krogstie 2017b; Jabareen 2006; Kärrholm 2011;Van Bueren et al. 2011) According to Jabareen (2006), the compact city emphasizes density, compactness, diversity, and mixed–land use, whereas the eco–city focuses on renewable resources, passive solar design, ecological and cultural diversity, urban greening, environmentally sound policies, and environmental management. In addition to land use patterns and design features, the compact city emphasizes sustainable transportation (e.g., transit–rich interconnected nodes), environmental and urban management systems (Handy 1996; Williams et al. 2000), energy–efficient buildings, closeness to local squares, more space for bikes and pedestrians, and green areas (Phdungsilp 2011). In general, the key defining elements of sustainable urbanism, which have been enacted in many cities across the world, include the following: • • • • • • • • • • • • • • • • • •

High density areas Contiguity and connectivity Economic and cultural diversity Diversity and proximity of land uses Biophilia Sustainable corridors High performance buildings Building operations resource management High performance building features and benefits Energy efficiency/clean energy resources Integrated renewable solutions Improved indoor environment Resource reduction Pollution prevention Waste reduction and recycling High performance infrastructure Component and multifunctional optimization Integrated and sustainable design. All in all, sustainable urbanism seeks to (Bibri 2018a, 2019b; Bibri and Krogstie 2017b; Farr 2008):

• • • • • • • • •

maximise the efficiency of energy and material use; create a zero–waste system; support renewable energy production and consumption; promote carbon–neutrality and lower pollution levels; decrease transport needs and encourage walking and cycling; provide efficient and sustainable transport; preserve ecosystems; emphasize design scalability and spatial proximity and contiguity; and promote livability and community–oriented human environments.

Not much has been said concerning the criticism of sustainable urbanism. There are some views that are concerned with the use of sustainable urbanism as a branding hoax that risks debasing the term ‘sustainable’ with developments being labeled as examples of, or involving only some aspects related to, ‘sustainable urbanism.’ These are not considered truly sustainable according to the Brundtland definition of sustainable development, or based on the criteria of sustainable development goals set in the United Nations’ 2030 Agenda for Sustainable Development (UN 2015).

2.6 Ecological Urbanism Closely related to sustainable urbanism movement is ecological urbanism movement which is another approach that focuses on creating urban environments based on ecological principles. Ecological urbanism focuses on developing multidimensional sustainable human communities within harmonious and balanced built environments. In other words, it draws from

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ecology to inspire an urbanism that is more socially inclusive as well as more sensitive to the environment. In many ways, it is an evolution of, and a critique of, several approaches to urbanism, such as new urbanism, urban containment, and landscape urbanism. It argues for a more holistic approach to the planning, design, and management of cities. The key tenets of ecological urbanism as associated with the eco–city, a leading paradigm of such urbanism, include the following: • • • • • • •

Self–contained, local economy Maximization of efficiency of energy resources Renewable energy production and carbon–neutrality Sustainable transport system (prioritizing walking, cycling, and public transportation) Zero–waste system Awareness of environmental and sustainability issues Reduction of material consumption.

In light of the above, if ecological urbanism as a holistic approach is to be successful, it needs to design and combine complex systems and social processes and reflects their synergy in ways that are dynamically interactive and fundamentally humane. It is of great necessity for cities to become masters of a stable, inclusive, just, resilient, and ecological urbanism. Ecological urbanism represents another common term for sustainable urbanism. Farr (2008) discusses combining elements of ecological urbanism and sustainable urban infrastructure, and goes beyond them to close the loop on resource use and bring everything into the city, along with making cities walkable. This is about increasing the quality of life by bringing more resources within a short distance. Further, ecological urbanism and green urbanism overlap or share several concepts, ideas, and visions in terms of the role of the city and positive urbanism in shaping more sustainable places, communities, and lifestyles. Beatley (2000, pp. 6–8, cited in Jabareen 2006) views, while arguing for the need for new approaches to urbanism to incorporate more ecologically responsible forms of living and settlement, a city exemplifying green urbanism as one that: • • • • • •

strives to live within its ecological limits; is designed to function in ways analogous to nature; strives to achieve a circular rather than a linear metabolism; strives toward local and regional self–sufficiency; facilitates more sustainable lifestyles; and emphasizes a high quality of neighborhood and community life.

While its principles are based on the triple–zero framework: zero fossil–fuel energy use, zero waste, and zero emissions, green urbanism involves relatively similar ideas and visions as sustainable urbanism as well. They both emphasize urban design with nature and the creation of better communities and lifestyles. The focus in sustainable urbanism is more on designing communities that are walkable and transit–served so that people will prefer to meet their daily needs by walking. This relates to the defining elements of the compact city, namely mixed–land use and sustainable transportation, as a model of sustainable urban form (e.g., Bibri 2018a; Jabareen 2006). Ecological design is defined by Van der Ryn and Cowan (1996, p. 19) as ‘any form of design that minimizes environmentally destructive impacts by integrating itself with living processes.’ It is an integrative ecologically responsible design discipline. It is associated with greening in the context of the eco–city, which is an important concept in sustainable urban planning. Green space has the ability to contribute positively to sustainability agenda (Bibri 2018a; Bibri and Krogstie 2017b). In addition to making urban places attractive and pleasant, greening urban spaces renders them more sustainable by bringing nature into the life of citizens through diverse open landscapes (Jabareen 2006). The inchoate developing nature of ecological design was initially associated with incorporating the environmental factor into the design process, but later it was focused on the details of the practice of eco–design. Ecological urbanism has been criticized as an idea that is loosely defined from a set of ostentatiously attractive or impressive projects as expensive schemes with economic ends intended to satisfy some kind of ambition to invest in ecology without posing a more globally applicable approach. Moreover, it is inadequately explained in terms of how it constitutes an evolution of such approaches as new urbanism and landscape urbanism, thereby serving as an addition to the other urbanism approaches without any valid content or of a fill–in–the–blank character.

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2.7 Strategic Smart Sustainable Urbanism The discourse of smart sustainable urbanism/urban planning and development has recently gained special importance and strong foothold and become powerful in academia and policymaking in response to the mounting challenges of sustainability and urbanization facing modern and future cities. This is manifested in the smart sustainable city as a new phenomenon becoming the leading paradigm of urbanism, its subject getting endlessly fascinating, and its research garnering growing attention and rapidly burgeoning with its status consolidating as one of the most enticing and fanciest areas of investigation today. This is therefore making the relevance and rationale behind the smart sustainable city debate of high significance in relation to the future form of urbanism. The evolving research and practice in the field of smart sustainable/sustainable smart urban planning and development tends to focus on exploiting, harnessing, and leveraging the unfolding and soaring deluge of urban data flooding from those urban systems (namely built form, urban infrastructure, ecosystem services, human services, and administration and governance) and urban domains (namely transport, traffic, mobility, energy, built and natural environment, land use, healthcare, education, science and innovation, and public and social safety) that are associated with the environmental, physical, social and economic dimensions of sustainability—in the needed transition towards sustainable development. This entails developing and applying new urban intelligence functions and simulation models on the basis of the useful knowledge to be extracted from such deluge by means of big data analytics, which is primarily directed for enhancing decision making associated in this context with sustainability advancement. The outcome of the analysis of this deluge involves valuable insights into how and the extent to which urban systems, urban domains, and urban networks can interrelate of interlink, as well as into how they can be integrated, coordinated, and coupled, respectively, for the purpose of enhancing and optimizing urban operations, functions, services, designs, strategies, and policies in line with the goals of sustainable development.

2.7.1 The City Planning Component of Urbanism Sustainable urban planning is the process of guiding and directing the use and development of land, urban environment, urban infrastructure, and related processes, activities, and services in ways that seek to achieve the required level of sustainability. As such, it involves defining the long–term goals of sustainability; formulating sustainable development objectives to achieve such goals; arranging the means and resources required for attaining such objectives; and implementing, monitoring, steering, evaluating, and improving all the necessary steps in their proper sequence towards reaching the overall aim (Bibri 2018a, d). Its technical features entails the application of advanced ICT as a set of computational and scientific approaches and technical processes to direct and guide the use and development of land use, natural ecosystems, physical structures, urban forms, spatial organizations, natural resources, urban infrastructures, socio–economic networks, and ecosystem and human services in line with the long–term goals of sustainability. Recent evidence (e.g., Al Nuaimi et al. 2015; Angelidou et al. 2017; Batty et al. 2012; Bettencourt 2014; Bibri 2019a; Bibri and Krogstie 2017b) lends itself to the argument that an amalgamation of these defining elements of urban planning with cutting–edge big data technologies as an advanced form of ICT in terms of such functions as control, automation, management, and optimization can create more sustainable, resilient, safe, and livable cities. All in all, the data–driven approach to urban planning is of paramount importance to strategic sustainable urban planning. Besides, the functioning and organization of urban domains and networks and related processes in the field of sustainable urban planning require not only complex interdisciplinary and transdisciplinary knowledge, but also sophisticated technologies and powerful data analytics capabilities. Sustainable development goals and smart targets should be well understood with respect to their synergy and integration (see, e.g., Ahvenniemi et al. 2017; Angelidou et al. 2017; Batty et al. 2012; Bibri 2018a, b, 2019b; Bibri and Krogstie 2017b; Bifulco et al. 2016; Kramers et al. 2014). This is a valuable force for defining or setting the kind of integrated objectives needed for achieving sustainability in the context of smart sustainable/sustainable smart urbanism. As a management function, sustainable urban planning involves formulating a detailed plan to achieve an optimum balance of demands for growth with the available resources and the need to protect the environment, or to provide and maintain a livable and healthy human environment in conjunction with minimal demand on resources and minimal impacts on the environment. This can be accomplished by integrating sustainable urban strategies with advanced technologies and their novel applications for sustainability, as well as by formulating and implementing effective policy instruments and institutional frameworks. On the whole, smart sustainable urban planning uses ICT in ways that ensure a continuous assess, improvement, and advancement of the contribution of the city to the goals of sustainable development. What is known about the relationship between urban planning interventions, advanced technologies, and sustainability objectives is a subject of much debate (Bibri 2018a). This means that realizing smart sustainable/sustainable smart cities

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requires making countless decisions about urban forms, urban designs, sustainable technologies, and governance. Regardless, the emerging urban planning initiatives should consist in adopting a holistic approach to decision–making, a pathway which can be pursued by employing advanced technological systems and sophisticated analytical approaches, thereby the relevance of big data analytics and related data–driven decision–making processes (Bibri 2018a, d). As noted by Angelidou et al. (2017), the incorporation of the systematic use of big data in the urban policy development and monitoring processes is a key success factor for formulating and implementing effective policies with significant positive impacts on multiple levels. Big data analytics and data–driven decision–making processes are of wide–ranging use in different areas of urban planning, including strategic thinking, sustainable development, transportation planning, environmental planning, land–use planning, policy recommendations, public administration, urban design, landscape architecture, and civil engineering. Indeed, the uses of big data analytics are associated with an array of multitudinous decisions involving control, management, optimization, evaluation, recommendation, and improvement associated with urban operations, functions, services, designs, strategies, and policies. This should be taken into account in, or constitute an integral part of, any comprehensive plan to be formulated for developing and implementing smart sustainable/sustainable smart cities, where the focus is typically on a wide range of sustainability issues, including energy consumption, pollution, waste, traffic congestion, land use inefficiency, and social and environmental policy (Bibri 2018a, d).

2.7.2 The City Design Component of Urbanism Urban design constitutes part of urban planning, or the latter overlaps with the former. Specifically, urban design is concerned with urban planning, landscape architecture, and civil engineering, as well as sustainable design, ecological design, sustainable urbanism, ecological urbanism, and strategic urban design, whereas urban planning involves transportation planning, environmental planning, land–use planning, policy recommendations, and public administration, as well as strategic thinking, sustainable development, landscape architecture, civil engineering, and urban design (Bibri 2018a; Bibri and Krogstie 2017a). However, the way cities are intelligently designed and planned is of paramount importance to strategic urban sustainability. In this regard, the link between the emerging urban intelligence functions being developed using advanced ICT and urban design concepts and principles lies in that the city structures, forms, and spatial organizations are generated by new powerful forms of simulation models and optimization and prediction methods fashioned on the basis of complexity science and urban science, which are in turn to be utilized by such functions as new conceptions of the way smart sustainable/sustainable smart cities function. In short, the way such cities are designed as informed by such models and methods determine or shape their operational functioning. Such functions represent a form of advanced decision support pertaining to diverse urban systems and domains in terms of their operational functioning. In light of the above, smart urban design entails a blend of sciences and artistic architectures, which the big data analytics system and related simulation models and optimization and prediction methods are extremely well placed to initiate and contribute to. Specifically, such models and methods generate urban structures and forms in terms of design concepts and principles and planning practices that can improve sustainability, efficiency, and resilience (Bibri 2018a; Bibri and Krogstie 2017b). In particular, such models hold great potential to inform future urban designs. Furthermore, in urban science, a field in which data science is practiced, the emerging disaggregate urban models entail exploring many different kinds of models based on complexity science, as well as building many different models of the same situation in the context of sustainability, efficiency, and resilience, a pluralistic approach which is key to enhancing the understanding of this complexity. In addition, the new immediacy of constructing urban simulation models is being enabled and motivated by the emerging ‘real–time city’ (Kitchin 2014) and related sensing infrastructures and networks advancing towards providing information about medium– and long–term changes (e.g., Batty et al. 2012; Bibri 2018a; Bibri and Krogstie 2017b) and their prediction. The emerging new models of scientific (knowledge) discovery are germane to how to figure out good designs for efficient, equitable (Nielsen 2011), sustainable, and resilient cities (Bibri 2018a). The idea of advanced ICT penetrating wherever it can to improve sustainability performance (or make urban living more sustainable) is central to the quest for making smart sustainable/sustainable smart cities function as a social organism by design (Bibri 2018a, 2019b). The emergence of smart sustainable/sustainable smart cities poses enormous challenges for traditional forms of simulation and optimization modeling. Consequently, advanced urban simulation models operating at different spatial scales and over different temporal spans are being developed for understanding how such cities function. These models are being focussed on understanding as a prelude to their use to inform the process of planning and design. They are characterized by their ability to simulate complex aspects of urbanity using various lenses, such as environmental, economic, social, and geographic, which enable, for example, transport, land use, mobility mode, travel behavior, population growth, and so on to be predicted using computer models of various sorts. Advanced extensions of such models are being made as in urban operation, function, and service provision research used in the context of intelligent planning and design support systems. In

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fact, the emergence of smart sustainable/sustainable smart cities are pushing for more sophisticated approaches to simulation and optimization modeling (e.g., Bibri 2018a).

2.7.3 The City Development Component of Urbanism Strategic sustainable urban development can be viewed as an alternative approach to urban thinking and practice focused primarily on addressing and overcoming the escalating environmental problems and the mounting socio–economic issues associated with the current path or predominant paradigm of city development by mitigating or eliminating its negative impacts on the environment and human well–being. In short, sustainable urban development is a strategic approach to achieving urban sustainability. As such, it seeks to guide and direct scholars, practitioners, organizations, institutions, and governments to agree upon concrete ways to determine the most strategic actions in a concerted effort to reach a sustainable future. In this respect, it is guided by a shared understanding of the agreed–upon sustainability principles that embody the end goal for sustainability. The sustainability principles for achieving socio–ecological sustainability as developed through scientific consensus and thus peer–reviewed by the international scientific community are derivative from basic laws of science, including laws of thermodynamics, cycles of nature, conservation of matter, and so on (e.g., Holmberg and Robèrt 2000), as well as stem from social, cultural, and ethical foundations. In the sustainable society, according to Holmberg and Robèrt (2000), nature is not subject to systematically increasing… 1. 2. 3. 4.

…concentrations of substances extracted from the Earth’s crust, …concentrations of substances produced by society, …degradation by physical means, and in that society… people are not subject to conditions that systematically undermine their ability to meet their needs.

The purpose of articulating sustainability with scientific rigor is to make it more intelligible, comprehensible, and useful for measuring, analyzing, and managing human activities within society. A significant contribution in this line was the development of the above four guiding sustainability principles. The sustainability principles should be, according to Holmberg and Robèrt (2000, p. 298): • • • • • •

Based on a scientifically agreed upon view of the world Necessary to achieve sustainability Sufficient to achieve sustainability General to structure all societal activities relevant to sustainability Concrete to guide action and serve as directional aides in problem analysis Non–overlapping or mutually exclusive in order to enable comprehension and structured analysis of the issues.

In relation to the city as a clear example of society, to be strategic in moving towards urban sustainability from an environmental perspective requires a clear understanding of environmental sustainability principles, which are employed to set the minimum requirements of a sustainable city in terms of being sensitive to, and in support of, the environment (Bibri 2018a). Further, sustainability principles define an end–goal for urban sustainability, which serves to plan strategically, knowledgeably, and holistically in terms of development to attain socio–ecological sustainability in the city. This relates to strategic sustainable urban development as a planned development that strives to address and overcome the environmental, social, and economic issues facing the city in the context of sustainability in a rigorous, meaningful, and scientific way to achieve a sustainable city as an instance of urban sustainability. This can occur through, or rather requires, tackling the root causes resulting in the systematic decline in the potential of the city by developing upstream solutions necessary to sustain the functioning of its systems and making it more resilient. Urban development must be based on a multifaceted kind of planning process. In this context, strategic sustainable urban development entails backcasting from basic sustainability principles, whereby a desirable sustainable future can be defined or set as the reference point for determining and implementing the necessary actions or steps towards attaining that specified future (see Bibri 2018a, d for a detailed account of backcasting as a scholarly and planning approach to strategic smart sustainable city development). Indeed, in terms of strategic planning and development, smart sustainable cities as a holistic urban development strategy represent an instance of sustainable urban development, a strategic approach to achieving the long–term goals of urban sustainability—with support of advanced technologies and their novel applications. Accordingly, achieving smart sustainable cities epitomises an instance of urban sustainability. This notion refers to a desired (normative)

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state in which a city strives to retain a balance of socio–ecological systems through adopting and executing sustainable development strategies as a desired (normative) trajectory (Bibri 2018a, c, d). This balance entails enhancing the physical, environmental, social, and economic systems of the city in line with sustainability over the long run—given their interdependence, synergy, and equal importance. This long–term strategic goal requires, as noted by Bibri (2018a, p. 601), ‘fostering linkages between scientific and social research, technological innovations, institutional practices, and policy design and planning in relevance to urban sustainability. It also requires a long–term vision, a trans–disciplinary approach, and a system–oriented perspective on addressing environmental, economic, social, and physical issues. All these requirements are at the core of backcasting as a scholarly and planning approach to futures studies.’ This approach facilitates and contributes to the development, implementation, evaluation, and improvement of future models for smart sustainable cities, with a particular focus on practical interventions for integrating and enhancing urban systems and coordinating and coupling urban domains using cutting–edge technologies in line with the vision of sustainability. As an appropriate response to smart sustainable city development, backcasting ‘involves the analysis of several factors, including past, present, and future situations; long–term visions; formulation, implementation, and follow–up; transfer and deployment of technologies; building and enhancement of human and social capacity; and regulatory policies. These factors are intertwined and thus cannot be isolated from each other in all kinds of urban sustainability endeavors. These indeed require a system–oriented perspective to address environmental, social, economic, and physical issues in a holistic way. Futures studies offer promising approaches to building smart sustainable city foresight’ (Bibri 2018a, p. 602). Backcasting as a scholarly and planning methodology is well suited to any multifaceted kind of planning process (e.g., Bibri 2018a, d, 2019b; Holmberg 1998; Holmberg and Robèrt 2000; Phdungsilp 2011), as well as to dealing with urban sustainability issues (Bibri 2018a, d, 2019b; Carlsson–Kanyama et al. 2003; Dreborg 1996; Miola 2008; Phdungsilp 2011). This form of planning for strategic smart sustainable urban development is deemed useful and effective as a way to act proactively to avoid potential negative impacts and think of future generations. There is a belief that future–orientated planning can change development paths (Bibri 2018a, d, 2019b). The interest in the future of the smart sustainable city is driven by the aspiration to transform the continued urban development path. In the context of this chapter, the smart dimension of sustainable urban development is of equal importance. Strategic smart sustainable urban development denotes a process of change in the built environment driven by big data technology and its novel applications that seeks to promote sustainable built form, environmental integration, economic regeneration, and social equity as a set of interrelated goals in the context of smart sustainable/sustainable smart cities. In other words, to foster economic development while conserving resources and promoting the health of the ecosystem and its users requires innovative solutions and sophisticated approaches resulting from unlocking the untapped potential and transformational power of advanced ICT in terms of its disruptive, substantive, and synergetic effects, coupled with its integrative and constitutive nature (Bibri 2018a, d). This process of change must be based on effectively integrating data science, urban science, and urban informatics in terms of ideas and tools with the objectives of sustainable urbanism as an applied domain. This implies justifying ICT investment and its innovation by environmental concerns and socio–economic needs within human settlements. This endeavor should in turn be supported by pertinent institutional structures and practices and policy frameworks and measures. Among the advantage of ICT in relation to sustainable urban development in terms of catalyzing and boosting its processes using big data technology and its novel applications include the following: • Data–driven applications for enhancing the performance of sustainable cities in terms operational functioning. • Advanced simulation models for evaluating and optimizing the performance of the typologies and design concepts of sustainable cities. • Sophisticated simulation models for enabling the design scalability and planning flexibility of sustainable cities that are necessary for responding to urban growth, environmental pressures, changes in socio–economic needs, unprecedented shifts, global trends, discontinuities, and societal transitions. • Innovative frameworks for smartening up the urban metabolism of sustainable cities to maintain their levels of sustainability. • Data–driven solutions for integrating urban systems, coordinating urban domains, and coupling urban networks in the context of sustainable cities. • Data–driven applications for enhancing participation, equity, fairness, safety, and accessibility, as well as service delivery and efficiency in relation to the quality of life and wellbeing. • Data–driven solutions for identifying risks, uncertainties, and hazards.

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2.8 Smart Sustainable/Sustainable Smart Cities: A Leading Paradigm of Urbanism The concept of smart sustainable/sustainable smart cities has emerged as a result of three important global shifts at play across the world, namely the rise of ICT, the diffusion of sustainability, and the spread of urbanization, and such cities as a new techno–urban phenomenon materialised and became widespread around the mid–2010s (Bibri 2018a, b, c, d, 2019a, b). In other words, the interlinked development of ICT, sustainability, and urbanization has recently converged under what is labeled ‘smart sustainable cities’ or ‘sustainable smart cities,’ as echoed by Höjer and Wangel (2015) and Bibri (2019b), respectively. While the focus here is on smart sustainable cities as a leading paradigm of urbanism, the way the idea of such cities is conceptualized pertains to sustainable smart cities to a great extent due to the relatively parallel emergence of these two urbanism approaches and the many overlapping technical, environmental, social, and economic aspects between them, coupled with their equal prominence and significance as research areas today in terms of urban analytics, planning, development, and governance. That being said, as an integrated framework and holistic approach, smart sustainable cities amalgamate the strengths of sustainable cities in terms of the design concepts and principles and planning practices of sustainability and those of smart cities in terms of the innovative solutions and sophisticated approaches being developed for sustainability and mainly offered by big data technology (Bibri 2018a; Bibri and Krogstie 2017b, c). The whole idea revolves around leveraging the convergence, ubiquity, advance, and potential of ICT of pervasive computing and its prerequisite enabling technologies, especially big data analytics, in the transition towards the needed sustainable development and sustainability advancement in an increasingly urbanized world (Bibri 2018a, c). Therefore, they are increasingly gaining traction and prevalence worldwide as a response to the imminent challenges of sustainability and urbanization. They are moreover being embraced as an academic pursuit, societal strategy, and, thus, evolving into a scholarly and realist enterprise around the world, not least within ecologically and technologically advanced nations (Bibri and Krogstie 2016; Bibri 2018a, c). In a nutshell, the concept and development of smart sustainable cities are gaining increased attention worldwide among research institutes, universities, governments, policymakers, and ICT companies. The term ‘smart sustainable city,’ despite not always explicitly discussed, is used to describe a city that is supported by the pervasive presence and massive use of advanced ICT, which, in connection with various urban systems and domains and how these are complexly integrated and are intricately coordinated, respectively, enables the city to control available resources safely, sustainably, and efficiently to improve economic and societal outcomes (Bibri 2018a; Bibri and Krogstie 2017a). As a result of analyzing around 120 definitions, ITU (2014) provides a comprehensive definition based on the notion of sustainable development, which states that ‘a smart sustainable city is an innovative city that uses ICT and other means to improve the quality of life, efficiency of urban operation and services, and competitiveness while ensuring that it meets the needs of present and future generations with respect to economic, social and environmental aspects.’ Another close definition put forth by Höjer and Wangel (2015, p. 10), which is deductively formulated based on the notion of sustainable development, states that ‘a smart sustainable city is a city that meets the needs of its present inhabitants without compromising the ability for other people or future generations to meet their needs, and thus, does not exceed local or planetary environmental limitations, and where this is supported by ICT.’ This entails primarily unlocking and exploiting the potential of ICT of pervasive computing as an enabling, integrative, and constitutive technology with embodied transformational, substantive, and disruptive effects for achieving the environmental, social, and economic goals of sustainability. Also, Bibri and Krogstie (2016, p. 11) provide a more complex conceptualization of the term based on innovation system and socio– technical perspectives, describing smart sustainable cities as: ‘being a dynamic and complex interplay between scientific innovation, technological innovation, environmental innovation, urban design and planning innovation, institutional innovation, and policy innovation, smart sustainable cities involve inherently complex socio–technical systems of innovation systems. Such systems, which focus on the creation, diffusion, and utilization of knowledge and technology, are of various types (variants of innovation models), including national, regional, sectoral, technological, and Triple Helix of university– industry–government relations.’ Further, as a set of techno–urban innovation systems, smart sustainable cities result from a dynamic network of relationships among universities, research institutes, governmental agencies, policy makers, industry consortia, and business communities involved in various innovation systems (Bibri and Krogstie 2016). In relation to the first question of Step 2 of the applied backcasting methodology, this chapter adheres to the socio–technical system approach to innovation system, which entails the components needed to fulfill a certain societal function (Bijker 1995; Geels 2004, 2005). The typically complex sets of socio–technical systems underlying smart sustainable cities involve different innovation entities operating at the intersection of ICT development and innovation and urban planning and development with the aim of advancing sustainability and integrating its dimensions as a societal function. In this regard, the technological innovation

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system should be of particular focus here considering the smart form of urban sustainability being investigated. This system refers to ‘socio–technical systems focused on the development, diffusion, and use of particular technologies’ (Bergek et al. 2008, p. 408). In other words, it denotes a dynamic network of actors interacting within a specific industrial sector (e.g., urban industry domains) under a particular institutional set–up (governmental agencies, policymakers, public research institutes, universities, etc.) in the production, diffusion, and utilization of new technologies (e.g., Carlsson and Stankiewicz 1991; Carlsson et al. 2002), specifically big data technologies. These are seen as systems of socio–technical elements interacting with each other, and this approach provides insights into understanding the development of new technologies (see Geels 2004). In light of the above, Bibri (2018a, p. 299) defines smart sustainable cities ‘as a social fabric and web made of a complex set of networks of relations between various synergistic clusters of urban entities that, in taking a holistic or systemic perspective, converge on a common approach into using and applying smart technologies to create, develop, disseminate, and mainstream the innovative solutions and sophisticated methods that help provide a fertile environment that is conducive to improving and advancing sustainability. This can occur through strategically assessing and continuously enhancing the contribution of such cities to the goals of sustainable development. Here, ICT can be directed towards, and effectively used for, collecting, processing, analyzing, and synthesizing the data on every urban system and domain as involving forms, structures, infrastructures, networks, facilities, processes, activities, and citizens.’ The resulting outcome can then be employed to develop urban intelligence and planning functions that utilize the science of complexity in fashioning new powerful forms of urban simulation models for guiding decision–making processes in the context of urban sustainability in terms of operations, functions, designs, services, strategies, and polities. In this regard, ‘smart sustainable cities are complex systems par excellence, more than the sum of their parts. They are inherently intricate through the very technologies being used to monitor, understand, and analyze them in relation to their operational functioning, management, planning, development, and governance to improve their contribution to sustainability and their ability to confront urbanization.’ (Bibri 2018a, p. 474) Overall, as dynamically changing environments and developed through a multitude of individual and collective decisions, they should rely on sophisticated technologies and their novel applications to realize their full potential and thus to respond to the challenges of sustainability and urbanization. All in all, smart sustainable cities can be viewed as an urban development approach or model which seeks to explicitly bring together the sustainable city and smart city endeavors in ways that address the relevant limitations of sustainable cities and the relevant deficiencies of smart cities by merging what each has to offer for sustainability in terms of design concepts and principles and planning practices and advanced technologies and their applications, respectively.

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Academic and Scientific Disciplines

An academic discipline is a branch of knowledge that is taught and researched as part of higher education. It involves research areas, research projects, research challenges, expertise, knowledge, scholars, communities, and studies as associated with a given scholastic subject area and related practice (Bibri 2018a). Academic disciplines are particularly of usefulness with respect to narrowing research efforts and creating ongoing dialogues about particular subjects. Thus far, there is no consensus on their classification in relation to the human, social, and natural sciences. With regard to scientific discipline, it refers to a particular branch of scientific knowledge as based on the scientific approach—hypothesize, model, and test. This approach denotes specifically a set of principles and procedures employed for the systematic pursuit of knowledge involving the formulation of hypotheses, the collection of data through observation and experiment, and the testing of hypotheses. This chapter is concerned with different branches of science, including applied sciences, which apply existing scientific knowledge to develop more practical applications, such as big data technologies; formal sciences, including mathematics and logic as related to big data computing; and social sciences as part of urban informatics and urban planning and development. The set of academic and scientific disciplines presented and discussed below are identified on the basis of their relevance to the interdisciplinary and transdisciplinary field of smart sustainable/sustainable smart urbanism in terms of its pursuit and practice.

3.1 Urban Planning and Design As an academic discipline, urban planning is concerned with research and analysis, strategic thinking, sustainable development, transportation planning, environmental planning, land–use planning, policy recommendations, public administration, urban design, landscape architecture, and civil engineering (e.g., Bibri 2018a; Nigel 2007). As a governmental function

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in most countries, urban planning is practiced on neighborhood, district, municipality, city, metropolitan, regional, and national scales, with land use, environmental, transport, and local planning representing more specialized foci. It has been approached from a variety of perspectives, often combined, including physical, spatial, geographical, ecological, technical, economic, social, cultural, and political. As a concept, it refers to the process of guiding and directing the use and development of land, urban environment, urban infrastructure, and ecosystem and human services—in ways that ensure the responsible management and efficient utilization of natural resources, the efficiency of urban operations and functions, optimal economic development, and high quality of life (Bibri 2018a). In more detail, urban planning entails drawing up, designing, developing, organizing, coordinating, standardizing, evaluating, and forecasting physical arrangements, spatial patterns, and infrastructural systems of a city and related processes, functions, and services. The ultimate aim of urban planning is to make cities more sustainable, efficient, safe, resilient, and attractive places (Bibri 2018a). Urban planning is associated with urban systems: the operating and organizing processes of urban life. Urban systems include the following: • Built form (buildings, streets and boulevards, neighborhoods, districts, residential and commercial areas, schools, parks, public spaces, etc.). • Urban infrastructure (transport systems, water and gas provision systems, sewage systems, power distribution systems, etc.). • Ecosystem services (provisioning energy, water, air, and food; regulating climate; supporting nutrient cycles and oxygen production; etc.). • Human services (public services, social services, cultural and recreational facilities, etc.). • Administration (organizational structures, governance arrangements, creating and implementing mechanisms for adherence to regulatory frameworks, practice enhancements, policy design and recommendation, technical and assessment studies, etc.). In terms of the operational processes, the above urban systems involve the following: • • • • •

Design and evaluation regarding built form Monitoring, operation, and control concerning urban infrastructure Provision and distribution as regards ecosystem services Delivery and optimization as to human services Development, use, and evaluation with respect to administration.

As mentioned above, urban design constitutes part of urban planning, or the latter overlaps with the former. As an academic field, it is concerned with planning, landscape architecture, and civil engineering (van Assche et al. 2013), in addition to sustainable urbanism, ecological urbanism, sustainable design, ecological design, and strategic urban design (Bibri 2018a; Bibri and Krogstie 2017a). Dealing with the design and management of the public domain and the way this domain is experienced and used by urbanites, urban design refers to the process of designing, shaping, arranging, and reorganizing urban physical structures and spatial patterns depending on diverse contexts (Bibri 2018a). Accordingly, it involves buildings, streets, neighborhoods, districts, public infrastructures and facilities, public spaces, parks, and so on. In relation to its sustainable dimension, it is aimed at making urban living more environmentally sustainable and urban areas more attractive, functional (e.g., Aseem 2013; Boeing et al. 2014; Larice and MacDonald 2007), and equitable. In this respect, urban design is about making connections between forms for human settlements and environmental and social sustainability, built environment and ecosystems, people and the natural environment, economic viability and well–being, and movement and urban form (Bibri 2018a).

3.2 Computer Science Often described as one of the parents of data science, computer science is concerned with the study of the theoretical foundations of information (e.g., structures, representation, etc.) and computation (e.g., mechanisms, algorithms, etc.) and the practical techniques and methods for their implementation in computer systems (Bibri 2018a). In other words, it is the scientific and practical approach to computation and its applications and the systematic study of the feasibility, structure, expression, and mechanization of the methodical procedures that underlie the acquisition, representation, storage, processing, analysis,

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communication of, and access to information. In short, it is the study of the theory, experimentation, and engineering that form the basis for the design and use of computer systems. As a discipline, computer science spans a range of topics from theoretical studies of algorithms and the limits of computation to the practical issues of implementing computing systems in hardware and software. Computer scientists deal with the systematic study and development of algorithmic processes that describe, create, and transform information and formulate abstractions (or conceptualizations) to model, simulate, and design complex systems (Bibri 2018a). They therefore specialize in the theory of computation and the design of computational systems. A number of computer scientists argue for the distinction of three separate paradigms in computer science. Wegner (1976) contends that those paradigms are science, technology, and mathematics. Denning et al. (1989) contends that they are theory, abstraction (modeling), and design. Eden (2007) describe them as the ‘rationalist paradigm’ (which treats computer science as a branch of mathematics, which is prevalent in theoretical computer science and mainly employs deductive reasoning), the ‘technocratic paradigm’ (which is found most prominently in software engineering), and the ‘scientific paradigm’ (which approaches computer–related artifacts from the empirical perspective of natural sciences, identifiable in some branches of artificial intelligence). Its fields can be divided into a variety of theoretical and practical disciplines, including computational complexity theory, programming language theory, computer programming, human–computer interaction, and artificial intelligence. In more detail, there are several areas that are crucial to the discipline of computer science, including theory of computation, algorithms and data structures, programming methodology and languages, and computer elements and architecture, in addition to software engineering, artificial intelligence, computer networking and communication, database systems, parallel computation, distributed computation, human–computer interaction, computer graphics, operating systems, and numerical and symbolic computation. Among the areas of computing science that underpin smart sustainable city development practice in terms of computational systems include the following: Data structures and algorithms: The study of commonly used computational methods and their computational efficiency. They are of relevance to the functioning of big data applications in the context of smart sustainable cities (Bibri 2018a; Bibri and Krogstie 2017c). Big data analytics should involve highly sophisticated and dedicated techniques and algorithms associated with machine learning, data mining, statistics, database query, and so on that can perform complex computational processing of data for timely and accurate decision–making purposes. New approaches to storing, managing, coordinating, and analyzing big data and processing context information, in particular in relation to smart sustainable city applications should rely on advanced artificial intelligence programs. Theory of computation: Deals with the fundamental question underlying computer science: ‘What can be (efficiently) automated?’ (Denning 2000). Theory of computation is focused on answering fundamental questions about what can be computed and what amount of resources are required to perform those computations. This is of particular relevance to many urban problems in the sense of using computability theory to examine which are computationally solvable on various theoretical models of computation (see Bettencourt 2014 for illustrative examples of computationally intractable problems in the context of smart urbanism). As regards to defining critical problems relating to smart sustainable cities, ICT is focussed on defining critical problems that emerge rapidly and unexpectedly, some of which reveal critical infrastructures. The analysis of such problems and their identification is crucial to the sustainability of smart sustainable cities. These are far– from–equilibrium, dominated by fast and slow dynamics in short and long cycles (e.g., Batty et al. 2012). Concurrent, parallel, and distributed systems: In such systems several computations execute simultaneously and potentially interact with each other. A distributed system extends the idea of concurrency onto multiple computers connected through a network. Computers within the same distributed system have their own private memory, and information is often exchanged among themselves to achieve a common goal. This relates to cloud computing and fog computing as models for performing big data analytics in relation to diverse applications in the context of smart sustainable cities (see Bibri 2018a for further discussion). Part of the process of coordination and integration using state–of–the–art data systems and distributed computing must involve ways in which the citizenry is able to participate and to blend their personal knowledge with that of experts who are developing these technologies (Batty et al. 2012). Computer network: Aims to manage networks between computers across different geographical areas. This is of high relevance to urban domains in the context of smart sustainable cities. To develop technologies that ensure widespread participation, new ICT is essentially network–based and enables extensive interactions across many domains and scales (Batty et al. 2012).

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Wireless network technologies include satellite–enabled GPS, mobile phone, LPWAN, and Wi–Fi networks for collecting and coordinating data in terms of the data themselves and how that data are stored and made accessible. ICT of pervasive computing will result in a blend of smart applications enabled by constellations of instruments across many spatial scales linked via multiple networks for providing continuous data flowing from various urban domains (processes, activities, movements, interactions, observations, etc.), This can provide a fertile environment conducive to advancing the contribution of smart sustainable cities to sustainability over the long run by monitoring, understanding, analyzing, and planning them in ways that strategically assess, improve, and sustain this contribution through the design and planning principles of sustainability (Bibri 2018a). Computer security: Aims to protect information from unauthorized access, disruption, or modification while maintaining the accessibility and usability of the system for its intended users (see Bibri 2018 for further discussion on different aspects of information security as part of risk management in the context of smart sustainable cities). It is highly important to ensure that all technological components associated with big data applications for smart sustainable cities are supported by security measures. Massive repositories of urban data are at stake, and failure to protect these data will pose risks and threats to the functioning of such applications, as well as to the safety and well–being of citizens. Therefore, security measures should be at the center of urban policy and governance practice associated with the design, development, deployment, and implementation of big data applications within smart sustainable cities. Any attempt of an unauthorized access, malicious attack, or abuse of information on citizens, infrastructures, networks, and facilities can compromise the integrity of such applications and related services. Smart sustainable cities generate colossal amounts of data on virtually every urban process, which must be securely maintained for processing, analysis, and sharing. Urban environments are now being continually forged in sensorial, informational, and communicative processes. It is a world where smart sustainable cities think of us, where the environment reflexively monitors our behavior, including the extent to which we behave in a sustainable way through the activities and processes we perform on a daily basis. Human–computer interaction (HCI): a common thread running through most definitions of HCI is that it deals with the study, development, and implementation of the interaction between users and computers. HCI can be defined as a discipline concerned with the design, evaluation and implementation of interactive computing systems for human use and with the study of major phenomena surrounding them (Bibri 2018a). HCI is the process of communicating information from or presenting services by computer systems via display units to human users as a result of the manipulation and control of such systems by means of explicit or implicit input devices. Its special concerns include: the joint performance of tasks by users and computers; the structure of communication between users and computers; human capabilities to use computers; algorithms and programming of user interfaces; engineering issues relating to designing and building interfaces, the process of analysis, design and implementation of interfaces; and design trade–offs (Bibri 2018a). HCI also deals with enhancing usability and learnability of interfaces; techniques for evaluating the performance of interfaces; developing new interfaces and interaction techniques; developing and applying design methodologies to real–world problems; prototyping new software and hardware systems; exploring new paradigms for interaction (e.g., natural interaction); and developing models and theories. As to developing new technologies for communication and dissemination, new sources of urban data, the articulation of urban problems, plans and policies, and all the apparatus used in engaging the community in developing smart sustainable cities require new forms of online participation making use of the latest ICT in terms of state–of–the–art HCI and distributed computation (Batty et al. 2012). Artificial intelligence: Is concerned with understanding the nature of human intelligence, and creating computer systems capable of emulating human intelligent behavior. Therefore, it involves the modeling and simulation of intelligent cognitive and behavioral aspects of humans into machines, such as learning, reasoning, problem solving, perception, learning, planning, creativity, language, language production, actuation, decision–making, and so forth. John McCarthy, who coined the term in 1956, defines it as ‘the science and engineering of making intelligent machines’ (McCarthy 2007). While there are many definitions of artificial intelligence in the literature, a common thread running through all definitions is the study of cognitive phenomena or the simulation of human intelligence into machines. Implementing aspects of human intelligence in computer systems is one of the main practical goals of artificial intelligence. Computer intelligence combines a wide range of advanced technologies, such as machine learning, data mining, artificial neural networks, multisensory devices, data fusion techniques, modeling techniques (knowledge representation and reasoning), natural user interfaces, computer vision, and intelligent agents. These areas are at the core of big data analytics as a set of technologies and their novel applications that are directed for use within diverse urban domains to advance sustainability in the context of smart sustainable cities (Bibri 2018a). Software engineering: Is the study of designing, implementing, and modifying software in order to ensure it is of high quality, affordable, maintainable, and fast to build. It is a systematic approach to software design, involving the application of

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engineering practices to software. It deals with the creation, organization, analysis, and maintenance of software. Engineering computer applications software and computer systems software are at the heart of smart sustainable city development in terms of big data applications. For a detailed account of software development activities as part of project management associated with urban complexity, the interested reader can be directed to Bibri (2018a).

3.3 Data Science Data science has recently become a catch phrase. As an interdisciplinary field, it employs scientific methods, systems, processes, and algorithms to extract useful knowledge and valuable insights from data in various forms, similar to data mining. It has been used interchangeably with such concepts as statistics, business intelligence, business analytics, and predictive modeling. It uses theories and techniques drawn from many fields within the context of statistics, mathematics, computer science, information science, software engineering, and data engineering, among others. Data science (and thus big data analytics) techniques, such as data mining and pattern recognition, statistical analysis, data visualization and visual analytics, and prediction and simulation modeling are largely in the early stages of their development given that the statistical methods that have prevailed over several decades were originally designed to perform data–scarce science, i.e., to identify significant correlations and relationships from small, clean sample data sizes with known attributes or properties. These analytics techniques rely on machine learning (artificial intelligence) techniques and huge computational power to process and analyze data. Nonetheless, recent years have witnessed a remarkable progress within computer science, information science and data science with regard to handling and extracting knowledge and insights from large masses of data and these have been utilized in urban science. Data science is a flourishing field, and its particular concerns are relatively new and its general principles are just evolving. Its ultimate goal is to enhance decision–making pertaining to a large number and variety of domains across many fields through the practice of data analytics—data–driven decision– making (DDD). Yet, data science requires a careful thinking about what kind of available data might be used and how these data can be used in relation to a given application domain, specifically in terms of the problem that is to be tackled. It assumes access to and utilization of large masses of data, and often benefits from sophisticated data engineering facilitated by data processing and other software technologies being in use within a wide variety of organizations and institutions. In light of the above, it becomes clear why several terms have become muddled, confused, mixed–up, and interchanged in the world of big data. They overlap and interweave with one another, but are still quite distinct. Ultimately, it becomes necessary to understand the purpose, value, and scope of each term so as to give the terms their real meaning, as all play an integral part in the world of big data. In the context of smart sustainable urbanism, data science involves a set of unified concepts, principles, processes, and techniques as fundamentals that are incorporated in cutting–edge technologies distributed across diverse urban entities or domains for understanding, explaining, and predicting urban processes and problems in relation to environmental, social, and economic sustainability via the automated analysis of the deluge of urban big data, coupled with specialized knowledge, creativity, and common sense of data analysts or scientists. Accordingly, data–science oriented analytic thinking enables one to evaluate urban sustainability/sustainable urbanism proposals for data mining projects in the context of smart sustainable/sustainable smart cities. If a planner, strategist, or expert proposes to improve a particular energy, transport, traffic, environment, or healthcare application by extracting useful knowledge from urban data, it is crucial for the data scientist (or urban analyst) to be able to assess the proposal systematically and decide whether and why it is sound or flawed. This concerns identifying weak spots, unrealistic assumptions, and unconnected and missing pieces rather than determining whether it will actually succeed. The fundamentals of data science incorporated in data science technologies underlie the functioning of big data analytics, e.g., data mining as a process of extracting useful knowledge from large masses of data for enhancing decision–making and generating deep insights. In organizing thinking and analysis, these fundamentals make it possible to deeply understand data science approaches instead of focusing in depth on the wide range of specific data mining algorithms (Provost and Fawcett 2013). Compared to other big data analytics techniques, coupled with the fact that data science is of a wider application than the use of data mining, data mining algorithms provide the most explicit illustrations of data science fundamentals, which differ from, and are complementary to, statistics and database querying. However, in the context of smart sustainable/sustainable smart urbanism, it has become important to foster the ability to approach urban sustainability problems ‘data–analytically,’ as well as to assess how urban data can improve sustainability performance in relation to diverse urban domains. This implies that the knowledge extracted from large bodies of urban data is assumed to be in the

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form of nontrivial, actionable models. This entails applying a set of fundamental concepts that facilitate careful urban data– analytic thinking, understanding data mining techniques and data science applications in relation to sustainability dimensions, and developing relevant frameworks for structuring urban thinking about data analytics (sustainability) problems so that it can be done systematically. Drawn from many fields concerned with data analytics, among the fundamental concepts of data science in connection with the domain of smart sustainable/sustainable smart urbanism include, but are not limited to, the following: • Extracting useful knowledge from a huge deluge of urban data to solve a large number and wide variety of urban sustainability/sustainable urbanism problems (e.g., physical, spatial, technical, environmental, social, and economic) related to diverse urban domains in terms of operations, functions, services, designs, strategies, and policies. This can be treated systematically by following a set of reasonably well–defined stages (see Chap. 9 for a systematic framework for urban analytics). • Using big data analytics techniques, especially data mining models, to discover informative descriptive attributes of such entities as citizens, transport systems, traffic systems, energy systems, healthcare systems, mobility patterns, spatial organizations, and so on that are of particular interest and relevance, with each entity described by a large number of attributes. Which of these attributes gives us information on these entities’s likelihood of contributing to the environmental, economic, and social goals of sustainable development in the context of smart sustainable/sustainable smart cities? How much information and how unexpected and unknown can it be? An urban analyst may be able to hypothesize some and investigate them further, and there are various tools available to facilitate this experimentation in the context thereof. This experimentation can alternatively be carried out in an automated and large–scale fashion by means of advanced ICT to automatically discover informative descriptive attributes of any entity of interest. This concept can be applied recursively to build models across various urban domains to predict events or situations based on multiple attributes, as well as prevent them from happening if they involve negative implications for one of the dimensions of sustainability within urbanism. • Formulating data mining solutions to urban sustainability problems across diverse urban domains and evaluating the results entail thinking carefully about the spatial, topographic, and temporal contexts in which such solutions will be implemented. This implies formulating the kind of solutions that can actually contribute beneficially to the goals of sustainable development when extracting potentially useful knowledge. This depends critically on the application domain of urban sustainability in terms of how exactly we plan to use the patterns extracted from urban data, whether in relation to operations, functions, services, designs, strategies, or policies. The underlying assumption is to ensure that the extracted patterns lead eventually to enhancing decision-making compared to other available reasonable alternatives in urban thinking about analytics problems pertaining to environmental, economic, and social sustainability. These are dozen other fundamental concepts of data science that can be considered in the domain of data–driven smart sustainable/sustainable smart urbanism. Several books focus on illustrating how such concepts aid in structuring data– analytic thinking and understanding data mining techniques from a general perspective. The popularity of the term ‘data science’ has exploded in the academia where many critical academics see no distinction between data science and statistics. However, many statisticians envision data science as an increasingly inclusive applied field that grows out of traditional statistics and goes beyond traditional analytics. This implies that data science differs from statistics. One key difference is that statisticians are able by means of data science methods, systems, and processes to develop models for highly complex systems that were unfathomable: incapable of being fully explored or understood, before. In addition, emerged in the wake of big data, data science, as argued by Donoho (2015), does not equate to big data in that the size of the data set is not a criterion to distinguish data science and statistics. Also, data science is a heavily applied field where academic programs currently do not sufficiently prepare data scientists for the jobs in that many graduate programs misleadingly advertise their analytics and statistics training as the essence of a data science program (Barlow 2013; Donoho 2015). From a technical perspective, while statistics emphasizes models grounded in probability theory to deal with data arising from real–world phenomena, and provides principles and tools for the construction of statistical hypotheses as models that involve such modeling processes as data generation, evaluation and assessment, prediction, and uncertainty quantification, data science brings to statistics large–scale compute (modern computational infrastructures), data–intensive techniques, algorithmic design and analysis, large datasets, and advanced mathematical models. Data science, most often linked to the big data explosion, is the amalgamation of numerous parental disciplines, as mentioned above. As one example of capturing this, Blei and Smyth (2017) describe data science is ‘the child of statistics

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and computer science,’ where the ‘child’ metaphor appropriately depicts that data science inherits from both its parents, but eventually evolves into its own entity. They further elaborate: ‘data science focuses on exploiting the modern deluge of data for prediction, exploration, understanding, and intervention. It emphasizes the value and necessity of approximation and simplification; it values effective communication of the results of a data analysis and of the understanding about the world and data that we glean from it; it prioritizes an understanding of the optimization algorithms and transparently managing the inevitable tradeoff between accuracy and speed; it promotes domain–specific analyzes, where data scientists and domain experts work together to balance appropriate assumptions with computationally efficient methods.’ (Blei and Smyth 2017). Data science is largely seen as the umbrella discipline that incorporates a number of other disciplines. As an interdisciplinary field, data science employs methodologies and practices from across several academic disciplines while morphing them into a new discipline. Data science is often said to include particularly the allure of big data, the fascination of unstructured data, the advancement of data–intensive techniques and algorithms, and the precision of mathematics and statistics. The practical engineering goal of data science: actionable knowledge and consistent patterns for generating predictive models takes it beyond traditional approaches to analytics. For the future of data science, Donoho (2015) projects an ever–growing environment for open science where data sets used for academic publications are accessible to all researchers. Open science also involves making scientific research available to all levels of an inquiring society, as well as disseminating, sharing, and developing knowledge through collaborative networks. The future of data science not only exceeds the boundary of statistical theories in scale and methodology, but data science will revolutionize current academia and research paradigms (Donoho 2015). The scope and impact of data science will, as concluded by Donoho (2015), continue to expand enormously in the upcoming decades as scientific data and data about science itself become overwhelmingly abundant and ubiquitously available. Already, significant progress has been made within data science, information science, computer science, and complexity science with respect to handling and extracting knowledge and insights from big data and these have been utilized within urban science (e.g., Bibri 2018a, 2019a; Bibri and Krogstie 2017c; Kitchin 2016).

3.4 Urban Informatics Urban informatics did not emerge as a notable field of research and practice until around the mid 2000s. Subsequently, a number of books have been published on the topic (e.g., Foth 2009; Shepard 2011; Foth et al. 2011; Ratti and Claudel 2016; Townsend 2013; Unsworth, Forte and Dilworth 2014), which further demonstrate the increasing notability and significance of the field of urban informatics. This field is concerned with the study of humans in their interaction with computer and information systems, or people creating, applying, and using ICT and data, in the context of urban environments or areas. There are different definitions of urban informatics as an interdisciplinary field of research and practice. According to Foth et al. (2011), urban informatics refers to ‘the study, design, and practice of urban experiences across different urban contexts that are created by new opportunities of real–time, ubiquitous technology, and the augmentation that mediates the physical and digital layers of people networks and urban infrastructures.’ Kitchin (2016) describes it as a human–computer interaction and informational approach to examining and communicating urban processes. Further, this field draws on three broad domains: people, place, and technology (Foth et al. 2011). People from different socio–cultural backgrounds include residents, citizens, and community groups, in addition to the social dimensions of organizations and institutions. Place entails both urban sites, locales, and habitats, as well as regions, districts, neighborhoods, public spaces, and other kinds of urban areas. Technology entails various forms and types of urban ICT and urban computing/ubiquitous computing. Given the emphasis being placed on the technology domain of urban informatics in the context of this chapter, it is of relevance and importance to elaborate further on this domain. According to Bibri (2018, pp. 61–62), ‘urban ICT includes hardware and software components. The former encompasses sensors (RFID, GPS, infrared sensors, wearable sensors, etc.), computers and terminals, urban screens, smartphones, Internet infrastructure, wireless communication networks, telecommunication systems, database systems, data processing platforms, and cloud computing/fog infrastructures. The latter includes all kind of software applications operating and running on these hardware systems, including big data analytics techniques (data mining, machine learning, statistical analysis, etc.), algorithms, database integration and management methods…, real–time operation methods, enterprise integration methods, decision support systems, and communication and networking protocols… Urban ICT can be best spoken of based on the context of use, e.g., smart transport, smart mobility, smart traffic, smart energy, smart planning, smart governance, smart environment, smart healthcare, smart education, smart safety, and smart parks.’ In terms of the link between the aforementioned domains of urban informatics, ICT can be

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integrated in the built environment at various spatial scales, urban systems, urban domains, urban networks, urban services, and physical objects, as well as attached to citizens and spread along the trajectories they follow during their daily activities. As to urban computing, as a process, it refers to the use of computer technology to complete a given task, i.e., perform big data analytics, which can be of a small, medium, or large scale compute. For example, a large scale compute related to big data analytics can be performed in relation to a given urban domain (e.g., transport, mobility, healthcare, etc.) in the context of smart sustainable/sustainable smart cities. In this regard, urban computing denotes collecting, integrating, processing, analyzing, and synthesizing heterogeneous data (Zheng et al. 2014) and what this entails in terms of core enabling technologies for serving some purpose. In the context of smart sustainable/sustainable smart urbanism, urban computing entails, according to Bibri (2018a), using a set of devices, systems, platforms, infrastructures, networks, and related algorithms, techniques, processes, and protocols for the purpose of addressing and overcoming the issues engendered by the rapid urbanization and the unsustainability of the built form and other urban systems by using, manipulating, and leveraging various kinds of urban data, such as transport data, human mobility data, traffic flow data, spatiotemporal data, environmental data, energy data, socio–economic data, government data (census data, open data, etc.), user data, and so on in ways that extract useful knowledge to enhance decision–making processes pertaining to urban operational functioning, management, planning, and governance in the context of sustainability. Urban computing as an interdisciplinary field involves a range of scientific and technological areas, including computer science, information science, data science, information technology, information systems, computer engineering, software engineering, and wireless networks, as well as city-related or urban planning fields, including sustainable development, strategic thinking, environmental planning, transportation planning, land–use planning, landscape architecture, and urban design, all converging in the context of urban environments or spaces (Bibri and Krogstie 2016, 2017a). As an academic and research field, urban computing deals with, according to Bibri (2018a, pp. 62–63), ‘the study, design, development, and implementation of computing technology in urban systems and domains. Specifically, it is concerned with designing and constructing urban–oriented systems and applications and making them behave intelligently as to decision support to serve multiple urban goals; representing, modeling, processing, and managing various kinds of urban data; collecting information and discovering knowledge for various purposes; and so forth. It employs many of the technological paradigms introduced by ubiquitous computing…, [which] represents an era when, in the urban context, computer technology in all its forms disappears into urban environments and recedes into the background of urban life… [Such paradigms] share the same core enabling technologies, namely sensing devices, data processing platforms, computing infrastructures, and wireless communication networks. These are to function unobtrusively and invisibly in the background of urban life as well to—by means of various ICT applications—help optimize urban operational functioning, improve urban management and planning, enhance the quality of life of citizens, understand the nature of urban phenomena, and predict urban changes and dynamics.’ Important to add, Foth (2009) differentiates urban computing from urban informatics by suggesting that the former focusses more on technology and computing, and the latter focusses more on the social and human implications of technology in cities, i.e., the relationship between technology and urbanity, as expressed through the many dimensions of urban life. In light of the above, the field of urban informatics draws on social, scientific, technological, spatial, and urban research domains, including, combined, urban sociology, cultural studies, communication studies, urban planning, urban design, spatial planning, urban studies, geography, urban engineering, transportation engineering, landscape architecture, environmental engineering, geo–informatics, computer science, data science, software engineering, and human–computer interaction. The research domains of urban informatics is reflective of the diversity of methodologies being used in its pursuit and practice. In view of that, this field borrows from a wide range of methodologies across the social sciences, humanities, arts, design, architecture, planning, ICT, and computing and applies them to the domain of urbanism. Examples of such methodologies include big data analytics and urban science, action research and participatory action research, critical theory (e.g., Batty 2013; Foth and Brynskov 2016; Hearn et al. 2009; Satchell 2008), grounded theory, spatial analysis, participatory design, and interaction design. In addition, there is a longer legacy of scientific and informatics approaches to cities that provide a bedrock of knowledge, which originates in digital mapping and geographic information systems, quantitative geography and urban modeling, and urban cybernetics theory and practice (Kitchin 2016). Since urban informatics became a notable field of research and practice in the mid 2000s, the prevalence of ICT, the growing popularity of ubiquitous computing, the access to open data, the use of big data analytics, as well as the spread of smart cities have contributed to a surge in interest in this field. This is manifested in various actors seeking to explore and exploit the new possibilities and opportunities of urban informatics. Specifically, there are numerous actors involved in the academic and practical aspects of the field, including scholars, technical planners, industry experts, engineers and architects, computer and data scientists, and applied urban scientists, all undertaking research and developing technologies to tackle the challenging elements of urbanism using new approaches and exploring new opportunities increasingly enabled and fueled by

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the emerging paradigm of big data computing. This adds to the work of urban policymakers and city governments in terms of formulating and implementing regulatory policies and devising and applying political mechanisms to promote and spur innovation within urban informatics. In a nutshell, as described by Foth et al. (2011) and Townsend (2013), urban informatics emphasizes the new opportunities (including real–time data) for both citizens and city administrations enabled and afforded by ubiquitous computing, in addition to the convergence of physical and digital aspects of the city.

3.5 Urban Science As a result of the increasing prevalence and widespread development of smart cities and the growing application and use of big data analytics within, the definition of urban informatics has become narrow and limited. As an example, urban informatics refers to big data analytics for efficiency and productivity gains in city contexts, unless the arts and social sciences are added to the interdisciplinary mix (Thrift 2014). This particular specialization within urban informatics has been referred to as urban science (Batty 2013) or ‘data–driven, networked urbanism’ (Kitchin 2015). Accordingly, urban informatics and urban science are often used interchangeably and do overlap in many aspects. However, the strong recursive relationship between urban science and data–driven urbanism lies in that the former provides the fundamental ideas and the key tools to enact city analytics and data–driven decision–making, and the latter provides the applied domain and raw material (Kitchin 2016). This can further serve to enact and envision more sustainable, efficient, resilient, and transparent cities in the context of smart sustainable/sustainable smart cities (Bibri 2019a). Urban science is an interdisciplinary field within which data science is practiced to inform and sustain the core of data– driven urbanism. Positioned at the intersection of science and design, it draws on new disciplines in the natural science and information science, and seeks to exploit the development of modern computation and the growing abundance of data. As a research field, urban science is concerned with the study of diverse urban issues and problems, and thereby aims to produce both theoretical and practical knowledge that contributes to understanding and solving them in contemporary society. In this respect, it entails making sense of cities as they are by identifying relationships and urban laws, as well as predicting and simulating likely future scenarios under different conditions, potentially providing valuable insights for planning and development decision–making and policy formulation (Kitchin 2015). As such, it involves data–analytic thinking and computational modelling and simulation approaches to exploring, understanding, and explaining urban processes, and also addressing several challenges posed by urban data. The two fundamental ones are: (1) how to handle and make sense of billions of observations that are being generated on a dynamic basis (Batty et al. 2012) and (2) how to translate the insight derived into new urban theory (fundamental knowledge) and actionable outcomes (applied knowledge) (Batty 2013; Foth 2009; Ratti and Offenhuber 2014). In this respect, urban science radically extends quantitative forms of urban studies, blending in data science, social physics, and geocomputation (Batty 2013). Urban science informs and sustains the instrumental rationality and realist epistemology underpinning the logic, calculative, and algorithmic rules and procedures to which urban life is reduced through the computational understanding of the underlying systems, which is at the core of the data–driven approach to smart sustainable urbanism. Indeed, the new urban science—which is underpinned by urban sustainability science, a transdisciplinary field that fuses theories from urban sustainability and sustainability science, seeks to make cities more sustainable, resilient, efficient, livable, and equitable by rendering them more measurable, knowable, and tractable in terms of their operational functioning, management, planning, design, development, and governance.

3.6 Systems Thinking As a discipline, systems thinking is concerned with the broad paradigm of thinking. It revolves around seeing the big picture of phenomena, or viewing systems from a holistic perspective rather than on the basis of specific events and visible interacting variables. Conceptions in contemporary science are concerned with what is termed wholeness, i.e., problems of organization, phenomena not resolvable into local events, and dynamic interactions manifest in the difference of the behavior of the system parts when isolated (Bertalanffy 1968). In many fields, the necessity of systems thinking is emphasized, and it can be little or no doubt that the systems perspective marks a necessary, consequential, and genuine development in science and worldview (László 1972). There are multiple definitions of systems thinking, which essentially mean the same thing. The common thread running through all these definitions is the concept of holistic thinking in the sense of considering all the aspects of the system (e.g.,

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characteristics, structures, properties, behavioral patterns, relationships, dependencies, dynamics, influences, cause–effect, etc.), or the entirety of the object of interest and the mutual interactions with others when studying complex phenomena. This results to a better understanding of why things are the way they are, and to more effective ways of developing solutions or designing interventions, which are highly likely to succeed and to enhance the conditions under consideration. As supported by Senge (1990, p. 69), ‘systems thinking is a discipline for seeing the ‘structures’ that underlie complex situations, and for discerning high from low leverage change. That is, by seeing wholes we learn how to foster health. To do so, systems thinking offers a language that begins by restructuring how we think.’ In addition, systems thinking enables to solve problems in such a way that they stay solved, and crucially to not create new problems in the process that we have to deal with later. This is at the core of urban sustainability or sustainable urbanism in the context of smart sustainable/sustainable smart cities. In this regard, the nexus is complex and this interwoven complexity ought not to be overlooked at our human and enviroemtonal perils. Such cities are characterized by wicked problems, and thus, related urbanism has to deal with them in terms of operational functioning, planning, design, and development. When tackling wicked problems, they become worse due to the unanticipated effects and unforeseen consequence which were overlooked because the systems in question were not approached from a holistic perspective or treated in too immediate and simplistic terms (Bibri 2018a). Accordingly, it is of critical importance to look at the whole rather than the parts of such cities when dealing with sustainability and urbanization as complex problems in order to gain a better understanding of what is happening, to enable more effective actions for tackling such problems or improving the situation, and to provide an approach to designing the best means for implementing these actions. Considering everything, systems thinking focuses on systems, sub–systems, patterns of behaviors, and system interrelationships in a complex situation. It is a set of general principles spanning diverse fields, including physics, social sciences, engineering, and management, representing a framework for seeing behavioral patterns rather than static snapshots and interrelationships rather than things (Senge 1990). Systems thinking has extensively been applied to, and greatly influenced, human endeavors to understand and change organizations of different scales, structures, and complexities. Unlike traditional forms of analysis that focus on fragmenting and separating the individual parts of the system under investigation, systems thinking focuses on how to understand the system from studying the behavior of the whole and not the parts which the system is composed of, to reiterate. Science tried in the past to explain observable phenomena and problems of organization by reducing them to an interplay of elementary units investigable independently of each other (Bertalanffy 1968). Instead of isolating the components of the system being studied, systems thinking focuses moreover on the dynamic interaction of the whole system with the environment. This approach has proven to be exceedingly effective in solving difficult problems involving complexity aspects. Indeed, by considering the whole as well as the parts and their mutual interactions, the systems view minimizes the risk of potentially losing the most relevant emergent characteristics of the system being studied, and develops a deeper level of understanding for determining relevant actions. Figure 3 represents a positioning of systems thinking, where each stage in the continuum provides a strengthening of the systems view. The explicit description of our thinking provided by each level of understanding enables to reduce potential ambiguity and misunderstanding in our communication pertaining to how we think or conceive of complex systems. Cause and Effect is about identifying the cause and act, and represents thoughts where actions are primarily reactionary. Patterns of Behavior entails recognizing the way things have changed over time and taking this into account before taking action. Systems Perspective involves standing back far enough in space and time to be able to see when looking at the underlying web of ongoing, reciprocal relationships cycling to generate the patterns of behavior being exhibited by complex systems. Influence Diagram is a kind of simple map of the reciprocal relationships thought to be principally responsible for generating the patterns of behavior exhibited by complex systems. Structural Diagram represents a more disciplined map showing what really makes complex systems tick, a process during which the mechanisms that complex systems are using to control themselves can be laid out. Simulations translate the structural diagram into a set of equations characterizing the nature of the relationships laid out in the structural diagram, as well as their direction and strength. Then comes the simulation of the system behavior on a

Fig. 3 Systems thinking continuum. Adapted from Richmond (1991)

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computer. The key question to answer here is whether the set of reciprocal relationships pieced together do generate the patterns of behavior exhibited by complex systems.

3.7 Complexity Science and Complex Systems As an emerging approach to research and multidisciplinary subject, complexity science is the scientific study of complex systems, systems composed of many parts connected and joined together by a web of relationships that interact to generate collective behaviors that cannot easily be explained on the basis of the interaction between the individual constituent elements. In this respect, complexity entails the way a vast number of complicated and dynamic sets of relationships, interactions, or dependencies can produce some behavioral patterns. Complexity science is a set of conceptual tools and theories from an array of disciplines (Benham-Hutchins and Clancy 2010; Paley and Gail 2011). It deals with complex systems as a collection of interconnected parts and relationships that are dynamical, unpredictable, and multidimensional in nature. It has been discussed in both natural and social sciences. In a wide range of related complex systems, computational modeling, as based on mathematical developments and modeling approaches from physics, is undertaken to study the behavior of such systems to better understand them. Software engineering expertise can be used to apply new results as well as to inspire new approaches in this regard (Batty et al. 2012). Complex systems are characterized by nonlinearity and indeed require more than simplistic linear thinking, as they feature a large number of interacting elements (patterns, agents, processes, etc.) whose aggregate activity (behaviors, relationships, interactions, etc.) does not emanate from the summations of the activity pertaining to the individual elements. As such, they typically exhibit hierarchical self–organization under some kind of selective pressures. Examples of complex systems include cities, ecosystems, organisms, global climate, neural network, human brain, ICT network, and the entire universe. For a detailed account of smart sustainable cities as complex systems and dynamically changing environments, the interested reader can be directed to Bibri (2018a), specifically to Chap. 6 of his book. The aim of this chapter as grounded in complexity science and systems thinking as theoretic approaches is ‘to systematically explore the key structures, behavioral patterns, conditions, relationships, interactions, and dependencies underlying smart sustainable cities as complex systems, and to elucidate the associated principles in terms of methods, mechanisms, and goals. The intent of offering the knowledge to describe and analyze such systems accordingly is to surface noteworthy relationships as well as their implications for sustainability so as to provoke thought, foster deeper understanding, and create fertile insights, with the primary purpose of making visible possible places for actions that improve the contribution of smart sustainable cities to the goals of sustainable development. This can be accomplished by developing new urban intelligence functions and powerful new forms of urban simulation models for strategic decision–making based on big data analytics in conjunction with the design concepts and principles and planning practices of urban sustainability. This chapter also discusses the potential of big data analytics and related urban intelligence functions and simulation models for boosting and advancing the process of sustainable urban development by proposing innovative approaches and solutions for monitoring, understanding, analyzing, and planning smart sustainable cities… The main argument is that the systems thinking and complexity science are integral to the understanding of such cities, which is a moving target in that they are becoming more complex through the very technologies being used to understand and deal with them. Moreover, advanced ICT is founded on the application of complexity theory to urban problems and issues in terms of tracking the changing dynamics, disentangling the intractable issues, and tackling the challenges pertaining to urban systems, which are in and of themselves becoming ever more complex. Besides, complex systems cannot be understood and studied without the use of sophisticated computational and data analytics.’ (Bibri 2018a, pp. 297–298). In light of this, complexity science is linked to many different disciplines and professional fields that have the city as their concern. In particular, urban planning and design is a distinct area that is central to urban scholarly and scientific research while data science and urban science are key to the development of big data analytics and underpinning technologies. Cities can only be studied in an interdisciplinary context and the perspective here involves developing a social physics and data–driven science of cities that are consistent with treating their structure and evolution as complex systems. However, as an approach to science, complex systems investigates how the dependencies, relationships, or interactions between the system’s parts give rise to its collective behaviors, and how the system interacts and forms relationships with its environment (Yaneer 2002). Thus, it is principally concerned with the behaviors and properties of systems. As a research approach, it deals with problems in many different disciplines, including information theory, computer science, mathematics, statistical physics, biology, ecology, nonlinear dynamics, sociology, and economy. As an interdisciplinary field, it draws on theoretical contributions and perspectives from those disciplines, e.g., spontaneous order from the social sciences, chaos from mathematics, cybernetics from technology, self–organization from physics, adaptation from biology, and many others.

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The concerns that complexity science addresses have grown out of investigations from a varied intellectual ancestry, including cybernetics, general systems theory, chaos theory in dynamical systems, complex systems, mathematical systems, and complex adaptive systems (social systems, technological systems, urban systems, etc.) where the parts actively change the way they interact. The increased use of computer simulation created research in the simulation of adaptive behavior in the 1990s (Bibri 2018a). From 2000s and onward, complexity science takes stock of what has been accumulated as substantive knowledge of all this rich background of work. A key part of the current emphasis of complexity science is its application to practical technological and engineering systems in that control systems need to be designed, managed, and constructed as they proliferate and increase in size and connectivity in a variety of contexts, e.g., control systems associated with big data technologies and engineering analytical solutions in relation to smart sustainable/sustainable smart cities. It is desirable to have the ability to build systems that are scalable, robust, and adaptive by using such properties as self– organization, self–adaptation, self–regulation, self–repair, and evolution as a way of mimicking biological systems. Complexity science is a subject of study that is well positioned to bringing together deep scientific questions pertaining to sustainability and urbanization with big data applications–driven goals within the field of smart sustainable/sustainable smart urbanism. Its contemporary applications are complemented by a rich background of theoretic work. Complexity science touches on all facets of science and technology, creating an array of multitudinous new opportunities within numerous research domains. Important to underscore in this context is that complexity is not just determined by the large number of parts of a system with very intricate design, but rather by such dynamical properties as self–organization, spontaneous order, adaptation, emergence, feedback loops, and nonlinearity. In the context of smart sustainable/sustainable smart cities, technological and engineering systems based on big data analytics are primarily designed to minimize these tricky dynamical properties. These can otherwise make such cities as complex systems difficult to design, predict, and control. However, if desirable emergent behaviors and processes can be managed, harnessed, and exploited, they can allow to move beyond the limits of conventional technological and engineering systems that are merely complicated. Apart from that, we are dealing with the traditional approach to tackling complexity, which aims to reduce or constrain it and thereby typically involves compartmentalization: dividing a large system into separate parts. Technological and engineering systems are susceptible to failure for they are often designed using modular components, and where failure usually results from the potential issues arising to bridge the divisions. Dynamical properties such as feedback loops, adaptation, nonlinearity, emergence, networks, and spontaneous order as important concepts specific to complex systems originate in systems theory. Complex systems is indeed a subset of systems theory. Accordingly, both complex systems and general systems theory focus on the collective or system–wide properties and behaviors of interacting entities. But the latter is concerned with a much broader class of systems, including linear systems where the effect is directly proportional to cause, or noncomplex systems where reductionism may hold viable. Indeed, systems theory entails the ordered arrangement of knowledge accumulated from the study of all classes of systems in the observable world. As such, it seeks to describe, explain, and explore all categories of systems, and one of its objectives is the invention of classes that are of value to researchers across a wide variety of fields. Generally, given the link between systems theory and complex systems, the former provides two key contributions to the latter: (1) an interdisciplinary perspective in that the shared properties linking systems across disciplines justify the quest for modeling approaches applicable to complex systems across disciplines, and (2) an emphasis on the way in which system’s components interact and depend on each other can determine system–wide properties that produce collective behaviors.

3.8 Systems Science and Theory To systems scientists, the world can be understood as a system of systems, both simple and complex. Systems science is an interdisciplinary field that studies the nature of systems—from simple to complex—in nature, society, city, engineering, technology, and science itself. M’Pherson (1974) defines systems science as the organized knowledge acquired from the study of systems in the observable world, together with the application of this knowledge to the design of man–made systems. For example, designing a smart sustainable city as a complex system should be grounded in efficiency, resiliency, sustainability, and equity, or ideally mimic natural patterns and processes. In more detail, however, systems science is a systematic enterprise that builds and organizes knowledge in the form of explanations and predictions about the simple and complex systems in the universe. In other words, it denotes the intellectual and practical activity encompassing the systematic study of and knowledge about the structure and behavior of such systems based on facts learned through experimentation and observation.

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Systems theory can be viewed as a key regulative device in science in terms of eschewing ungrounded assumptions in science and hence their detrimental effects on practice. As a major scientific discovery and systematic approach to thinking, systems theory aims at surfacing noteworthy behavioral patterns and reciprocal relationships and their implications pertaining to complex systems so as to provoke thought and discussion, foster deeper understanding, and create fertile insights, with the purpose of enabling more effective actions for improvement in terms of the functioning, adaptation, and evolution of such systems toward a desired state (Bibri 2018a). Concerned with the study of systems, systems theory brings together laws, principles, and concepts from a number of disciplines in terms of integration and fusion, including computer science, data science, physics, philosophy of science, biology, engineering, organizational theory and management, ecology, environmental science, and sociology. In short, it is an interdisciplinary and transdisciplinary field for research and practice, and as such, it serves as a bridge for dialogue within the field of systems science as well as between autonomous fields of study. To note, the social sciences were of particular importance to the establishment of systems theory since its inception. Systems theory emerged as an alternative approach to scientific thinking, and strives to revive the unity of science. Thus, it is about broadly applicable concepts and principles, as opposed to those applicable to one discipline or field of knowledge. Its primary goal is to systematically discover the structures, patterns, conditions, dynamics, relationships, interactions, and constraints of complex systems, and to illuminate the related principles (methods, mechanisms, tools, goals, etc.) that can be discerned and applied at every level of nesting pertaining to such systems, as well as to every field for achieving optimized equifinality. Accordingly, it focuses on the arrangement (structure) of, and the relations (interaction) between, the components that connect them to a whole that determines a system, which is independent of the concrete substance of the individual elements and cannot be reduced to the properties of its components. Bertalanffy (1962) points out that real systems are open to, and interact with, their environments, which allows them to acquire qualitatively new properties through emergence, resulting in continual evolution. Also, a system is an entity which maintains its existence through the mutual interaction of its parts (Davidson 1983). In systems theory, emergence as a phenomenon and central in theories of complex systems entails larger entities arising through interactions among smaller entities such that the former exhibit properties that the latter do not. Almost all accounts of emergentism involve a form of ontological or epistemic irreducibility to the lower level (O’Connor and Wong 2012). As far as general system theory is concerned, it involves models, laws, formulation, and derivation of universal principles that apply to generalized systems or their subclasses, irrespective of their particular kind, the nature of their components, and the relation between them (Bertalanffy 1968). It should be a key regulative device in science, to guard against superficial analogies that are useless in science and harmful in their practical implications (Bertalanffy 1950).

3.9 Sustainability Science Sustainability is a new domain of science that focuses on understanding the dynamic interactions of socio–ecological systems, of which the city represents a clear illustration, and holds much promise as an approach to dealing with the kind of wicked and intractable problems presented by the city. Sustainability has theoretical foundations and assumptions from which it has grown that have solidified it into a defined science whose focus is on general truths and laws, as well as on particular methods of enquiry. As a flourishing academic discipline, sustainability science has emerged in the early 2000s (e.g., Kates et al. 2001; Clark 2007; Clark and Dickson 2003; Lee 2000). Sustainability science is concerned with ‘advancing knowledge on how the natural and human systems interact in terms of the underlying (changing) dynamics and patterns for the purpose of designing, developing, implementing, evaluating, and perennially enhancing human engineered systems as practical solutions and interventions that support the idea of the socio–ecological system in balance, as well as nurturing and sustaining the linkages between scientific research and technological innovation and policy and public administration processes in relevance to sustainability.’ (Bibri and Krogstie 2017a, p. 6). Just like the definition of sustainability, a consensual definition of sustainability science is difficult to pin down. Kieffer et al. (2003, p. 432) define sustainability science as ‘the cultivation, integration, and application of knowledge about Earth systems gained especially from the holistic and historical sciences…coordinated with knowledge about human interrelationships gained from the social sciences and humanities, in order to evaluate, mitigate, and minimize the consequences…of human impacts on planetary systems and on societies across the globe and into the future.’ As an interdisciplinary and transdisciplinary field, it mixes and fuses disciplines across the natural sciences, social sciences, and applied and engineering sciences. Speaking of interdisciplinarity and transdisciplinarity and in relation to smart sustainable/sustainable smart urbanism, these two scholarly approaches remain most relevant to study smart sustainable/sustainable smart cities as holistic approaches to urban planning and development. Understanding the tenets of several pertinent theories from diverse disciplines allows a more complete

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understanding of such cities as leading models of urbanism. This entails setting side–by–side elements of a set of theories that have clear implications for the concept of such cities. Further to the point, the philosophical and analytic framework of sustainability draws on and links with numerous different disciplines and fields. It is studied and examined in various contexts of environmental, social, economic, and cultural development, and also managed over many temporal and spatial scales. The focus ranges from macro levels starting from the (sustainability) of planet Earth to the sustainability of societies, regions, cities, and neighborhoods, as well as economies, ecosystems, and communities, and to micro levels encompassed in streets, buildings, and individual lifestyles. The solutions to the kind of wicked and intractable problems, i.e., difficult to delineate and control, unpredictable and unmanageable, and defying standard principles of science and rational decision making, associated with sustainability are to be anchored in the recognition that the world has increasingly become integrated, complex, intricate, and uncertain (Bibri 2018a). Such problems relate particularly to the domain of urban planning and development for which sustainability science should constitute a theoretical foundation. This could result in a new paradigm of urbanism that can effectively engage with the wicked and intractable problems pertaining to cities and their sustainability with support of advanced technologies and their novel applications, especially big data computing. This entails linking the wicked and intractable problems associated with the domain of urbanism to the type of problems explored and probed by sustainability science, as well as demonstrating how the understanding of cities as instances of socio–ecological systems provides a conceptual and analytical framework for addressing and overcoming some of the challenges characteristic to the kind of wicked and intractable problems inherent in urbanism. There is a host of new practices that sustainability science could bring to urbanism, an argument that needs to be developed further and become part of mainstream debates in urban and academic circles as being stimulated through the ongoing discussion and development of the new ideas about the untapped potential of advanced technologies and their novel applications for advancing sustainability science and thus urban sustainability. As a research field, sustainability science probes the complex mechanisms involved in the profound interactions between environmental, social and economic systems to understand their behavioral patterns and thus changing dynamics, in order to develop upstream solutions for tackling the complex challenges associated with the systematic degradation of the natural system and the concomitant perils to human well–being. That is, those challenges that imperil the integrity of the planet’s life support systems and compromise the future of human life. In short, sustainability science centers around the interactions between the resource system, the human system, and the governance system, and in doing so, attempts to identify and solve potential problems through devising and implementing holistic solutions. This research field seeks to give the ‘broad–based and crossover approach’ of sustainability a solid scientific foundation (Bibri 2018a). It also provides a critical and analytical framework for sustainability (Komiyama and Takeuchi 2006), and ‘must encompass different magnitudes of scales (of time, space, and function), multiple balances (dynamics), multiple actors (interests), and multiple failures (systemic faults)’ (Reitan 2005, p. 77). In addition, sustainability science can be viewed as ‘neither “basic” nor “applied” research but as a field defined by the problems it addresses rather than by the disciplines it employs; it serves the need for advancing both knowledge and action by creating a dynamic bridge between the two’ (Clark 2007, p. 1737). From a broader perspective of sustainability science, some views highlight the need to probe the root causes of the fundamental unsustainability of the predominant paradigms of technological, economic, and societal development. In this line of thinking, Bibri (2015) provides an analytical account of the implications of ICT of pervasive computing as a form of advanced science and technology for environmental and societal sustainability. Arguably, sustainability science must involve the role of technology both in exasperating the unsustainability of social practices (e.g., urbanism) as well as in tackling the problems such practices generate (environmental risks, social inequality, etc.), in addition to including the study of the societal structures as to material consumption (see, e.g., Bibri 2018a; Brown 2012). To grasp the integrated whole of the socio–ecological system in terms of the complex social and multidimensional environmental characteristics, behavioral patterns, relationships, interactions, and dynamics to tackle the underlying problems necessitates global political consensus and collaboration between institutional, social, economic, scientific, and technological disciplines in terms of scholars and practitioners, as well as the active engagement of citizens, communities, organizations, and institutions. One of the key missions of sustainability science as a more disciplined framework is to aid in coordinating cross–disciplinary integration necessary as a critical step towards a global joint effort and concerted action. In addition, the way in which sustainability science as a scholarly community can best contribute to the understanding and implementation of the goals of sustainable development should be based on an in–depth critical analysis and evaluation through scenario analysis, scientific research, technological innovation, stakeholder relationships, participatory decision– making, and policy recommendations and impacts (Bibri 2018a). In a nutshell, to achieve these goals requires taking an all– inclusive approach by mobilizing diverse actors, factors, and resources.

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3.10 Scientifically Oriented Sustainable Development The link between sustainable development and science stems from the idea that the former is an aspiration that should, as realized by many scholars over the past two decade or so, be achieved only on the basis of scientific knowledge. This has justified the establishment of a new branch of science due to the fact that humanity is, arguably, confronted at an ever unprecedented rate and larger scale with the ramifications of its own success as a species. The way things have changed in recent years (and the attempts being undertaken to take this into account) calls for a scientific approach to understanding the underlying web of ongoing, reciprocal relationships that are cycling to generate the patterns of behavior that the ecosystems are exhibiting, and to figuring out the mechanisms these ecosystems are using to control themselves. The point is that the complexities, uncertainties, and hazards of the human adventure are triggering drastic changes increasingly requiring insights from all the sciences to tackle them if there is a shred of seriousness about the aspiration to enhance and sustain the quality of life, i.e., sustainable development. The real challenge stemming from the fragmented character of science lies in understanding and acting upon the causal mechanisms and behavioral patterns in response to the reciprocal relationships between different complex systems across several time– and space–scales. This calls for fusing disciplines and theories, a transdisciplinary approach that reconciles and amalgamates the theoretical and practical knowledge, the quantitative and qualitative perspectives, and the natural and social sciences. Sustainability science is what such integrative approach entails, and whose emphasis is on understanding changes in states rather than just their characterization. Systems theory and system analysis approaches become the most coherent expression of this insight (Bossel 2004). Sustainability science is perhaps the most clear and desirable illustration of the endeavor of reinforcing the unified approaches and unifying tendencies in science, as well as of liberating the study of real–world processes from the boundaries between the scientific disciplines (de Vries 2013).

4

Discussion and Conclusion

This chapter endeavored to systematize the complex field of smart sustainable/sustainable smart urbanism by identifying, distilling, mixing, fusing, and thematically analytically organizing the core dimensions of a foundational approach consisting of a set of relevant concepts, theories, discourses, and academic and scientific disciplines that underpin this field for research and practice. The primary intention of setting such approach was to conceptually and analytically relate urban planning and development, sustainable development, and urban science while emphasizing why and the extent to which sustainability and big data computing have particularly become influential in urbanism in modern society. The conceptual and theoretical dimensions of the foundational approach involve the following: • • • • • • • • • • •

Big data computing Big data Big data analytics Big data technology Big data application Urban sustainability planning Sustainable urban development Sustainable urbanism Ecological urbanism Strategic smart sustainable urbanism Smart sustainable/sustainable smart cities.

This chapter primarily serves to facilitate collaboration and integration between the different foundational dimensions that underlie the interdisciplinary and transdisciplinary field of smart sustainable/sustainable smart urbanism for the sheer purpose of generating the kind of interactional and unifiable knowledge necessary for a broader and more inclusive understanding of the topic of smart sustainable/sustainable smart urban planning and development. This is a key contribution that supports the ethos of interdisciplinarity and transdisciplinarity characterizing the research field of smart sustainable/sustainable smart urbanism. While the interdisciplinary approach is about pooling approaches and insights from several disciplines and adjusting them in such a way that the resulting outcome becomes well suited to examining the problems of such urbanism, the transdisciplinary approach goes beyond pooling and adjusting disciplinary approaches to include their fusion for readily exploring such problems in their complexity. Therefore, adopting interdisciplinary and

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transdisciplinary scholarly approaches in this context makes it possible to flexibly respond to the topic under study and thus uncover the best way of addressing it. Such approaches are primarily aimed at contributing to an integral reflection over where the still–emerging field of smart sustainable/sustainable smart urbanism is coming from and where it is believed it should be headed, and how it will evolve in the upcoming decades. With that in regard, the outcome of this scholarly book provides fertile insights into rethinking such urbanism in terms of data–driven planning and development, with the ability to create new approaches into, and opportunities for, informing the sustainability and ICT strands of urban planning and development and related policies, and to propose a holistic approach to conceiving of, thinking about, understanding, and practicing urbanism. The conceptual and theoretical dimensions of the foundational approach underpinning the research field of smart sustainable/sustainable smart urbanism in the form of a collection of interrelated concepts, theories, and academic discourses are intended to support and guide this scholarly work, as well as to elucidate why the research issues being addressed exist and how they should be, or are being, dealt with. In short, they represent a sort of lens through which to look at this scholarly work. As such, they can explain, predict, and help understand the phenomenon of smart sustainable/sustainable smart urbanism, as well as challenge, interrogate, reconfigure, and extend existing knowledge within the confines of critical bounding assumptions. They serve additionally to construct new knowledge by validating or evaluating explicitly stated assumptions. Their particular relevance to this scholarly work lies in that they aid in eliminating potential preconceived notions pertaining to the topic of smart sustainable/sustainable smart urbanism, which may otherwise result from biases or taken–for–granted assumptions, thereby avoiding the issue of not noticing things that might fit the implicit approach guiding this scholarly work. This indeed deals with a rather complex and multifarious topic. The point is to make what can be an implicit approach more explicit by seeing the topic through conceptual and theoretical lenses. The overall intention is to strengthen this scholarly work by allowing the reader to reflectively and critically assess the assumptions explicitly enunciated, to connect to existing and dominating knowledge, and to transition from portraying the topic of smart sustainable/sustainable smart urbanism to uncovering and generalizing about its various dimensions—while identifying the limits to this generalization process. This is predicated on the premise that the conceptual and theoretical dimensions of any foundational approach may, in general, determine the specific assumptions that the research work involves with regard to analyzing and interpreting the scientific literature. Tacking everything into consideration, the value of these dimensions in this context lies in fulfilling one primary purpose: to explain the nature, meaning, implications, and challenges associated with the multifaceted phenomenon of smart sustainable/sustainable smart urbanism. In this regard, the commonly held view is that all phenomena are often experienced and explained from certain perspectives, but unexplored from somewhat other perspectives in the world in which we live, and that the multi–perspectival approaches are accordingly useful for a broader understanding of multifaceted phenomena. In addition, in the subject of smart sustainable/sustainable smart urbanism, theories from academic and scientific disciplines constitute a foundation for action—data–driven smart sustainable urbanism and related urban big data development as informed by data science practiced within the fields of urban science and urban informatics, as well as by sustainability science and sustainable development. In short, the theoretical and disciplinary dimensions of the foundational approach have strong implications for such urbanism as a set of practices. The synergic interaction between these dimensions produces a combined effect greater than the sum of their separate effects. This implies that such approach has a supporting, underpinning, and shaping role in smart sustainable urban planning and development. Of importance to underscore in this regard is that the theories of sustainability science, urban science, urban informatics, urban computing, data science, and ICT have become influential in many aspects of urbanism and urbanity, whether in relation to urban forms, urban systems, urban domains, urban networks, urban ecosystems, or urban services. This scholarly work focuses specifically on how sustainability can be integrated with big data technology as an advanced form of ICT in their application to urbanism, and how this functions and is useful in an increasingly technologized, computerized, and urbanized world. Thus, this integration is of paramount importance as to how the subject of smart sustainable/sustainable smart urbanism should be studied and applied. In other words, how sustainability and ICT theories are applied in the real urban world, how they work and provide value, constitute relevant subjects for this scholarly work. While there are many theories that are influential in how this subject can be studied and applied, some of them are strongly based on scientific evidence that would need expert knowledge to challenge, and others are more philosophical and institutional and thus more open to general critical examination. What the implications of the integrated theories in this scholarly work are and whether such theories deliver what is claimed can be best studied by examining actual case studies (projects, programs, strategies, and future plans pertaining to sustainable cities and smart cities). However, the theories and their effects are of importance to this scholarly work.

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The disciplinary dimension of the foundational approach involves the following academic and scientific disciplines: • • • • • • •

Urban planning and design Data science Urban informatics Urban science Complexity science and complex systems Sustainability science Scientifically oriented sustainable development.

One of the intentions of providing the disciplinary dimension of the foundational approach to smart sustainable/sustainable smart urbanism is to enable a holistic understanding of this multifaceted phenomenon as enabled by the interdisciplinary and transdisciplinary perspectives framing such approach. This is justified by the pursuit of normative actions associated with integrating sustainability and big data technology in the future form of urban planning and development as a set of practices. This form requires the understanding of diverse academic and scientific disciplines, as well as their integration and fusion, to solve complex problems and hence facilitate practical endeavors. Another intention is to induce and motivate scholars and researchers to further integrate and fuse several disciplines to create new perspectives based on interactional and unifiable knowledge beyond those disciplines—with a result that both yields new ideas by thinking across disciplinary boundaries as well as exceeds the simple sum of each discipline. This can be accomplished by combining different analyzes, using insights and methods in parallel and conjunction, and spilling over and blurring boundaries. Pooling various disciplinary approaches together is of importance to arrive at a theoretically solid and analytically informed multi–perspective on smart sustainable/sustainable smart urbanism, as well as to holisticize knowledge for enhancing and rethinking related practices. Besides, interdisciplinary and transdisciplinary perspectives are necessary to address the complex issues related to such urbanism, as well as to respond knowledgeably and critically to the enormous challenges facing contemporary cities in terms of sustainability and urbanization.

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Sustainable, Smart, and Data-Driven Approaches to Urbanism and their Integrative Aspects: A Qualitative Analysis of Long-Lasting Trends

Abstract

Smart sustainable/sustainable smart cities, a defining context for ICT for sustainability, have recently become the leading global paradigm of urbanism. With this position, they are increasingly gaining traction and prevalence worldwide as a promising response to the mounting challenges of sustainability and the potential effects of urbanization. In the meantime, the research in this area is garnering growing attention and rapidly burgeoning, and its status is consolidating as one of the most enticing areas of investigation today. A large part of research in this area focuses on exploiting the potentials and opportunities of advanced technologies and their novel applications, especially big data computing, as an effective way to mitigate or overcome the issue of sustainable cities and smart cities being extremely fragmented as landscapes and weakly connected as approaches. In this context, one of the most appealing strands of research in the domain of smart sustainable urbanism is that which is concerned with futures studies related to the planning and development of new models for smart sustainable cities. Not only in the futures studies using a backcasting approach to strategic planning and development, but also in those using other approaches, is trend analysis a necessary step to perform and a critical input to the scenario analysis as part of such studies. With that in regard, this chapter aims to provide a detailed qualitative analysis of the key forms of trends shaping and driving the emergence, materialization, and evolvement of the phenomenon of smart sustainable cities as a leading paradigm of urbanism, as well as to identify the relevant expected developments related to smart sustainable urbanism. It is more likely that these forms of trends reflect a congeries of long-lasting forces behind the continuation of smart sustainable cities as a set of multiple approaches to, and multiple pathways to achieving, smart sustainable urban development. As part of the futures studies related to smart sustainable city planning and development using a backcasting methodology, both the trends and expected developments are key ingredients of, and crucial inputs for, analyzing different alternative scenarios for the future or long-term visions pertaining to desirable sustainable futures in terms of their opportunities, potentials, environmental and social benefits, and other effects. This study serves to provide a necessary material for scholars, researchers, and academics, as well as other futurists, who are in the process of conducting, or planning to carry out, futures research projects or scholarly backcasting endeavors related to the field of smart sustainable urbanism. Keywords


















Smart sustainable/sustainable smart cities Sustainable cities Smart cities Smarter cities Sustainability Sustainable development Trends Futures studies Backcasting

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Big data computing




Introduction

Not long ago, smart sustainable cities became the leading global paradigm of urban planning and development. The rapidly evolving body of work, the countless research endeavors going on, and the multitudinous unexplored opportunities within the domain of smart sustainable urbanism reflect the characteristic spirit and prevailing tendency of the ICT–sustainability– urbanization era as manifested in its aspirations for increasingly directing the advances in ICT of pervasive computing toward addressing and overcoming the challenges of sustainability and containing the potential effects of urbanization in the © Springer Nature Switzerland AG 2019 S. E. Bibri, Big Data Science and Analytics for Smart Sustainable Urbanism, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-17312-8_4

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context of smart sustainable cities of the future. The subject of ‘smart sustainable cities’ is endlessly enticing and magnetizing, whether from an intellectual or practical perspective, as there are numerous actors involved in the academic and practical aspects of the endeavor, including engineers and architects, green technologists, built and natural environment specialists, environmental and social scientists, ICT experts, computer and data scientists, and applied urban scientists. All these actors are undertaking research and developing strategies, approaches, and programs to tackle the challenging elements of smart sustainable urbanism. This adds to the work of policymakers and political decision-makers in terms of formulating and implementing regulatory policies and devising and applying political mechanisms and governance arrangements to promote and spur innovation and monitor and maintain progress within such urbanism. The notion of smart sustainable cities has come to the fore in recent years as a result of three global shifts at play across the world today, namely the rise of ICT, the diffusion of sustainability, and the spread of urbanization (Bibri 2018a, b, c, d, 2019a, b), aside from the other long-lasting and influential trends. These are the object and focus of this chapter. With this conspicuous position, it is increasingly gaining traction and prevalence worldwide as a promising response to the mounting challenges of sustainability and the potential effects of urbanization. In the meantime, the research on smart sustainable cities is garnering increased attention and rapidly burgeoning, and its status is consolidating as one of the most enticing areas of research today, making the relevance and rationale behind the smart sustainable city debate of high significance and value with respect to the future form of urbanism. This area is typically concerned with addressing a large number and variety of issues related to the amalgamation of sustainable cities and smart cities as landscapes and approaches in the context of sustainability. A large part of research in this area focuses on exploiting the potentials and opportunities of advanced technologies and their novel applications, especially big data computing, as an effective way to mitigate or overcome the issue of sustainable cities and smart cities being extremely fragmented as landscapes and weakly connected as approaches, especially at the technical and policy levels, under what is labeled ‘smart sustainable cities’ (Bibri 2019b). This integrated approach to urbanism tends to take multiple pathways toward combining the strengths of sustainable cities and smart cities based on how the concept of smart sustainable cities can be conceptualized and operationalized, just as it has mainly been the case for sustainable cities as a paradigm of urban planning and development that has prevailed for more than three decades: There are multiple processes of, and pathways toward achieving, sustainable urbanism (Bibri 2019b). As a corollary of this, there is a host of unexplored opportunities toward new approaches to smart sustainable urbanism. Furthermore, the quest for, and the challenge of, finding more effective ways to merge the physical and informational landscapes of the emerging smart sustainable cities in ways that can continuously improve, advance, and maintain their contribution to the goals of sustainable development and advance their sustainability is currently motivating, inducing, and inspiring many researchers, scholars, academics, and practitioners within smart sustainable urbanism, as well as real-world cities (Bibri 2019b). Building such model will play a pivotal role in laying the foundation for, and spurring, the development, implementation, and deployment of such cities. This will in turn stimulate its replication in different places around the world, thereby mainstreaming this drastic urban transformation. Smart sustainable cities as a holistic urban planning and development approach aim primarily at substantiating and strengthening the growing potential and role of advanced ICT in enabling sustainable cities to improve, advance, and maintain their contribution to the goals of sustainable development and to rise to the challenges of urbanization (Bibri 2018a, 2019b; Bibri and Krogstie 2016, 2017a, b). With that in mind, the way forward for developing and realizing smart sustainable cities is through amalgamating the sustainable city and smart city landscapes and approaches, a process which typically takes many forms depending on several factors, including objectives, requirements, resources, and interpretations, in addition to the social, cultural, national, and local contexts in which these factors arise and are embedded as pertaining to particular urban projects and initiatives (Bibri 2019b). This is to achieve the required or optimal level of sustainability with respect to such urban aspects as operations, functions, services, designs, strategies, and policies as manifestations of processual outcomes of urbanization, irrespective of the ambition of the projects and initiatives considered to be smart sustainable cities, which there will indeed be multiple ways to achieve (Bibri 2018a, 2019b). One of the most appealing strands of research in the domain of smart sustainable urbanism is that which is concerned with futures studies related to the planning and development of future models for smart sustainable cities as an instance of sustainable urban development, a strategic approach to achieving the long-term goals of urban sustainability—with support of advanced technologies and their novel applications, more specifically, in this context, big data computing. The relevance and rationale behind futures research approach are inextricably linked to the strategic planning and development associated with long-term sustainability endeavors, initiatives, or solutions in the domain of smart sustainable urbanism. In this context, achieving smart sustainable cities epitomizes an instance of urban sustainability. This notion refers to a desired (normative) state in which a city strives to retain a balance of socio-ecological systems through adopting and executing sustainable development strategies as a desired (normative) trajectory (Bibri 2018c, d, 2019b). This balance entails enhancing the

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Introduction

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physical, environmental, social, and economic systems of the city in line with sustainability over the long run—given their interdependence, synergy, and equal importance. This long-term strategic goal requires, as noted by Bibri (2018a, p. 601), ‘fostering linkages between scientific and social research, technological innovations, institutional practices, and policy design and planning in relevance to urban sustainability. It also requires a long-term vision, a trans-disciplinary approach, and a system-oriented perspective on addressing environmental, economic, social, and physical issues.’ All these requirements are at the core of several approaches to futures studies, in particular backcasting. This approach facilitates and contributes to the development, implementation, evaluation, and improvement of future models for smart sustainable cities, with a particular focus on practical interventions for integrating and enhancing urban systems and coordinating and coupling urban domains using cutting-edge technologies in line with the vision of sustainability. As an appropriate response to smart sustainable city development, backcasting ‘involves the analysis of several factors, including past, present, and future situations; long-term visions; formulation, implementation, and follow-up; transfer and deployment of technologies; building and enhancement of human and social capacity; and regulatory policies. These factors are intertwined and thus cannot be isolated from each other in all kinds of urban sustainability endeavors. These indeed require a system-oriented perspective to address environmental, social, economic, and physical issues in a holistic way. Futures studies offer promising approaches to building smart sustainable city foresight’ (Bibri 2018a, p. 602). Backcasting as a scholarly methodology is well suited to any multifaceted kind of planning process (Holmberg 1998; Holmberg and Robèrt 2000; Phdungsilp 2011), as well as to dealing with urban sustainability issues (Bibri 2018d; Carlsson-Kanyama et al. 2003; Dreborg 1996; Miola 2008; Phdungsilp 2011). Not only in the futures studies using a backcasting approach to strategic planning and development, but also in those using other approaches, is trend analysis a necessary step to perform and a critical input to the scenario analysis as part of such studies. In addition, in future studies, while trend analysis is too often used as a separate approach in itself, it is sometimes combined with other approaches, depending on the nature and complexity of the futures research project to undertake. With that in regard, this chapter aims to provide a detailed qualitative analysis of the key forms of trends shaping and driving the emergence, materialization, and evolvement of the phenomenon of smart sustainable cities as a leading paradigm of urbanism, as well as to identify the relevant expected developments related to smart sustainable urbanism. It is more likely that these forms of trends reflect a congeries of long-lasting forces behind the continuation of smart sustainable cities as a set of multiple approaches to, and multiple pathways to achieving, smart sustainable urbanism. As part of the futures studies related to smart sustainable city planning and development using a backcasting methodology, both the trends and expected developments are key ingredients of, and crucial inputs for, analyzing different alternative scenarios for the future or long-term visions pertaining to desirable sustainable futures in terms of their opportunities, potentials, environmental and social benefits, and other effects. The motivation for this work is to provide a necessary material for scholars, researchers, and academics, as well as other futurists, who are in the process of conducting, or planning to carry out, futures research projects or scholarly backcasting endeavors related to the field of smart sustainable urbanism. This chapter consists of six sections. Section 2 provides a conceptual definition and analytical approach in relevance to the focus and aim of the study. In Sect. 3, an account of futures studies covering their dimensions, objectives, types, and approaches is given, with a focus on sustainability issues. Section 4 introduces, describes, and discusses backcasting approaches to futures studies in the context of urban sustainability. Section 5 presents a descriptive and analytical account of the key prevailing and emerging trends in relation to the phenomenon of smart sustainable cities, as well as identifies the most relevant expected developments in relation to smart sustainable urbanism. The concluding remarks and some discussable issues are the object of Sect. 6.

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Conceptual Definition and Analytical Approach

The term ‘trend’ can have different meanings depending on the context of use. For example, it refers to a type of behavior that develops in a large population, such as lifestyles, customs, norms, social behaviors, social relations, and so on. In this respect, it happens when a large group of people begins liking some objects and things or adopting some habits and modes in a short period of time. This connotation of trend is not the focus here considering the kinds of trends this chapter is concerned with. The meaning of trend in this chapter pertains to such long-lasting or influential trends as global shifts, paradigms, intellectual and academic discourses, and technological innovations. In this context, a trend is a term that can be used to describe a pattern of change over time in some phenomena of importance and relevance to the observer (Bibri 2018a, d, 2019b). In relation to the phenomenon of smart sustainable cities, the forms of trends identified include, global shifts (e.g., urbanization, sustainability, and ICT), intellectual discourses (e.g., urban development, sustainable urban development, smart growth, smart sustainable/sustainable smart urban development, and smart sustainable/sustainable smart urbanism),

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academic discourses (e.g., sustainable cities, smart cities, smarter cities, smart sustainable cities, and sustainable smart cities), computing and scientific paradigms (e.g., pervasive/ubiquitous computing, big data computing, data-intensive scientific development, and the IoT), and technological innovations (e.g., big data analytics, big data applications, data processing platforms, fog/edge computing, and multifusion sensors, and sensor networks). This chapter is also concerned with the way these forms of trends interrelate and inform one another in relevance to the phenomenon of smart sustainable cities. Trend analysis is the widespread practice of collecting information and attempting to spot some patterns of change or shifting patterns pertaining to some phenomena. In some fields of study, the term ‘trend analysis’ has more formally defined meanings and is often used to predict future events. In this case, it can be applied to areas involving solid and historical kind of data. In this respect, among the mechanical methods that can be used to analyze historical sequence, data are based on complex mathematical structures or statistical analyses, including trend extrapolations, time series, cycle analyses, forecasts, and long waves analyses (e.g., Chatterjee and Gordon 2006). These processes involve objectivity (Banister and Stead 2004). However, the quantitative kinds of trend analysis are associated with some limitations in the sense of their propensity to accept the results as a kind of truth about the future rather than simply a starting point for discussion (Banister and Stead 2004). Regardless, such analyses remain, as pointed out by Bibri (2018d), most appropriate for projecting forward in a stable or nonlinear system. Hence, they are often criticized for their lack of creativity and consideration of potential changes; there is a tendency to solely project from the past to the future in a straight line and to overlook less predictable possibilities (Miola 2008). The type of the trend analysis this chapter is concerned with constitutes an integral part of the backcasting approaches adopted in futures studies and not an approach to such studies in itself. This would otherwise involve the use of a variety of techniques based on solid and historical data in relation to quantitative analyses, as mentioned above. Accordingly, the trend analysis as to the way it is meant to be conducted in this chapter entails identifying the key forms of trends at play in the world today and then performing an analysis to understand their nature, meaning, as well as their implications in relevance to the phenomenon of smart sustainable cities of the future. In this case, the way forward is to look at a number of studies previously done on the diverse topics related to smart (and) sustainable cities to identify a set of pertinent, intertwined patterns of change of various kinds pertaining to such phenomenon, and then to envision certain developments. One form of this envisioning in the context of this chapter could be approached from the perspective on the integration, synergy, and complementarity of the respective forms of trends—of which the outcome is the development of multiple visions of smart sustainable cities as new approaches to smart sustainable urbanism, as well as how this phenomenon will evolve and the extent to which it will spread in the years ahead. This also involves other expected developments than smart sustainable cities and the continuation of this paradigm of urban planning and development in the future. In addition, the trend analysis in this context requires probing what is causing the identified forms of trends to emerge, whether the causes will continue in that direction, what other external forces may affect the trends, whether they are part of rather larger societal shifts with far-reaching and long-term implications, and if there are some limitations and challenges associated with the trends (Bibri 2018a, d, 2019b).

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On Futures Studies

Since the dawn of civilization, people have tried to develop different methods for predicting the future. But in recent years, scientists, sociologists, researchers, and others futurists within different disciplines have developed qualitative and quantitative methods for rationally predicting the future. Rationality in this context of use signifies a recognition or awareness that many different futures are possible and that the future is far from being determined or known with absolute certainty. This is typically contingent upon the kinds of the decisions people make and actions they take in the present (Bibri 2018d). Given that the aim of this chapter relates to the futures studies using backcasting approaches, this kind of studies does not pretend to predict the future, although assessing the probabilities of alternative futures in this regard constitutes a key aspect of the approach to studying the future. Futures studies are intended to assist decision-making under uncertainty which is to be defined as indeterminacy, rather than to predict the future (Dreborg 1996). Their core purpose is to get a better understanding of future opportunities as alternatives with their differences and feasibilities. In light of this, they help people to examine and clarify their normative scenarios of the future, to transform their visions and then to develop action plans on the basis of a wide range of techniques (Bibri 2018a, d). Long-lasting and substantive transformations, including sustainability transitions, can only come about through the accumulation of several integrated smaller-scale actions associated with strategically successful initiatives and programs.

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On Futures Studies

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They also operate at the interface of policy domains (Bibri 2018a, d). Methods for futures studies can help to highlight such initiatives and programs and to identify such interface (Bibri 2018a). In relation to the aim of this chapter, they can be used to illustrate what might happen to smart sustainable cities in order to allow them to adapt to the perceived future trends. Researchers, scientists, sociologists, and other futurists employ methods for futures studies as an attempt to manage uncertainty rather than reduce it. As such, these methods aid in dealing with this uncertainty by clarifying what the most desirable possibilities are, what can be known, what is already known, as well as how today’s decisions and actions may play out in each of a variety of plausible futures (Bibri 2018a, d). The effectiveness of futures studies lies in defining a broader conceptual and analytical framework for discussing the future as well as for contributing to policy formulation, transition governance, and the emergence of new possibilities. The kind of decision-making such studies seek to assist under uncertainty pertains, especially to long-term decisions. In the context of smart sustainable cities, decisions are to be made in ways that reduce uncertainty about what may happen in the future in terms of urban development or in ways that analyze the effects of today’s decisions taken in line with the vision of sustainability as enabled by advanced ICT in the future (Bibri 2018a, d). Futurists often divide the purpose of futures studies as assessing the probable, imagining the possible, and deciding on the preferable. As pointed out by Banister and Stead (2004), futures studies can be classified based on the three modes of thinking about the future: • Possible futures (what might happen?). Scenario studies as descriptions of possible future states and their developments are included in this category. • Probable futures (what is most likely to happen?). This category includes forecasting studies, which are characterized by a predictive nature and mainly focused on historical data and trend analysis. • Preferable futures (what we would prefer to happen?). This category is of relevance to futures studies dealing with urban sustainability, as it involves studies focusing on normative or desirable futures, such as backcasting and normative forecasting. Several authors have elaborated on futures studies in relation to sustainability. Dreborg (1996) identifies four different types of futures studies in connection with sustainability, namely • Directional studies which investigate different economics and other measures in the short term that will probably work in the right direction toward sustainability. • Short-term studies which take immediate official goals as a starting point or a small step toward sustainability and attempt to find means of achieving them. • Forecasting studies which usually apply to a long-term perspective, but restricted presumptions of the possibilities of major change make this approach fail to reach sustainability. • Alternative solutions and visions where the development of future (normative) scenarios as desirable futures allows them to be explored by using backcasting where the results describe a desirable future with criteria for sustainability providing the systemic framework for change. It can be as many approaches to futures studies as futurists, since futurists develop different ways to look ahead or envision the future. But some consensus in this regard is evolving. According to Chatterjee and Gordon (2006), futures studies can be categorized on the basis of the context that is being studied in terms of simplicity and complexity. Specifically, if the context is predictable and largely controllable, then a planning approach such as forecasting may be appropriate, and if it is unpredictable and uncertain, an alternative approach such as scenario planning is more suitable. Another consensual perspective among futurists is the need to employ multiple approaches to address most futures problems. There is a valid argument that supports the idea of developing future research programs that combine different approaches to futures studies to gain much greater insight than relying on a single approach (Bibri 2018a, d). There are a number of different approaches to strategy analysis and future analysis that investigate what will, could, or should happen in the future that are in their application not mutually exclusive, including, but not limited to, the following:

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• • • • •

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Trend analysis; Technological forecasting; Visioning; Scenario planning; Cyclical pattern analysis.

For a detailed, descriptive account of the above approaches, which can be combined in futures studies, the interested reader can be directed to Bibri (2018a). The backcasting approach, which relates to the aim of this chapter, is addressed separately in more details in the next subsection.

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Backcasting Approaches to Future Studies and Urban Sustainability

Backcasting is viewed as a natural step in operationalizing sustainable development (Holmberg and Robèrt 2000) within different societal spheres. In terms of its practical application, backcasting is increasingly used in futures studies in the fields related to sustainable urban planning and development as a formal element of future strategic initiatives (Bibri 2018a, d, 2019b). It is a methodology that starts with defining a desirable future and then works backward to identify programs and policies in the form of strategic decisions and actions that will connect that specified future to the present. Backcasting approaches the challenge of discussing the future of cities from the opposite direction, unlike forecasting which involves predicting the future based on the currently prevailing trends (Bibri 2018a, 2019b). It is ‘a methodology in which the future desired conditions are envisioned and steps are then defined to attain those conditions, rather than taking steps that are merely a continuation of present methods [or trends] extrapolated into the future’ (Holmberg and Robèrt 2000, p. 294). Envisioning smart sustainable cities as a desirable future has a normative side: What future is desired? Backcasting this preferred vision has an analytical side: How can this desirable future be attained? In this context, backcasting is a process of starting from a smart sustainable future as a vision of success, then looking back to the present to identify the most strategic steps necessary for achieving that specified future, to reiterate. It is about analyzing possible ways of attaining certain futures, as well as their feasibility and potential (e.g., Quist et al. 2006). Specifically, in the quest for the answer to how to reach specified outcomes in the future, backcasting involves finding ways of linking goals that may lie far ahead in the future to a set of steps to be performed now and designed specifically to achieve that end (Bibri 2018a, d, 2019b) and also facilitates discovery (Dreborg 1996). Furthermore, when applied in sustainable urban planning, backcasting can increase the likelihood of handling the complex issues in a systematic and coordinated way. The aim of the recent study conducted by Bibri (2018a, d) is fourfold: 1. To provide a comparative account of the most commonly applied approaches in futures studies dealing with sustainability and technology (backcasting and forecasting). 2. To review the existing backcasting methodologies and discuss the relevance of their use in terms of their steps and guiding questions for investigating and analyzing smart sustainable city development. 3. To synthesize a backcasting approach based on the outcome of this review. 4. To examine backcasting as a scholarly and planning approach by looking at its use in a city planning project, as well as to use this case to illustrate the core of the synthesized approach. The subject or topic categories addressed by the above study are listed below in a series of bullet points, which can be referred to for more details and better insights into backcasting methodology: • • • •

Strategic smart sustainable urban planning; Strategic smart sustainable urban development; Futures studies’ dimensions, objectives, categories, and approaches; Backcasting approach in terms of its historical origins, characteristic features, and comparison with forecasting, as well as in terms of its relevance and purpose as a scholarly methodology for strategic smart sustainable city development, multiplicity and adaptation as a set of methodological frameworks, and participatory orientation in terms of stakeholders; • The premises and assumptions underlying the synthesis of the scholarly and planning approach as applied to strategic smart sustainable city development; and • A case study on city planning: aims, stakeholders, agreements, outcomes, and foresight methodology.

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Backcasting is the most applied approach in futures studies when it comes to urban sustainability problems and the identification and exploration of their solutions (Bibri 2018a, d, 2019b). This pertains to a wide variety of topics, including, but not limited to, the following (Bibri 2018a, 2019b): • • • • • •

Strategic urban planning; Sustainable urban development; Sustainable mobility; Sustainable transportation systems; Sustainable technologies and sustainable system innovation; Sustainable transformation of organizations.

The backcasting approach is ‘found to be well suited for long-term urban sustainability solutions due to its normative, goal-oriented, and problem-solving character. Also, it is particularly useful when: dealing with complex problems and transitions, the current trends are part of the problem, and different directions of development can be allowed given the wide scope and long time horizon considered. A number of recent futures studies using backcasting have underlined the efficacy of this scholarly and planning approach in terms of indicating policy pathway for sustainability transitions and thus supporting policymakers and facilitating and guiding their actions.’ In addition, backcasting is well suited for long-term problems and long-term sustainability solutions (Dreborg 1996). It might be useful, if not important, to briefly elucidate the pertinence and significance of the kind of the trend analysis this chapter is concerned with and to show which step of the backcasting approach such analysts belongs to in the context of smart sustainable cities of the future. The relevance of describing and analyzing the broader context within which the backcasting analysis takes place lies in identifying or defining, among others, the key forms of trends and expected development related to such cities in terms of their strategic planning and development that could act as direct inputs to the scenario analysis (Bibri 2018a, 2019b). Moreover, Bibri (2018d) provides a synthesized backcasting approach to strategic smart sustainable city planning and development, whose steps are distilled from a number of notable studies done on backcasting approaches to futures studies, with a particular emphasis on their relevance to such planning and development. The short version of this synthesized backcasting approach is illustrated in Table 1 in which the second step to which the trend analysis belongs is highlighted in bold. Of high relevance to highlight as a commonly held view is that the researchers’ worldview and purpose remain the most important criteria in or for determining how futures studies can be developed and conducted in terms of the details concerning the questions guiding the steps involved in a particular backcasting approach or methodology to help identify and implement strategic decisions associated with urban sustainability (Bibri 2018a, d). Also, they will too often need different approaches to carry out their futures studies. Both premises are valid beyond, and irrespective of, any kind of the classification of such studies. In fact, there is neither consensus on a single classification of such studies nor a guide for the use of the most suitable approaches to such studies. Most of these focus on one or two of these goals: assessing the probable, imagining the possible, and deciding on the preferable (e.g., Bibri 2018a, d; Miola 2008). Futures studies related to smart sustainable cities as to their strategic planning and development are typically concerned with deciding on the preferable in terms of how to prefer their planning and development to play out.

Table 1 Steps of backcasting as a scholarly and planning approach Steps of backcasting as a scholarly and planning approach Step 1: Defining the normative assumptions and setting the goals, objectives, and criteria in relation to smart urban sustainability Step 2: Describing and analyzing the key prevailing and emerging trends, clarifying the relevant issues of the current situation, and identifying the main expected developments Step 3: Constructing an image of the future Step 4: Backcasting analysis Step 5: Elaboration and implementation Source Bibri (2019b)

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Of importance to highlight before delving into this analysis of the identified forms of trends related to the socio-technical system of the smart sustainable city is that this trend analysis is meant to be of a comprehensive nature as advocated in the literature on the futures studies using particularly backcasting. The purpose is to enable the reader to gain a rather broad understanding of the phenomenon of smart sustainable cities, as well as to make sense of the rationale behind the futures research that can be, or is being, conducted in relation to the field of smart sustainable urbanism as one approach among many others into amalgamating the landscapes of, and merging the approaches to, sustainable cities and smart cities. Otherwise, in terms of specificity, the attention in this trend description and analysis is to be particularly given to smart sustainable cities: sustainable cities and their relation to smart technologies in terms of big data technology and its novel applications pertaining to sustainability, assuming that the latter is associated with smart cities of the future or smarter cities. The rationale is that sustainability is at the core of their conceptualisation and operationalisation (see, e.g., Batty et al. 2012; Bettencourt 2014; Bibri 2018a, 2019a). Next, however, the identified forms of trends and related issues, as well as the main expected developments, are described and analyzed below in accordance with the qualitative approach elucidated in Sect. 2.

5.1 Sustainable Cities Sustainable cities have been the leading global paradigm of urban development (Bibri 2019b; Whitehead 2003; Williams 2009). The concept of sustainable cities has become more established as a result of the widespread diffusion of sustainability as an important global shift, which is still at play across the world today. In more detail, while this concept has been around for more than three decades or so, it did gain strong foothold and become powerful a few years after the inception and dissemination of the notion of sustainable development by the World Commission on Environment and Development (WCED) in 1987. Indeed, this notion has been applied to, or adopted within, urban planning and development since the very early 1990s (e.g., Bibri and Krogstie 2017a; Wheeler and Beatley 2010). This adoption was marked by the emergence of the notion of sustainable urban development and by solidifying the theoretical and technological foundations (i.e., environmental science, applied urban science, urban computing, sustainable development engineering, green technology engineering, and transport engineering) from which urban sustainability had grown into a defined science. This happened a few years after the emergence of sustainability science. This in turn coming into prominence and focusing on general truths and laws as well as on particular methods of enquiry occurred in the early 2000s (e.g., Clark 2007; Clark and Dickson 2003; Kates et al. 2001; Lee 2000). However, the research on and the development of sustainable cities (e.g., Girardet 2008; Williams 2009) have gained traction and prevalence worldwide as a response to the imminent challenges of sustainability in cities. In view of that, it has been supported and embraced by governments, policymakers, research institutions, universities, and industries (especially green and energy efficiency technologies) across the globe. The usefulness and relevance of the findings produced by the research in the field of urban sustainability and sustainable urban development have led sustainable cities as a drastic urban transformation to figure in many documents and agenda of policymakers of influential weight, such as the United Nations (UN), the European Union (EU), and the Organization for Economic Co-operation and Development (OECD). Also, such transformation has been provided as political statements and argumentations by many governments and organizations. The point is that urban politics and policy around the world are infused with the language of sustainability (Williams 2009). The whole idea is that the subject of ‘sustainable cities’ remains endlessly fascinating and enticing, as there are numerous actors involved in the academic and practical aspects of the endeavor, including engineers and architects, green technologists, built and natural environment specialists, and environmental and social scientists. All these actors are undertaking research and developing strategies and programs to tackle the challenges of sustainable urbanism. This adds to the work of policymakers and political decision-makers in terms of formulating and implementing regulatory policies and devising and applying political mechanisms and governance arrangements to promote and spur innovation and monitor and maintain progress in such urbanism. There are multiple views on what a sustainable city should look like or be and hence multiple ways of defining it or conceptualizing it. Generally, it can be understood as a set of approaches into operationalizing sustainable development in cities or practically applying the knowledge about sustainability and related technologies to the operation, design, and planning of existing and new cities or districts (Bibri 2018a; Bibri and Krogstie 2017a). A sustainable city represents an instance of sustainable urban development, which in turn is a strategic approach to achieving the long-term goals of urban sustainability. Thus, it is designed with the primary aim to simultaneously retain a balance between environmental

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integration and protection, social equity and justice, and economic development and regeneration over the long run. This can be attained through a process of change aiming at fostering innovation and advancement in built environment, urban infrastructure, operational functioning, management, planning, governance, as well as human and ecosystem service provisioning, while continuously optimizing efficiency gains, all in line with the vision of sustainability (Bibri 2018a). The connotation of efficiency here differs from that which is associated with smart cities in terms of the technological solutions proposed in relation to economic growth. Such solutions pertain to productivity, management, cost-effectiveness, time saving, and so forth. On the whole, as succinctly put by Bibri and Krogstie (2017a, p. 11), sustainable cities ‘strive to maximise the efficiency of energy and material use, create a zero-waste system, support renewable energy production and consumption, promote carbon-neutrality and reduce pollution, decrease transport needs and encourage walking and cycling, provide efficient and sustainable transport, preserve ecosystems, emphasise design scalability and spatial proximity, and promote livability and community-oriented human environments.’ There are various instances of sustainable city as a meta-model, but compact cities and eco-cities are advocated as more sustainable urban forms and, indeed, the most prevalent and environmentally sound models of such forms. Following the advocacy and recommendation of several international policymakers, many state and local governments in varying contexts around the world have promoted both compact city and eco-city developments and policies for what these models entail that is indispensable for sustainable urban futures. For example, the Commission of European Communities (1990) has, since the early 1990s, advocated very strongly the compact city because it enhances the quality of life and makes urban areas more environmentally sustainable. Further, similar to sustainable cities, there are multiple definitions of compact cities and eco-cities in the literature (e.g., Hofstad 2012; Jabareen 2006; Jenks et al. 1996a, b; Joss 2010, 2011; Joss et al. 2013; Rapoport and Vernay 2011; Register 2002; Roseland 1997). See Chap. 8 for a detailed definitional and descriptive account of the compact city and the eco-city. In addition, however, the formulation of these definitions tends to be based on the socio-cultural context in which these two models of sustainable urban form are embedded in terms of projects and initiatives. In relation to eco-cities, for example, Rapoport and Vernay (2011) uncover the diversity underneath the various uses of the concept of eco-city and determine the extent of convergence or divergence on the way projects conceive of what an eco-city should be. The authors suggest that there is a great deal of diversity among eco-city projects, going beyond just their size, location, and ambition to expand to their vision of what a sustainable urban future looks like, the techniques that planners and designers should use to achieve it, as well as the kind of actors that should be involved. In this sense, they argue that it is better to think of the eco-city as an objective which there will be multiple ways to achieve. In relation to compact city, there are great differences between cities in terms of their urban form whose key elements can be distinguished: density, surface, land use, public transport infrastructure, and the economic relationship with the surrounding environment (Van Bueren et al. 2011). Further, however, a sustainable urban form can be defined as a form for human settlements that seeks to meet the required level of sustainability and enable the built environment to function in a constructive and efficient way in terms of operations, functions, services, designs, strategies, and policies associated with urban systems and domains (Bibri 2018a). According to Jabareen (2006), the compact city and the eco-city as the most prevalent models of sustainable urban form entail overlaps among them in their concepts, ideas, and visions: The compact city emphasizes density, compactness, diversity, and mixed-land use, whereas the eco-city focuses on renewable resources, passive solar design, ecological and cultural diversity, urban greening, environmentally sound policies, and environmental management. In addition to land use patterns and design features, the compact city emphasizes sustainable transportation (e.g., transit-rich interconnected nodes), environmental and urban management systems (Handy 1996; Williams et al. 2000), energy-efficient buildings, closeness to local squares, more space for bikes and pedestrians, and green areas (Phdungsilp 2011). In view of that, Jabareen (2006) ranks compact city as more sustainable than eco-city from a conceptual perspective by using a thematic analysis to develop a matrix of sustainable urban forms for assessing the level of their sustainability performance based on the underlying topologies and design concepts and principles. This work has been revisited by Bibri and Krogstie (2017b) from a technological perspective, i.e., big data analytics and its novel applications. The focus in their work is on integrating the typologies and design concepts and principles underlying such forms with big data applications. However, the effects of these models are compatible with the goals of sustainable development in terms of transport provision, mobility and accessibility, travel behavior, energy conservation, pollution and waste reduction, economic viability, life quality, and social equity (Bibri 2018a). See Chap. 8 for a detailed account of compact cities and eco-cities in terms of their effects, benefits, aspirations, as well as characteristic features.

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5.2 Smart Cities The concept of smart cities became widespread during the mid-1990s due to the rise of ICT as a global shift. In recent years, it has become associated with urbanization as another global shift given the synergy and integration between these global shifts, which are strongly at play across the world today. On this note, Townsend (2013) portrays urban growth and ICT development as a form of symbiosis. This entails an interaction that is of advantage to, or a mutually beneficial relationship between, both ICT and urbanization (Bibri and Krogstie 2017a). One way of looking at this form of tie-in is that urbanization can open entirely new windows of opportunity, or simply provide a fertile environment, for cities to act as vibrant hubs of technological innovation in a bid to solve a wide variety of environmental, social, and economic problems and challenges, thereby containing the potential effects of urbanization. Further to the point, however, according to a recent review conducted by Bibri and Krogstie (2017a), the roots of the smart city concept date back to the 1960s under what is labeled the ‘cybernetically planned cities’ and then in urban development and planning proposals associated with networked or wired cities since the 1980s. In this respect, the common faces that emerged before, or in parallel with (only for a few of them), the adoption of the concept of smart city in urban planning and development around the mid-1990s include networked cities, wired cities, cybercities, digital cities, virtual cities, intelligent cities, knowledge cities, and cyberphysical cities, among other nomenclatures. For example, digital cities tend to focus on the hard infrastructure, whereas intelligent cities on the way such infrastructure are used (Batty 1989, 1990, 1997). Several views claim that the concept was introduced in 1994 (Dameri and Cocchia 2013) and that it is only until 2010 that the number of publications and scientific writings on the topic increased considerably, after the emergence of smart city projects as supported by the European Union (Jucevicius et al. 2014). As echoed by Neirotti et al. (2014), the smart city concept’s origin can be traced back to the smart growth movement during 1990s. Yet, it is not until recently that this movement led this concept to be adopted within urban planning and development (Batty et al. 2012). However, regarding its early conceptualization and connotation, the concept was mostly associated with the efficiency of technological solutions with respect to the operational functioning, management, and planning pertaining to energy, transport, physical infrastructure, distribution and communication networks, economic development, service delivery, and so forth. Smart growth implies the ability of achieving greater efficiencies through coordinating the forces that lead to laissez-faire growth: transportation, land use speculation, resource conservation, and economic development, rather than letting the market dictate the way cities grow (Batty et al. 2012). At present, however, many cities across the globe compete to be smart cities in the hopes of reaping the efficiency benefits economically, socially, or, more recently, environmentally by taking advantage from the opportunities made possible by big data analytics and its wider application across urban domains. It is also in this context that it has increasingly become attainable to achieve the required level of sustainability, resilience, and equity, in addition to improving the quality of life and ensuring higher levels of transparency and openness and hence democratic and participatory governance, citizenry participation, and social inclusion (Bibri 2019a). Achieving all these benefits require sophisticated approaches, advanced technologies and their novel applications and services, resources, financial capabilities, regulatory policies, and strategic institutional frameworks, as well as an active involvement of citizens, institutions, and organizations as city constituents. Worth noting is that the growing interest in building smart cities based on big data analytics as an advanced technology is increasingly driven by the needs for addressing the challenges of sustainability and to contain the effects of urbanization. Besides, the main features of smart cities have become a high degree of information and technology integration and a comprehensive application of computing resources. In recent years, the smart city as a catchphrase and phenomenon has drawn increased attention and gained traction among universities, research institutes, governments, policymakers, businesses, industries, consultancies, and international organizations across the globe. Notwithstanding this prevalence worldwide, the concept of smart city is still without a universally agreed definition. In other words, a shared definition of smart city is not yet available or offered. Consequently, multiple meanings have been, and continue to be, adopted by different people within different contexts. Bibri (2019a) provides a detailed discussion on this issue for those who are interested to read more about it and thus gain further insights. The concept having different connotations and being approached from a variety of perspectives is clearly manifested in the various ways in which many governments set initiatives or implement projects to enable their cities to become, badge, or regenerate themselves as, or manifestly plan to be, smart. A large number and variety of definitions (e.g., Al Nuaimi et al. 2015; Bibri 2019a; Albino et al. 2015) and views (Bibri 2018a, 2019a) have been suggested with different emphases, orientations, and scopes. Regardless, scholars, academics, planners, ICT experts, and policymakers converge on the idea that the use of ICT pertains to all domains of smart cities and hence on considering it as an inseparable facet thereof (Bibri 2018a).

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In the context of this chapter, however, a smart city can be described as a city that is increasingly composed of, and monitored and operated by, ubiquitous and pervasive computing, as well as whose planning and governance are being driven by innovation as enacted by various stakeholders that capitalize on and exploit cutting-edge technologies in their endeavors and practices. In this light, being instrumented and pervaded with digital devices, systems, and platforms that generate big data, smart cities can enable real-time analysis of urban life, environment, and dynamics as well as new modes of urban planning and governance and also provide the conditions that are conducive to envisioning and enacting more sustainable, efficient, resilient, transparent, and open human and urban environments (Bibri 2019a).

5.3 Smarter Cities In general, smart cities come in many faces depending on the way ICT is applied, the extensiveness of its use, the degree and form of its ubiquity, and/or the focus of its orientation, as well as the kind of digital technology by which it is coordinated and integrated (Bibri 2018a). Among these faces are those cities that are inspired by the prevalent ICT visions of pervasive computing, including ambient cities, sentient cities, ubiquitous cities, real-time cities, and cities as Internet of everything (e.g., Kitchin 2014; Kyriazis et al. 2014; Perera et al. 2014; Rathore et al. 2018; Shepard 2011; Shin 2009; Thrift 2014; Zanella et al. 2014). These cities have materialized as a result of the advance of ICT of pervasive computing or rather the evolvement of the dominant ICT visions into achievable and deployable computing paradigms. Seen as future forms of smart cities, they are quite different from what has been experienced hitherto in terms of smartness and its effects on human life at several levels. They have come to be identified or labeled as ‘smarter cities’ due to the magnitude of ICT and the extensiveness of data with regard to their application and use across urban systems and domains. The prospect of smarter cities is increasingly becoming the new reality with the massive proliferation of the core enabling technologies underlying ICT of pervasive computing, namely sensor networks, data processing platforms, wireless communication networks, and cloud and fog computing models across different spatial scales (Bibri and Krogstie 2017a). The initiatives of smarter cities in several countries across Europe, the USA, and Asia are considered as national urban development projects epitomising the increasing significance and role of advanced ICT, especially big data analytics, in enhancing the operations, functions, services, strategies, and policies of smart cities of the future associated with planning, management, development, and governance (Bibri 2018a). The conceptualization of smarter cities is built upon the core features of the prevalent ICT visions in terms of the pervasion of technology into the very fabric of the city, the omnipresence and always-on interconnection of computing resources, applications, and services across several spatial and temporal scales. The emerging connotations of smart cities of the future or smarter cities are numerous (see, e.g., Bibri 2018a, 2019a; Kitchin 2014; Kyriazis et al. 2014; Perera et al. 2014; Rathore et al. 2018; Shepard 2011; Shin 2009; Thrift 2014; Zanella et al. 2014; Piro et al. 2014; Su et al. 2011; Townsend 2013). A smarter city can be understood as a city where advanced ICT is combined with physical, infrastructural, architectural, operational, functional, and ecological systems across many spatial scales, as well as with urban planning processes and governance models, with the primary aim of improving efficiency, sustainability, resilience, equity, and livability. Indeed, the concept of smarter cities has particularly been associated with the orientation of smart cities toward achieving the goals of sustainability in the future. In this line of thinking, Chourabi et al. (2012) describe a smart city as a city which strives to become smarter as to making itself more sustainable, equitable, and livable. This is also consistent with what smart cities of the future entail according to Batty et al. (2012). Worth noting is that smarter cities have tremendous potential compared to current smart cities as to advancing sustainability. Indeed, there has recently been a conscious push for cities in Europe to be smarter and thus more sustainable, leading to the need to benchmark these cities’ efforts using advanced assessment frameworks to rank them based on how smarter and more sustainable they are (Bibri 2019a). For a detailed account of smarter cities, the interested reader can be directed to Bibri (2018a) where there is a whole chapter about the transition of smart cities to smarter cities and the future potential of the underlying ICT of pervasive computing for advancing environmental sustainability. This is projected to happen because of the prospective advancements and innovations pertaining to big data analytics as an advanced form of ICT (see, e.g., Al Nuaimi et al. 2015; Batty et al. 2012; Bibri 2018a, 2019a). Irrespective of which ICT vision smarter cities tend to instantiate, they are taken to mean urban spaces loaded with clouds of data intended to shape the life and experience of citizens and bring about major transformations to their environments. Here, big data analytics is given a prominent role, as all over the city, the underlying core enabling technologies can monitor urban areas (in terms of activities, citizen behaviors, events, social dynamics, locations, spatiotemporal settings, environmental states, etc.); analyze, interpret, evaluate, model, and simulate the continuously collected streams of data; and then deploy the obtained results in the form of intelligence and planning functions applicable to various urban domains across several spatial scales.

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5.4 Sustainable Smart Cities There are several differences between sustainable smart cities and smart sustainable cities. One obvious distinction to highlight is that the former involves those cities that badge themselves as smart (e.g., Barcelona) and are striving to become sustainable, and this class of cities often relates to technologically advanced nations. The latter entails those cities that badge themselves as sustainable (e.g., Stockholm) and are striving to improve, advance, and maintain their contribution to sustainability using the advanced forms of ICT, and this class of cities pertains to ecologically advanced nations. However, a sustainable smart city can be described, according to UNECE (2015), as an innovative city that uses ICT and other means to improve the efficiency of urban operations, functions, and services as well as enhance the quality of life of citizens, ‘while ensuring that it meets the needs of present and future generations with respect to economic, social, environmental as well as cultural aspects.’ It is not until very recently that smart sustainable/sustainable smart urban planning and development as an intellectual discourse did elicit and attract attention among urban scholars, practitioners, and policymakers, as well as ICT experts and computer scientists working within the area of applied urban science or urban informatics, especially in the subfield of big data and its relation to urban analytics, planning, and development. Smart sustainable/sustainable smart cities evolving subsequently into a more powerful and established techno-urban discourse and realist enterprise emanates from the fact that many strategic urban actors are increasingly relating to such discourse and enterprise in a structured way in different contexts of their practices—socially anchored and culturally institutionalized actions (Bibri and Krogstie 2016; Bibri 2019a, b). The accordingly increasing insertion, functioning, and dissemination of such discourse, in particular, are increasingly shaped and influenced by the emerging sophisticated technologies and their future generation being under vigorous investigation and scrutiny by the ICT industry consortia, collaborative research institutes, policy networks, and quadruple helix of university– industry–government–citizen organizations in terms of research, development, and innovation within ecologically technologically or technologically advanced nations (Bibri 2018a). Concurrently, just like the concept of smart sustainable cities (Bibri and Krogstie 2016), the concept of sustainable smart cities has gained momentum as both a holistic approach to urban development and an academic and societal pursuit, not least in technologically advanced nations. That is to say, it has become important not only in urban planning and policymaking, but also in urban research and practice, generating worldwide attention as a powerful framework for strategic sustainable urban development (Bibri 2019a). Therefore, the development of sustainable smart cities, just like that of smart sustainable cities, is increasingly gaining traction and pre-eminence worldwide, surpassing all other urban development approaches, especially in the world’s major cities, supported by policymakers, governments, research institutions, universities, and industries. Given the apparent relevance and usefulness of the findings produced in the field of smart cities, the related research and development have been embraced and advocated by the United Nations (UN), the European Union (EU), and the Organization for Economic Co-operation and Development (OECD) (e.g., Lytras and Visvizi 2018). For example, a common understanding shared by the European Commission and reflected in the Smart Cities and Communities European Innovation Partnership (SCCEIP) is that smart technologies in their various forms hold great potential for achieving sustainability in smart cities, particularly in relation to the intersection between energy, transport, and ICT, where the associated industries have been invited to collaborate with cities to address their challenges and needs (European Commission 2011). This will enable innovative, integrated, and efficient technologies to roll out and enter the market more smoothly, making cities the nexus of innovation (European Commission 2011). Accordingly, the European Union’s policies highlight the synergy between smart technologies and sustainable urban development, as manifested additionally by the EU’s current 10-year development strategy through which the objectives of fostering smart, inclusive, and sustainable development in Europe were set, and at the heart of which innovation is seen as a means to tackle the environmental challenges associated with climate change and intensive energy use and its inefficiency. Moreover, recent research and policy reports highlight synergies and benefits at the intersection of smart and sustainable urban development (Angelidou et al. 2017). The most widely cited report of the World Urbanization Prospects series of the United Nations (UN 2014) clearly states that this trend of integrating both urban development paradigms in terms of policies and practices will continue to rise at least up to 2050, highlighting the growing role of ICT in mitigating the rising challenges of sustainability. As stated in the report, the policy implications drawn from this study include the use of ICT in facilitating a sustainable mode of urbanization, one that enhances and efficiently delivers services to diverse urban stakeholders, as well as the necessity to have accurate, consistent, and timely data to inform city-related policymaking, among others. The United Nations has already begun to explore the role of big data for sustainable development in the form of action-oriented research in that direction (UN 2015).

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In addition, many governments have recently set ambitious targets to transition their cities to being sustainable smart using a variety of initiatives and programs or have adopted the concept of smart city and implemented big data applications to reach the required level of sustainability and to improve the living standards. Accordingly, it has become of crucial importance to develop and utilize new methods for measuring the performance of sustainable smartness (e.g., Chourabi et al. 2012; Jimenez et al. 2014; Webb et al. 2016). This is due to the growing realization of the untapped potential of the emerging smart technologies, especially big data analytics and its application, for addressing the challenges of sustainability and containing the effects of urbanization. While there is a growing interest in the flourishing field of sustainable smart city research, the academic discourse on sustainable smart urban development within the relevant literature is still scant and also heavily weak on empirical grounding —yet rapidly burgeoning (Bibri 2019a). Indeed, a few studies exploring the subject of sustainable smart cities have been published in the mainstream journals. The case is evidently different from smart cities as an urban development strategy that has been around for more than two decades or so, thereby witnessing a proliferation of academic publications and scientific writings and thus demonstrating a large body of successful practices. However, the extent to which the field of sustainable smart cities is blossoming gives a clear indication of its future developmental path and research direction. In fact, this field of research has materialized in response to the need for overcoming the numerous challenges and issues pertaining to the existing approaches to smart cities with regard to sustainability and urbanization. The research on sustainable smart and smarter cities, just like smart sustainable cities, is garnering increased attention, and its status is consolidating as one of the fanciest and fertile areas of research today. This hot topic and recent wave of research have started to highlight and explore, respectively, the growing significance and role of the advanced forms of ICT in increasing the contribution of smart and smarter cities to the goals of sustainable development (as well as in smartening up sustainable cities or sustainable urban forms). This research wave has become more established about two decades or so after the adoption of the concept of smart city in the domain of urban planning and development in 1994 and in parallel with the emergence and success of the discourse of sustainable smart urban development (Bibri 2019a). Explicitly, when this concept has become widespread and mature, and concurrently, most of the core enabling technologies (sensor technology, cloud computing, fog computing, distributed computing, data processing platforms, wireless communication networks, etc.) of smart and smarter cities have become relatively financially affordable, technically advanced, and widely deployed across urban environments. This has been enabled and fueled by the most prevalent ICT visions of pervasive computing becoming deployable and achievable computing paradigms and thus the new reality in different parts of the world, especially Europe, Asia, and the USA (Bibri 2019a). This new paradigm shift in computing as heralding a drastic change in ICT in its various forms and thereby giving rise to innovative solutions and sophisticated approaches increasingly pervading urban domains and environments has made the vision of building and living in sustainable smart and smarter cities an achievable and attainable reality (see Bibri and Krogstie 2016). Other driving factors for, or global shifts triggering, the wave of research and phenomenon in question, in addition to the rise, advance, prevalence, and convergence of ICT, is the unprecedented urbanization of the world’s population and the rising concerns over its multidimensional effects, coupled with the mounting challenges of urban sustainability (Bibri 2018a). In particular, as pointed out by Angelidou et al. (2017), what has brought the two disciplines of smart urban growth and sustainable urban development closer than ever before, despite the different development trajectories followed until recently, is the growing realization of the role of technological advancements in monitoring urban environments and making well-informed technical and policy decisions, as well as in reducing resource consumption whose unsustainability is bringing humanity closer to a future where basic goods will be unavailable to large parts of the population. Taking everything into account, research on sustainable smart/smart sustainable cities has attracted attention and evolved on the basis of these different, yet related, developments: smart cities, sustainable cities, ICT of pervasive computing, sustainable development, sustainability, and urbanization.

5.5 Smart Sustainable Cities Much of what is said above concerning sustainable smart cities does, as either explicitly stated or implicitly suggested, apply to smart sustainable cities due to the relatively parallel emergence of these two urbanism approaches and the many overlapping technical aspects between them, coupled with their prominence and significance as research areas today in terms of urban analytics, planning, development, and governance. In this subsection, the focus is only on the main, yet unaddressed, aspects specific to smart sustainable cities as an academic discourse and its relation to the other forms of trends, in addition to some reiterations for clarity purposes. In particular, several definitions are provided and discussed given that the concept of smart sustainable cities is at the core of this book and of particular relevance to this chapter.

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The concept of smart sustainable cities has emerged as a result of three important global shifts at play across the world, namely the rise of ICT, the diffusion of sustainability, and the spread of urbanization (e.g., Bibri 2018a, b, c). As echoed by Höjer and Wangel (2015), the interlinked development of sustainability, urbanization, and ICT has recently converged under what is labeled ‘smart sustainable cities.’ Accordingly, smart sustainable cities are a new techno-urban phenomenon that materialized and became widespread around the mid-2010s (e.g., Ahvenniemi et al. 2017; Al-Nasrawi et al. 2015; Bibri 2018a, b; Bibri and Krogstie 2016, 2017a, c; Höjer and Wangel 2015; ITU 2014; Kramers et al. 2014, 2016; UNECE 2015). As an integrated framework and holistic urban development approach, they amalgamate the strengths of sustainable cities in terms of the design concepts and principles and planning practices of sustainability and those of smart cities in terms of the innovative solutions and sophisticated approaches being developed for sustainability and mainly offered by big data technology (Bibri 2018a; Bibri and Krogstie 2017b, c). The whole idea revolves around leveraging the convergence, ubiquity, advance, and potential of ICT of pervasive computing and its prerequisite enabling technologies, especially big data analytics, in the transition toward the needed sustainable development and sustainability advancement in an increasingly urbanized world (Bibri 2018a, c). Therefore, they are increasingly gaining traction and prevalence worldwide as a response to the imminent challenges of sustainability and urbanization. They are moreover being embraced as an academic pursuit, societal strategy, and, thus, evolving into a scholarly and realist enterprise around the world, not least within ecologically and technologically advanced nations (Bibri and Krogstie 2016; Bibri 2018a, c). In a nutshell, the concept and development of smart sustainable cities are gaining increased attention worldwide among research institutes, universities, governments, policymakers, and ICT companies. The term ‘smart sustainable city,’ despite not always explicitly discussed, is used to describe a city that is supported by the pervasive presence and massive use of advanced ICT, which, in connection with various urban systems and domains and how these are complexly integrated and are intricately coordinated, respectively, enables the city to control available resources safely, sustainably, and efficiently to improve economic and societal outcomes (Bibri 2018a; Bibri and Krogstie 2017a). As a result of analyzing around 120 definitions, ITU (2014) provides a comprehensive definition based on the notion of sustainable development, which states that ‘a smart sustainable city is an innovative city that uses ICT and other means to improve the quality of life, efficiency of urban operation and services, and competitiveness, while ensuring that it meets the needs of present and future generations with respect to economic, social, and environmental aspects.’ Another close definition put forth by Höjer and Wangel (2015, p. 10), which is deductively formulated based on the notion of sustainable development, states that ‘a smart sustainable city is a city that meets the needs of its present inhabitants without compromising the ability for other people or future generations to meet their needs, and thus, does not exceed local or planetary environmental limitations, and where this is supported by ICT.’ This entails primarily unlocking and exploiting the potential of ICT of pervasive computing as an enabling, integrative, and constitutive technology with embodied transformational, substantive, and disruptive effects for achieving the environmental, social, and economic goals of sustainability. Also, Bibri and Krogstie (2016, p. 11) provide a more complex conceptualization of the term based on an innovation system and socio-technical perspective, describing smart sustainable cities as: ‘being a dynamic and complex interplay between scientific innovation, technological innovation, environmental innovation, urban design and planning innovation, institutional innovation, and policy innovation, smart sustainable cities involve inherently complex socio-technical systems of innovation systems. Such systems, which focus on the creation, diffusion, and utilization of knowledge and technology, are of various types (variants of innovation models), including national, regional, sectoral, technological, and Triple Helix of university-industry-government relations.’ Further, as a set of techno-urban innovation systems, smart sustainable cities result from a dynamic network of relationships among universities, research institutes, governmental agencies, policymakers, industry consortia, and business communities involved in various innovation systems (Bibri and Krogstie 2016). In relation to the first question of Step 2 of the applied backcasting methodology, this chapter adheres to the socio-technical system approach to innovation system, which entails the components needed to fulfill a certain societal function (Bijker 1995; Geels 2004, 2005). The typically complex sets of socio-technical systems underlying smart sustainable cities involve different innovation entities operating at the intersection of ICT development and innovation and urban planning and development with the aim of advancing sustainability and integrating its dimensions as a societal function. In this regard, the technological innovation system should be of particular focus here considering the smart form of urban sustainability being investigated. This system refers to ‘socio-technical systems focused on the development, diffusion, and use of particular technologies’ (Bergek et al. 2008, p. 408). In other words, it denotes a dynamic network of actors interacting within a specific industrial sector (e.g., urban industry domains) under a particular institutional setup (governmental agencies, policymakers, public research institutes, universities, etc.) in the production, diffusion, and utilization of new technologies (e.g., Carlsson and Stankiewicz 1991; Carlsson et al. 2002), specifically big data technologies. These are seen as systems of socio-technical elements interacting with each other, and this approach provides insights into understanding the development of new

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technologies (see Geels 2004). In light of the above, Bibri (2018a, p. 299) defines smart sustainable cities ‘as a social fabric and web made of a complex set of networks of relations between various synergistic clusters of urban entities that, in taking a holistic or systemic perspective, converge on a common approach into using and applying smart technologies to create, develop, disseminate, and mainstream the innovative solutions and sophisticated methods that help provide a fertile environment that is conducive to improving and advancing sustainability. This can occur through strategically assessing and continuously enhancing the contribution of such cities to the goals of sustainable development. Here, ICT can be directed towards, and effectively used for, collecting, processing, analyzing, and synthesizing the data on every urban system and domain as involving forms, structures, infrastructures, networks, facilities, processes, activities, and citizens.’ The resulting outcome can then be employed to develop urban intelligence and planning functions that utilize the science of complexity in fashioning new powerful forms of urban simulation models for guiding decision-making processes in the context of urban sustainability in terms of operations, functions, designs, services, strategies, and polities. In this regard, ‘smart sustainable cities are complex systems par excellence, more than the sum of their parts. They are inherently intricate through the very technologies being used to monitor, understand, and analyze them in relation to their management, planning, and development to improve their contribution to sustainability and their ability to confront urbanization’ (Bibri 2018a, p. 474). Overall, as dynamically changing environments and developed through a multitude of individual and collective decisions, they should rely on sophisticated technologies and their novel applications to realize their full potential and thus to respond to the challenges of sustainability and urbanization. All in all, smart sustainable cities can be viewed as an urban development approach or model which seeks to explicitly bring together the sustainable city and smart city endeavors in ways that address the relevant limitations of sustainable cities and the relevant deficiencies of smart cities by merging what each has to offer for sustainability in terms of design concepts and principles and planning practices and advanced technologies and their applications, respectively. Given the general consensus about the benefits of smart sustainable cities, coupled with the relevance and usefulness of the findings produced thus far in the field, the related research and development have been supported and advocated by the United Nations (UN), the European Union (EU), and the Organization for Economic Co-operation and Development (OECD), as stated in the recently published studies on the topic. Also, many city governments in ecologically advanced nations have recently set ambitious targets to smarten up their sustainable cities using a variety of initiatives and programs. Or, they have adopted the concept of smart sustainable cities by implementing big data applications to reach the required level of sustainability. Accordingly, it has become of crucial importance to develop and utilize new methods for measuring the smart performance of urban sustainability (e.g., Al-Nasrawi et al. 2015).

5.6 Big Data Computing/Analytics 5.6.1 Characteristics, Concepts, and Prospects Advances in ICT and its widespread development, diffusion, and integration into many spheres of society and hence numerous domains of all kinds of specializations, including urban, scientific, medical, technological, engineering, economic, environmental, ecological, social, and political, are resulting in data explosion as manifested in the huge data deluge flooding from new and extensive sources, rapidly unfolding, and endlessly soaring. Data mining/knowledge discovery and decision-making from voluminous, varied, real-time, exhaustive, fine-grained, indexical, dynamic, flexible, evolvable, relational data are a daunting challenge/task in terms of storage, management, organization, processing, analysis, evaluation, interpretation, modeling, and simulation, as well as in terms of the visualization and deployment of the obtained results for enhancing and optimizing operations, functions, services, designs, strategies, and policies. This is an emerging form of trend known as big data computing, which is influential, formative, groundbreaking, pioneering, innovative, and long-lasting. As a new paradigm, it amalgamates, as underpinning technologies, large-scale computation as well as new data-intensive techniques and algorithms and advanced mathematical models to build and perform data analytics. The expansion and success of this computing paradigm are increasingly stimulating smart sustainable/sustainable smart city projects and initiatives as well as research opportunities to an increasing extent, within both ecologically technologically and technologically advanced nations (Bibri 2019a, b). There is no definite definition of big data, and therefore, many definitions are available in the literature (see Chap. 3 for an example). However, a great deal of the existing definitions tends to converge on three main attributes of big data: the huge volume of data, the wide variety of data types, and the velocity at which the data can be collected and analyzed. These are identified as the most agreed upon Vs (e.g., Fan and Bifet 2013; Laney 2001). Against the background of this chapter, the term ‘big data’ is essentially used to mean datasets that are too huge, heterogeneous, and relationally intricate for conventional data

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processing or analytic systems to deal with—in other words, a massive volume or colossal amount of both structured and unstructured data that are so large, widely varied, and complexly arranged that it is difficult to process and analyze using traditional database and software techniques (Bibri 2019a). The term ‘big data analytics’ denotes ‘any vast amount of data that has the potential to be collected, stored, retrieved, integrated, selected, preprocessed, transformed, analyzed, and interpreted for discovering new or extracting useful knowledge. Prior to this, the analytical outcome (the obtained results) can be evaluated and visualized in an understandable format before their deployment for decision-making purposes (e.g., an enhancement of, or a change in, operations, functions, services, strategies, and designs) … In the context of smart sustainable/sustainable smart cities, big data analytics refers to a collection of sophisticated and dedicated software applications and database systems run by machines with very high processing power, which can turn a large amount of urban data into useful knowledge for well-informed decision-making and deep insights in relation to various urban domains, such as transport, mobility, traffic, environment, energy, land use, education, health care, planning, and design’ (Bibri 2018b, p. 234). Big data computing as a paradigm has emerged as a result of the rise, advance, and prevalence of ICT as a global shift, as well as of the maturity and evolvement of the dominant ICT visions into achievable and deployable computing paradigms, especially UbiComp and the IoT as a form of it. The latter development took an identifiable shape in the early 2000s, and ever since, big data analytics has been rapidly evolving as a research area in the business domain, predominantly (e.g., Bibri 2018a, b, 2019a). Indeed, it is not until recently that big data analytics came to the fore and became of importance and relevance as a research area within smart sustainable/sustainable smart urban development (see, e.g., Al Nuaimi et al. 2015; Batty et al. 2012; Bettencourt 2014; Bibri 2018a, b, 2019a; Bibri and Krogstie 2016, 2017b; Khan et al. 2015; Kumar and Prakash 2014; Pantelis and Aija 2013). The multifaceted potential of smart city approaches has been under investigation by the UN (2015) through their study on ‘Big Data and the 2030 Agenda for Sustainable Development.’ The notion of big data analytics and its application in sustainable urban development have gained traction and foothold among urban scholars, academics, scientists, practitioners, and policymakers over the past few years. Manifestly, big data computing is fundamentally changing the way modern cities can sustainably be operated, managed, planned, developed, and governed, shaping and driving decision-making processes within many urban domains (Bibri 2018a, 2019a), especially with regard to optimizing resource utilization, mitigating environmental risks, responding to socio-economic needs, and enhancing the quality of life and well-being of citizens in an increasingly urbanized world (Bibri 2019a). This paradigm is clearly on a penetrative path across all the systems and domains of smart, smarter, sustainable, and sustainable smart/smart sustainable cities that rely on sophisticated technologies in relation to their operational functioning, management, planning, and development. This is manifested in the proliferation and increasing utilization of the core enabling technologies of big data analytics across those cities badging or regenerating themselves as one of such cities for storing, managing, processing, analyzing, and sharing colossal amounts of urban data for the primary purpose of extracting useful knowledge in the form of applied intelligence functions and simulation models directed for multiple purposes, especially sustainability. Big data are regarded as the most scalable and synergic asset and resource for modern cities to enhance their performance on many scales, as they have become the fundamental ingredient for the next wave of urban analytics and planning (Bibri 2018a). As a result, many governments have started to exploit urban data and their numerous benefits to support the development of their cities with regard to sustainability, efficiency, resilience, equity, and the quality of life.

5.6.2 Research Status of Big Data Analytics as an Enticing Investigation Area Having recently, as a research wave and direction, permeated and dominated academic circles and industries, coupled with its research status being consolidated as one of the most appealing and fertile as well as fanciest areas of investigation beyond the realm of sustainable smart/smart sustainable urbanism, big data analytics has attracted researchers, scholars, scientists, experts, and practitioners from diverse disciplines and professional fields—given its importance and relevance for generating well-informed decisions and deep insights of highly useful value to many sectors of society. Therefore, big data analytics is a rapidly expanding research area merging computer science, data science, and complexity sciences (Batty et al. 2012; Bibri 2018a), and becoming a ubiquitous term in understanding and solving complex challenges and problems in many fields. The big data movement has been propelled by the intensive R&D activities taking place in academic and research institutions, as well as in industries and businesses—with huge expectations being placed on the upcoming innovations and advancements in the field. This includes the high-influence big data analytics, and its application will have on many facets of smart sustainable/sustainable smart cities (e.g., Al Nuaimi et al. 2015; Batty et al. 2012; Bettencourt 2014; Bibri 2018a, 2019a; Bibri and Krogstie 2017b; Khan et al. 2015; Kitchin 2014; Kumar and Prakash 2014; Pantelis and Aija 2013; Townsend 2013). Further to the point, however, a large part of ICT investment is being directed by giant technology companies, such as Google, IBM, Oracle, Microsoft, SAP, and CISCO, toward creating novel computing models and enhancing existing practices pertaining to the storage, processing, analysis, management, modeling, simulation, and

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evaluation of big data, as well as to the visualization and deployment of the analytical outcome for different purposes (Bibri 2018a). Adding to this is the active, ongoing research within so many universities across the globe, especially in relation to smart sustainable/sustainable smart cities, for the purpose of enhancing the acquisition of data from multiple distributed sources, the management of data streams, the integration of heterogeneous data into coherent databases as well as the definition of observables to extract relevant information from available datasets, data transformation and preparation, methods for distributed data mining and network analytics, the organization and composition of the extracted models and patterns as well as the evaluation of their quality, tools for visual analytics to study the behavioral patterns and models, methods for the simulation and prediction of the mined patterns and models, and so forth. Big data analytics is considered as a prerequisite technology for realizing the novel applications and services offered and promised by the ICT visions of pervasive computing, which is a determinant enabler and powerful driver for smart sustainable/sustainable smart cities of the future.

5.6.3 Data Growth Projection and Related Core Driving Technologies The deluge of fast, exhaustive, indexical data is, and will continue to be, unfolding and soaring, amounting to hundreds of exabytes every year and covering so many aspects of urbanity in its complexity, breadth, depth, and heterogeneity as demonstrated in, among others, the nature of urban systems and their continuous integration that of urban domains and their coordination and that of urban networks and their coupling This urban data growth will undoubtedly continue in this direction, and expectedly, the resulting datasets are set to proliferate and be coalesced, integrated, and coordinated. Generally, the digital data are projected to grow from 2.7 to 35 zettabytes by the year 2020 (Malik 2013; Zikopoulos et al. 2012). Manyika et al. (2011) project a growth of about 45% in the global data produced per year. It is estimated that more data are produced every 2 days at present than in all of history prior to 2003 (Kitchin 2014; Smolan and Erwitt 2012). Such explosive data growth is due to a number of the core enabling and driving technologies of ICT of various forms of pervasive computing, including data sensing devices and sensor networks, data processing platforms, data analytics techniques and processes, cloud and fog computing infrastructures, and wireless networking technologies. These are being fast embedded into the very fabric of contemporary cities, everyday practices and spaces, whether badging or regenerating themselves as smart sustainable/sustainable smart to pave the way for utilizing and adopting the upcoming innovative solutions to overcome the challenges of sustainability and urbanization in the years ahead. In the meantime, the increasing convergence, advance, and ubiquity of ICT are giving rise to new computationally augmented urban environments that are enabling sophisticated operating and organizing processes of urban life. This is in response to the event of cities becoming more and more complex and dynamically changing systems together with their domains getting more and more coordinated, their systems integrated, and their networks coupled (Bibri 2019a). This concerns particularly those domains, systems, and networks that rely heavily on complex technologies to realize their full potential for responding to the challenges of sustainability and urbanization. 5.6.4 The Enabling Capabilities of the Deluge of Urban Data The deluge of urban data involves large datasets collected and coalesced through data warehousing for wide-city uses that are directed toward advancing different aspects of smart sustainable/sustainable smart urbanism, namely sustainability and the integration of its dimensions and the quality of life. This deluge enables real-time analysis of urban systems, new modes of urban planning and governance, and advanced approaches to interconnecting data from across urban domains to provide detailed views of the relationships between urban data and urban synoptic intelligence, as well as provides the raw material and favorable conditions for enacting and envisioning more sustainable, resilient, efficient, equitable, open, and transparent cities. In short, the data from distributed and heterogenous sources cascade into vast troves of information, which calls for prudent big data applications that can churn out useful knowledge and valuable insights for various purposes. The sustainability of smart cities and the smartness of sustainable cities are being digitally fueled and driven by the data being generated for processing, analysis, and deployment for enhanced decision-making purposes and innovative solutions development. In this respect, the unfolding and soaring deluge of urban data are increasingly stimulating wide-scale attempts to extract value from and make sense of such data, which is driven primarily by the desire to translate contextual and actionable data and data analytics into data-driven operational functioning, planning, design, development, and governance focused more and more on advancing smart sustainable urbanism. In more detail, the value of the useful knowledge resulting from data analytics lies in enhancing physical forms, infrastructures, resources, networks, facilities, and services by developing urban intelligence functions for automating and supporting decisions pertaining to control, automation, optimization, management, and prediction for the purpose of improving, advancing, and maintaining the contribution of smart sustainable/sustainable smart cities to the goals of sustainable development (Bibri 2018a, 2019a, b).

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5.7 Data-Intensive Scientific Development and Smart Sustainable Urbanism The data-intensive scientific development as a new paradigm, which has materialized as a result of the recent advances in data science systems, processes, and methods and thus big data computing and the underpinning technologies, is instigating a drastic shift in city-related academic and scientific disciplines or fields (Bibri 2018a) These include, but are not limited to, urban planning, urban design, urban development, urban sustainability science, environmental science, urban computing, urban science, and urban informatics. Adding to this is the opportunity for such disciplines or fields to be potentially integrated into new interdisciplinary or transdisciplinary disciplines or fields. Turing award winner Jim Gray envisions data science as the new paradigm of science, and asserts that everything about science is changing because of the impact of advanced ICT and the unfolding data deluge (Bell et al. 2009). The Exabyte Age is upon us, and the data deluge makes the scientific approach—hypothesize, model, test—obsolete. This is the way science has worked for hundreds of years: that hypothesized models as systems visualized in the minds of scientists are tested, and then, experiments confirm or falsify the models of how the world works. Data-intensive scientific discovery is the fourth paradigm of scientific development where science involves the exploration and mining of scientific data and using advanced data mining techniques to unify theory, simulation, and experimental verification—with the first paradigm being where science used empirical methods thousands of years ago; the second paradigm where science became a theoretical field a few hundred years ago, involving the process of generating and testing hypotheses; and the third paradigm where science used calculation, conducting simulation and verification by computation in recent decades (Bell et al. 2009). Using the process of data mining is increasingly gaining traction and foothold in many academic and scientific research fields, taking over the method of formulating and testing hypotheses, which has prevailed for centuries. Here, the use of this process is seen as an important and effective way to, in addition to conducting scientific exploration and discovery based on big data, solve complex problems within a wide number and variety of domains, including smart sustainable/sustainable smart urbanism. By mining urban data, it is possible to discover laws and principles of sustainable development pertaining to environmental and socio-economic aspects of the city (Bibri 2018a). This development will allow an inference of the varied city stakeholders’ responses to operations, functions, services, designs, strategies, and policies in relation to multiple urban domains with respect to sustainability. Indeed, data-analytic and sustainable thinking and practice as an integrated approach into urbanism connect the best elements of data science technologies and urban sustainability practices. Data science has brought a novel approach to the way problems can be conceived of, understood, and tackled within a wide variety of domains. Accordingly, big data computing is changing the paradigm of scientific development, shifting from mainly formulating and testing hypotheses as well as collecting data manually and examining and reflecting on them to relying more and more on data generation, organization, processing, analysis, modeling, simulation, and verification (Bibri 2018a). This paradigm shift obviously spans many major academic and scientific research domains. In this context, it will help make decisions easier to judge, knowledge-driven, and strategic, and hence support and enhance existing, and create new, practices, strategies, and policies. For instance, big data analytics and related simulation models and optimization and prediction methods hold great potential to completely redefine urban problems, as well as offer entirely innovative opportunities to tackle them as part of new urban intelligence and planning functions, thereby doing more than merely enhancing existing urban practices. Further, experiences have shown that traditional scientific and academic research paradigms lead to questionable and challengeable assumptions about the evolution of social practices. Therefore, it is more beneficial and effective to search for new practices by rather using data-driven research approaches (as part of data-driven science and inductive empiricism) and thus the wider application of big data analytics techniques in the domain of smart sustainable/sustainable smart urbanism. In this sense, new practices can develop around big data technology, which can in turn be adapted and integrated into these practices, thereby advancing further its use in a way that fits into a wider strategy or formula that makes this technology more meaningful and relevant at the practical level (Bibri 2018a). In a nutshell, big data analytics is becoming increasingly a salient factor for academic and scientific innovation with regard to addressing complex challenges, wicked problems, and pressing issues, i.e., responding to major environmental concerns and socio-economic needs, mitigating the risks posed by ICT itself to environmental and social sustainability and containing the potential effects of urbanization. Indeed, the best opportunity for using big data is to harness and analyze data not as an end in itself—but rather to develop big theories about how smart sustainable/sustainable smart cities can be operated, managed, planned, designed, developed, and governed as to their quest for overcoming the challenges of sustainability and urbanization. In this respect, as part of academic and research endeavors, big data analytics can be exploited to reveal hidden and previously unknown patterns and discover meaningful correlations in large datasets pertaining to natural and social sciences so to develop more effective ways of responding to population growth, environmental pressures, changes in socio-economic needs, global

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shifts/trends, discontinuities, and societal transitions in the form of new processes, systems, designs, strategies, and policies, as well as products and services. In the meantime, to really get a grip on the use of big data to address the challenges of urban sustainability in an increasingly urbanized world, a new theory about big data theory is necessary. West (2013) vividly argues that big data require big theories. As to smart sustainable/sustainable smart urbanism, discovering patterns and making correlations from the deluge of urban data can only ever occur through the lens of a new kind of theory (Batty 2013; Bibri 2018a). There are enormous opportunities being created by the new and extensive sources of the deluge of urban data to effectively monitor, understand, analyze, and plan smart sustainable/sustainable smart cities to improve their contribution to the goals of sustainable development through enhancing and optimizing operations, functions, services, and designs in line with the vision of sustainability (e.g., Bibri 2018a, 2019a; Bibri and Krogstie 2017b). The use of big data computing and the underpinning technologies offers the prospect of cities in which natural resources can be managed sustainably and efficiently to enhance societal and economic outcomes by means of data-driven methods. This in fact epitomizes what smart sustainable/sustainable smart cities of the future entail and aim for: a set of transformative, innovative urban processes and approaches that amalgamate technological capabilities and strategic, data-driven decisions for boosting the performance of urban systems on the basis of a quest for promoting the health of individual citizens, communities, and natural ecosystems; conserving resources; and fostering economic development (Bibri 2018a). In light of this, the prospect of developing and implementing such cities based on big data analytics and its novel applications is fast becoming the new reality as manifested in the ever-growing embeddedness of advanced data sensing, data processing platforms, cloud and fog computing infrastructures, and wireless communication technologies as core enabling technologies of big data analytics into the fabric of urban environments for the purpose of solving the challenges of sustainability (e.g., Al Nuaimi et al. 2015; Batty et al. 2012; Bibri 2018a, 2019a). As part of the emerging urban analytics approach (Bibri 2018a), it has become of critical importance for urban professionals, analysts, and researchers to understand the fundamental concepts of data science and thus data mining/knowledge discovery even if they never intend to approach urban sustainability or sustainable urbanism problems from a data-analytical perspective merely because data analysis has now become so critical to urbanism practices. Both sustainable cities and smart cities are increasingly driven by big data analytics (e.g., Al Nuaimi et al. 2015; Batty et al. 2012; Bibri 2018a, b, 2019a, b; Bibri and Krogstie 2017a, b; Kitchin 2014, 2015, 2016), so there is a great professional and academic advantage in terms of interacting data-analytically with smart sustainable/sustainable smart cities as an emerging holistic approach to urbanism in an efficient and capable way. Understanding the fundamental concepts of data science and making an effective use of the available frameworks for organizing data-analytic thinking, especially the process of data mining/knowledge discovery, not only will allow urban professionals, analysts, and researchers to interact competently with such cities, but will help to envision tremendous opportunities for improving urbanism practices in the context of sustainability based on data-driven decision-making in the ambit of smart sustainable/sustainable smart urbanism. The emerging cities badging or regenerating themselves as smart sustainable/sustainable smart are exploiting new and existing data resources for environmental and socio-economic gains and benefits. They gather data science teams and urban scholars and practitioners on common ground to bring big data computing and the underpinning technologies as well as sustainability practices to bear to increase the contribution of such cities to the goals of sustainable development. Increasingly, urban administrators need to oversee analytics teams and analysis endeavors across multiple urban domains, local city governments must be able to invest wisely in urban projects and initiatives with substantial data assets directed for improving the different aspects of sustainability, and urban strategists and policymakers must be able to devise plans and design regulatory policies, respectively, that exploit and leverage data in the needed transition toward sustainability and its advancement (Bibri 2018a).

5.8 The Key External Forces Affecting the Combination of the Trends: The Role of Political Action in Smart Sustainable/Sustainable Smart Cities It is important to recognize the interplay between smart sustainable/sustainable smart cities as a form of sustainability transition and other societal scales, as well as the links to political processes on a macro level, i.e., regulatory policies and governance arrangements. This relates to the dialectic relationship between societal structures and such cities in the sense of each affecting and being affected by the other (see Bibri and Krogstie 2016 for a detailed discussion). The focus in this subsection is rather on how the former affects the latter, which is driven by one of the aims of the trend analysis. This one-way relationship has been approached from a variety of perspectives, including transition governance, innovation system, and discourse analysis. From a transition governance perspective, government is one of the key actors involved in

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any form of sustainability transition through various governance arrangements, including funding schemes, research management (regulation of public research institutes), innovation and technology policies, regulatory standards, market manipulations, public–private collaborations and partnerships, and so on (Bibri 2015, 2018a). In this respect, the government generates top-down pressure from regulation and policy and the use of market and other forms of incentives, while promoting, spurring, and stimulating the collective learning mechanisms by supporting innovation financially and providing access to the needed knowledge (Rotmans et al. 2001). Further, recommendations for smart sustainable cities as a major urban transformation, which entails a set of intertwined socio-technical systems and a cluster of interrelated discourses embedded in the wider socio-technical landscape, are unlikely to proceed without parallel political action (Bibri 2018a; Bibri and Krogstie 2016). Drastic shifts to sustainable (and) technological regimes ‘entail concomitantly radical changes to the socio-technical landscape of politics, institutions, the economy, and social values’ (Smith 2003, p. 131). Furthermore, political action is of influence with regard to smart sustainable/sustainable smart cities as both a techno-urban discourse and an amalgam of innovation systems (Bibri and Krogstie 2016). Indeed, it is at the core of discourse theory (e.g., Foucault 1972) in terms of the material mechanisms and practices that can be used to translate the idea or vision of such cities into concrete projects and strategies and their institutionalization in urban structures and practices (Bibri 2018a). Likewise, it is at the heart of the theoretical models of innovation system (e.g., Chaminade and Edquist 2010; Kemp 1997; Kemp and Rotmans 2005; Rånge and Sandberg 2015), as adequately discussed above in relation to the academic discourse of smart sustainable cities. Only to reiterate, political processes represent the setup under which dynamic networks of urban actors can interact within diverse industrial sectors in the development, diffusion, and utilization of knowledge and technology pertaining to smart sustainable urban development. Concerning the macro-processes of regulation as one of the key components of political action, ‘the act of regulating entails a set of principles, rules, or laws designed to govern urban behavior in terms of development and planning by carrying out legislations. Regulating city development and planning through policies is the responsibility of many different government departments and agencies. In other words, regulations are issued and enforced by various regulatory bodies formed or mandated by city governments to carry out the provision or intent of legislations. A city government affects urban development and planning through regulatory policies as a way to promote sustainability efforts. Most city governments have some regulations covering a variety of urban areas, including transport, traffic, mobility, natural environment, built environment, energy, land use, health, education, and safety [as well as science and innovation as an urban domain] in the context of sustainability’ (Bibri 2018a, p. 648). On the whole, political action is of critical importance to, if not determining in, the emergence, insertion, functioning, and evolution of smart sustainable/sustainable smart cities as an academic discourse, or rather to the construction, dissemination, and establishment of smart sustainable/sustainable smart urban development as an intellectual discourse. Related two urban transformations have a quite strong governmental and policy support within ecologically and technologically advanced nations, respectively. The main argument is that smart sustainable/sustainable smart cities—as instances of smart sustainable/sustainable smart urban development approaches—are not an element closed in the ‘ivory tower’ of the research and industry communities, but they are influenced by the macro-political practices in connection with sustainable development and ICT innovation (Bibri 2018a). Such cities figure in many policy documents and agenda as well as political statements and argumentations, in addition to being used by many institutions and organizations of influential weight at the national and international levels, as mentioned above. In short, as a corollary of its dynamic interaction with academic and intellectual discourses, politics forces their emergence, insertion, functioning, and evolution (Foucault 1972). Bibri and Krogstie (2016) provide a detailed account of some of the common political mechanisms used in this process, which represent facets of the operations that link smart sustainable cities and political action. They include the following: • Creating regulatory and policy instrument and incentives and carrying out legislation; • Assigning scholarly roles and institutional positions to particular institutions and organizations, thereby authorizing them and legitimizing their actions as to R&D activities, technology, and innovation policy formation, and constructing and implementing new visions, and so on; • Government involvement in projects and initiatives through funnelling investments, providing positive incentives, advocating product and service adoption, organizing forums and symposiums, encouraging national and local programs, and devising comprehensive plans; • Accumulating and preserving the relevant body of knowledge as well as disseminating and teaching concepts, visions, and principles, which is typically carried out inside centers for research and innovation and higher educational institutions.

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In light of the aim of this chapter, macro-processes of political regulation are also of particular relevance in the context of backcasting as a form of strategic urban planning and development as associated with sustainability and its advancement based on ICT as part of larger societal shifts or transitions. To move cities toward sustainability by improving their contribution to the goals of sustainable development using the innovative solutions and sophisticated approaches being offered by big data technology, policy actions should be, according to Bibri (2018, p. 547), ‘fostered through relevant principles and values, and the environmental, social, and economic impacts associated with sustainability need to be anticipated and assessed. Being normative, backcasting in turn is a suitable and useful framework for supporting policymakers and facilitating their actions to guide sustainability transitions. The choice of such framework to develop scenarios of smart sustainable/sustainable smart cities is supported and justified by its appropriateness to reach the policy targets (e.g., sustainable development goals) in tandem with societal and economic development.’ In addition, scenarios based on backcasting may be capable of generating new policy directions needed if cities are to become smart sustainable (see OECD 2002 for guidelines toward environmentally sustainable transportation). Furthermore, the use of backcasting approaches in futures studies assumes a vision of an evolutionary process of policy with a time frame of a generation or so, which is a basic principle to allow the policy actions to pursue the path toward, and potentially achieve, smart sustainable cities as a form of sustainability transition (Bibri 2018a, d, 2019b). The backcast of an alternative future is intended to reveal the relative implications of different policy actions and related goals or targets (Robinson 1982).

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Discussion and Conclusion

Smart sustainable cities are seen as the most important arena for sustainability transitions, as they constitute the hubs and sites of innovation within different, yet related, systems, including national, regional sectoral, technological, and quadruple helix of university–industry–government–citizen relations. Thereby, they are well positioned to instigate major, and make a significant contribution to, urban transformations by linking technological development with sustainable development. Drastic changes of such kind require long-term strategic planning and development, and backcasting, and other relevant futures, studies can serve as a basis for inspiration in the discussions and decision-making processes within such planning and development. In particular, backcasting studies allow for a better understanding of future opportunities and exploring the implications of alternative development paths that can be relied on either to adapt or avoid the impacts of the future (Bibri 2018d). Also, when applied in strategic urban planning and development toward sustainability, backcasting can increase the likelihood to envision certain changes (see, e.g., Bibri 2019b; Holmberg and Robèrt 2000). There is a belief that future-orientated planning can change development paths. The interest in the future of smart sustainable cities is driven by the aspiration to transform the continued urban development path. As part of the futures studies related to smart sustainable city planning and development using a backcasting methodology, both the trends and expected developments are key ingredients of, and crucial inputs for, analyzing different alternative scenarios for the future or long-term visions pertaining to desirable sustainable futures in terms of their opportunities, potentials, environmental and social benefits, and other effects. Using a qualitative approach, this chapter intended to provide a detailed analysis of the key forms of trends shaping and driving the emergence, materialization, and evolvement of the phenomenon of smart sustainable cities as a leading paradigm of urbanism, as well as to identify the most relevant expected developments related to smart sustainable urbanism. A number of separate, yet intertwined, trends associated with this phenomenon have been identified, described, and elaborated, including: • • • •

Global shifts: sustainability, ICT, and urbanization; Intellectual discourses: sustainable urbanism, smart urbanism, data-driven urbanism, and sustainable development; Academic discourses: sustainable cities, eco-cities, compact cities, smart cities, smart sustainable/sustainable smart cities; Computing and scientific paradigms: pervasive computing, ubiquitous computing, the IoT, big data computing, and data-intensive science; • Technological innovations: big data analytics, technologies, and applications. The dynamic interplay between these varied forms of trends, which will undoubtedly continue to evolve simultaneously and affect one another in a mutual process for many years yet to come, is the backcloth against which many recent urban innovation and transition endeavors or enterprises have emerged and materialized, and hence, numerous opportunities have been created and exploited in the context of data-driven smart sustainable urbanism within both ecologically and

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technologically advanced nations. In particular, these trends are shaping and driving not only the emergence and materialization of smart sustainable cities as a leading paradigm of urbanism, but also their evolvement, success, expansion, and evolution. Furthermore, the interrelationships between these trends have been discussed in relation to this phenomenon. The forms of trends identified include global shifts, intellectual discourses, academic discourses, computing and scientific paradigms, and technological innovations. In addition, envisioning how smart sustainable cities and related developments will evolve has been supported by the status of the recent and ongoing research endeavors in the field as involving most of the trends identified in this context. Also, the causes triggering the various forms of trends to emerge have been examined, so has whether they will continue in that direction. Moreover, the key external forces affecting these forms of trends have been elucidated and discussed, adding to highlighting the fact that their combination constitutes an integral part of larger societal shifts with far-reaching and long-term implications, namely sustainability transitions. In addition, the most relevant expected developments related to smart sustainable urbanism have been identified and linked. They tend to revolve around smart sustainable cities of the future and the continuation of this paradigm of urban planning and development in the future, which is projected to be fueled and driven by new technological innovations and sustainability advancements in the long term. They include the following, as regarded as likely to happen or believed to be already happening or to arrive soon: • Sustainable cities embracing big data technologies and their novel applications to improve, advance, and maintain their contribution to the goals of sustainable development toward achieving sustainability; • Smart cities incorporating the goals of sustainable development in their conceptualization and operationalization as part of new pathways toward achieving sustainability by relying heavily on big data computing and the underpinning technologies; • Big data analytics increasingly pervading urban systems and domains in terms of operations, functions, services, designs, strategies, and policies; • Both smart cities and sustainable cities becoming increasingly instrumented, datafied, and computerized to operate properly—and even to function at all with regard to many domains of urban life; • The practice of both sustainable urbanism and smart urbanism becoming predominately data-driven; • Smart sustainable/sustainable smart cities gaining traction and prevalence worldwide as a promising response to the challenges of sustainability and urbanization in an increasingly technologized world; • Data-intensive science as a fourth scientific paradigm drastically changing urban analytics and studies within the field of urban science, with the aim of transforming the knowledge of smart sustainable/sustainable smart cities or advancing urban sustainability science. One of the most sensible ways to understand the future as to, in this context, how smart sustainable cities will evolve is to postulate that the past and present can exhibit shifting patterns relatively in the same direction toward the future, along with some potential changes and less predictable possibilities (Bibri 2018a, 2019b). In addition, using backcasting approaches, futures studies are intended to help people better understand the future opportunities and possibilities of novel models for smart sustainable cities and their feasibility and potential in order to make better decisions today, not least of a strategic long-term nature They are also aimed at challenging the present systems or influencing the future or adapting to the most likely one. Creating choices of the future by outlining normative alternatives forms the basis for strategic urban planning and development. Therefore, the role of future studies has become of central importance for policymaking processes in connection with smart sustainable urbanism. Concluded by Bibri (2018a, d, 2019b) as the most suitable approach to the strategic planning and development of smart sustainable cities as part of related futures studies, the backcasting approach is prescriptive (normative) by focusing on what such cities should be or look like in the future, an aspect for which the trend analysis is of crucial importance in terms of constructing the alternative scenario for the future. Generally, prescriptive approaches to futures studies try to aid people in clarifying their values and preferences so that they can develop visions of desirable futures. Indeed, the backcasting approach allows researchers to understand what they would prefer the future to be or to play out and then take the necessary steps or actions to create and attain that preferred future. In this respect, most approaches to futures studies as part of qualitative inquiry rely on subjective human judgment; nevertheless, a range of tools and methods is available for use to mitigate such

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judgment through generating ideas to produce different judgments, encouraging collective judgment, and identifying discrepancies between competing views on the future, as well as through substantiating inconsistencies and consistencies among and within such views (Bibri 2018a, d).

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The Underlying Technological, Scientific, and Structural Dimensions of Data-Driven Smart Sustainable Cities and Their Socio-Political Shaping Factors and Issues

Abstract

We are moving into an era where instrumentation, datafication, and computation are routinely pervading the very fabric of cities, coupled with the interlinking, integration, and coordination of their systems and domains. As a result, vast troves of contextual and actionable data are being produced and used to operate, regulate, manage, and organize urban life. This data-driven approach to urbanism has recently become the mode of production for smart sustainable cities, which are accordingly becoming knowable, tractable, and controllable in new dynamic ways, responsive to the data generated about them by reacting to the analytical outcome of many domains of urban life in terms of enhancing and optimizing operational functioning, planning, design, development, and governance in line with the goals of sustainable development. However, topical studies tend to deal mostly with data-driven smart urbanism while barely exploring how this approach can improve and advance sustainable urbanism under what is labeled ‘data-driven smart sustainable cities’ as a leading paradigm of urbanism. Having a threefold aim, this chapter first examines how data-driven smart sustainable cities are being instrumented, datafied, and computerized so as to improve, advance, and maintain their contribution to the goals of sustainable development through enhanced practices. Secondly, it highlights and substantiates the real potential of big data technology for enabling such contribution by identifying, synthesizing, distilling, and enumerating the key practical and analytical applications of this advanced technology in relation to multiple urban systems and domains with respect to operations, functions, services, designs, strategies, and policies. Thirdly, it proposes, illustrates, and describes a novel architecture and typology of data-driven smart sustainable cities. This chapter intervenes in the existing scholarly conversation by calling attention to a relevant object of study that previous scholarship has neglected and whose significance for the field of urbanism is well elucidated, as well as by bringing new insights to and informing the ongoing debate on smart sustainable urbanism in light of big data science and analytics. This work serves to bring data-analytic thinking and practice to smart sustainable urbanism, and seeks to promote and mainstream its adoption, in addition to drawing special attention to the crucial role and enormous benefits of big data technology and its novel applications as to transforming the future form of such urbanism. Keywords








Data-driven smart sustainable cities Urbanism Urban science Sustainability Innovation labs computing

1














Big data analytics Big data applications Datafication Urban intelligence functions Cloud and fog




Introduction

Contemporary cities have a central and defining role in strategic sustainable development; therefore, they have gained a central position in operationalizing this notion and applying this discourse. This is clearly reflected in the Sustainable Development Goal 11 (SGD 11) of the United Nations’ 2030 Agenda, which entails making cities more sustainable, resilient, inclusive, and safe (UN 2015a). In this respect, the UN’s 2030 Agenda regards ICT as a means to promote socio-economic development and protect the environment, increase resource efficiency, achieve human progress and © Springer Nature Switzerland AG 2019 S. E. Bibri, Big Data Science and Analytics for Smart Sustainable Urbanism, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-17312-8_5

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knowledge in societies, upgrade legacy infrastructure, and retrofit industries based on sustainable design principles (UN 2015b). Hence, the multifaceted potential of the smart city approach as enabled by ICT has been under investigation by the UN (2015c) through their study on ‘Big Data and the 2030 Agenda for Sustainable Development.’ In particular, there is an urgent need for developing and applying data-driven innovative solutions and sophisticated methods to overcome the environmental and social challenges of urbanization (UN 2016) and sustainability (Bibri 2018a, 2019; Bibri and Krogstie 2017b, 2019). In other words, the world is drowning in data—and if planners and policymakers realize the potential of harnessing these data in collaboration with urban scientists, data scientists, and computer scientists, the outcome could solve major global challenges. In recent years, there has been a marked intensification of datafication. This is manifested in a radical expansion in the volume, range, variety, and granularity of the data being generated about urban environments and citizens (e.g., Bibri 2018a, 2019; Bibri and Krogstie 2019; Kitchin 2014, 2015a, 2016a), with the primary aim of quantifying the whole of the city, putting it in a data format so it can be organized and analyzed for a variety of uses and applications. We are currently experiencing the accelerated datafication of the city in a rapidly urbanizing world and witnessing the dawn of the big data era not out of the window, but in everyday life. Our urban everydayness is entangled with data sensing, data processing, and communication networking, and our wired world generates and analyzes overwhelming and incredible amounts of data. The modern city is turning into constellations of instruments and computers across many scales and morphing into a haze of software instructions, which are becoming essential to the operational functioning, planning, design, development, and governance of the city. The datafication of spatiotemporal citywide events has become a salient factor for the practice of smart sustainable urbanism. Indeed, in the wake of datafication, a new era is presently unfolding wherein smart sustainable urbanism is increasingly becoming data-driven (Bibri 2018a, b, 2019). At the heart of such urbanism is a computational understanding of urban systems and processes that renders urban life a form of logic, calculative, and algorithmic rules and procedures. Such understanding entails drawing together, interlinking, and analyzing urban big data to provide a more holistic and integrated view and synoptic intelligence of the city. This is being increasingly directed for improving and maintaining the contribution of sustainable cities to the goals of sustainable development in an increasingly urbanized world. We are living at the dawn of what has been termed as ‘the fourth paradigm of science,’ a scientific revolution that is marked by the recent emergence of big data science and analytics as well as the increasing adoption of the underlying technologies in scientific and scholarly research practices. Everything about science development and knowledge production is fundamentally changing thanks to the unfolding and soaring data deluge. The upcoming data avalanche is thus the primary fuel of this new age where powerful computational processes or analytics algorithms burn this fuel to generate useful knowledge and deep insights directed for a wide variety of practical uses, e.g., generating and enhancing strategic decisions and developing and adopting innovative solutions to create more sustainable, efficient, resilient, livable, equitable, and safe cities. Big data science and analytics possesses unparalleled potential to revolutionize society in a way that no one is able to predict in terms of the dramatic change that it will have on our lives. Indeed, it embodies an unprecedentedly transformative and constitutive power—manifested not only in the form of revolutionizing science and transforming knowledge, but also in advancing social practices, producing new discourses, creating and catalyzing major shifts, and fostering societal transitions. Of particular relevance to this chapter, this new area of science and technology is instigating a drastic change in the way cities are studied and, hence, in the practice of operating, managing, planning, designing, developing, and governing them in the context of sustainability in the face of urbanization. To put it differently, these practices are becoming highly responsive to a form of data-driven urbanism that is the key mode of production for what have widely been termed smart sustainable cities whose monitoring, understanding, and analysis are accordingly increasingly relying on big data computing and the underpinning technologies. In a nutshell, the Fourth Scientific Revolution is set to erupt in cities, break out suddenly and dramatically, throughout the world. This is manifested in bits meeting bricks on a vast scale as instrumentation, datafication, and computation are permeating the spaces we live in. The outcome will impact most aspects of urban life, raising questions and issues of urgent concern, especially those related to sustainability and urbanization. This pertains to what dimensions of cities will be most affected; how urban planning, design, development, and governance should change and evolve; and, most importantly, how cities can embrace and prepare for looming technological disruptions and opportunities. However, topical studies tend to deal mostly with data-driven smart urbanism (e.g., Bettencourt 2014; Kitchin 2014, 2015a, b, 2016a, b; Kitchin et al. 2017) while barely exploring how this approach can improve and advance sustainable urbanism under what is labeled ‘data-driven smart sustainable cities’ as a leading paradigm of urbanism. Moreover, research on big data applications in the context of smart cities tends to deal largely with economic development, the quality of life,

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and governance (e.g., Batty 2013; Bibri 2018a; Bibri and Krogstie 2017a; Khan et al. 2015; Khanac et al. 2017; Kitchin 2014, 2015b; Hashem et al. 2016; Rathore et al. 2018) while overlooking the rather more urgent issues and complex challenges related to sustainability. This paucity of research pertains particularly to the untapped potential of big data technologies and their novel applications for enhancing the environmental and social aspects of sustainability in the context of smart sustainable cities (Bibri 2018a, 2019). Indeed, many of the emerging smart solutions are not aligned with sustainability goals (Ahvenniemi et al. 2017; Bibri 2019). This relates to the deficiencies and shortcomings of smart cities in this regard (see Bibri 2019 for a detailed review). Having a threefold aim, this chapter first examines how data-driven smart sustainable cities are being instrumented, datafied, and computerized so as to improve, advance, and maintain their contribution to the goals of sustainable development through enhanced practices. Second, it highlights and substantiates the real potential of big data technology for enabling such contribution by identifying, synthesizing, distilling, and enumerating the key practical and analytical applications of this advanced technology in relation to multiple urban systems and domains with respect to operations, functions, services, designs, strategies, and policies. Third, it proposes, illustrates, and describes a novel architecture and typology of data-driven smart sustainable cities. This work serves to bring data-analytic thinking and practice to smart sustainable urbanism, and seeks to promote and mainstream its adoption, in addition to drawing special attention to the crucial role and enormous benefits of big data technology and its novel applications as to transforming the future form of such urbanism. The remainder of this chapter is structured as follows. Section 2 introduces and describes the key conceptual definitions in relevance to the topic of the study. Section 3 provides a survey of related work. Section 4 delves into the heart of data-driven smart sustainable cities, covering a range of constituents and underpinnings. Section 5 identifies and enumerates the key practical and analytical applications of big data technology. In Sect. 6, a novel architecture and typology of data-driven smart sustainable cities are illustrated and described. Section 7 sheds light on the social shaping dimensions of such cities. Section 8 provides a critical discussion of urban science and big data computing in the context of data-driven smart sustainable urbanism. Section 9 identifies and compiles the key challenges and concerns associated with the use of big data technology and its applications. The chapter ends, in Sect. 10, with concluding remarks, discussion, contribution, and further research.

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Conceptual Definitions

2.1 Data-Driven Smart Sustainable Cities ‘Data-driven smart sustainable cities’ is a term that has recently gained traction in academia, government, and industry to describe cities that are increasingly composed and monitored by ICT of ubiquitous and pervasive computing and thereby have the ability of using advanced technologies by city operations centers, planning and policy offices, research centers, innovation labs, and living laboratories for generating, processing, and analyzing the data deluge in order to enhance decision-making processes and to develop and implement innovative solutions for improving sustainability, efficiency, resilience, and the quality of life. It entails developing a citywide instrumented system (i.e., inter-agency control, planning, innovation, and research hubs) for creating and inventing the future. For example, a data-driven city operations center, which is designed to monitor the city as a whole, pulls or brings together real-time data streams from many different agencies spread across various urban domains and then analyze them for decision-making and problem-solving purposes: optimizing, regulating, and managing urban operations (e.g., traffic, transport, energy, etc.).

2.2 Datafication The big data revolution will transform the way we live, work, and think in the city. Datafication has become a buzzword in the era of big data revolution. This buzzword describes an urban trend of defining the key to core city operations and functions through a reliance on big data computing and the underpinning technologies. In other words, the notion of datafication denotes that cities today are dependent upon their data to operate properly—and even to function at all with regard to many domains of urban life (Bibri 2019). It also refers to the collective tools, processes, and technologies used to transform a city to a data-driven enterprise. In short, datafication involves turning many aspects of urban life into computerized data and transforming this information into value. As such, this concept helps better frame the changes taking place

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now (Cukier and Mayer-Schoenberger 2013). A city that implements datafication is said to be datafied. To datafy a city is to put it in a quantified format, so it can be structured and analyzed. Cities are taking any possible quantifiable metric and squeezing useful knowledge out of it for enhanced decision-making and deep insights pertaining to many domains of urban life. Datafication entails that in a modern data-oriented urban landscape, a city’s performance is contingent on having control over the storage, management, processing, and analysis of the data, as well as on the extracted knowledge in the form of applied intelligence. Tackling sustainability and urbanization issues is one of the key concerns of the datafication of the contemporary city. To put it differently, the urban world is drowning in data—and if planners and policymakers realize the potential of harnessing these data in collaboration with urban scientists and data scientists, the outcome could solve major global challenges. The point at issue is that we generate enormous amounts of data on a daily basis, a binary trail of breadcrumbs that forms a map of urban life in terms of citizens’ experiences and urban dynamics, and hence the resulting disparate datasets can, if harnessed properly, open up a unique window of, and represent a goldmine, opportunity for making cities more sustainable and in tune with citizens’ actual needs and aspirations.

2.3 Big Data Computing The term ‘big data’ is essentially used to mean collections of datasets whose volume, velocity, variety, exhaustivity, relationality, and flexibility make it so difficult to manage, process, and analyze the data using the traditional database systems and software techniques. In other words, big data refer to humongous volumes of both structured and unstructured data that cannot be processed and analyzed with conventional applications, or that exceed their computational and analytical capabilities. The term ‘big data analytics’ denotes ‘any vast amount of data that has the potential to be collected, stored, retrieved, integrated, selected, preprocessed, transformed, analyzed, and interpreted for discovering new or extracting useful knowledge. Prior to this, the analytical outcome (the obtained results) can be evaluated and visualized in an understandable format before their deployment for decision-making purposes (e.g., improving or changing an operation, function, service, strategy, or policy). Other computational mechanisms involved in big data analytics include search, sharing, transfer, querying, updating, modeling, and simulation. In the context of sustainable smart cities, big data analytics refers to a collection of sophisticated and dedicated software applications and database management systems run by machines with very high processing power, which can turn a large amount of urban data into useful knowledge for enhanced, well-informed decision-making and deep insights in relation to various urban domains, such as transport, mobility, traffic, environment, energy, land use, waste management, education, health care, public safety, planning and design, and governance’ (Bibri 2018b, p. 234). Big data computing is an emerging paradigm of data science, which is of multidimensional data mining for scientific development and knowledge production over large-scale infrastructure. Data mining/knowledge discovery and decision-making from voluminous, varied, real-time, exhaustive, fine-grained, indexical, dynamic, flexible, evolvable, relational data is a daunting challenge/task. As a new paradigm, it amalgamates, as underpinning technologies, large-scale computation as well as new data-intensive techniques and algorithms and advanced mathematical models to build and perform data analytics. Accordingly, big data computing demands a huge storage and computing power for data curation and processing for the purpose of discovering new or extracting useful knowledge intended typically for immediate use in an array of multitudinous decision-making processes to achieve different purposes.

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A Survey of Related Work

We are moving into an era where instrumentation, datafication, and computation are routinely pervading the very fabric of cities, coupled with the interlinking, integration, and coordination of their systems and domains, and vast troves of data are being generated and exploited to operate, manage, and organize urban life, or a deluge of contextual and actionable data is produced and acted upon in real time. This data-driven approach to urbanism has become the mode of production for smart sustainable cities (Bibri 2018a, b, 2019). Accordingly, such cities are becoming knowable, tractable, and controllable in new dynamic ways, responsive to the data generated about them by reacting to the analytical outcome of many domains of urban life in terms of enhancing and optimizing operational functioning, planning, design, development, and governance in line with the goals of sustainable development. This occurs in a synergistic way thanks to big data computing and the underpinning technologies and their ubiquity presence.

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With the above in mind, in one of the earlier works on data-driven urbanism, Batty (2013) describes how the growth of big data is shifting the emphasis from longer term strategic planning to short-term thinking about how cities function and can be managed. His argument revolves around the sea change in the kinds of data that are emerging about what happens where and when in cities, and how it is drastically altering the way we conceive of, understand, and plan smart cities. Bettencourt (2014) explores how big data can be useful in urban planning by formalizing the planning process as a general computational problem. The focus in his paper is on scientific (complexity science) and engineering principles (big data technologies) pertaining to data-driven urbanism, and how they particularly relate to urban policy, management, and planning as to achieving new solutions to wicked and intractable urban problems. In his article ‘The Real-time City? Big Data and Smart Urbanism’ Kitchin (2014) focuses on smart cities as increasingly composed of and monitored by pervasive and ubiquitous computing, and drawing on a number of examples, details how cities as being instrumented with digital devices and infrastructure produce big data which enable real-time analysis of city life, new modes of urban governance, and provide the raw material for envisioning and enacting more efficient, competitive, productive, open, and transparent cities. He, moreover, provides a critical reflection on the implications of big data and smart urbanism, examining five emerging concerns: the politics of big urban data; technocratic governance and city development; corporatization of city governance and technological lock-ins; buggy, brittle and hackable cities; and the panoptic city. A large part of this examination is also the aim of Kitchin’s (2015a) paper, which indeed provides a critical overview of data-driven, networked urbanism and smart cities focusing in particular on the relationship between data and the city (rather than network infrastructure or computational or urban issues), and critically examines a number of urban data issues, including corporatization, ownership, control, privacy and security, anticipatory governance, and technical challenges. Kitchin (2016a) examines the forms, practices, and ethics of smart cities and urban science, paying particular attention to: instrumental rationality and realist epistemology; privacy, dataveillance, and geosurveillance; and data uses such as social sorting and anticipatory governance. Overall, the above works lack an important strand to the topic of smart or data-driven urbanism: sustainability, and also tend to focus on either technical or political issues related to urban big data. In this light, Bibri (2019) provides a comprehensive, state-of-the-art review and synthesis addressing the sustainability and unsustainability of smart urbanism and related big data applications in terms of research issues and debates, knowledge gaps, technological advancements, as well as challenges and common open issues. With respect to the latter, the author identifies significant scientific and intellectual challenges and common open issues that need to be addressed and overcome prior to achieving a more effective utilization of big data analytics and its applications in the realm of sustainable smart and smarter cities. Such challenges and issues pertain, by extension, to smart sustainable cities, as addressed in Bibri (2018a). The challenges are mostly of computational, analytical, technical, and logistic kinds. While most of the challenges and open issues are currently under investigation and scrutiny by the relevant research and industry communities, supported by technology and innovation policies, deploying big data technologies and their novel applications in smart sustainable/sustainable smart cities of the future requires overcoming other organizational, institutional, political, social, ethical, and regulatory challenges (see Bibri 2019 for an overview). Research on big data analytics and its application in the context of smart and smarter cities tends to deal largely with economic development (i.e., management, optimization, effectiveness, innovation, productivity, etc.), the quality of life in terms of service efficiency and betterment, and governance (e.g., Batty 2013; Bibri 2018a, 2019; Bibri and Krogstie 2017a; DeRen et al. 2015; Khan et al. 2015; Khanac et al. 2017; Kitchin 2014, 2015a; Hashem et al. 2016; Rathore et al. 2018) while overlooking and barely exploring the rather more urgent issues and complex challenges related to sustainability. This paucity of research pertains particularly to the untapped potential of big data technologies and their novel applications for enhancing the environmental and social aspects of sustainability in the context of smart sustainable/sustainable smart cities (Bibri 2018a, 2019). Indeed, many of the emerging smart solutions are not aligned with sustainability goals (Ahvenniemi et al. 2017; Bibri 2019). This relates to the deficiencies and misunderstandings of smart and smarter cities in this regard (see Bibri 2019 for a detailed review). Such cities have been subject to much debate, generating a growing level of criticism that essentially questions their added value to sustainability due to the lack of incorporating the fundamental goals of sustainable development in their conceptualization, as well as falling short in considering the environmental and social indicators of sustainability (see, e.g., Ahvenniemi et al. 2017; Bibri 2018a, b, 2019; Bibri and Krogstie 2017a; Höjer and Wangel 2015; Kramers et al. 2014; Marsal-Llacuna 2016). Consequently, a recent research wave has started to focus on enhancing smart and smarter city approaches to achieve the required level of sustainability using big data applications under what is labeled ‘sustainable smart cities’ (e.g., Al Nuaimi et al. 2015; Batty et al. 2012; Bibri 2018a, 2019). Therefore, there are only a few studies that have recently focused on the uses of big data applications in relation to the different aspects of sustainability in the context of smart sustainable/sustainable smart cities (see, e.g., Bibri 2018a, b, 2019; Bibri and Krogstie 2017b). This paucity of research can be, nevertheless, explained by the fact that such cities are a new urban phenomenon, and the concept only became widespread during the mid-2010s (Bibri 2018a; Bibri and Krogstie 2017a).

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What Lies at the Heart of the Data-Driven Smart Sustainable City

As cities are routinely embedded with all kinds of ICT forms, including infrastructure, platforms, systems, devices, sensors and actuators, and networks, the volume of data generated about them is growing exponentially and diversifying, providing rich, heterogenous streams of information about urban environments and citizens. This data deluge enables the real-time analysis of different urban systems and interconnects data across different urban domains to provide detailed views of the relationships between different forms of data that can be utilized for advancing the various aspects of urbanity through new modes of operational functioning, planning, design, development, and governance in the context of sustainability, as well as provides the raw material for envisioning more sustainable, efficient, resilient, and livable cities. The point at issue is that we generate enormous amounts of data on a daily basis, a binary trail of breadcrumbs that forms a map of urban life in terms of citizens’ experiences and urban dynamics, and these disparate datasets, if harnessed properly, open up a unique window of, and represent a goldmine, opportunity for making cities more sustainable and in tune with citizens’ actual needs and aspirations.

4.1 On the Evolving Integration of Data-Driven Smart Cities and Sustainable Cities Both smart cities and sustainable cities are becoming ever more computationally augmented and digitally instrumented and networked, their systems interlinked and integrated, their domains combined and coordinated, and thus their networks coupled and interconnected, and consequently, vast troves of urban data are being generated and used to regulate, control, manage, and organize urban life in real time. In other words, the increasing pervasiveness of urban systems, domains, and networks utilizing digital technologies is generating enormous amounts of digital traces capable of reflecting in real time how people make use of urban spaces and infrastructures and how urban activities and processes are performed, an information asset which is being leveraged in steering smart cities and sustainable cities. Indeed, citizens leave their digital traces just about everywhere they go, both voluntarily and involuntarily, and when cross-referenced with each citizen’s spatial, temporal, and geographical contexts, the data harnessed at this scale offers a means of describing, and responding to, the dynamics of the city in real time. In addition to individual citizens, city systems, domains, and networks constitute a key source of data deluge, which is generated by various urban entities, including governmental agencies, authorities, administrators, institutions, organizations, enterprises, and communities by means of urban operations, functions, services, designs, strategies, and policies. Smart cities of the future seek to solve a fundamental conundrum of cities—improve sustainability, services, equity, and the quality of life at the same time as reducing costs and increasing efficiency and resilience by utilizing a fast-flowing torrent of urban data and the rapidly evolving data analytics technologies; algorithmic planning and governance; and responsive, networked urban systems. In particular, the generation of colossal amounts of date and the development of sophisticated data analytics for understanding, monitoring, probing, regulating, and planning the city is one significant aspect of smart cities that is being embraced by sustainable cities to improve, advance, and maintain their contribution to the goals of sustainable development (Bibri 2018a, b, 2019; Bibri and Krogstie 2017a, b, c, 2018). Generally, sustainable cities can be understood as a set of approaches into operationalizing sustainable development in cities or practically applying the knowledge about sustainability and related technologies to the operational functioning and thus planning and design of existing and new cities or districts (Bibri 2018a; Bibri and Krogstie 2017a, 2019). Sustainable cities represent an instance of sustainable urban development, which is a strategic approach to achieving the long-term goals of sustainability. As such, they, as put succinctly by Bibri and Krogstie (2017a, p. 11), ‘strive to maximize the efficiency of energy and material use, create a zero-waste system, support renewable energy production and consumption, promote carbon neutrality and reduce pollution, decrease transport needs and encourage walking and cycling, provide efficient and sustainable transport, preserve ecosystems, emphasize design scalability and spatial proximity, and promote livability and community-oriented human environments.’ For supranational states, national governments, and city officials, smart cities offer the enticing potential of environmental and socio-economic development—more sustainable, livable, functional, safe, equitable, and transparent cities, and the renewal of urban centers as hubs of innovation and research (e.g., Al Nuaimi et al. 2015; Batty et al. 2012; Bibri 2018a, 2019; Bibri and Krogstie 2019; Kitchin 2014; Kourtit et al. 2012; Townsend 2013). While there are several main characteristics of a smart city as evidenced by industry and government literature (see, e.g., Hollands 2008; Kitchin 2014 for an overview), the one that this chapter is concerned with focuses on environmental and social sustainability.

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There has recently been much enthusiasm in the domain of smart sustainable urbanism about the immense possibilities and fascinating opportunities created by the data deluge and its extensive sources with regard to enhancing and optimizing urban operational functioning, management, planning, design, and governance in line with the goals of sustainable development as a result of thinking about and understanding sustainability and urbanization and their relationships in a data-analytic fashion for the purpose of generating and applying knowledge-driven, fact-based, strategic decisions in relation to such urban domains as transport, traffic, mobility, energy, environment, education, health care, public safety, public services, governance, and science and innovation (Bibri 2018a, 2019). Therefore, the operational functioning, management, planning, and design of smart sustainable cities as a set of interrelated systems is increasingly being dominated by the use of advanced data, information, and communication technologies. The provision of data from urban operations and functions is offering the prospect of urban environments, wherein the implication of the way such cities are functioning and operating is continuously available, and urban planning is facing the prospect of becoming continuous as the data deluge floods from different urban domains and is updated in real time, thereby allowing for a dynamic conception of planning and a scalable and efficient form of design.

4.2 Digital Instrumentation The big data revolution is set to erupt in both smart cities and sustainable cities throughout the world. This is manifested in bits meeting bricks on a vast scale as instrumentation is routinely pervading the spaces we live in. Smart sustainable cities are depicted as constellations of instruments for measurement and control across many spatial scales that are connected through fixed and wirelessly ad hoc and mobile networks with a modicum of intelligence, which provide and coordinate continuous data regarding different aspects of urbanity in terms of the flow of decisions about the physical, infrastructural, operational, functional, and socio-economic forms of smart sustainable cities (Bibri 2018a). As such, the instrumentation of such cities offers the prospect of an objectively measured, real-time analysis of urban life and infrastructure, and opens up dramatically different forms of social organization. It is the domain of the ICT industry that is providing the detailed hardware and software to provide the operating system for smart sustainable cities. This infrastructure entails integration, data collection and mining, decision-making, practice enhancement, and service delivery in relation to sustainability, efficiency, resilience, equity, and the quality of life. While there are different approaches to generating the deluge of urban data (e.g., directed, indirected, volunteered, etc.), the automated one is the most common and prominent among them. It pertains to various automatic functions of the devices and systems that are widely deployed and networked across urban environments. Indeed, the automated approach to urban data deluge generation has recently captured the imagination of those concerned with understanding, operating, managing, and planning cities, as well as seeking useful insights into urban systems, in particular in relation to the environment (Bibri 2018b). Especially, there has been increased interest in sensor networks and the IoT as well as the tracking and tracing of people and objects (Kitchin 2014). For example, sensor networks can be used to monitor the operation and condition of urban and public infrastructures, such as roads, rails, tunnels, sewage systems, water systems, power and gas provision systems, hospitals, facilities, and parks, as well as environmental conditions. In this context, smart sustainable/sustainable smart cities offer the prospect of real-time analysis of the processes operating and organizing urban life, which is of paramount importance to advancing the different aspects of sustainability. There are a number of tools and techniques used in the automated approach to generating urban data deluge (Batty et al., 2012; Bibri 2018b; Dodge and Kitchin 2007; Kitchin 2014; Kitchin and Dodge 2011), including sensors: • • • • • • • • •

GPS in vehicles and on people; smart tickets that are used to trace passenger travel; RFID tags attached to objects and people; sensed data generated by a variety of sensors and actuators embedded into the objects or environments that regularly communicate their measurements; capture systems in which the means of performing tasks captures data about those tasks; digital devices that record and communicate the history of their own use; digital traces left through purchase of goods and related demand supply situations; transactions and interactions across digital networks that not only transfer information, but also generate data about the transactions and interactions themselves; Clickstream data that record how people navigate through web sites or apps;

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• automatic meter reading (AMR) that communicates utility usage on a continuous basis; • automated monitoring of public services provision; • the scanning of machine-readable objects such as travel passes, passports, or barcodes on parcels that register payment and movement through a system; • machine-to-machine interactions across the IoT; • uniquely indexical objects and machines that conduct automatic work as part of the IoT, communicating about their use and traceability if they are mobile (automatic doors, lighting and heating systems, washing machines, security alarms, wifi router boxes, etc.); • transponders that monitor throughput at tollbooths, measuring vehicle flow along a road or the number of empty spaces in a car park, and track the progress of buses and trains along a route. In the domain of urban planning and management, these categories of instrumentation provide abundant, systematic, dynamic, varied, well-defined, resolute, relatively cheap data about urban processes and activities, allowing for real-time analytics and adaptive forms of planning and management (Kloeckl et al. 2012). In view of that, embedding more and more advanced ICT in various forms into smart sustainable/sustainable smart cities will undoubtedly continue and even escalate for the purpose of providing the most suitable tools and methods for handling the underlying complexity as systems and thus dealing with the challenges they are facing and will continue to face. From a different perspective, not all data are equally generated, and their variety is associated with the purpose of their use, among others. There are opportunistic data which are collected for one purpose and then used for another, e.g., data owned by cellphone companies to run their operations but used by transport companies to better understand urban mobility. User-generated data result from the engagement of citizens, e.g., data from social media platforms which provide valuable information to better understand today’s cities. Purposely sensed data, e.g., automated data, reflect the power of ubiquitous urban sensors that can be deployed ad hoc in public and private spaces to better understand some aspects of urban life and dynamics. Moreover, the various sensor-recording parameters, their length as to the collected data, where they are located, what kinds of sensors are embedded in which environments, their settings and calibration, their integration and fusion, and their exhaustiveness as technical configurations and deployments determine the nature of the produced data and the way they are stored, managed, processed, analyzed, and disciplined.

4.3 Big Data Ecosystem and Its Components Big data trends are associated with pervasive and ubiquitous computing, which involves myriads of sensors pervading urban environments on a massive scale. Therefore, the volume of the data generated is huge and thus the processes, systems, platforms, infrastructures, and networks involved in handling these data are complex. Mechanisms to store, integrate, manage, process, analyze, and visualize the generated data through scalable applications remain a major scientific and technological challenge in the ambit of data science, urban science, and computer science. The evolving data deluge is due to a number of the core enabling and driving technologies of ICT of pervasive and ubiquitous computing and thus big data computing. These are being fast embedded into the very fabric of contemporary cities, everyday practices and spaces, whether badging or regenerating themselves as smart sustainable to pave the way for adopting the upcoming innovative solutions to overcome the challenges of sustainability and urbanization in the years ahead. Further, like many areas to which big data computing can be applied, smart sustainable cities require the big data ecosystem and its components to be put in place as part of their ICT infrastructure prior to designing, developing, deploying, implementing, and maintaining the diverse applications that support sustainability and reduce the negative effects of urbanization. As a scientific and technological area, the core enabling technological components underlying the big data ecosystem are under vigorous investigation in both academic circles as well as the ICT industry toward the development of computationally augmented urban environments as part of the informational landscape of such cities (Bibri 2019). Big data ecosystems are for capturing data to generate useful knowledge and deep insights. In the sphere of smart sustainable cities, the big data landscape is daunting, and there is no one ‘big data ecosystem’ or single go-to solution when it comes to building big data architecture. The big data ecosystem involves multivarious technologies in terms of quality and form, which allow to store, manage, process, analyze, visualize data, and deploy the obtained results. It consists of infrastructure and tools for storing, managing, processing, and analyzing data; specialized analytics techniques; and applications. Bibri and Krogstie (2017c) provide a comprehensive, state-of-the-art review of the core enabling technologies of big data analytics in

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relation to smart sustainable cities, including a synthesis and illustration of the key computational and analytical techniques, processes, and models associated with the functioning and application of big data analytics. The components addressed by the authors in rather more detail include, but are not limited to, the following: • pervasive sensing in terms of collecting and measuring urban big data; the IoT and related RFID tags; sensor-based urban reality mining; and sensor technologies, types, and areas in big data computing; • wireless communication network technologies and smart network infrastructures; • data processing platforms; • cloud and fog/edge computing; • advanced techniques and algorithms; • conceptual and analytical frameworks. Generally, big data ecosystems entail a number of permutations of the underlying core enabling technologies as shaped by the scale, complexity, and extension of the city projects and initiatives to be developed and implemented. In this respect, it is necessary to, as suggested by Chourabi et al. (2012), take into account flexible design, quick deployment, extensible implementation, comprehensive interconnections, and advanced intelligence. Regardless, while there are some permutations that may well apply to most urban systems and domains, there are some technical aspects and details that remain specific to smart sustainable cities, more specifically to the requirements, objectives, and resources of related projects and initiatives, which are usually determined by and embedded in a given context (Bibri 2019; Bibri and Krogstie 2017c). Yet, most of, if not all, the possible permutations involve sensing technologies and networks, data processing platforms, cloud computing and/or fog computing infrastructures, and wireless communication and networking technologies. These are intended to provide a full analytic system of big data and related functional applications based on advanced decision-support systems and strategies—urban intelligence functions and related simulations models and optimization and prediction methods. On this note, Batty et al. (2012) state that much of the focus on sustainable smart cities of the future ‘will be in evolving new models of the city in its various sectors that pertain to new kinds of data and movements and actions that are largely operated over digital networks while at the same time, relating these to traditional movements and locational activity. Very clear conceptions of how these models might be used to inform planning at different scales and very different time periods are critical to this focus… Quite new forms of integrated and coordinated decision-support systems will be forthcoming from research on smart cities of the future.’

4.4 Cloud Computing for Big Data Analytics 4.4.1 Characteristics and Benefits The term ‘cloud computing’ has been defined in multiple ways by ICT experts and researchers and a wide range of organizations (e.g., government agencies) and institutions (e.g., educational institutions). Common threads running through most definitions are that cloud computing denotes a computing model in which standardized, scalable, and flexible ICT-enabled capabilities delivered in real time via the Internet in the form of three types of services: (1) software as a service (SaaS), (2) platform as a service (PaaS), and (3) infrastructure as a service (IaaS) to external users or customers. SaaS and PaaS denote the provider’s software applications and software development platforms, respectively, and IaaS means virtual servers, storage facilities, processors, and networks as resources, all being delivered over the cloud. Thus, cloud computing consists of several components, which can be rapidly provisioned with minimal management effort. However, the diversity of the definitions of, coupled with the lack of agreement over what constitutes, cloud computing has created confusion as to what it really means as an emerging computing model, and consequently its definitions have been criticized for being too broad and unclear (Bibri 2018a). Users of cloud computing, including individuals, organizations, and government agencies employ it to, as a variety of enabled services, store and share information; manage, sift, and analyze databases; and deploy web services, including processing huge datasets for complicated problems of scientific kinds (Bibri 2018a). Cloud computing can also be used to process urban big data and context data in relation to smart sustainable city applications. Big data analytics can be performed in the cloud. This involves both big data platform as a service (PaaS) and infrastructure as a service (IaaS). Having attracted attention and gained popularity worldwide, cloud computing is becoming increasingly a key part of the ICT infrastructure of both smart cities and sustainable cities (Bibri 2018a) as an extension of distributed and grid computing due to the prevalence of sensor technologies, storage facilities, pervasive computing infrastructures, and wireless communication networks. Especially, most of these technologies have become technically

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mature and financially affordable by cloud providers. By commoditizing services, low-cost open-source software, and geographical distribution, cloud computing is becoming increasingly an attractive option. Big data analytics is associated with cloud computing, an Internet-based computing model that is increasingly seen as the most suitable solution for highly resource-intensive and collaborative applications as an on-demand network access to a shared pool of computing resources (memory capacity, energy, computational power, network bandwidth, interactivity, etc.). This entails that computer-processing resources, which reside in the cloud, are virtualized and dynamic, implying that only display devices for information and services need to be physically present in relation to urban domains where diverse stakeholders (e.g., planners, administrators, sustainability strategists, urbanists, data analysts, urban scientists, authorities, organizations, institutions, enterprises, communities, citizens, etc.) can make use of software applications and services to improve the different aspects of urban sustainability. Such stakeholders can access cloud-based software applications through a web browser and a lean client (a computer program that depends on its server to fulfill its computational roles) or mobile devices while software tools and urban data of all kinds are stored on servers at a remote location. Indeed, cloud computing model is based on hosted services in the sense of application service provisioning running client server software locally. In this respect, the smart sustainable city applications for transport, traffic, mobility, energy, health care, civil security, governance, education, and so on reside ‘in the cloud’ and can be accessible per demand. Moreover, the software development platform can be offered in a public, private, or hybrid network, where the cloud provider manages the platform that runs the applications and relieves the cloud clients from the burden of securing dedicated platforms, which would otherwise be very demanding and costly in terms of resources and time. The cloud clients can therefore benefit from tested, scalable, reliable, and maintainable platforms offered by the cloud provider. Another advantage involves service process optimization through advanced functionalities of software development platforms, namely flexibility, interoperability, reusability, scalability, and cooperation. Moreover, there is a great opportunity to slash or minimize energy consumption associated with the operation of ICT infrastructure, especially when it comes to large-scale deployments like in the case of data-driven smart sustainable cities as to different city departments and agencies. Beloglazov et al. (2012) develop policies and algorithms that aim at increasing energy efficiency in cloud computing. Energy consumption is way too lower than if all urban entities have their own software development platforms. These are indeed shared by multiple users as well as dynamically reallocated per demand. This approach maximizes the use of computational power and reduces energy usage and thus mitigates GHG emissions associated otherwise with powering a variety of functions and data centers dispersed throughout the departments and agencies of data-driven smart sustainable cities. Whether public or private, the cloud provider includes the cloud environment’s servers, storage, networking, and data center operations. This implies that the cloud provider has the actual energy-consuming computational resources; users or clients can simply log on to the network without installing anything, thereby curbing energy usage and making the best of the available computational power. Energy efficiency in cloud computing can result from energy-aware scheduling and server consolidation. In fact, cloud computing is seen as a form of green computing, especially if it is based on renewable energy like solar panels. In addition, cloud computing has other intuitive benefits because it relies on sharing of resources and maximizing the effectiveness of the shared resources, thereby reducing the costs otherwise incurred by ICT operations as to human, technical, and organizational resources. In cloud computing, supercomputers in large data centers as a distributed system of many servers are used to deliver services in a scalable manner as well as to enable the storage and processing of vast quantities of data. Cloud computing offers tremendous opportunities for streamlining data processing. All in all, cloud computing constitutes an efficient and elegant solution in terms of facilitating the huge demand for computing resources associated with big data analytics for decision-making processes in relation to the operational functioning, planning, development, and governance of cities in relation to sustainability. Through the use of cloud computing, data-driven smart sustainable cities can therefore have higher possibilities to perform more effectively and efficiently thanks to the advanced technological features underlying the functioning of cloud computing model. Furthermore, cloud computing performs service-oriented computing. In this regard, it can rapidly process large and complex data produced from urban activities and simultaneously serve citizens in relation to health care, education, housing, utility, and so on, providing a kind of integrated and specialized center for information services to both the general public and urban departments across various urban domains. In light of this, with reference to data-driven smart sustainable cities, cloud computing has the ability to run smart applications on many connected computers and smartphones at the same time for different purposes associated with increasing sustainability performance. In sum, among the key advantages provided by cloud computing technology include cost reduction, location and device independence, virtualization (sharing of servers and storage devices), multitenancy (sharing of costs across a large pool of cloud provider’s clients), scalability, performance, reliability, and maintenance (Bibri 2018a). Therefore, opting for cloud computing to perform big data analytics in the realm of smart sustainable cities (see Bibri 2018a, b for an illustrative

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example of the application of cloud computing) remains thus far the most suitable option for the operation of infrastructures, applications, and services whose functioning is contingent upon how urban domains interrelate and collaborate, how efficient they are, and to what extent they are scalable as to achieving and maintaining the required level of sustainability (Bibri 2018a).

4.4.2 Elements of Big Data In line with the definition of cloud computing, there are three main elements of big data cloud (Fig. 1). Konugurthi et al. (2016) describe them below: 1. Big Data Infrastructure Services (BDIS): This layer offers core services, such as compute, storage, and data services for big data computing, as described below: • Basic storage service: Provides basic services for data delivery, which is organized either on physical or virtual infrastructure, and supports various operations, such as create, delete, modify, and update, with a unified data model supporting various types of data. • Data organization and access service: Provides management and location of data resources for all kinds of data, as well as selection, query transformation, aggregation and representation of query results, and semantic querying for selecting the data of interest. • Processing service: Mechanisms to access the data of interest, transferring to the compute node, efficient scheduling mechanism to process the data, programming methodologies, and various tools and techniques to handle the variety of data formats. The elements of BDIS are described below: • Computing Clouds: On-demand provisioning of compute resources, which can expand or shrink based on the analytics requirements. • Storage Clouds: Large volume of storages offered over the network, including file system, block storages, and object-based storage. Storage Clouds offer to create file system of choice and also elastically scalable. They can be accessed based on the pricing models which are usually based on data volumes or data transfer. The several services provided in this regard are raw, block, and object-based storages. • Data Clouds: Are similar to Storage Clouds but unlike storage space delivery. They offer data as a service. Data Clouds offer tools and techniques to publish the data, tag the data, discover the data, and process the data of interest. Data Clouds

Fig. 1 Big data cloud components. Source Konugurthi et al. (2016)

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operate on domain-specific data leveraging the Storage Clouds to serve data as a service based on the four step of the Standard Scientific Model, such as data collection, analysis, analyzed reports, and long-term preservation of the data. 2. Big Data Platform Services (BDPS): This layer offers schedulers, query mechanisms for data retrieval, and data-intensive programming models to address several big-data-analytic problems. 3. Big Data Analytics Services (BDAS): Big data analytics as services over big data cloud infrastructure.

4.4.3 Fog and Edge Computing Fog Computing Versus Cloud Computing Fog computing (Numhauser and Jonathan 2012), also known as fogging or edge computing, can be viewed as an alternative computing model to cloud computing in relation to the IoT and its underlying big data analytics. It is an architecture that uses one or more collaborative near-user edge devices to carry out a substantial amount of storage (rather than stored primarily in cloud data centers), communication (rather than routed over the Internet backbone), control, configuration, measurement, and management (rather than controlled primarily by network gateways). Although both fog computing and cloud computing provide storage, applications, and data to end users, fog computing has a bigger proximity to end users and bigger geographical distribution (Bonomi et al. 2012). On the data plane which constitutes one of the components of fog networking, fog computing enables computing services to reside at the edge of the network as opposed to servers in a data center like in cloud computing. Accordingly, fog computing emphasizes proximity to end users and client objectives, dense geographical distribution, and local resource pooling, latency reduction and backbone bandwidth savings to achieve better quality of service (QoS) (Brogi and Forti 2017), as well as edge analytics/stream mining resulting in redundancy in case of failure Arkian et al. (2017). Moreover, it is said that fog computing is a medium weight and intermediate level of computing power, whereas cloud computing can be a heavyweight and dense form of computing power, as it uses a network of remote servers hosted on the Internet to store, manage, and process data, rather than a local server. Mist computing is a lightweight form of computing power that resides directly within the network fabric at its extreme edge using microcomputers and microcontrollers to feed into fog computing nodes and potentially onward toward the cloud computing platforms. Furthermore, fog computing extends cloud computing to the edge of an organization or city’s network. In this context, it facilitates the operation of computation, storage, communication, and networking services between end devices and cloud computing data centers, and entails the distribution of the related resources and services on or close to devices and systems in the control of end users (Zhang 2016; Ostberg et al. 2017). In the context of data-driven smart sustainable cities, fog computing can be seen in big data structures as well as in large cloud systems, making reference to the growing difficulties in accessing information objectively. This results in a lack of quality of the obtained content. The effects of fog computing on cloud computing and big data applications may vary; however, a common aspect that can be extracted is a limitation in accurate content distribution, an issue that has been tackled with the creation of metrics that attempt to improve accuracy (Numhauser and Jonathan 2012). Cloud computing performs complex and full data validation, storing, processing, and processing (big data analytics), whereas fog computing carries out more complex data processes, namely validation, storage, and forwarding. In fog computing, transporting data from things to the cloud requires several components (steps), namely: • the automation controller for automating the physical assets or things; • the server or protocol gateway for receiving the data from the control system program and then converting the data into a protocol Internet systems; • the fog node or the IoT gateway on the LAN to which the data are to be sent for performing higher-level processing and analysis. This system filters, analyzes, processes, and may even store the data for transmission to the cloud or WAN at a later date. The disadvantage of fog computing is the extent of complexity it brings to the computation, storage, and networking as part of the overall architecture. This has implications for the time taken to perform analysis as well as the cost of ownership since physical things have to be secured and maintained due to the fact that fog computing pulls processing capabilities to a fog as a form of decentralized location.

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Fog and Edge Computing for the IoT Fog and edge computing models have been developed to respond to the sheer, monumental increase of data bandwidth required by the end devices that underpin the IoT. The concept has indeed been fueled by the explosion of the IoT, a computing paradigm that has made it necessary to process the generated data much closer to the source in real time, pushing the cloud closer to the requester as a way to minimize latency as well as to increase quality. Fog networking supports the IoT concept, in which most of devices and everyday objects are to be connected to each other, such as smartphones, wearable devices, connected vehicle and augmented reality using devices (e.g., Bonomi et al. 2012). The fundamental objective of the IoT is to obtain and analyze data from physical assets or things that were previously disconnected from most data processing tools. This relates to sensor-based big data applications pertaining to smart sustainable cities with respect to environmental sustainability. The urban big data are generated by physical assets or things deployed at the very edge of the network that perform specific tasks to support environmental sustainability. The IoT is about connecting the unconnected devices (things) and sending their data to the cloud or Internet to be analyzed. In traditional IoT cloud architecture, all these data from physical assets or things are transported to the cloud for storage and advanced analysis associated with big data applications. The IoT devices generate a constant stream of data that has to be validated, analyzed, and processed in real time. Data validation needs to, with the explosion of the IoT devices, take place closer to the requester. In this regard, fog and edge computing can crunch through data at a fast pace compared to cloud computing (data center). It, moreover, allows disconnected validation of data, a feature that lowers bandwidth costs, as it helps to reduce the total amount of end-to-end bandwidth needed. Fog and Edge Computing: Commonalities and Differences Fog and edge computing in smart sustainable city applications are network and system architectures that attempt to collect, analyze, and process data from physical assets closer to the requester and more efficiently than traditional cloud architecture. In light of this, these two computing models are closely related and aimed at reducing latency cost and increase quality, to reiterate. Both are able to filter data prior to reaching a big data lake for further consumption, thereby decreasing the amount of data that need to be processed. Data reduction is an important process of big data analytics techniques. Though there is a key difference between the two concepts, fog and edge computing architectures share similar objectives, namely reducing the amount of data sent to the cloud, decreasing network and Internet latency, and improving system response time in remote applications. Also, data in both are generated from the same source—physical assets like sensing devices which perform a task in the physical world, i.e., sensing the world around them as related to environmental phenomena, dynamics, changes, parameters, patterns, and so on in the context of smart sustainable cities. There is such a wide variety and large number of physical things augmented with sensing, actuation, and communication capabilities that make up the IoT system as part of the overall ICT infrastructure of data-driven smart sustainable cities. In addition, both involve pushing processing and intelligence capabilities down closer to where the IoT data originate—at the network edge—and the physical things are connected together. The key difference between the two architectures lies in exactly where such capabilities are placed, i.e., the location of the devices. In fog computing, intelligence and computing power are pushed down to the local area network (LAN) level of network architecture, processing data in a fog node or the IoT gateway. Whereas in edge computing, data validation and processing intelligence together with communication capabilities of an edge gateway are pushed directly into central edge devices like routers. This is crucial to strengthening security measures by implementing encryption in the local network before the data traverse through insecure or unprotected parts of the Internet. Furthermore, the need for validating and preprocessing data either within a fog (a LAN) or an edge (a gateway device) emanates from the fact that it is not sensible to install a full data center on a plane. On the whole, the basic idea of fog and edge computing is to move data logic (data validation) to an outer ring of processing capabilities. In fog computing, transporting data from things to the cloud requires several components (steps), namely: • the automation controller for automating the physical assets or things; • the server or protocol gateway for receiving the data from the control system program and then converting the data into a protocol Internet systems; • the fog node or the IoT gateway on the LAN to which the data are to be sent for performing higher-level processing and analysis. This system filters, analyzes, processes, and may even store the data for transmission to the cloud or WAN at a later date.

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Therefore, fog computing involves in the sphere of the IoT many layers of complexity and data conversion (e.g., Bonomi et al. 2012). To move data from the physical world of assets across the domains of smart sustainable cities into the digital world of ICT requires many links in a communication chain, which fog computing architecture relies on and in which each link is a potential point of failure. This is in contrast to edge computing which simplifies the communication chain and decreases the number of potential points of failure in the IoT-enabled big data applications. Indeed, edge computing is a direct response to the mammoth increase of bandwidth required by the end devices that underpin the IoT, to reiterate. In addition, edge computing saves time by reducing the complexity associated with system and network architecture as well as streamlining IoT communication. This feature is of crucial importance to the success of the IoT-enabled big data applications pertaining to smart sustainable cities. In edge computing, the focus is on the automation controller into which physical assets like sensors are physically wired, and which automate things by executing an onboard control system program. Intelligent programable automation controllers with edge computing capabilities collect, process, and analyze data from the physical assets they are linked to. Subsequently, they use edge computing capabilities to determine what data should be stored locally or sent to the cloud for further analysis.

4.5 Urban Operating Centers and Strategic Planning and Policy Offices The consequence of the evolving and soaring data deluge is that data-driven urbanism is changing how we know, operate, regulate, manage, plan, and govern city systems, both within particular domains and across them (e.g., Batty et al. 2012; Bibri 2018a, b, 2019; Bibri and Krogstie 2019; Kitchin 2016a; Kitchin et al. 2015; Marvin et al. 2016; Townsend 2013). Indeed, one of the implications of such urbanism is that urban systems are becoming much more tightly interlinked and integrated and urban domains highly coordinated, especially in the context of sustainability (Bibri 2018a; Bibri and Krogstie 2017b). New data streams from such domains are changing how to use data science to extract and analyze these data to make a real impact. There has recently been a marked tendency supported by practical endeavors to draw all the kinds of analytics associated with the city in terms of its urban domains into a single hub, supported by broader public and open data analytics. This entails creating a citywide instrumented or centralized system that draws together data streams from many agencies (across city domains) for large-scale analytics (see Bibri and Krogstie 2018). For example, urban operating systems explicitly link together multiple urban technologies to enable greater coordination of urban systems and domains (Kitchin 2016a), especially for the purpose of advancing sustainability (Bibri 2018a, b). Similarly, urban operating centers attempt to draw together and interlink urban big data to provide integrated and holistic views and synoptic city intelligence (Kitchin 2016a; Kitchin et al. 2015) through processing, analyzing, visualizing, and monitoring the vast deluge of urban data that is used for real-time decision-making using advanced data analytics techniques. A notable example is the Centro De Operacoes Prefeitura Do Rio, an urban operations center staffed by 400 professional works for monitoring the operational functioning of the city (Kitchin 2016a). Here, the aim is to knock down silos between different urban departments and to combine each one’s data to help the whole enterprise (Singer 2012) as a complex endeavor. Indeed, this urban operations center draws together real-time data streams from 30 agencies, including public transport and traffic, mobility, power grid, municipal and utility services, emergency services, weather feeds, information sent in by the public via smartphones, and social media networks into a single data analytics center (Kitchin 2014, 2016a). Urban operations centers provide a powerful means for making sense of, managing, and living in the city in the here-and-now, as well as for planning the city in terms of envisioning and predicting future scenarios, which is of value for those developing and using integrated, real-time city data analytics (Kitchin 2014). Examples of city operating systems or control rooms include Microsoft’s CityNext, Urbiotica’s City Operating System, IBM’s Smarter City, and PlanIT’s Urban Operating System, with the latter representing enterprise resource planning (ERP) systems as intended to operate and coordinate the activities of large companies repurposed for cities (Kitchin 2015a). There has been a transformation in the attributes of the data being collected, stored, and organized in datasets, This transformation has been enabled by new networked, digital technologies embedded into the fabric of urban environments that underpin the drive to create smart sustainable cities. In this context, many different initiatives in collecting data from new varieties of digital access are being fashioned, such as the satellite-enabled global positioning system (GPS) in vehicles and on citizens, from social media sites, from transactions, and from access to numerous kinds of web sites. Satellite remote-sensing is increasingly widely deployed, in addition to a variety of scanning technologies associated with the IoT (Bibri 2018b). Other technologies include digital cameras, sensors, transponders, meters, actuators, and transduction loops that monitor various phenomena and continually send data to an array of control and management systems, such as urban

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operations centers, centralized control rooms, intelligent transport systems, logistics management systems, energy grids, and building management systems that can process and respond in real time to the data flow (Graham and Marvin 2001; Kitchin 2014, 2016a). For example, data on traffic flow generated by sensors, cameras, transponders, and transduction loops in public transport systems can be produced in a real-time manner, fed back to a control room where analysts can monitor traffic levels using advanced software applications and alter traffic light sequencing and road speeds to try and maintain traffic flow (Bibri 2018b; Kitchin 2016a). This relates to smart traffic lights and signals (see Bibri 2018a, b for a descriptive account). The big data application for traffic also involves the possibility of determining travel patterns across times of the day and days of the week concerning all nodes on the network, such as bus stops, sensor locations, and junctions, as well as creating and improving models and simulations to guide future urban development (e.g., to simulate what might happen to travel patterns by closing a road on the network). For a detailed account of diverse big data applications for environmental sustainability in the context of smart sustainable cities, including, in addition to traffic, mobility, energy, power grid, environment, buildings, infrastructure, and large-scale deployment, the reader can be directed to Bibri (2018a, b). In addition, the Policy and Strategic Planning Office in New York City has sought to create a data-analytic hub to weave together data from a diverse set of city agencies in order to try to manage, regulate, plan, and govern the city more efficiently and effectively (Kitchin 2014). Huge amounts of data amounting to petabytes stream through the office on a daily basis for analysis in terms of cross-referencing data, spotting patterns and identifying and solving city problems (Feuer 2013; Kitchin 2014). A more ambitious endeavor in this direction would be to realize a joined-up planning, which entails an integration that enables systemwide effects to be understood, analyzed, tracked, and built into the very designs and responses that characterize the operations, functions, and services of the city. This involves connection, networks, and data integration in regard to urban agencies or domains. A team of data analysts and other data operatives, aided by various data analytics software, monitor, manage, process, analyze, and visualize the vast deluge of urban data, alongside data aggregated over time and huge volumes of other kinds of data in terms of velocity, i.e., released on a more periodic basis, often mashing the datasets together to investigate particular aspects of city life and changes over time, and to build predictive models with respect to city management, planning, design, and development in the context of sustainability. The outcome is to be used for real-time decision-making and problem solving pertaining to urban operations and functions, as well as to other urban practices. In this respect, the data-driven city enables to make decisions by assessing what is happening at any one time and by responding and planning appropriately with respect to sustainability. Such assessment entails interlinking diverse forms of data, which provides a deeper, more holistic, and robust analysis. This, therefore, allows for developing, running, regulating, and planning the city on the basis of strong, rationale evidence. The implication and prospect of the above endeavors is a new form of highly responsive urbanism in which big data technologies and their systems are prefiguring and setting the urban agenda for sustainable development and influencing and controlling how city systems respond to and perform as to the goals of sustainable development.

4.6 Living Laboratories Smart sustainable cities revolve around the idea of a living laboratory for new technologies that can handle all the major systems a city requires and the key domains it involves. There are several descriptions and definitions of a living laboratory, according to different sources (e.g., Kusiak 2007; Niitamo et al. 2006; Schumacher and Feurstein 2007). In the context of this chapter, a living laboratory as a research concept (e.g., Almirall and Wareham 2011; Chesbrough 2003; Von Hippel 1986) refers to a user-centered, open-innovation ecosystem operating in the city and targeted at improving sustainability through data-driven smart solutions and approaches, integrating innovation processes and concurrent research within a partnership involving public and private organizations and institutions, as well as citizens and communities. As such, it brings together interdisciplinary and transdisciplinary scholars, researchers, experts, and practitioners to develop, deploy, implement, and test in actual urban environments new technologies and strategies for design that respond to the long-term goals of sustainability. The endeavor here spans in city scale from the physical to the social and ecological, and addresses challenges related to the built environment in the context of sustainable urban forms. Especially, the effects of such forms are compatible with the goals of sustainable development in terms of transport provision, mobility and accessibility, travel behavior, energy conservation and efficiency, pollution and waste reduction, public health and safety, economic viability, and life quality (Bibri 2018a). In addition, in terms of the living laboratory process, the act of cocreating, exploring, experimenting, testing, and discovering—all of breakthrough scenarios, visions, ideas, concepts, and related technological

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artefacts in real-life setting in terms of urban design and services can generate scientific and practical innovations of high potential for advancing sustainability. This approach allows all the involved city stakeholders to concurrently consider both the global performance of data-driven smart sustainability solutions and their potential adoption by cities on different spatial scales. Considering everything, the concept of the living laboratory this chapter is concerned with relates, to planners, scholars, researchers, scientists, experts, policymakers, and citizens for codesigning, exploring, experiencing, and refining new urban functions, services, strategies, policies, and regulations in real-life scenarios for evaluating their potential impacts on sustainability before their implementations. Today, new technologies are giving citizens more opportunities to participate in the functioning, design, and governance of the city, which is being increasingly leveraged in the transition toward the needed sustainable development. Changes driven by digital technologies can happen without heavy infrastructure, as they can arise from bottom-up actions instead of being necessarily determined by city governments. These should therefore develop knowledge sharing platforms that get citizens engaged as much as possible and excited about smart sustainable urban transformations through open innovation and participatory research. Indeed, citizens can really be the ones to bring such transformations, if the right platforms can be created, and the installation and control of hardware can be done for no more than what citizens wish their city to become and how they aspire to see it evolving in the future. An example of a living laboratory is the multipurpose experimental facility built by Zero Emission Buildings (ZEB), Faculty of Architecture and Fine Arts, the Norwegian University of Science and Technology (NTNU). As a test facility occupied by real persons using the building as their home, it focuses on the occupants and their use of innovative building technologies. This living laboratory is used to study various technologies and design strategies in a real-world living environment: • User-centered development of new and innovative solutions: The test facility is used within a comprehensive design process focusing on user needs and experiences. • Performance testing of new and existing solutions: Exploring building performance in a context of realistic usage scenarios. • Detailed monitoring of the physical behavior of the building and its installations as well as the users influence on them. ZEB researchers within the fields of architecture, social science, materials science, building technologies, energy technologies, and indoor climate jointly study the interaction between the physical environment and the users. This living laboratory and other similar initiatives related to different areas of sustainability are at the core smart sustainable cities in terms of their specific structural components. Examples of such initiatives relate to the design concepts and typologies characterizing the compact city and the eco-city as combined landscapes and approaches, notably compactness, density, mixed-land use, diversity, sustainable transport, passive solar design, and ecological design. Specifically, the multipurpose experimental facilities the proposed model is concerned with will focus on the significant themes evident in the current debates on various strategies and their effects and benefits in the context of sustainable urban forms. See Bibri (2018a) for a detailed list of these themes and strategies.

4.7 Innovations Laboratories Exploring the notion of smart sustainable cities as an innovation lab is about evolving urban intelligence functions associated with optimizing and enhancing operations, functions, services, designs, strategies, and policies across various urban domains in line with the goals of sustainable development. This can take the form of laboratories. Especially, building models of cities functioning in real time from routinely sensed data is becoming increasingly achievable and deployable (e.g., Batty et al. 2012; Bibri 2018a, 2019; Kitchin 2014). Although innovation labs are springing up everywhere, becoming now commonplace across industries, most of such initiatives still relate to the business domain. In the context of this chapter, an innovation lab denotes a working space designed to optimize and enhance sustainability innovation in the form of urban intelligence functions. It is a unique environment devoted to or exclusively intended for sharing and building new and expert knowledge, creating new ideas and alignment, and developing comprehensive solutions for sustainability in response to the needs, aspirations, and goals of the city and its stakeholders and citizens. An innovation lab also serves as an environment where a team of researchers, scientists, practitioners, and professionals can gather and design thinking for innovation can directly happen in relation to sustainability solutions, meaning it is designed to host innovation workshops. The key strengths lie in the team’s multidisciplinary knowledge and skills, long-standing experience,

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international know-how, and access to global networks in the sphere of urban sustainability and related technologies. Further, among the questions that an innovation lab for urban sustainability involve transport and traffic, mobility, energy, power grid, environment, buildings, infrastructures, design and planning, scientific research, governance, health care, public safety, and big data technology. This implies that such laboratory should host many interdisciplinary and transdisciplinary teams concerned with different city domains or sub-domains and the associated solutions. Applicable solutions for various areas of sustainability should be developed considering the interests of city stakeholders as well as citizens. The positioning of such laboratory should make it possible to offer a platform where the many, scientifically excellent research initiatives of the city in these areas can cooperate even more strongly with each other. The idea is to make a scientific contribution to the social discourse of the data-driven smart sustainable city within the framework of the innovation lab for urban sustainability. One way to support innovation within smart sustainable cities involves a set of strategic and goal-focused units, focused on specific areas that link big data technology to sustainability, tasked with creating anything from a new solution to a new method, model, or technology. Another innovation initiative, which may not be physically colocated, can involve setting up a group to collaborate with industry and academia. Setting up an innovation lab involves significant challenges, which pertain to the many questions that the smart sustainable city stakeholders need to ask themselves in the course of creating an innovation lab for sustainability. These questions involve what roles should be filled, what types and combinations of people make the best innovators, what governance model or framework should be applied, which projects should be prioritized, how to establish synergies with the rest of city projects, what kind of infrastructure should be in place, how can ideas and models be tested, and so on. ICT is being developed to increase the efficiency of energy systems and the delivery of public and social services, to improve transportation and mobility, and to enhance the quality of life, among others. This reflects the notion of the smart sustainable city as a laboratory for innovation or research center. For example, the Zero Emission Neighbourhoods (ZEN) Research Centre at NTNU, which was established in 2017 by the Research Council of Norway, is a research center for environmentally friendly energy. More specifically, it conducts research on zero emission neighborhoods in smart cities. Its goal is to develop solutions for future buildings and neighborhoods with no greenhouse gas (GHG) emissions and thereby contribute to a low-carbon society. Its main objective is to develop products and processes that will lead to the realization of sustainable neighborhoods as to their production, operation, and transformation. In line with the goals of smart sustainable cities, the ZEN research is driven by the vision and convinced that future communities and cities should ensure optimal energy use and be good places for people to live and work in. The main question the ZEN research center is concerned with —which indeed is at the core of how sustainable urban forms should be monitored, understood, and analyzed to improve, advance, and maintain their contribution to the goals of sustainable development—is how the sustainable neighborhoods of the future should be designed, built, transformed, and managed to reduce their GHG emissions toward zero. As with most of innovation centers, the idea of the ZEN research center is to bring together like-minded people to share ideas and create the future. The partners of this center cover the entire value chain and include representatives from municipal and regional governments, property owners, developers, consultants and architects, ICT companies, contractors, energy companies, manufacturers of materials and products, and governmental organizations. The Norwegian University of Science and Technology (NTNU) is the Center’s host and leads it together with SINTEF Building and Infrastructure and SINTEF Energy. In order for the ZEN research center to achieve its high ambitions, the process of strategizing and planning is done together with these partners to: • develop neighborhood design and planning instruments while integrating science-based knowledge on GHG emissions; • create new business models, roles, and services that address the lack of flexibility toward markets and catalyze the development of innovations for a broader public use; • create cost-effective and resource and energy efficient buildings by developing low-carbon technologies and construction systems based on lifecycle design strategies; • develop technologies and solutions for the design and operation of energy flexible neighborhoods; • develop a decision-support tool for optimizing local energy systems and their interaction with the larger system; • create and manage a series of neighborhood-scale living laboratories, which will act as innovation hubs and a testing ground for the solutions developed in the ZEN Research Center. Similar to ZEB, this research center and other similar initiatives related to different areas of sustainability are at the core of smart sustainable cities in terms of their specific components. Examples of such initiatives relate to the design concepts and typologies characterizing the compact city and the eco-city as combined landscapes and approaches, notably compactness,

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density, mixed-land use, diversity, sustainable transport, passive solar design, and ecological design. Specifically, the research centers that the proposed model is concerned with will focus on the significant themes evident in the current debates on various strategies and their effects and benefits in the context of sustainable urban forms. For example, cleaner modes of transportation such as bike-sharing systems is a potential area for research and innovation based on mapping how and when people travel so as to know where to invest in such modes, and hence mobilize and align stakeholders.

4.8 Urban Intelligence Functions In the context of this chapter, the concept of urban intelligence refers to the planning, development, integration, and deployment of big data computing and the underpinning technologies as an ecosystem (both physical and virtual assets) to support the interoperability between resources and technologies and hence the integration of urban systems and the coordination of urban domains to serve the city and its stakeholders and citizens with respect to sustainability dimensions. In short, urban intelligence entails the use of big data analytics and the underlying core enabling technologies to address and overcome the problems and challenges facing cities in the context of sustainability. As an advanced form of decision support, urban intelligence functions integrate, synthesize, and analyze data flows for the purpose of improving the sustainability, efficiency, resilience, equity, and quality of life in cities. This relates in this context to exploring the notion of smart sustainable cities as innovation labs. Accordingly, the kind of urban intelligence functions that such city should evolve in the form of laboratories that enable its monitoring, planning, design, and development include, but are not limited to, the following. • • • • • • • • • • •

The efficiency of energy systems; The improvement of transportation and communication systems; The improvement of water, power, and sewage systems; The enhancement of urban metabolism; The effectiveness of distribution systems; The robustness and resilience of urban infrastructures in terms of their ability to withstand adverse conditions and to quickly recover from difficulties; The efficiency and scalability of urban design in terms of forms, structures, and spatial organizations; The optimal use and accessibility of facilities; The efficiency of social and public services delivery; The optimization of ecosystem services provision; The dynamic, continuous, and short-term forms of planning.

Such functions represent new conceptions of how smart sustainable cities function and utilize and combine complexity science and urban science in fashioning new powerful forms of urban simulations models and optimization and prediction methods that can generate urban structures and forms as well as spatial organizations and scale stabilizations that improve sustainability, efficiency, resilience, and the quality of life (Bibri 2018a). Especially, building models of such cities operating and functioning in real time from routinely and automatically sensed data has become a clear prospect (see, e.g., Batty et al. 2012; Bibri 2018a; Kitchin 2014). Furthermore, the sort of intelligence functions envisaged for smart sustainable cities should be woven into the fabric of its institutions whose mandate is promoting and supporting sustainability and producing a better quality of life for its citizenry. Urban intelligence functions are best to take the form of laboratories for scientific and social research and innovation directed primarily for improving, advancing, and maintaining the contribution of smart sustainable cities to sustainability, to reiterate. The kind of intelligence functions envisioned in this regard will be woven into the institutional direction with respect to promoting sustainability and enhancing the quality of life for citizens. However, the decision-support systems associated with new urban intelligence functions and related simulation models and optimization and prediction methods are still in their infancy (Bibri 2018a), and also much needs to be done to provide the raw material for the development and implementation of such functions across multiple urban domains. Urban intelligence labs are intended to work directly with various urban entities (e.g., government agencies, public authorities, organizations, institutions, companies, communities, citizens, etc.) to acquire, process, and analyze data and then derive knowledge and insight from data in the form of applied intelligence. Their core aim is to solve tangible and significant problems of city operational functioning, management, planning, design, development, and governance through data-driven

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decision-making. This involves delivering problem-oriented research that serves the dual purpose of advancing the scientific understanding of cities in terms of sustainability and urbanization and how they intertwine with and affect one another, as well as in terms of having a direct impact on decision-making and action taking in the sense of enhancing and advancing urbanism practices. In this context, urban intelligence labs take a multidisciplinary approach to urbanism, bringing the perspectives of design thinking and planning to the visions and practices of such urbanism. In addition, with the projected advancements and innovations in big data computing and the underpinning technologies, the process of building intelligence functions will shift from top-down (expert and professional organizations) to engaging citizens with experts due to the complexity underlying urban planning, design, development, and governance in the context of sustainability (Bibri 2018a). This entails integrating databases and models from across various urban domains for supporting the development of this sort of integrated intelligence functions, with new or refashioned ways at different levels, including visualization of data and urban sustainability problems, using tools for informing and predicting the impacts of future sustainability scenarios, and engaging citizens and their useful, relevant recommendations, all into a form of a holistic system that operates in accordance with sustainability requirements at various spatial and temporal scales (Bibri 2018a). Chapter 10 examines and discusses this evolving approach to urbanism in terms of computerized decision-support and making, intelligence functions, simulation models, and optimization and prediction methods. It also documents and highlights the potential of the integration of these advanced technologies for facilitating the synergy between the operational functioning, planning, design, and development of smart sustainable cities for the primary purpose of improving, advancing, and maintaining their contribution to the goals of sustainable development. Indeed, at the core of smart sustainable urbanism is the interaction or cooperation of these urban practices to produce a combined effect greater than the sum of their separate effects in the context of sustainability. In this respect, urban planning determines the way urban structures and forms should be designed, which shapes urban operational functioning that in turn drives urban development. This entails using advanced technologies, notably big data computing, as an enabler for such synergy as well as a determinant of its outcomes. This is owing to the underlying powerful engineering solutions as a set of novel applications and sophisticated approaches. Big data analytics and related simulation models and optimization and prediction methods might completely redefine urban problems, as well as offer entirely innovative opportunities to tackle them on the basis of new urban intelligence and planning functions, thereby doing more than merely enhancing existing urban practices.

4.9 Public, Private, and Open Data and Their Analysis To provide a very rich nexus of possibilities in terms of providing new and open sources of urban data necessary for better understanding the way smart sustainable cities function entails linking GPS, satellite remote-sensing and other forms of sensing, scanning technologies, and online interactive data systems focused on crowd sourcing, all with the automation of standard secondary sources of data (Bibri 2018a). In this respect, as elucidated by Bibri (2018a, p. 220), ‘in the urban domain, some data are open and thus accessible to the public for use while other data are confidential and hence pose privacy issues. Also, some data are available virtually for free while other data require effort to obtain or even need to be acquired. Still not all the data needed for building solutions to a given urban sustainability problem exist. Hence, some data are likely to necessitate entire ancillary projects (providing necessary support to the primary activities or operation of the involved urban stakeholders) as organizations, institutions, and enterprises to arrange their collection and storage.’ Urban systems, domains, and networks constitute the main source of data deluge, which is generated by various urban entities, including governmental agencies, authorities, administrators, institutions, organizations, enterprises, communities, and individual citizens by means of urban operations, functions, services, designs, strategies, and policies. Examples of urban data include observational data, transactional data, environmental data, socio-economic data, geospatial data, temporal data, administrative records, household-level surveys, collective mobility records, transportation and travel data, citizenry participation, official statistics, social media and participatory sensing, social network surveys, and so on (Bibri 2018a). Moreover, the outcome of the data collected, stored, and organized in digital databases and hence conjoined and shared is vast troves of varied, real-time, exhaustive, fine-grained, indexical, flexible, evolvable, relational, contextual, and, more importantly, actionable data, which are routinely generated about urban environments and citizens (Bibri 2018a; Kitchin 2014, 2015a, 2016a). This is being done by a range of public and private organizations, including the following (Kitchin 2016a): • government bodies and public administration (services, performance, surveys, etc.); • crowdsourcing and citizen science (maps, local knowledge, weather, etc.);

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social media sites (location/movement, opinions, personal information, etc.); smartphone operators (location/movement, app use and behavior, etc.); utility companies (use of electricity, gas and water); home appliances (behavior, consumption, etc.); financial institutions and retail chains (consumption and location); transport providers (location/movement, travel flow, mobility mode, traffic flow, etc.); travel and accommodation web sites (location/movement, reviews, consumption, etc.); surveillance and security firms (location and behavior); and emergency services (security, crime, response, policing, etc.).

The above indicates that much of these data constitute a private asset which is closed in nature, but can still be freely shared with city governments and authorities. In some cases, these data are open in nature through data infrastructures for the purpose to be meshed with the data generated by local authorities and governmental agencies for analytical purposes and endeavors. Indeed, universities, research centers, innovation labs, urban operations centers, and governments are increasingly working together to share information and thereby becoming partners in the process of urban planning, design, and development, especially in relation to sustainability. Similarly, urban big data from heterogenous and distributed sources produce a highly granular, longitudinal, holistic understanding of urban systems and enable them to be managed in real time. Data about how urban systems are performing can be streamed back from across the data infrastructure (related to the shared data considered as private asset), analyzed together with the data generated by local authorities and state agencies, and appropriate responses returned to control and management systems. Open data have become a key tool in redefining this process. That is why many governments are using such data to understand how sectors are affecting strategies to mitigate or overcome the challenges of sustainability. Open data usage can also promote transparency and build trust in government decision-making and official policies. In addition, one of the most significant innovations being embraced by the world’s urban operations centers, research centers, and innovation labs is the movement of open data, a form of information sharing aimed at improving any aspect of urban life or urbanity. In an open-data environment, datasets from a number of urban sectors and countless other municipal sectors and state authorities are made available to optimize and enhance urban operations, functions, services, designs, strategies, and policies. For instance, when combined with data from government sources, such as information on air quality, traffic patterns, or health statistics, user-generated information can lead to building cities that are more in tune with the needs and aspirations of citizens using advanced technologies. Within cities, citizens, activities, movements, processes, physical structures, urban infrastructure, distribution systems and networks, natural ecosystems, spatial organizations, scale stabilizations, socio-economic networks, facilities, services, spaces, and citizen objects all contribute to the generation of the colossal amounts of data from heterogeneous and distributed sources. Basically, virtually every aspect of urbanity has become open to, and instrumented for, data collection, processing, and analysis. As a result, vast troves of information have become widely available on numerous aspects of urbanity, including social trends, global shifts, environmental dynamics, socio-economic needs, spatial and scalar patterns, land use patterns, travel and mobility patterns, traffic patterns, energy consumption patterns, life-quality levels, and citizens’ lifestyles and participation levels (Bibri 2018a, 2019; Bibri and Krogstie 2017c). The data from these sources and on these aspects cascade into urban data deluge, which calls for prudent big data applications that can churn out useful knowledge and valuable insights from this huge deluge. The sustainability of smart cities and the smartness of sustainable cities are being digitally fueled and driven by the enormous data collected for analysis and deployment for enhanced decision-making purposes and innovative solutions development. In that respect, the unfolding and soaring deluge of urban data is increasingly stimulating wide-scale attempts to extract value from and make sense of such data, which is driven primarily by the desire to translate actionable data and data analytics into new modes of data-driven operational functioning, management, planning, and governance focused more and more on advancing smart sustainable urbanism. In more detail, the value of the useful knowledge extracted from the deluge of urban data lies in improving physical forms, infrastructures, resources, networks, facilities, and services by developing urban intelligence functions for automating and supporting decisions pertaining to control, automation, optimization, management, and prediction. Indeed, there are numerous uses of big data analytics within different urban domains for enhanced decision-making in the context of smart sustainable/sustainable smart cities, which is enabled by the useful knowledge and valuable insights extracted from large masses of urban data as a result of their analysis, including, but not limited to, the following:

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• • • • • • • • • • • • • • • • •

geospatial and temporal analysis; analysis of population growth and distribution; analysis of energy consumption and GHG emissions; analysis of socio-economic network’s performance; analysis of citizens and their behaviors and lifestyles; analysis of transport systems and networks; analysis of commuting and travel behavior; analysis of mobility modes and accessibility effectiveness; analysis of water supply and waste generation; analysis of urban metabolism; analysis of the impact and efficiency of land use; analysis of spatial organizations and scale stabilizations and their effects; analysis of urban forms with respect to design concepts and typologies; analysis of policies and their impact and effectiveness; analysis of communication systems and distribution networks performance; analysis of urban infrastructure and facilities performance and efficiency; analysis of the operational energy of buildings on different spatial and temporal scales.

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There are many examples of cities that show ways in which vast quantities of data can improve sustainability, efficiency, resilience, equity, and the quality of urban life. They suggest the range of opportunities that could open up when planners, scholars, urban scientists, and citizens use their imagination to leverage the Fourth Paradigm of Scientific Revolution’s capacity to produce information and discover new knowledge. To scale up the opportunities, these examples of cities demonstrate, it is crucial that an increasing number of people have access to data and participate in a collective discussion on their use, potential, and benefits in terms of sustainability. Big data should become, as much as possible, open data to have a profound impact on smart sustainable cities. However, a world of truly open data will take time to build. But governments have already recognized the importance of open data in solving key sustainability challenges. There are many ways of how improved access to data can streamline sustainable urban planning and design. However, it is critically important to develop and implement guidelines and principles to facilitate the integration of all the different cross-thematic data categories into coherent databases prior to any kind of analytics (e.g., data mining, statistical analysis, predictive modeling, regression analysis, etc.). The underlying assumption is that urban big data are generated from widely different and at times unstructured sources, each with particular format and related technical and methodological challenges (Bibri 2018a). In this regard, research within smart sustainable/sustainable smart urbanism should focus on addressing several questions and issues related to the public policy domain of urban big data, including the following (Bibri 2018a): • • • • •

How to collect, store, and coalesce various types of large data in city data warehouse? Which urban entities (or stakeholders) should be involved within different urban domains? What concerns are of relevance for the diffusion of big data technologies and platforms? The interoperability between various data standards (open, proprietary, etc.); How urban citizens should be involved in the decision-making process pertaining to the selection and deployment of urban big data innovations? • The ethical and legal dimensions in terms of data access and control and thus privacy and security.

4.10 Data-Driven Urbanism, Urban Science, and Data-Intensive Science Smart cities and sustainable cities are increasingly captured as big urban data, and data-driven urbanism is underpinning the materialization, functioning, success, expansion, and evolution of smart sustainable cities (e.g., Batty et al. 2012; Bibri 2018a, b, c, 2019; Bibri and Krogstie 2017b, 2019; Bettencourt 2014). Accordingly, a new era is presently unfolding, wherein smart sustainable urbanism is increasingly becoming data-driven. At the heart of such urbanism is a computational understanding of city systems that brings urban life to a set of logic, calculative, and algorithmic rules and procedures thanks to the datafication and thus quantification of the city. Such understanding entails drawing together, interlinking, and

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analyzing urban big data to provide a more holistic and integrated view and synoptic intelligence of the city, to reiterate, which is being increasingly directed for improving and maintaining the contribution of sustainable cities to the goals of sustainable development in an increasingly urbanized world (Bibri 2019). What underlies data-driven smart sustainable urbanism is underpinned by epistemological realism and instrumental rationality. Epistemological realism is a subcategory of objectivism which holds that what is known about an object exists independently of human mind. More specifically, it posits that there exist an external reality which operates independently of an observer and which can be objectively and accurately measured, tracked, analyzed, modeled, simulated, and visualized to reveal the world as it actually is (Kitchin 2016a). Instrumental rationality is a pursuit of any suitable means necessary to achieve a specific end. Specifically, it is practical reasoning serving for making decisions on how to efficiently perform technical tasks, solve problems, overcome challenges, and resolve conflicts by regarding the factors involved in a situation as variables to be controllable and knowable. As such, it underpins the conception that cities can be operated, regulated, managed, planned, and developed through a set of data levers and analytics, and that urban issues can be solved through a range of technical solutions thanks to the ability of probing the deluge of urban data in neutral, value-free, and objective ways to reveal the truth about cities. Further, epistemological realism and instrumental reality are informed by urban science, which seeks to make cities more sustainable, resilient, efficient, livable, and equitable by rendering them more knowable, controllable, and tractable in terms of their operational functioning, management, planning, design, and development. Urban science is an interdisciplinary field within which data science is practiced to inform and sustain the core of data-driven urbanism. Positioned at the intersection of science and design, it seeks to exploit the development of modern computation and the growing abundance of data. As a research field, urban science is concerned with the study of diverse urban issues and problems, and thereby aims to produce both theoretical and practical knowledge that contributes to understanding and solving them in contemporary society. In this respect, it entails making sense of cities as they are by identifying relationships and urban laws, as well as predicting and simulating likely future scenarios under different conditions, potentially providing valuable insights for planning and development decision-making and policy formulation (Kitchin 2015a). As such, it involves data-analytic thinking and computational modeling and simulation approaches to exploring, understanding, and explaining urban processes, and also addressing several challenges posed by urban data. The new urban science—which is underpinned by urban sustainability science, a transdisciplinary field that fuses theories from urban sustainability and sustainability science, seeks to make cities more sustainable, resilient, efficient, livable, and equitable by rendering them more measurable, knowable, and tractable in terms of their operational functioning, management, planning, design, development, and governance. The urban science approach is shaped by the above-mentioned two epistemological positions. The first is a form of inductive empiricism in which the data deluge, through analytics as manifested in the data being wrangled through an array of multitudinous algorithms to discover the most salient factors concerning complex phenomena, can speak for itself free of human framing and subjectivism, and without being guided by theory (as based on conceptual foundations, prior empirical findings, and scientific literature). As argued by Anderson (2008), ‘the data deluge makes the scientific method obsolete’ and that within big data studies ‘correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all.’ The second is data-driven science, which seeks to generate hypotheses out of the data rather than out of the theory, thereby seeking to hold to the tenets of the scientific method and knowledge-driven science (Kelling et al. 2009, p. 613). Here, the conventional deductive approach can still be employed to test the validity of potential hypotheses but on the basis of guided knowledge discovery techniques that can be used to mine the data to identify such hypotheses. It is argued that data-driven science will become the new dominant mode of scientific method in the upcoming Exabyte/Zettabyte Age because its epistemology is suited to exploring and extracting useful knowledge and valuable insights from enormous, relational datasets of high potential to generate more holistic and extensive models and theories of entire complex systems rather than parts of them, an aspect which traditional knowledge-driven science has failed to achieve (Kelling et al. 2009; Miller 2010). However, both epistemological positions are evident in urban science, with a preference on the latter. One of the main activities in data-intensive science is data analysis, and there are numerous technologies that can assist urban data analysts and scientists in the various aspects of data analysis, supporting analysis processes in holistic and integrated ways that promote system interoperability, integration, and automation, as well as scientific reproducibility and efficient data handling in relation to data-driven smart sustainable cities. This, however, presents significant challenges pertaining to software design, which calls for devising innovative solution to address them by finding efficient ways of integrating the existing and emerging technologies together to meet the analysis needs of the urban data deluge and urban scientists. Yao and Rabhi (2014) review different architectural design approaches that can be used to address these challenges and propose a service-oriented framework called the Ad Hoc Data Grid Environment, which consists of an

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architectural pattern and its associated operational guidelines. The guidelines prescribe a number of activities based on an iterative decomposition approach to produce and evolve software architectures according to constantly changing user needs. However, in contrast with urban knowledge derived from longer standing, more traditional urban studies, data science offers the potential for the kind of urban knowledge that is inherently longitudinal, and has greater breadth, depth, scale, and timeliness (Batty et al. 2012; Kitchin 2016a; Lazer et al. 2009) in the context of smart sustainable urbanism. This is being enabled and afforded by the unfolding and soaring deluge of urban big data. With respect to the data-driven urban knowledge, the emphasis has been on the development of new big data analytics that utilize sophisticated techniques and advanced mathematical models designed to process and analyze enormous datasets (e.g., Batty 2013; Bibri 2018a; Bibri and Krogstie 2017c; Kitchin 2014, 2016a; Miller 2010). This pertains particularly to the process of knowledge discovery, which involves carefully choosing variable selection mechanisms, encoding schemes, preprocessing, reductions, and projections of the data prior to discovering the intended patterns and building the relevant models, as well as their evaluation, interpretation, and visualization (Bibri 2018a). The pursuit of mastering the complexity of the process of knowledge discovery for smart sustainable/sustainable smart cities requires building an entirely new holistic system for big data analytics. The entire analytical process able to create the needed knowledge services or associated with extracting useful knowledge and valuable insights in the form of applied intelligence should be expressible within a system that supports the following: • • • • • • • • • • •

the acquisition of data from multiple distributed sources; the management of data streams; the integration of heterogeneous data into coherent databases; the definition of observables to extract relevant information from available datasets; data transformation and preparation; methods for distributed data mining and network analytics; the organization and composition of the extracted models and patterns as well as the evaluation of their quality; tools for visual analytics to study the behavioral patterns and models; the availability of visualizations to planners, strategists, and decision-makers; methods for the simulation and prediction of the mined patterns and models; mining strategies for overcoming the scalability issues associated with big data in distributed environments.

What will be exciting to witness in the near future is how data science will evolve and affect urban science and sustainability science; what new techniques will be invented that would not have come into existence if not for the amalgamation of the parental disciplines of data science, as well as the extent to which they will radically change urban sustainability science; and what new kinds of urban problems will urban sustainability science, using advanced big data computing and the underpinning technologies, be able to solve. On the whole, the deluge of urban data manifestly hides in itself the solutions to many challenges and problems presented by sustainability and urbanization, provides raw ingredients to build tomorrow’s human engineered systems, and plays a key role in understanding urban constituents as data agents. To put it differently, as concluded by Bibri and Krogstie (2018), there is tremendous potential for transforming the knowledge and practice of smart sustainable cities through the creation of a data deluge whose analysis can provide much more sophisticated, finer-grained, wider-scale, real-time understanding and control of various aspects of urbanity in the undoubtedly upcoming Exabyte/Zettabyte Age.

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Key Practical and Analytical Applications of Big Data Technology for Urban Systems and Domains

Smart sustainable cities are increasingly being permeated with big data technologies and their novel applications in terms of their systems and domains (Bibri 2018a, b, 2019; Bibri and Krogstie 2017b, 2019). The smart dimension of such cities can be seen as a new ethos added to the era of sustainable urbanism in response to the rise of ICT and the spread of urbanization as major global shifts at play today. The characteristic spirit of the era of smart sustainable urbanism is manifested in the behavior and aspiration of smart sustainable cities toward embracing what big data computing has to offer in order to bring about sustainable development and achieve sustainability. This is due to the tremendous potential of this advanced form of ICT for adding a whole dimension to sustainability in an increasingly technologized, computerized, and urbanized world. The range of the emerging big data applications as novel analytical and practical solutions that can be utilized in this regard is potentially huge, as many as the case situations where big data analytics may be of relevance to enhance some sort of

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decision or insight in connection with urban systems and domains. The most common big data applications are listed below in relation to the key systems and domains of smart sustainable cities in terms of operations, functions, services, designs, strategies, and policies (see Chap. 8 for a descriptive account of these applications). • • • • • • • • • • • • • •

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Transport and traffic Mobility Energy Power grid Environment Buildings Infrastructures Urban planning Urban design Academic and scientific research Governance Health care Education Public safety.

A Novel Architecture and Typology of Data-Driven Smart Sustainable Cities

6.1 Specialized Constituents for Making up a Whole There exist a range of city architectures that essentially aim to provide the appropriate infrastructure for big data systems and applications for steering urban processes and enhancing urban practices, and whose components serve to form, compose, or make up a whole. These architectures typically influence the relationship between their components and urban constituents and entities. The architecture of data-driven smart sustainable cities illustrated in Fig. 2 entails specialized urban, technological, organizational, and institutional elements dedicated for improving, advancing, and maintaining the contribution of such cities to the goals of sustainable development. Underlying the idea of such cities is the process of drawing all the kinds of analytics associated with urban life into a single hub, supported by broader public and open data analytics. This entails creating a citywide instrumented or centralized system that draws together data streams from many agencies (across city domains) for large-scale analytics and direct it for different centers and laboratories. Urban operating systems explicitly link together multiple urban technologies to enable greater coordination of urban systems and domains. Urban operating centers attempt to draw together and interlink urban big data to provide integrated and holistic views and synoptic city intelligence through processing, analyzing, visualizing, and monitoring the vast deluge of urban data that is used for real-time decision-making pertaining to sustainability using big data ecosystems. Strategic planning and policy centers serve as a data-analytic hub to weave together data from many diverse agencies to control, manage, regulate, and govern urban life more efficiently and effectively in relation to sustainability. This entails an integration that enables systemwide effects to be understood, analyzed, tracked, and built into the very designs and responses that characterize urban operations, functions, and services. As far as research centers and innovation labs are concerned, they are associated with research and innovation for the purpose of developing and disseminating urban intelligence functions.

6.2 Typological Dimensions and Functions As a leading paradigm of and holistic approach to urbanism, data-driven smart sustainable cities represent a class of cities which are composed of and monitored by ICT of ubiquitous and pervasive computing and underpinned by big data technology and its novel applications that aim at harnessing physical, economic, and social infrastructures as well as leveraging knowledge and conserving resources through enhanced and optimized operational functioning, planning, design, development, and governance. This occurs in ways that ensure environmental integration, social justice, and economic regeneration as fundamental goals of sustainable development toward achieving sustainability. Smart sustainable cities as an

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integrated approach to urbanism takes multiple forms of combining the strengths of sustainable cities and smart cities based on how the concept of smart sustainable cities can be conceptualized and operationalized, as well as on the multiple processes of, and pathways toward achieving, their status. As a corollary of this, there is a host of opportunities yet to explore toward new approaches to smart sustainable urbanism. This will result in the multiplicity of models of smart sustainable cities in the future. Below is an exemplar of a model of data-driven smart sustainable cities (Table 1) encompassing nine distinct dimensions and functions. This model also shows how various urban systems and domains might connect up as shaped by the use of big data technology and its novel applications.

Fig. 2 An architecture of data-driven smart sustainable cities

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Table 1 A typology of data-driven smart sustainable city dimensions and functions Smart sustainable built environment

Smart sustainable citizens

Smart sustainable governance

• • • • • • •

• • • • • • • •

Cultural enhancement Lifelong learning Creativity Social plurality/cultural diversity Sustainable lifestyles Tolerance and open-mindedness Active involvement in public life Innovative and meaningful use of technology • Personal knowledge sharing • Motivation for participation

• New forms of e-government • New modes of operational governance • Coordination of governmental agencies toward collaboration, integration, and optimization • Evidence-based approach to decision-making, system control, and policy formation • Improved models and simulations for future development • Democratic processes • Public and social services • Equity and fairness • Transparent, participatory, and accountable government

Smart sustainable mobility

Smart sustainable environment

Smart sustainable living

• • • • •

• • • • • •

• Social cohesion and inclusion • Cultural facilities • Education facilities • Public safety and civic security • Housing quality • Public utility (water, electricity, gas, etc.) • Health conditions • Job opportunities • Efficient and tailored services • Participation and empowerment • Well-informed citizenry and fostered creativity

• • • •

Data-driven compactness Data-driven density Data-driven mixed-land use Data-driven diversity Data-driven sustainable transportation Data-driven ecological design Data-driven integration of design concepts and typologies at different spatial scales

Spatial and non-spatial accessibility Virtual mobility Balanced mobility and accessibility Car and bicycle sharing Innovative, intelligent, and safe transport systems Walking and cycling Proximity of services and facilities Diversity of commuting modes Efficient, interoperable multimodal public transport

Green and resilient infrastructure Attractive urban places and images Open urban landscapes Air quality and environment protection Ecological diversity of urban places Sustainable and intelligent resource management

Smart sustainable planning

Smart sustainable economy

Smart sustainable energy

• • • • • • • • •

• Green entrepreneurship • Integration of environmental concerns into economic decision-making • Data-driven business processes • Optimum balance of technological and human resources in labor market • Efficient utilization of resources • Green investments • Green ICT for economic innovation • Sustainable productivity • New forms of economic development (e.g., sharing and open data economy)

• • • • • • • • •

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Data-driven Data-driven Data-driven Data-driven Data-driven Data-driven Data-driven Data-driven Data-driven

environmental planning sustainable development transportation planning land use planning economic forecasting policy recommendations strategic thinking research and analysis administration

Integrated renewable solutions Clean/green technology Data-driven grid management Context-aware operation of buildings Dematerialization and demobilization Context-aware operation of appliances Data-driven transport systems Data-driven urban efficiency Context-aware power supply and distribution

Socio-Political Shaping Factors

In relation to the unfolding shifts in smart sustainable/sustainable smart urbanism, advanced ICT as a set of applications of scientific discoveries in computing has been evolving just as the underlying social knowledge of how to understand technology and the way in which it can be applied to transform and advance urbanism as a social practice have been evolving. This is predicated on the assumption that science-based technologies develop dependently of cities as a microcosm of society, in a reciprocal shaping process where they both are shaped at the same time and thus affect one another and evolve (Bibri and Krogstie 2016). Consistent with this premise, Batty et al. (2012, p. 506) state, ‘the crucible for technological innovation is the cultural context in which it takes place. Technology is a social construction as much as it is a material or ethereal one, and its application is intrinsically social. There is an increasing consensus that cities represent the crucible for technological innovations and that larger cities with a highly educated workforce represent the best places where progress can be made with their invention and application… ICT holds the key to a better society and it will be most clearly demonstrated in large cities.’ In a nutshell, science and technology (S&T) shape and influence, and are shaped and influenced by, cities as social organizations. Accordingly, big data computing and the underpinning technologies in the form of scientific and applied knowledge are embedded in and thus shaped and influenced by the urban context as part of the wider social context within which they arise, and which they in turn shape and influence. By the same token, the urban

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conditions as social structures and processes shape scientific knowledge and activity in terms of data science, urban science, and urban informatics, which in turn shape urbanism as part of such conditions. On this note, as concluded by Bibri and Krogstie (2016, p. 1), smart sustainable/sustainable smart cities and what they entail in terms of their operational functioning, planning, design, and development being responsive to a form of data-driven urbanism ‘are mediated by and situated within ecologically technologically advanced societies. As urban manifestations of scientific knowledge and technological innovation, they are shaped by, and also shape, socio-cultural and politico-institutional structures,’ particularly those pertaining to the urban sphere of society. From a Science, Technology, and Society (STS) perspective, visions of future advances in S&T (and predominately computer science and ICT) inevitably bring with them wide-ranging common visions on how societies and hence cities as social fabrics will evolve in the future, as well as the immense opportunities this future will bring (Bibri and Krogstie 2016). This relates to the role of science-based technology in modern society in terms of its development, a subject area which is positioned within the research and academic field of STS. This is concerned with the ways in which new technology emerges from different perspectives, why it becomes institutionalized and interwoven with politics and policy—cultural dissemination, as well as the risks it poses to environmental and social sustainability. However, in this context, S&T is associated with big data computing and its technological applications and the increasing role this form of ICT of pervasive computing plays in advancing sustainability within contemporary cities. This rapidly evolving form of S&T and related role in smart sustainable/sustainable smart cities has recently permeated urban and academic debates as well as politics and policy across the globe, and is accordingly seen as key for solving the environmental and socio-economic challenges pertaining to sustainability and urbanization facing modern and future cities. Big data computing and its technological applications are drastically changing long-standing forms of city structures, systems, and processes, and revolutionizing city transformation models in terms of sustainability and the quality of life. Major urban transformations are promised as a result of the future advancements and innovations in big data analytics and its application. The existing evidence (e.g., Batty et al. 2012; Al Nuaimi et al. 2015; Angelidou et al. 2017; Bibri 2018a, 2019; Bettencourt 2014) already lends itself to the argument that the use of big data technology and its novel applications across various urban domains makes this technology a salient factor for improving the goals of sustainable development and thereby advancing sustainability. If its research, development, and innovation continue further to be linked with the agenda of sustainable development and the goals of sustainability, i.e., to be utilized meaningfully and strategically, big data computing will have positive, profound, and long-term impacts on smart sustainable/sustainable smart cities of the future. It is projected to yield hitherto unrealized environmental gains and socio-economic benefits, owing to its technological superiority in terms of the novel applications and services that provide high performance and concrete value (Bibri 2018a). From a societal standpoint, big data computing and its technological applications are socio-culturally constructed to have a determinant role in instigating major social changes on multiple scales due to its transformational power residing or embodied in its disruptive, synergistic, and substantive effects. In relation to this, the coalescence of computing, data processing, and communication technology is unleashing a wealth of opportunities and proving a powerful driver for innovation and change, as well as blurring the boundaries between domains within different societal spheres. Big data computing does not just enable us to do new things; it shapes how we do them. It is important not to underplay the radical social transformations that are likely to result from the implementation of this advanced technology. Likewise, smart sustainable/sustainable smart cities are the product of socio-culturally conditioned frameworks, including how and why the underlying data-driven urbanism practices have emerged and become disseminated at the urban level and hence discursively constructed and materially produced through diverse socio-political institutions and organizations (Bibri and Krogstie 2016, 2019). In this respect, it is important to recognize the interplay between smart sustainable/sustainable smart cities as a form of sustainability transition and other societal scales, as well as the links to political processes on a macro-level, i.e., regulatory policies and governance arrangements. This relates to the dialectic relationship between societal structures and such cities in the sense of each affecting and being affected by the other. The focus here is rather on how the former affects the latter. This one-way relationship has been approached from a variety of perspectives, including transition governance, innovation system, and discourse analysis. From a transition governance perspective, government is one of the key actors involved in any form of sustainability transition through various governance arrangements, including funding schemes, research management (regulation of public research institutes), innovation and technology policies, regulatory standards, market manipulations, public–private collaborations and partnerships, and so on (Bibri 2015, 2018a, b). In this respect, the government generates top-down pressure from regulation and policy and the use of market and other forms of incentives, while promoting, spurring, and stimulating the collective learning mechanisms by supporting innovation financially and providing access to the needed knowledge (Rotmans et al. 2001). Further, recommendations for smart sustainable/sustainable smart cities as a major urban transformation and a leading paradigm of data-driven urbanism, which

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entails a set of intertwined socio-technical systems and a cluster of interrelated discourses embedded in the wider socio-technical landscape, are unlikely to proceed without parallel political action (Bibri 2018a). Drastic shifts to sustainable (and) technological regimes ‘entail concomitantly radical changes to the socio-technical landscape of politics, institutions, the economy, and social values’ (Smith 2003, p. 131). Furthermore, political action is of influence in the context of smart sustainable/sustainable smart cities as both a techno-urban discourse and an amalgam of innovation systems (Bibri and Krogstie 2016, 2019). Indeed, it is at the core of discourse theory (e.g., Foucault 1972) in terms of the material mechanisms and practices that can be used to translate the idea or vision of such cities into concrete projects and strategies and their institutionalization in urban structures and practices. Likewise, it is at the heart of the theoretical models of innovation system in relation to the academic discourse of smart sustainable/sustainable smart cities. In this regard, political processes represent the setup under which dynamic networks of urban actors can interact within diverse industrial sectors in the development, diffusion, and utilization of knowledge and technology pertaining big data computing in the context of smart sustainable/sustainable smart urbanism.

8

Recasting Urban Science and Big Data Computing Technology

Several scientific and computational approaches to cities, such as digital mapping and geographical information systems, quantitative geography and urban modeling, and urban cybernetics theory and practice, as well as knowledge discovery/data mining as an advanced form of decision support, are based on realist epistemology. This approach postulates ‘the existence of an external reality which operates independently of an observer and which can be objectively and accurately measured, tracked, statistically analyzed, modeled, and visualized to reveal the world as it actually is. In other words, urban data can be unproblematically abstracted from the world in neutral, value-free, and objective ways and are understood to be essential in nature; that is, fully representative of that which is being measured (they faithfully capture its essence and are independent of the measuring process)… And these data when analyzed in similarly objective ways reveal the truth about and a ‘God’s eye’ view of cities. As such, they promote an instrumental rationality that underpins the notion that cities can be steered and managed through a set of data levers and analytics and that urban issues can be solved through a range of technical solutions’ (Kitchin 2016a, p. 4). One of the implications of such a framing as to the criticism of urban science is that the scientific and computational approaches wilfully ignore the role of politics, social norms, social structures, ideology, and culture, as well as the metaphysical aspects of human life, in shaping urban relations, governance, planning, and development (Harvey 1973/2009). Another implication of such a framing associated with urban science being roundly criticized within the social sciences is that it is too atomizing, reductionist, mechanistic, essentialist, deterministic, parochial, and closely aligned with positivist thinking, collapsing diverse complex, multidimensional social structures and relationships to abstract data points and universal formulae and laws (Buttimer 1976). In addition, it produces the kind of policy interventions that both did much damage to city operations as well as failed to live up to their promises (Flood 2011). Computational and scientific approaches to cities have been perceived as inadequate to solve urban problems due to their wicked nature. It is argued that such problems are often best solved through political/social solutions, citizen participation, and deliberative democracy, rather than technocratic forms of governance (Greenfield 2013; Kitchin et al. 2015). Moreover, such approaches are claimed to produce a limited and limiting understanding of how cities work and how they should be managed. The former pertains to foreclosing what kinds of questions can be asked and how they can be answered (Kitchin 2016a), and the latter is associated with foreclosing other forms of urban knowledge, such as knowledge derived from practice and deliberation and based on experience (Parsons 2004). Nonetheless, while such approaches have been criticized for failing to recognize that cities are complex, intricate, multifaceted, and unpredictable systems, full of contestations and intractabilities that are not easily captured or steered, a view which undoubtedly still holds (Bibri 2018a; Bibri and Krogstie 2016; Kitchin 2016a; Kitchin et al. 2015), advocates of computational social and urban science counter that in the age of big data the variety, exhaustivity, resolution, flexibility, evolvability, and relationality of data, coupled with the growing power of big data computation and analytics, address some of the raised critiques, especially those related to reductionism and universalism, by providing more finely grained, sensitive, and nuanced analysis that can take account of context and contingency (Kitchin 2014). Nonetheless, how smart sustainable cities are conceived needs a re-orientation. ‘Rather than being cast as bounded, knowable and manageable systems that can be steered and controlled in mechanical, linear ways, cities [in general] need to be framed as fluid, open, complex, multilevel, contingent and relational systems that are full of culture, politics, competing interests and wicked problems and often unfold in unpredictable ways… [C]ity analytics and its instrumental rationality should not be allowed to simply trump reason and experience, or other sources of information and

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Recasting Urban Science and Big Data Computing Technology

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insight such as those based on ‘small data’ studies, in shaping and driving urban governance. Instead, they should be used contextually and in conjunction with each other’ (Kitchin 2016a, p. 11). From another critical perspective, in examining the practices of urban science, paying particular attention to instrumental rationality and realist epistemology, Kitchin (2016a) concludes that urban science needs to be recast in this way: a reconfiguring of the underlying epistemology to openly recognize the contingent and relational nature of urban systems as well as urban processes and science. This relates to the social shaping of science-based technology and the social construction of scientific knowledge as analytical and philosophical approaches (see Bibri 2015; Bibri and Krogstie 2016 for a detailed discussion). In light of this, the recasting in question involves recognizing that the realist assumptions, which posit that urban science can reveal fundamental truths about the city, are flawed. Urban science can only produce a particular view through a specific lens, and cannot provide neutral, objective, God’s eye views of the city (Kitchin 2016a). On the one hand, the data used do not exist independently of the ideas, instruments, systems, practices, and knowledge employed—and embedded within a multidimensional context (e.g., local, national, social, political, cultural, organizational, regulatory, etc.) —to generate and process them (Ribes and Jackson 2013). To put it differently, data are never raw, but always already cooked to a particular recipe for a particular purpose (Bowker 2005; Gitelman 2013). On the other hand, big data computing and the underpinning technologies are socio-technical in nature. As such, they are not neutral, purely technical means of assembling and making sense of data; instead, they are shaped by philosophical ideas, socio-political frameworks, and ideological means (Bibri 2018a; Bibri and Krogstie 2016; Kitchin 2016a). In particular, big data technology is ‘cultural’ since it can be conceptualized as a discourse prioritizing specific concepts, ideas, claims, assumptions, and visions about the nature and practice of science and technology in society and the role of diverse actors in shaping them, to draw on Bibri (2015). There is potential for realizing that the big data-driven technologized nature of the city is neither apolitical nor inevitable. Furthermore, when engaging in a discursive-material analysis, the politics of this science-based technology does not become the result of the unconditioned agency of the involved actors, e.g., scholars, scientists, experts, engineers, and technologists. Rather, such technology can be conceived as specific techno-socio-political practice which depends on the agency of various actors promoting it and forming coalitions on particular technological innovations and on the political regulation of science and technology in society. On the whole, big data technology is the outcome of social processes involving diverse intertwined factors and many stakeholders with a vested interest. Accordingly, urban science as a field in which data science and big data computing are practiced needs to recognize that it does not reflect the world as it actually is and to openly acknowledge its contingencies, limitations, and inherent politics, but rather actively frames and produces the world (Kitchin 2016a; Kitchin et al. 2015). This is, though, not to say: ‘the fundamental approach of analytics, modeling, and simulation is radically altered, but rather that how these approaches work in messy practice is detailed and grand claims as to their veracity or validity is tempered. This would include detailing how ethical issues were considered and the research design altered appropriately’ (Kitchin 2016a, p. 11). The main argument is that the way the technical systems are designed, operated, and steered is influenced by what Foucault (1977, p. 194) calls a ‘dispositif’ and defines it as ‘a thoroughly heterogeneous ensemble consisting of discourses, institutions, architectural forms, regulatory decisions, laws, administrative measures, scientific statements, philosophical and moral propositions.’ To put it differently, a data assemblage possesses, in Kitchin’s (2015a) terminology, systems of thought, the regulatory environment, organizational priorities and internal politics, institutional collaborations, funding and resourcing, technical know-how, and marketplace demand. These institutional apparatuses and their techniques relate to what Foucault (1972) terms ‘power/knowledge,’ that is, knowledge produced by a system of procedures to fulfill a strategic function or to achieve a particular purpose. In other words, urban big data are situated, context-dependent, contingent, framed, and selective for the purpose to achieve certain goals or ends, i.e., to monitor, empower, dictate, discipline, regulate, control, steer, centralize, produce profit, and so forth. In this context, it is legitimate to scrutinize and challenge the inner-working of technical systems and the data they produce: the mechanisms that function internally to such systems and data generation and are not outwardly visible as to the underlying politics. Or, it is necessary to critically unpack the data assemblage associated with urban big data when being under examination so as to document how this assemblage is formed and functions in practice to help generate urban processes and formations (Kitchin 2015a) for the benefit of sustainability and citizens. Such assemblage includes the core enabling technologies underlying big data computing in the context of data-driven urbanism, including sensor networks, data processing platforms, cloud or fog computing infrastructures, data warehouses, and so on. For example, the sensor-recording parameters, their length as to the collected data, where they are located, what kinds of sensors are embedded in which environments, their settings and calibration, their integration and fusion, and their exhaustiveness all pertain to technical configurations and deployments that determine the nature of the produced data and the way they are stored, managed, processed, analyzed, and disciplined. All in all, data are the products of

5 The Underlying Technological, Scientific, and Structural …

124 Table 2 A data assemblage Data assemblage System/process performs a task

Context frames the system/task

• • • • • •

• • • • • • • •

Reception/Operation (user/usage) Interface Code/algorithms (software) Data(base) Code Platform (operating system) Medical Platform (infrastructure–hardware)

Systems of thought Forms of Knowledge Finance Political economies Governmentalities & legalities Organisations and Institutions Subjectivities and commuinities Marketplace

Source Kitchin (2015a)

complex socio-technical constellations or assemblages that are framed and shaped by a range of technical, social, economic, and political forces, as illustrated in Table 2, and are designed to produce particular outcomes (Kitchin 2014, 2015a). Nonetheless, in terms of its success and expansion, data-driven smart sustainable urbanism is influenced by the effects of the power induced by the underlying scientific knowledge: data science, urban science, computer science, information science, engineering science, and so on. These scientific disciplines have legitimization capacity due to their association with the scientific discourse, one of today’s main sources of legitimacy and authority in knowledge production, decision-making, and policymaking. In particular, the success and expansion of such urbanism is associated with the exercise of power for the view of having a scientific function in the transformation of cities by instrumenting, datafying, and computerizing them on a massive scale. In a nutshell, the sheer scientificity and objectivity of urban science and big data computing and the underpinning technologies are behind the ongoing success and expansion of such urbanism, and which are used as all-embracing solutions to persuade the majority of the city that all urban problems can be contained and solved by what they can offer as novel applications and sophisticated approaches. They are also used as rhetorical elements in the decision-making process, utilized as a symbolic element: the process of deploying the core enabling technologies of big data analytics gives a ritualistic assurance that decision-makers hold appropriate attitudes toward decision-making pertaining to urbanism and its policy. With the above being said, given the scientific discourse and related legitimation capacity underlying data-driven smart sustainable urbanism, one can subsume a range of socio-political effects under the kind of discursive mechanisms through which the discourse of such urbanism operates, which both show its power and empower the agents that promote it.

9

Challenges and Concerns

While there is a growing consensus among urban scholars and planners and urban and data scientists that big data analytics and its application will be a salient factor in the operational functioning, management, planning, design, and development of smart sustainable cities, there are still significant scientific and intellectual challenges as well as concerns that need to be addressed and overcome for building such cities based on big data computing and the underpinning technologies, and then for accomplishing the desired outcomes related to sustainability. Such challenges and issues pose interesting and complex research questions, and constitute fertile areas of investigation awaiting interdisciplinary and transdisciplinary teams of scholars, academics, scientists, and experts working in the field of smart sustainable urbanism. The rising demand for big data analytics and its core enabling technologies, coupled with the growing awareness of the associated potential to transform the way the city can function in the context of sustainability, comes with major challenges and concerns related to the design, engineering, development, implementation, and maintenance of data-driven applications in smart sustainable cities. The challenges are mostly computational, analytical, and technical in nature, and sometimes logistic in terms of the detailed organization and implementation of the complex technical operations involving the installation and deployment of the big data ecosystem and its components as part of the ICT infrastructure of such cities. They include, but are not limited to, the following, as compiled from Bibri (2018a, 2019): • design science and engineering constraints; • data processing and analysis;

9

Challenges and Concerns

• • • • • • • • • • • •

data management in dynamic and volatile environments; data sources and characteristics; database integration across urban domains; data sharing between city stakeholders; data uncertainty and incompleteness; data accuracy and veracity (quality); data protection and technical integration; fault tolerance and scalability; data governance; urban growth and data growth; cost and large-scale deployment; evolving urban intelligence functions and related simulation models and optimization and prediction methods as a part of exploring the notion of smart cities as innovation labs; building and maintaining data-driven city operations centers or citywide instrumented system; relating the urban infrastructure to its operational functioning and planning through control, automation, management, optimization, enhancement, and prediction; creating technologies that ensure fairness, equity, inclusion, and participation; balancing the efficiency of solutions and the quality of life against environmental and equity considerations; privacy and security.

• • • • •

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There are many studies available (e.g., Al Nuaimi et al. 2015; Batty 2013; Batty et al. 2012; Bibri 2018a, b, 2019; Kharrazi et al. 2016; Kitchin 2014, 2015a, 2016a; Lacinák and Ristvej 2017; Townsend 2013; van Zoonen 2016; Vinod Kumar and Dahiya 2017) that provide a descriptive or analytical account of the above-listed challenges as related to big data analytics and its applications and uses in smart (and) sustainable cities. For example, Bibri (2018a) provides an overview of some of those challenges and potential ways to address and overcome them in the context of smart sustainable cities, including data management, database integration across urban domains, urban growth and data growth, data sharing, data uncertainty and incompleteness, data accuracy and quality, and data governance. Most of the challenges of big data analytics and its application arise from the nature of the data generated in smart (and) sustainable cities in terms of their attributes (e.g., Batty et al. 2012; Bibri 2018a, 2019; Kitchin 2014; Laney 2001; Marz and Warren 2012; Mayer-Schonberger and Cukier 2013; Zikopoulos et al. 2012) as: • consisting of exabytes or terabytes of data; • being structured and unstructured in nature; • being often tagged with spatial and temporal labels; being commonly streamed from a large number and variety of sources; • being mostly generated automatically and routinely; being created in, or near, real time; • being exhaustive in scope and scale by striving to capture entire populations or systems; • dramatically exceeding sample sizes commonly in use for small data studies; • being relational in database systems by containing common fields that enable the conjoining and combination of different datasets; • being fine-grained in resolution by aiming to be very detailed and uniquely indexical in identification; and holding the traits of extensionality (can add new fields easily), evolvability (can change dynamically), and scalability (can expand in size rapidly). Adding to the above primarily technological challenges are the financial, organizational, institutional, social, political, regulatory, and ethical ones, which are associated with the implementation, retention, and dissemination of big data across the domains of smart sustainable cities (Bibri 2018a). In this regard, controversies over the benefits of big data analytics and its application involve limited access and related digital divides and other ethical concerns about accessibility (see Bibri 2019 for an overview). For a detailed discussion of the challenges of urban big data and sustainable development, the reader can be directed to Kharrazi et al. (2016). Kitchin (2014) provides a critical reflection on the implications of big data and smart urbanism, examining five emerging concerns, namely:

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1. 2. 3. 4. 5.

10

the politics of big urban data; technocratic governance and city development; corporatization of city governance and technological lock-ins; buggy, brittle, and hackable cities; the panoptic city.

Discussion and Conclusion

Building smart sustainable cities based on big data computing and the underpinning technologies is deemed necessary to address and overcome many of the complex challenges and pressing issues of sustainability. Big data applications are seen as a critical enabler and powerful driver for smart sustainable cities in the future. Many cities across the globe have already started to exploit the untapped potential of such applications across diverse urban systems and domains. We stand at a threshold in beginning to make sense of big data analytics and data-driven decision-making that are projected to be of massive use in, and interwoven into the very fabric of, smart sustainable cities within the next few decades. The ultimate goal is to improve, advance, and maintain the contribution of such cities to sustainability by employing more effective digital ways to monitor, understand, probe, and plan them. However, there are currently many challenges and concerns that need to be addressed and overcome for achieving the desired outcomes and sought goals. This chapter examined how data-driven smart sustainable cities are being instrumented, datafied, and computerized so as to improve, advance, and maintain their contribution to the goals of sustainable development through enhanced practices. In this respect, different topics have been identified and discussed, namely the integration of data-driven smart cities and sustainable cities, digital instrumentation living laboratories, innovations laboratories, urban intelligence functions, urban operating centers and strategic planning and policy offices, data types and the role of open data, and data-driven urbanism and urban science and how they relate to one another from a scientific and scholarly perspective. The essence of the idea of data-driven smart sustainable cities revolves around the need to harness and leverage big data technologies that have hitherto been mostly associated with smart cities but have clear synergies in the functioning of sustainable cities and tremendous potential for enhancing their performance and need to be steered or directed for this purpose so that many new opportunities can be enabled and realized. From a societal standpoint, big data computing and its technological applications are socio-culturally constructed to have a determinant role in instigating major social changes on multiple scales due to its transformational power residing or embodied in its disruptive, synergistic, and substantive effects on different forms of social organization. Also, this chapter highlighted and substantiated the real potential of big data technology for improving, advancing, and maintaining the contribution of smart sustainable cities to the goals of sustainable development by identifying, synthesizing, distilling, and enumerating the key practical and analytical applications of this advanced technology in relation to multiple urban systems and domains with respect to urban operations, functions, services, designs, strategies, and policies. The most common data-driven applications identified include: transport and traffic, mobility, energy, power grid, environment, buildings, infrastructures, urban planning, urban design, academic and scientific research, governance, health care, education, and public safety. The potential of big data technology lies in enabling smart sustainable cities to harness and leverage their informational landscape in effectively understanding, monitoring, probing, and planning their systems and environments in ways that enable them to reach the optimal level of sustainability. To put it differently, the use of big data analytics is projected to play a significant role in realizing the key characteristics of such cities, namely the efficiency of operations and functions, the prudent utilization of natural resources, the intelligent management of infrastructures and facilities, the improvement of the quality of life and well-being of citizens, and the enhancement of mobility and accessibility. Moreover, this chapter proposed, illustrated, and described an architecture and typology of data-driven smart sustainable cities. Their unique features lie in their novelty in terms of bringing new ingredients and the way they are integrated and affect and shape the relationships between the urban entities specific to smart sustainable cities in light of the use of big data technology and its applicability to sustainability. The proposed architecture and typology are developed in response to the need for improving, advancing, and maintaining the contribution of such cities to the goals of sustainable development. However, just as there are many new opportunities and benefits ahead to embrace, there are significant challenges ahead to address and overcome in relation to big data analytics to achieve a successful implementation of related novel applications in the context of smart sustainable cities. These challenges are mostly of computational, analytical, technical, and logistic kinds. While most of these challenges are currently under investigation and scrutiny by the relevant research and industry

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communities, supported by technology and innovation policies, deploying big data applications in smart sustainable cities requires overcoming other organizational, institutional, political, social, ethical, and regulatory challenges. These are likely to hinder the development and implementation of big data applications in such cities. Nevertheless, with all the success factors in place, coupled with a deep understanding of the emerging phenomenon of smart sustainable cities and an acknowledgment of the potential of big data computing, realizing such cities becomes an attainable goal in an increasingly urbanized world. Important to add, while smart city and big data computing research is still in its infancy, the solutions to the involved challenges and issues can make it a very practical field. Also, while data-driven smart sustainable urbanism provides a set of advanced solutions to complex problems, it still involves limitations and unsettled issues. Most of topical studies have tended to focus mainly on data-driven smart urbanism, addressing a number of its aspects or areas in the context of smart cities, as well as approaching the topic from a wide variety of perspectives while overlooking its potential for sustainability. This chapter focuses on, and highlights, the growing role and significance of data-driven smart sustainable urbanism, with the aim of bringing this topic to the forefront of the minds of planners, scholars, scientists, and policymakers given the increasing influence of big data science and analytics on strategic sustainable development. Concerning the value of this work, the outcome will help strategic city stakeholders to understand what they can do and invest in more to advance smart sustainable urbanism on the basis of data-driven solutions and approaches, and also give policymakers an opportunity to identify areas for further improvement while leveraging areas of strength with regard to the future form of such urbanism. In addition, it will enable researchers and scholars to direct their future work to the emerging paradigm of data-driven smart sustainable urbanism, and practitioners and experts to identify common problems and potential ways to solve them, all as part of future research and practical endeavors, respectively. Lastly, this chapter provides a form of grounding for further discussion to debate over the disruptive, synergetic, and transformational effects of big data computing and the underpinning technologies on forms of the operational functioning, management, planning, design, development, and governance of smart sustainable cities in the future. Also, it presents a sort of basis for stimulating more in-depth research in the form of both qualitative analyses and quantitative investigations focused on establishing, uncovering, substantiating, and/or challenging the assumptions underlying the relevance of big data technology and its advancements as to accelerating sustainable development.

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Smart Sustainable Urbanism: Paradigmatic, Scientific, Scholarly, Epistemic, and Discursive Shifts in Light of Big Data Science and Analytics

Abstract

As a new area of science and technology (S&T), big data science and analytics embodies an unprecedentedly transformative power—manifested not only in the form of revolutionizing science and transforming knowledge, but also in advancing social practices, producing new discourses, catalyzing major shifts, and fostering societal transitions. Of particular relevance, it is instigating a massive change in the way both smart cities and sustainable cities are studied and understood, and in how they are planned, designed, operated, managed, and governed in the face of urbanization. This relates to what has been dubbed data-driven smart sustainable urbanism, an emerging approach which is based on a computational understanding of city systems that reduces urban life to logical and algorithmic rules and procedures and that employs new scientific methods and principles, while also harnessing urban big data to provide a more holistic and integrated view or synoptic intelligence of the city. This is underpinned by epistemological realism and instrumental rationality, which sustain and are shaped by urban science. However, all knowledge is socially constructed and historically situated, so too are research methods and applied research as related to S&T and as historically produced social formations and practices that circumscribe and produce culturally specific forms of knowledge and reality. This chapter examines the unprecedented paradigmatic, scientific, scholarly, epistemic, and discursive shifts the field of smart sustainable urbanism is undergoing in light of big data science and analytics and the underlying advanced technologies, as well as discusses how these shifts intertwine with and affect one another, and their sociocultural specificity and historical situatedness. I argue that data-intensive science as a new paradigmatic shift is fundamentally changing the scientific and practical foundations of urban sustainability. In specific terms, the new urban science—as underpinned by sustainability science—is increasingly making cities more sustainable, resilient, efficient, livable, and equitable by rendering them more measurable, knowable, and tractable in terms of their operational functioning, management, planning, design, and development. Keywords

















Smart sustainable urbanism Urban science Data science Data-intensive science Paradigm Episteme Historical a priori Big data computing and the underpinning technologies

1




Paradigm shift




Introduction

We are living at the dawn of what has been termed as ‘the fourth paradigm of science,’ a scientific revolution that is marked by the recent emergence of big data science and analytics as well as the increasing adoption of the underlying technologies (large-scale computation, data-intensive techniques and algorithms, and advanced mathematical models) in scientific and scholarly research practices. This fourth paradigm, after the three interrelated paradigms of empirical, theoretical, and computational science, is known as data-intensive science, a data-driven, exploration-centered form of science, where big data computing and the underpinning technologies are heavily used to help scientists and scholars manage, analyze, and share data for multiple purposes (e.g., decision-making, deep insights, discoveries, etc.). Everything about science development and knowledge production is fundamentally changing thanks to the unfolding and soaring data deluge. The © Springer Nature Switzerland AG 2019 S. E. Bibri, Big Data Science and Analytics for Smart Sustainable Urbanism, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-17312-8_6

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upcoming data avalanche is thus the primary fuel of this new age where powerful computational processes or analytics algorithms burn this fuel to generate useful knowledge and valuable insights directed for a wide variety of practical uses (e.g., decisions to create more sustainable, efficient, resilient, livable, and equitable cities). Big data science and analytics is a new area of science and technology (S&T). Data science is concerned with the collection, storage, management, processing, and analysis of massive-scale data. Big data denote collections of datasets whose volume, velocity, variety, exhaustivity, relationality, and flexibility make it so difficult to manage, process, and analyze the data using the traditional database systems and software techniques. In other words, big data refer to humongous volumes of both structured and unstructured data that cannot be processed and analyzed computationally with conventional applications. The scope and impact of big data science and analytics will continue to expand enormously in the upcoming decades as scientific data and data about all branches of science become overwhelmingly abundant and ubiquitously available. Especially, significant progress has been made within data science, information science, computer science, and complexity science with respect to handling and extracting knowledge and insights from big data, and these have been utilized within urban science (e.g., Bibri 2018a; Bibri and Krogstie 2017c; Kitchin 2016). Big data computing is an emerging paradigm of data science, a typical model that is of multidimensional data mining for scientific discovery over large-scale infrastructure (Bibri and Krogstie 2017c). It employs sophisticated computational methods to automatically extract useful knowledge and valuable insights from large masses of data—huge in volume, high in velocity, created in near or real time, diverse in variety, exhaustive in scope, fine-grained in resolution, relational in structure, and extensible and scalable in nature—using data science methods, processes, and systems. Data mining as one of big data analytics techniques provides some of the clearest illustrations of the fundamental concepts and principles of data science. It denotes the computational process of probing colossal datasets in order to find frequent, hidden, and previously unsuspected and unknown patterns and subtle relationships; to make useful, meaningful, and valid correlations from these discoveries; and to summarize the results in novel ways and then visualize them in understandable formats prior to their deployment for decision-making purposes (Bibri 2018a; Bibri and Krogstie 2018), among other things. In recent years, the emphasis has been on the development of new data analytics that utilizes advanced techniques designed to manage, process, and analyze enormous datasets, such as data mining and pattern recognition, data visualization and visual analytics, statistical analysis, and prediction and simulation modeling (Batty 2013; Bibri 2018a; Kitchin 2014a; Miller 2010). These techniques are different from traditional statistical methods that were designed to perform data-scarce science; that is, identify significant relationships from small, clean sample sizes with known properties. Overall, data science unifies statistics, data analysis, and machine learning, and the related methods in order to understand and analyze actual natural and social phenomena based on massive data. However, many big data analytics projects are still struggling to deliver useful results, often as a result of poor management and utilization of resources and inadequately trained data analysts and scientists. Data science is a heavily applied field where academic programs currently do not sufficiently prepare data scientists for the task (Barlow 2013; Donoho 2015). The idea of big data computing is to conceive a novel way of thinking about data computation in the world. This drastic shift is manifested in the big data technology uses and users, the incorporation of big data technology into research and social practices, the knowledge required to make use of big data technology, the scale and extension of new big data applications and services, and the multiplicity and diversity of the actors involved. There is a strong organizational, institutional, and governmental support for and commitment to big data technology—industry associations and consortia, business communities, scholarly and scientific research communities, policy bodies, and governmental agencies due to its tremendous (yet untapped) potentials and rapidly expanding success in relation to academic research and social practice, including urban science and smart sustainable/sustainable smart urbanism. The increasing adoption of big data technology and its novel applications (e.g., urban domains), coupled with the ongoing, intensive research, development, and innovation within academic circles and industries, reflects its morphing power, that is, its productive and constitutive network operates on all scales of the city not only in terms of revolutionizing science, transforming knowledge, advancing academic studies, and producing new discourses, but also in terms of creating new technological artifacts and environments, orientating technological innovations, captivating technological investments, shaping organizational and institutional developments, constituting policy bodies, catalyzing important shifts, and fostering major transitions. As hinted at, power here represents a productive and constitutive network which runs through the whole social body, of which the city is a significant example. Of importance to highlight in that regard, huge investments are being funneled into smart sustainable/sustainable smart city projects and initiatives pertaining to big data technology and its novel applications, and considerable resources are being mobilized for urban research and studies on the basis of big data analytics (Bibri 2018a, 2019a). And the rapidly evolving expansion of big data computing and the underpinning technologies through particularly the proliferation of such projects and initiatives as well as academic endeavors demonstrate their increasing benefits and future prospects. Big data analytics

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and its core enabling technologies benefit from the provisioning of novel applications in response to new urban market demands, as well as from the growing realization of exploiting the related innovations and advancements for invigorating the application demand for the sustainability solutions that advanced ICT can offer. This signifies that they exhibit positive feedbacks such that the more they are deployed and implemented, especially in connection with smart sustainable/sustainable smart cities, the more likely they are to be further deployed and implemented. Social apparatuses ‘behind this phenomenon commonly entail network effects, scale, adaptation, and learning, which fuel or stimulate further adoption of new technologies’ (Bibri 2015, p. 140). Of particular relevance, a new era is presently unfolding wherein smart sustainable/sustainable smart urbanism is increasingly becoming data-driven. At the heart of such urbanism is a computational understanding of city systems that reduces urban life to logic, calculative, and algorithmic rules and procedures, while also harnessing urban big data to provide a more holistic and integrated view and synoptic intelligence of the city directed primarily for improving, advancing, and maintaining the contribution of both sustainable cities and smart cities to the goals of sustainable development in an increasingly urbanized world. This is underpinned by epistemological realism and instrumental rationality. Epistemological realism is a sub-category of objectivism which holds that what is known about an object exists independently of human mind. More specifically, it posits that there exists an external reality which operates independently of an observer and which can be objectively and accurately measured, tracked, analyzed, modeled, simulated, and visualized to reveal the world as it actually is (Kitchin 2016). Instrumental rationality is a pursuit of any suitable means necessary to achieve a specific end. Specifically, it is practical reasoning serving for making decisions on how to efficiently perform technical tasks, solve problems, overcome challenges, and resolve conflicts by regarding the factors involved in a situation as variables to be controllable and knowable. As such, it underpins the conception that cities can be operated, managed, planned, and developed through a set of data levers and analytics and that urban issues can be solved through a range of technical solutions thanks to the ability of probing the deluge of urban data in neutral, value-free, and objective ways to reveal the truth about cities. Further, epistemological realism and instrumental reality are informed by and sustain urban science (a field in which data science and analytics is practiced), which seeks to make cities more measurable, knowable, and controllable in terms of their operational functioning, management, planning, and development and thereby more sustainable, resilient, efficient, and equitable. These practices are indeed becoming highly responsive to a form of data-driven urbanism that is the key mode of production for what have widely been termed smart sustainable/sustainable smart cities whose monitoring, understanding, and analysis are accordingly increasingly relying on the core enabling technologies of big data analytics. On the whole, the sea of change triggered by big data computing is heralding a once-and-for-all transition from a resource-intensive world to a data-intensive one. However, cities are complex par excellence and thus multifaceted, dynamically changing, contextual, contingent systems, full of wicked problems that are not easily steered and of contestation that is not easily captured, and that urban issues are often best solved through solutions involving social and political interventions, although in the age of big data science and analytics, the nature of data in terms of variety, velocity, exhaustivity, granularity, resolution, relationality, scalability, and extensibility, coupled with the growing computational processing power and new data analytics techniques, can, arguably, provide more finely grained, sensitive, and nuanced analysis that can take account of complexity, dynamicity, contextuality, and contingency. From a conceptually different angle, based on the premises of the social shaping of science-based technology or the social construction of scientific knowledge and its practical application, which relate to the analytical and philosophical framework of Science, Technology, and Society (STS), the realist assumptions, which posit that urban science can reveal fundamental truths about the city, remain flawed, thereby the need for reconfiguring the epistemology of urban (sustainability) science. In addition, recent years have—due to the advent of big data computing and the underpinning technologies as innovations —witnessed an outburst of, or an upsurge in, claims for new paradigms and paradigm shifts in relation to a number of domains, including smart sustainable/sustainable smart urbanism. There has been a near passion for labeling it as a paradigm shift in urbanism to describe a certain stage of urban planning and development. While such urbanism emanates from the transformational effects of ICT of pervasive computing, where paradigm and paradigm shift do actually hold as to computing science, information science, and data science as scientific disciplines, it is still laden with discursive aspects in the sense of particular ways of thinking and talking about some aspects of urban life or about the urban world, i.e., a set of concepts, ideas, visions, claims, and categorizations that are historically contingent and socio-culturally specific and generate truth effects accordingly. The underlying assumption is that while big data computing and the underpinning technologies are the results of scientific discovery in computing, they are targeted at complex, dynamic urban realities made of an infinite richness of circumstances and involving several intertwined factors and situated dynamics. Increasingly, the claim is that smart sustainable/sustainable smart urbanism as an enabler of new forms of urban actions represents a paradigm shift in

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urbanism. This is a problematic idea due to several reasons: Whether paradigms apply to techno-urban phenomena (or the social sciences in general) is questionable; in such urbanism, the concern is with science, technology, research, and innovation policies frameworks rather than explanatory and meta-theoretical frameworks; such urbanism involves normative orientations or pertain to normative values; there are different understandings and views on whether an epistemological break with the existing paradigm of urbanism; and finally the actual divergence in assumptions about and the approaches to a radical and data-driven smart sustainable/sustainable smart urban transformation is in some respects questioned and in others technologically deterministic. Therefore, it can be argued that there is no paradigm in smart sustainable/sustainable smart urbanism—nor should there be. Rather, being discursive in nature, such urbanism involves non-paradigmatic, preparadigmatic, and postparadigmatic dimensions. This is an attempt at an inquiry into the critical currents in urban thinking, and the aim is set to go beyond the rhetorical consensus in questioning and criticizing such urbanism, and to hold the claims of these inquisitorial and analytical positions themselves against the light. The intent of this theoretical workout is to address the question about what these positions tell us analytically and in what ways they shape various policies pertaining to science, technology, research, innovation, practice, and so on. It seems that smart sustainable/sustainable smart urbanism presents a loose of technological practices and social-related critical sensibilities, which leaves so many areas open that the claim to assume a paradigm and paradigm shift in urbanism is, to a great extent, misrepresented and misplaced. Against the preceding background, this chapter examines the unprecedented paradigmatic, scientific, scholarly, epistemic, and discursive shifts the field of smart sustainable urbanism is undergoing in light of big data science and analytics and the underlying advanced technologies, as well as highlights and discusses how these shifts intertwine with and affect one another, and their sociocultural specificity and historical situatedness. This chapter consists of six sections. Section 2 introduces, describes, and discusses the conceptual and theoretical constructs that make up this study. Section 3 provides an account of Michel Foucault and Thomas Kuhn’s Contribution to the philosophy of scientific knowledge. Section 4 examines the key scientific, paradigmatic, and scholarly shifts being instigated by big data science and analytics in relevance to smart sustainable/sustainable smart urbanism. Section 4 analyzes and discusses the discursive, material, epistemic, historical a priori, institutional, non-paradigmatic, preparadigmatic, and postparadigmatic dimensions of such urbanism, as well as their relationships. This chapter ends, in Sect. 6, with concluding remarks along with critical discussions and final thoughts.

2

Conceptual and Theoretical Background

2.1 Science and Philosophy The main difference between science and philosophy lies in the way they work and treat knowledge. Science is concerned with natural phenomena, while philosophy attempts to understand the nature of humans, existence, and the relationship that exists between the two concepts, with the latter being the concern of science. As a way of thinking about the world, the universe, and society, philosophy is concerned with the fundamental nature of knowledge, reality, and value, where ideas are often general and abstract. The focus in this chapter is on the nature of knowledge as related to the philosophical field of epistemology, which is one of the four main branches of philosophy, in addition to metaphysics, logic, and axiology (meta-ethics). Further, philosophy uses logic and reason as tools to analyze the ways in which humans experience the world. It imparts logical analysis as well as critical thinking, close reading, and clear writing as part of the process of reasoning, which helps to understand the language we use to make sense of and describe the world, and our place within it. Indeed, the study of philosophy aids in enhancing the human ability to solve problems as well as develop communication and writing skills, persuasive powers, and argumentation: the process of reasoning systematically in support of an idea, action, or theory. In addition, a key function of philosophy is to foster deeper reflection on the concepts, methods, and issues that are fundamental within other disciplines. There is no single, simple way to define science, as largely agreed upon by the philosophers of science. Within the scope of this chapter, science refers to a systematic enterprise that builds and organizes knowledge in the form of explanations and predictions about the universe (Wilson 1999) using testable hypotheses as models to confirm or falsify the theoretical models of how the world works. In other words, it denotes the intellectual and practical activity encompassing the systematic study of and knowledge about the structure and behavior of the natural and physical world based on facts learned through experimentation and observation. A scientific field or particular area of scientific study/branch of science refers to a systematically organized body of knowledge on a particular subject. In other words, it is a branch of knowledge or study

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dealing with a body of facts or truths showing the operation of general laws, e.g., environmental sciences. The National Research Council report, Taking Science to School (Duschl et al. 2007), identifies four strands of proficiency scientific proficiency, which are of equal importance and have an interwoven relationship, namely: 1. 2. 3. 4.

know, use, and interpret scientific explanations of the natural world; generate and evaluate scientific evidence and explanations; understand the nature and development of scientific knowledge; and participate productively in scientific practices and discourse.

By experiencing all four strands, it is more likely to grasp important ideas in science. In that vein, science is a way of helping our cognition and brain grow in finding new knowledge and defeat the curiosity of how the world evolves. In more detail, science as a collective institution aims to understand the world around us by generating more and more accurate explanations of how it works, and how it got to be the way it is now, based on hypotheses and related predictions. Hence, it is important because it influences most aspects of everyday life and improves human life at every level, from individual comfort to global issues. In relation to research, science is commonly organized mainly by academic and research institutions as well as government agencies. The practical impact of scientific research has led to the emergence of science policies that seek to influence the scientific enterprise. Modern science is typically divided into three major branches consisting of the following, with the disciplines using science being described as applied sciences: • the natural sciences, which study nature in the broadest sense; • the social sciences, which study individuals and societies; and • the formal sciences (e.g., theoretical computer science), which study abstract concepts without relaying on any empirical evidence.

2.2 The Scientific Method The scientific method—hypothesize, model, test—is an empirical method of knowledge acquisition and production, which has characterized the development of natural science for at least four centuries. It is built around testable hypotheses. In more detail, it involves formulating hypotheses, by means of induction, based on careful observations, which do entail rigorous skepticism (questioning doubt toward items of putative knowledge) about what is observed, given that cognitive assumptions about how the world works influence how scientists interpret an object of perception. The percept can bind sensations from multiple senses into a whole, or describes a perception that is independent from perceivers. In addition, however, the scientific method involves experimental and measurement-based testing of deductions (a process of reasoning from premises to reach a logically certain conclusion) drawn from the hypotheses; and refinement or rejection of the hypotheses based on the experimental findings. In regard to making observations and asking questions about the natural world as part of the scientific method, scientists and researchers are naturally inquisitive and curiosity-driven, so they often develop hypotheses about why things are the way they are. The chosen hypothesis (based on certain selection criteria) leads to predictions that can be tested in various ways. Its most conclusive testing comes from reasoning based on carefully controlled experimental data. Depending on how well additional tests match the predictions, the chosen hypothesis may require refinement, alteration, expansion, or even elimination. If this hypothesis becomes very well supported and thus survived testing, a general theory may be developed. In the coherentist approach to science development, a theory is validated if it makes sense of observations and experiments as part of a coherent whole. The scientific method consists of nine steps, namely: 1. 2. 3. 4. 5. 6.

Make an observation. Ask a question. Conduct background research. Form a hypothesis. Test hypothesis: design and perform a study. Analyze the data: observe and record.

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7. Draw conclusion. 8. Replicate the study in its entirety. 9. Report results/share findings. There are some difficulties in a formulaic statement of the scientific method. Indeed, the above steps are not always done in the same order, and not all of them take place in every scientific inquiry, nor to the same degree. Although they vary from one field of inquiry to another, they are frequently the same from one to another. Moreover, despite the scientific method being often presented as a fixed sequence of steps, the steps are better considered as general principles (Gauch 2003). Nevertheless, the overall process of scientific inquiry involves making hypotheses, deriving predictions from them as logical consequences, and then carrying out experiments based on those predictions to determine whether the original hypothesis was correct (Peirce 1908). To the discredit of the scientific method, some philosophers of science have argued that there is no scientific method, and the whole idea of a theory of scientific method is yesteryear’s debate.

2.3 Hypothesis and Hypothesis Testing The scientific method is based on the assumption that everything in the universe is linked by cause and effect, causality. There is a logical explanation for all the observed behavior of natural phenomena. So a scientist or researcher using the scientific method of inquiry starts by making assumptions about what to expect to find after conducting research. This initial assumption is called a hypothesis. A hypothesis means an idea or explanation made from limited evidence, thereby constituting a starting point for further investigation through observation and experimentation. In other words, it is a conjecture or postulation that is based on knowledge obtained while seeking answers to the question formed after making an observation. In science, in contrast to a theory which is a tested, well-substantiated, unifying explanation for a set of verified, proven factors, a hypothesis whether be it very specific or broad is either a suggested explanation for an observable phenomenon, or a reasoned prediction of a possible causal correlation among multiple phenomena. With respect to the latter, hypothesis statement is a prediction about what one think will happen or is happening in one’s experiment that can be tested to answer one’s question or problem. A hypothesis as a novel suggestion that no one wants to believe is guilty, until found effective through testing by conducting an experiment. The purpose of an experiment is to determine whether observations agree with or conflict with the predictions derived from a hypothesis. The application of hypothesis testing is predominant in data science. It is imperative to simplify and deconstruct it, and hypothesis testing based on data leads us from a novel suggestion to an effective proposition. This pertains to what is termed a statistical hypothesis, one that is testable on the basis of observing a process that is modeled via a set of random variables (Stuart et al. 1999). The most common application of hypothesis testing is in the scientific interpretation of experimental data, which is naturally studied by the philosophy of science. This is equally true of hypothesis testing which can justify conclusions even when no scientific theory exists. Commonly, two statistical datasets are compared, or a dataset obtained by sampling is compared against a synthetic dataset from an idealized model. A hypothesis is proposed for the statistical relationship between the two datasets, and this is compared as an alternative to an idealized null hypothesis that proposes no relationship between two datasets. A null hypothesis generally asserts that there is no meaningful relationship between two observed phenomena. Performing a hypothesis testing consists of a seven-step process: 1. 2. 3. 4. 5. 6.

Make assumptions. Take an initial position. Determine the alternate position. Set acceptance criteria. Conduct fact-based tests. Evaluate the results and confirm whether the evaluation supports the initial position, and whether there is confidence in that the results are not due to chance. 7. Reach one of the following conclusion: Reject the original position in favor of alternate position or fail to reject the initial position. A thought experiment (Robert Brown 2014) considers some hypothesis or principle for the purpose of thinking through its consequences. The common goal of a thought experiment is to explore the potential consequences of the principle in question:

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‘A thought experiment is a device with which one performs an intentional, structured process of intellectual deliberation in order to speculate, within a specifiable problem domain, about potential consequents (or antecedents) for a designated antecedent (or consequent)’ (Yeates 2004, p. 150). An explanatory thought experiment is put forward as explanation using relevant principles (e.g., parsimony—the idea that, all other things being equal, we should prefer a simpler explanation over a more complex one) and is generally expected to seek consilience: fitting well with other accepted facts related to the phenomena (Wilson 1999).. This new explanation is used to make falsifiable predictions that are testable in nature. Disproof of a prediction is evidence of progress, which is done through the observation of natural phenomena, but also through experimentation that tries to simulate natural events under controlled conditions as appropriate to the discipline. Experimentation is especially important in science to help establish causal relationships or to avoid the correlation fallacy. In addition, scientists may generate a model, an attempt to describe the phenomenon in terms of a logical, physical, or mathematical representation and to generate new hypotheses that can be tested on the basis of observable phenomena. However, based on the outcome of the observation and experimentation, a hypothesis is either modified or discarded. If the hypothesis survived testing, it may become adopted into the framework of a scientific theory, a logically reasoned, self-consistent model for describing the behavior of certain natural phenomena. While theories are formulated according to most of the same scientific principles as hypotheses, a theory typically describes the behavior of much broader sets of phenomena; commonly, a large number of hypotheses can be logically bound together by a single theory.

2.4 Scientific Models In science, the term model can have different meanings depending on the context of its use (e.g., a physical model of a system that can be used for demonstrative purposes, an idea about how something works, an object or process that is used to describe and explain phenomena that cannot be experienced directly, etc.). Models are central to what scientists do in their research and when communicating their explanations related to new scientific theories. As a research method, scientific modeling means creating a mathematical or logical model—a set of equations that indirectly represents a real-world system or process—as a basis for simulation: an imitation or emulation of the operation of a real-world system. These equations (often characterizing the nature of the reciprocal relationships pertaining to the system) are based on relevant information about the system and on sets of hypotheses about how the system works. The act of simulating the system requires that a model be developed, which represents the key characteristics, behaviors, and functions of that system. The model represents the system itself, whereas the simulation represents the operation of the system over time. In this regard, given a set of parameters, a model can generate expectations about how the system will behave in a particular situation. A model and the hypotheses it is based upon are supported when the model generates expectations that match the behavior of its real-world counterpart. For what it entails in terms of oversimplification, modeling often involves idealizing the system in some way— leaving some aspects of the real system out of the model in order to make the model easier to work with computationally in terms of simulation.

2.5 Scientific Theories In general, theories are created to explain and predict phenomena and, in many cases, to challenge, enhance, and extend existing knowledge within the limits of critical bounding assumptions. A scientific theory is a well-substantiated explanation of some aspect of the natural world—based on a body of facts that have been repeatedly confirmed through observation and experiment. In other words, it is a logically reasoned, self-consistent model for describing the behavior of much broader sets of natural phenomena. Commonly, a large number of hypotheses, surviving testing, can be logically bound together by, or become adopted into the framework of, a single theory. As often integrating and generalizing many hypotheses, scientific theories are concise, coherent, systematic, predictive, and broadly applicable, as well as strongly supported by many different lines of evidence. However, theories may be modified, overturned, or disregarded if warranted by new evidence and perspectives (e.g., using inductive empiricism or data-driven science approaches based on big data analytics for knowledge discovery).

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Fig. 1 Scientific theory and law

2.6 Scientific Laws Developed either from facts and hence being strongly supported by empirical evidence, scientific laws or principles are solidified and formal statements that describe or predict the outcome of a range of natural phenomena in the form of large collections of facts based on repeated experimental observations describing some aspects of the universe-verified scientific knowledge. The meaning of scientific laws varies based on the context or case of use: accurate, approximate, broad, or narrow theories, across all scientific disciplines and fields. Commonly, scientific laws reflect causal relationships fundamental to reality and are discovered rather than invented. While their accuracy does not change when new theories are worked out as manifested in the fixity of the underling equations, scientific laws, as with other scientific knowledge, do not have absolute certainty, as it is always possible for future observations to overturn them. Scientific laws differ from scientific theories, as well as from hypotheses, which are proposed during the scientific inquiry before and during validation by experiment and observation. They are merely distillations of the outcome of repeated experimental observations, while theories posit an explanation of phenomena. In other words, a scientific theory as a framework for observations and facts explains why the phenomenon exists or what causes it, whereas a scientific law is the description of an observed phenomenon. As such, it seeks to objectively explain the events of the natural world in a reproducible way: the closeness of the agreement between the results of measurements of the same measurand performed with the same methodology described in the corresponding scientific evidence. As illustrated in Fig. 1, scientific theories explain why something happens, whereas scientific laws record what happens. Further, while scientific hypotheses may lead to the formulation of scientific laws, they lack the level of verification such laws involve and may not be sufficiently general. A scientific hypothesis moves to a scientific theory in the scientific method and becomes accepted as a valid explanation of a natural phenomenon upon the accumulation of enough evidence for supporting it. However, although scientific laws describe nature mathematically and are practical conclusions reached by the scientific method, a question that is addressed by the philosophy of science, they are meant to be neither laden with statements of logical absolutes nor ontological commitments.

2.7 Theoretical Models As mentioned above, the term model enjoys a broad range of uses in science. Here, I shall examine one important use of this term: Theoretical models which are quite distinct from other conceptions sometimes called models. The philosophers of science have highlighted the importance of models and have claimed that their consideration will illuminate the structure, interpretation, and development of scientific thinking. A theoretical model is a group of related theories designed to provide explanations within a scientific domain for a community of practitioners. As a coherent whole, it is characterized by (Bibri 2015): • • • • • • •

involving a conceptual foundation for a scientific domain; understanding and describing problems within that domain and specifies solutions; being grounded in prior empirical findings and scientific literature; being able to predict outcomes in situations where these outcomes can occur far in the future; guiding the specification of a priori postulations and hypotheses; using rigorous methodologies to investigate them; and providing a framework for the interpretation and understanding of the unexpected results of scientific investigations.

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2.8 The Philosophy of Science The philosophy of science is a branch of philosophy concerned with the foundations, methods, and implications of science. It is a complex field of rigorous academic study that deals specifically with what scientific knowledge is, how it works, and the logic through which we build and organize it. The central questions of this study concern what qualifies as science, the reliability of scientific theories, the relevance of scientific approaches, and the ultimate purpose of science. There are three basic assumptions that the scientists usually take for granted and that are needed to justify the scientific method, namely (Heilbron 2003): (1) There is an objective reality shared by all rational observers. (2) This objective reality is governed by natural laws, and (3) These laws can be discovered by means of systematic observation and experimentation. The aim of the philosophy of science is a deep understanding of what these underlying assumptions mean and whether they are valid. The belief that scientific theories should and do represent metaphysical reality is known as realism. Metaphysics is the branch of philosophy that studies the essence of a thing (e.g., its cause and purpose) or its qualities of being and thus beyond the questions of its nature, including questions of being, becoming, existence, and reality. Accordingly, the field of the philosophy of science overlaps with other branches of philosophy, such as metaphysics, epistemology, and ontology, for example, when it explores the relationship between science and truth. The philosophers of science actively study such questions as: • What kind of hypothetical testing can be used in the scientific interpretation of experimental data? • What kind of data can be used to distinguish between real causation and accidental correlation? • Is there an external reality operating independently of an observer? Can it be objectively and accurately measured, tracked, statistically analyzed, modeled, and visualized to reveal the world as it actually is? • What is a law of nature? Are there any in non-physical sciences? • What kind, and how much, of evidence is needed to accept a hypothesis as a starting point for further investigation through experimentation? • Why do scientists continue to rely on models and theories, notwithstanding their partial inaccuracy? Usually, opinions on issues related to answering these questions and the extent to which they may be difficult to answer satisfactorily vary widely within the field, and occasionally part ways with the views of scientists themselves, who mainly focus on doing science, not analyzing it abstractly. In fact, there is no consensus among philosophers about many of the central problems concerned with the philosophy of science, including whether science can reveal the truth about unobservable things and whether scientific reasoning can be justified at all. In this line of thinking, Feyerabend (1993) argues that the idea that science can or should operate according to universal and fixed rules is unrealistic, pernicious, and detrimental to science itself. In addition to these general questions about science as a whole, the philosophers of science consider problems that apply to particular sciences, and also use contemporary results in science to reach conclusions about philosophy itself. Moreover, Feyerabend (1993) argue that there is no such thing as the scientific method, and hence all approaches to science should be allowed, including explicitly supernatural ones. Also, the general questions of the philosophy of science also arise with greater specificity in some particular sciences, e.g., the question of the validity of scientific reasoning is seen in a different guise in the foundations of statistics. In the spirit of the philosophy of science: To formulate criteria for ensuring all philosophical statements’ meaningfulness and objectively assessing them, Kuhn (1962) challenges the view of scientific progress as steady, cumulative acquisition of knowledge based on a fixed method of systematic experimentation, and instead argues that any progress is relative to a paradigm, the set of questions, concepts, and practices that define a scientific discipline in a particular historical period, and that the process of observation and evaluation takes place within a paradigm, a logically consistent portrait of the world that is consistent with observations made from the way it is framing. This topic is addressed in more details in the next subsection. Additionally, the philosophy of the social sciences explores whether the scientific studies of human nature can achieve objectivity or are inevitably shaped by social relations and values (see, e.g., Bibri 2015; Bibri and Krogstie 2016).

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One of the most popular schools of thought in the philosophy of science is empiricism, which holds that knowledge is created by a process involving observations, and that scientific theories are the result of generalizations from these observations (Godfrey-Smith 2003). Empiricism entails a set of philosophical approaches to building knowledge that emphasizes the importance of observable evidence: to note, record, or attend to a result, occurrence, or phenomenon using directly our senses or indirectly tools, from the natural world. It is associated with one of the recent epistemological positions enabled by data-intensive science as a paradigm shift and shaping urban science: inductive empiricism in which the deluge of data, through analytics as manifested in such data being wrangled through an array of multitudinous algorithms to discover the most salient factors concerning complex phenomena, can speak for itself free of human framing and subjectivism and without being guided by theory. Indeed, empiricism generally encompasses inductivism, a position that tries to explain the way general theories can be justified by the finite number of observations humans can make and hence the finite amount of empirical evidence available to confirm scientific theories. Induction is method of reasoning in which a generalization is argued to be true based on individual examples that seem to fit with that generalization. This is necessary because the number of predictions scientific theories make is infinite, which means that they cannot be known from the finite amount of evidence using deductive logic only. Deduction is a method of reasoning in which a conclusion is logically reached from premises. However, many versions of empiricism exist, with the predominant one being the hypothetico-deductive method and Bayesianism (Godfrey-Smith 2003). Other schools of thought in the philosophy of science include the following: • Rationalism holds that knowledge is created by the human intellect—not by observation (Godfrey-Smith 2003). • Instrumentalism emphasizes the utility of scientific theories as instruments for explaining and predicting phenomena (Newton-Smith 1994), and views them as black boxes with only their input (initial conditions) and output (predictions) being relevant. • Constructive empiricism, which is close to instrumentalism, holds that the main criterion for the success of a scientific theory is whether what it says about observable entities is true. • Epistemological anarchism holds that there are no useful and exception-free methodological rules governing the progress of science, and that the idea that science can or should operate according to universal and fixed rules is unrealistic, pernicious, and detrimental to science itself (Feyerabend 1993). • Methodological naturalism holds that a difference between natural and supernatural explanations should be made, and that science should be restricted methodologically, not ontological, to natural explanations, which means that science should not consider supernatural explanations itself, but should not claim them to be wrong either; rather, supernatural explanations should be left a matter of personal belief outside the scope of science (Godfrey-Smith 2003). Methodological naturalism maintains that proper science requires strict adherence to empirical study and independent verification as a process for properly developing and evaluating explanations for observable phenomena (Brugger 2004).

2.9 Paradigm and Paradigm Shift The word paradigm can be used to describe or indicate a typical pattern, archetype, or model of something (e.g., smart sustainable cities are a leading paradigm of urbanism). In this regard, a paradigm, as argued, does not impose a rigid approach, but can be taken more or less flexibly. Endeavoring to give this concept its contemporary meaning, the historian of science Kuhn (1962) uses the word to refer to the set of concepts and practices that define a scientific discipline at any particular period of time. In his book, The Structure of Scientific Revolutions (1962/1996), Kuhn defines a scientific paradigm as: universally recognized scientific achievements that, for a time, provide model problems and solutions for a community of practitioners. In short, characteristic of the sciences is the existence of a single (historically contingent or conditioned) reigning paradigm, a worldview that dominates science for a period of time during which that worldview is (determined to be) extended. This implies that scientific facts are never really more than opinions, or theories are just beginnings, whose dominance is transitory and far from conclusive. However, in more detail, a paradigm denotes, in science and philosophy, a distinct set of thought patterns, including theories, research methodologies, assumptions, and standards for what constitutes legitimate contributions to a scientific domain. In other words, it refers to a philosophical and theoretical framework of a scientific discipline within which theories, generalizations, principles, and experiments are formulated, or subsequent work is structured. As such, it represents the entire worldview in which the current theory exists, and all the ensuing implications. In this respect, a paradigm entails the explanatory power—and hence the universality of a theoretical model—and its broader institutional implications for the structure, organization, and practice of science (Kuhn 1962). This

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power means the ability of a theory (or a group of related theories) to effectively explain the subject matter it pertains to, and a theoretical model is a theory designed to provide explanations within a scientific domain for a community of practitioners —in other words, a scientific discipline shared intellectual framework encompassing the basic assumptions, ways of reasoning, and methodologies that are universally acknowledged by a scientific community (Bibri 2015). All in all, Kuhn’s notion of paradigm is based on the existence of an agreed upon set of concepts, theories, and practices for a scientific domain, and this set constitutes the shared knowledge and specialized language of a discipline (e.g., data science, computer science, urban science, etc.), i.e., an all-encompassing set of assumptions resulting in the organization of scientific theories and practices. In relation to the social sciences, the word paradigm is equated with worldview: the set of experiences, beliefs, and values that affect the way people perceive reality and respond to that perception in society. In other words, worldview refers to the fundamental cognitive orientation of society encompassing the whole of its knowledge and point of view. Thus, this concept is fundamental to epistemology, and also to episteme as a wide world perception or involving the underlying global structure that determines the evident and functioning life of all the cultural manifestations of a particular historical epoch. Moreover, worldview entails discourses and social practices and their dialectic relationship (see Bibri 2018a for a detailed account). Mechanisms similar to the original Kuhnian paradigm have been invoked in various disciplines, including the idea of major cultural themes (Spradley 1979), worldviews, ideologies, and mindsets. They have somewhat similar meanings that apply to smaller- and larger-scale examples of disciplined thought. Handa (1986) introduced the idea of ‘social paradigm’ in relation to the social sciences, identifying the basic components of a social paradigm and addressing the issue of paradigm shift. In this respect, he focused on social circumstances that precipitate such a shift as well as its effects on social institutions, which changes the way the individual perceives reality. A dominant paradigm refers to the system of thought in a society that is most standard and widely held at a given time. This is somewhat similar to the concepts of episteme and historical a priori (described below), which are credited to Foucault (1972). Especially, dominant paradigms are determined and shaped by the episteme of a given culture and its historical context. On the whole, social scientists have adopted the Kuhnian paradigm shift to denote a change in how a given society goes about organizing and understanding reality. After a given scientific discipline has changed from one paradigm to another, this is called, in Kuhn’s terminology, a scientific revolution or paradigm shift. Characterizing a paradigm shift (also referred to as radical theory change) is a fundamental change in the basic concepts and experimental practices of a scientific discipline, i.e., in thought patterns. Following the Kuhnian paradigm shift, a scientific revolution represents an epistemological shift, or denotes a significant change within belief systems. This occurs when the dominant paradigm (worldview) becomes eroded, when scientists encounter inconsistencies and anomalies (typically brushed away as acceptable levels of error, or simply ignored and not dealt with, yet entailing various levels of significance to the practitioners of science at the time) that mount up to such an extent that researchers can no longer work within the philosophical and theoretical framework of a scientific discipline. This implies that these inconsistencies and anomalies cannot be explained by the universally accepted paradigm within which scientific progress has thereto been made. The history of science has shown that the turbulence that sets in can lead to a paradigm shift that takes place over years or decades rather than centuries. According to Kuhn (1962/1996, p. 12), ‘successive transition from one paradigm to another via revolution is the usual developmental pattern of mature science.’ Accordingly, the sciences go through alternating periods of normal science when an existing model of reality dominates a protracted period of puzzle-solving, and revolution, when the model of reality itself undergoes sudden drastic change. This revolution involves the science to be thrown into a state of crisis, according to Kuhn, during which new ideas are tried. And eventually, a new paradigm is formed, which gains its own new followers, and an intellectual battle takes place between them and the hold-outs of the old paradigm. Sometimes, the convincing force is just time itself and the human toll it takes, Kuhn stated, using a quote from Max Planck: ‘a new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it’ (Kuhn 1962/1996, p. 150). By and large, a paradigm shift in a scientific discipline should meet three conditions or encompass three criteria: It must be grounded in a meta-theory: theory about theory, be accepted by practitioners of a scientific community, and have a body of successful practices (Kuhn 1962).

2.10 Discourse: Concepts and Theories 2.10.1 Discourse The term discourse can be used in varying ways, with different meaning in different contexts. Scholars from the social sciences tend to construe the Foucauldian approach to the concept of discourse from different perspective. According to Hall

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(1997, p. 44) Foucault defines discourse as ‘[A] group of statements which provide a language for talking about–a way of representing the knowledge about–a particular topic at a particular historical moment… But since all social practices entail meaning, and meanings shape and influence what we do–our conduct–all practices have a discursive aspect’. To Gordon (2000, pp. i–xli), Foucault conceives of it as ‘an identifiable collections of utterances governed by rules of construction and evaluation which determine within some thematic area what may be said, by whom, in what context, and with what effect.’ In addition, where a particular set of statements are ideological, discourse is defined as a system of representation developed socially to create and circulate a coherent set of meanings, which serve the interests of certain groups of society. Overall, common to all the definitions of discourse is that it denotes a particular way of thinking and talking about some aspects of social life or the world language and its constitution role. In this context, underlying the term ‘discourse’ is the idea that language as a form of discursive practice is structured according to a system of statements (e.g., what can be said about smart sustainable/sustainable smart urbanism) used by people (e.g., urban planners, ICT experts, data scientists, urban scientists, researchers, scholars, policymakers, etc.) as a particular way of understanding, talking about, and producing a particular kind of knowledge about the urban world (e.g., the physical, spatial, environmental, economic, and social dimensions of the city), as well as taking part in different domains of urban life (e.g., urban planning, urban design, urban research, urban sustainability, applied urban science, and urban analytics) (Bibri 2018a). Discourse, as defined by Foucault (1972), refers to the ways of constituting knowledge together with the social practices, forms of subjectivity and power relations which inhere in such knowledge. In short, discourses are practices which form the object that discourses talk about (Foucault 1972). In this regard, they constitute the conditions of possibility for the kind of socially anchored and institutionalized practices. And the object takes the form of a coherent set of ideas, concepts, terminologies, claims, assumptions, categorizations, visions, and prospects pertaining to smart sustainable/sustainable smart urbanism that is constructed, reconstructed, transformed, and challenged in the underlying practices, and that through which meaning is given to such urbanism.

2.10.2 Academic Discourse An academic discourse refers to the various ways of thinking, using language, and producing meaning in academic institutions, more succinctly, the specific styles of communication used in the academic world (Bibri 2018a). It represents a privileged form of argument in modern society, offering a model of rationality and detached reasoning (Hyland and Bondi 2006). As such, it provides a somewhat objective description of what the human and social world is actually like, and this, in turn, serves to distinguish it from the socially contingent form of description (Bibri 2018a). This pattern of persuasion, which involves the use of language to relate independent beliefs to shared experience, is seen as a guarantee of reliable knowledge, and we invest it with cultural authority, free of the cynicism with which we view other discourses such as politics (see Hyland 2000). To put it differently, an academic discourse depends on the demonstration of absolute truth or empirical evidence, representing what Lemke (1995) refers to as the discourse of truth. 2.10.3 Discursive Truth Based on Foucault (1972) assertion that there are various systems of knowledge, the rules for what is considered to be true or false, that is, different discourses determine what can be true and false, truth is a discursive construction and thus socio-culturally specific. For example, an array of discourses on the city (e.g., smart urbanism, sustainable urbanism, ecological urbanism, etc.) is available today to people; each carries with it a supporting body of knowledge and thus ‘truth’ for those who see things that way. While these discourses as ways of understanding and talking about the city and its transformation carry different ‘truths,’ only one or a few of them at a certain point in history gain dominance and authority within a given society, while other understandings become inconsequential or discredited. This depends on the technological, scientific, ecological, economic, political, social, and cultural ‘baggage’ (knowledge and truth) brought to the question of urban transformation. In addition, as Foucault asserts, ‘it is not possible to gain access to universal truth since it is impossible to talk from a position outside discourse; there is no escape from [social] representation. Truth effects are created within discourses’ (Phillips and Jørgensen 2002, p. 14). In this sense, truth becomes unattainable, and hence, discourse can only, as being socio-culturally situated, make a non-conclusive claim to truth (Bibri 2015). To reiterate, Michel Foucault and Thomas Kuhn adhere to one of the premises shared by social constructionist approaches that our (scientific) knowledge of the world is not a mere reflection of reality or an objective representation of nature, and thus should not be treated as absolute truths. However, Foucault’s argument that discourses produce the truth meanings of objects and practices is an idea that tends to make the nihilistic proposition that discourses determine everything, or that there is nothing outside discourse (Bibri 2015). Nevertheless, some argue that this idea is one of the reasons why Foucault is regarded as an intellectual icon of postmodernism, with this argument similar to the polemical statement made by Derrida as a postmodernist that nothing exists ‘beyond the text’ (Bibri 2015).

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2.10.4 Power as a Productive and Constitutive Force One of Foucault’s (1970) central assertions is that a discourse of knowledge represents a discourse of power, as knowledge is an effort not only to order facts, objects, and social actions and events, but also to order human subjects according to a given center. In dissecting the nature of power, Foucault (1991) contends that it is far more than simple force, radiating around in a complex web of directions and thus operating at all levels of society. In common with discourse, power is everywhere, spread across social practices, and thus not held by particular actors with particular interests (Bibri 2015). Hence, power should not be seen as exclusively exercised in domineering or oppressive acts of particular individuals or social groups, but may be enacted in the myriad taken-for-granted social practices and actions of daily life (Bibri 2015). Foucault (1980, p. 19) asserts that power ‘needs to be considered as a productive network which runs through the whole social body, much more than as a negative instance whose function is repression’. Discourses and their functions are strategically productive and integrative notions: They produce the social world and separate objects from one another so they attain their distinct characteristics and relationships to one another. Foucault (1980) states that power holds good because it forms knowledge, produces discourse, creates things, and induces pleasure. From another perspective, Foucault (1991, cited in Gordon 2000, pp. i–xii), asserts that there is constant articulation ‘of power on knowledge and of knowledge on power’: The exercise of power constantly creates and causes to emerge new knowledge and its accumulation, which perpetually induces power effects. In addition, Foucault’s conception of knowledge/power relationship suggests that knowledge is useful and essential to the exercise of power because of its practical use rather than its correctness (Gordon 2000). In view of the above, the power of smart sustainable/sustainable smart urbanism as a combination of scientific and social knowledge provides the conditions of possibility for the techno-urban/socio-technological. 2.10.5 The Relationship Between Power, Knowledge, and Truth Foucault’s (1972) notion of power/knowledge has implications for his conception of truth. What particularly draws his attention is knowledge/power relationship, and the way it can lead to the generation of certain ‘truths’ about the human subject. In essence, he concurs with the adage ‘knowledge is power,’ contending that power is involved in the way in which certain knowledge and thus truth is applied (Hall 1997). In his later work, Foucault (1980) investigated the way in which discourses are applied to the social world, focusing on the ‘discursive formations’ in a given society, which comprise institutional apparatuses and their techniques, including the episteme, the rules, the institutional frameworks, the subjects, and the things. Such regularities relate to what he calls ‘regimes of truth,’ that is, the historically specific social mechanisms which produce discourses that function as true in particular times and places and that are made true through discursive practices. He thus views truth as a system of procedures for the production, regulation, and distribution of all forms of discursive statements. Accordingly, smart sustainable/sustainable smart urbanism as techno-urban imaginaries is discursively constructed and materially reproduced through institutional and organizational practices and structures specific to the Western culture/European society where they are to be perceived, interpreted, and consumed as a corpus of statements. As Foucault (1972) notes in relation to discourse, truth as knowledge induces effects of power and is created by virtue of multiple, diverse forms of constraint; each society has its ‘general politics’ of truth (e.g., scientific knowledge), i.e., the historically specific systems that generate discourses and make them function as true, enabling one to distinguish between the statements that are true and those that are not, and differentiate the status of those actors that are charged with saying and advancing what counts as true knowledge in particular times and places. Therefore, truth is entrenched in, and produced by, systems of power. In light of this, smart sustainable/sustainable smart urbanism as systems of truth are infused with power relations and, thus, ways of seeing which has impact upon the human subject. To put it differently, the agents behind the discourse of such urbanism operate within the limits of the regime of truth and the institutional apparatuses of Western culture, to reiterate, in the current historical period. However, some views argue that Foucault renders the notion of ‘truth’ problematic. He exposes his argument of discursive ‘regimes of truth’ ‘to the charge of relativism,’ as his focus on discourse tends to overlook the material structural factors implicated in the distribution of knowledge/power (Hall 1997, p. 51). Notwithstanding Foucault’s theories of power/knowledge being insightful, ‘their totalising, omnipotent, metaphysical position places almost too much stress on the Foucauldian paradigm to account for everything’ (Hobbs 2008, p. 13). 2.10.6 Discursive and Social Practices Our understanding of virtually all aspects of social life is based on some kind of discourses: this is the way we make sense of, or ascribe meaning to, the world around us. Indeed, discourses are at the core of social constructionism, which develops understandings about the world that constitute the basis for shared assumptions about reality (Bibri 2018a). Social reality ‘is produced and made real, i.e., social and political actions are engineered and become meaningful, through discourses, and social interactions with their various forms of social processes cannot be utterly understood without reference to the

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discourses that give them meaning and form’ (Bibri 2015, p. 39) in the first place. In other words, the constitution of social life occurs through discursive practices. A discursive practice refers to the process through which (dominant) reality comes into being (Foucault 1972). Here, practice is not meant to be understood as the opposite of theory; instead, it entails activities that people engage in, deliberately, with the goal of developing knowledge and skills. It also denotes the construction and reflection of social realities through actions. The basis of a discursive practice approach is the insistence that discourse is action and not merely representation. In this regard, meaning is negotiated in interaction rather than present once-and-for-all in our utterances. Foucault was concerned with the discursive practices of episteme, that is, those actions taken as part of the real-world application of discourses. In essence, he agreed with the adage ‘knowledge is power,’ arguing that power is implicated in the manner in which certain knowledge is applied (Bibri 2015). Foucault (1972) explored the manner by which a discourse is applied to the social world, focusing on the institutional apparatuses and their technologies (e.g., the systems of thought, the rules, the institutions, the subjects, and the things). These together comprise particular discursive formations: the regularities that produce discourses (e.g., scientific disciplines). Accordingly, socio-cultural reproduction and change take place through discursive practices (e.g., Foucault 1972; Phillips and Jørgensen 2002). This means that discourses produce knowledge through language use and meaning construction, and also entail how this knowledge is institutionalized and conventionalized, thereby shaping social practices and setting new ones into play. On this note, Foucault (1972) asserts that all social practices have a discursive aspect because they involve meanings that shape and influence our actions. In a nutshell, there is a dialectical relationship between discourse and social practice. Certain social practices become legitimate forms of actions from within discourse as a system of understanding the world, and these practices, in turn, reproduce and support the discourse which legitimates them in the first place. Constructionist worldview posits that particular understanding of the world leads to particular social actions, whereby some forms of actions become unthinkable. For example, the discourse of smart sustainable/sustainable smart urbanism reshapes the actions of urban planners, scientists, scholars, and policymakers as societal actors, as well as the meanings they ascribe to their urban endeavors. However, particular discursive constructions and the position contained within discourses open up and close down opportunities for actions by constructing particular ways of seeing the world and positioning an array of subjects within them in particular ways (see Bibri 2015 for an analytical account of subject positioning in discourse).

2.11 Epistemology, Episteme, Historical a Priori, and Their Interrelationships Meaning knowledge (episteme), epistemology is a branch of philosophy that is concerned with the theory of knowledge. It is the study of the nature of knowledge, justification, and the rationality of belief. In other words, it deals with what knowledge is, how we justify the acceptance of things, and how we come to accept them as true. Among the common questions addressed by epistemology include: What it mean to say that we know something and fundamentally how we know that we know. The philosophical analysis of the nature of knowledge and how it relates to truth and justification, the questionability of different knowledge claims, the criteria for knowledge and justification, and the sources and scope of knowledge are the common four areas that are at the center of debate in epistemology. In research paradigm, the epistemological question involves the nature of the relationship between the knower and what can be known, and the methodological question entails the way in which the inquirer can go about finding out whatever they believe can be known. The nature of human knowledge and understanding can be acquired through different types of research methodology (e.g., scientific approach). Epistemology is an essential driver for thinking and reasoning about reality. This pertains to how the inquirer goes about producing new knowledge about what they consider important to be known, which involves the maps applied by the knower examining or studying what is there to or can be known in order to generate new knowledge. The terms ‘episteme’ and ‘historical a priori’ are two of the Foucault (1972) most widely used and often quoted discursive notions. According to him, episteme refers to the epistemological field which forms the conditions of possibility for knowledge, the orderly structures underlying the production of knowledge, or a principled system of understanding or a body of ideas which give shape to knowledge, in any particular time and place. Foucault (1972) asserts that knowledge is a matter of episteme, the space of knowledge in which configurations are grounded in a set of claims, assumptions, premises, and truths basic to how the whole culture decides and justifies what is certain of—in other words, a precognitive space that determines ‘on what historical a priori, and in the element of what positivity, ideas could appear, sciences be established, experiences be reflected in philosophies, rationalities be formed, only, perhaps, to dissolve and vanish soon afterwards’ (Foucault 1970, p. xxi–xxii). Foucault’s central argument is that different periods of history constitute different systems of thought or epistemological fields, and all social constructions of scientific knowledge (e.g., data-intensive science) fall under the episteme of a historical epoch.

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In light of the above, episteme entails the set of relations between, or a level of various shifting relations involving, science and knowledge as they materialize within a discursive positivity (Bibri 2015). Hence, it is not itself a tangible form of knowledge, and thus has no content in and of itself. This use of episteme has been asserted to equate to Kuhn’s (1962) notion of paradigm in the sense of distinct thought patterns in any scientific discipline. Foucault uses episteme as an overarching term for all scientific paradigms, thereby exceeding Kuhn’s notion of paradigm—aspects of paradigm in Kuhn’s original sense. In other words, Foucault’s meaning of episteme involves both the transcending historical process as well as the underlying global structure that determines the currently evident and functioning (or active) life of all the cultural manifestations of a particular historical epoch. Historical a priori (also referred to as the ‘positive unconscious of knowledge’) denotes the order underlying any given culture at any given period of history, of which episteme, scientific forms of knowledge, is a subset (Bibri 2015). It involves —and shifts with the transformations of—the positivities that constitute discursive formations (scientific disciplines), sub-formations, and statements, as well as the relations between them. The term ‘positivity’ is used by Foucault (1972) to refer to, in addition to different positivities, an approach to discourse that excludes anything hidden within it, missing from it, or lying beneath it—only its visible, traceable relations. Indeed, characteristic to discourses is that they are based on an interlacing of knowledge and practice, which leaves out certain topics or themes and organize specific rationality (Bibri 2015). As to discursive formations, they consist of institutional apparatuses and their techniques (e.g., rules, institutions, systems of thought, subjects, things, etc.), which are used to apply discourse to the social world. Concerning statements, a discourse denotes a coherent body of statements that are structured and organized in a systematic way to create a self‐ confirming account of social reality, and that attempt to make it true and real. In Foucault’s conception of discourse, what may be said within some topics and by whom, where, and with what implication is determined by the rules of construction and evaluation governing a distinguishable set of utterances (Gordon (2000). He investigates the conditions of existence for meaning production in discourses, and how statements emerge on the basis of historical rules and conditions (historical a priori), which delimit what can be uttered. He states that discourse consists of ‘a limited number of statements for which a group of conditions of existence can be defined. Discourse in this sense is not an ideal, timeless form…it is, from beginning to end, historical–a fragment of history…posing its own limits, its divisions, its transformations, the specific modes of its temporality’ (Foucault 1972, p. 117). Accordingly, as a discursive field, data-driven smart sustainable/sustainable smart urbanism creates a network of rules as preconditions for statements to exist and to be meaningful. Such rule-bound sets of statements impose restrictions on what gives meaning in the discourse of such urbanism, and as a consequence, innumerable statements are not articulated and would never be accepted as meaningful. This relates to Foucault’s (1980) conception of power as a constraining force: Power is responsible for the particular manners in which the social world can be understood and talked about, which excludes alternative ways of understanding and talking. On the whole, the historical a priori underpins the epistemological field which, in turn, forms the conditions of possibility for social and scientific knowledge and its varied discourses. Accordingly, such conditions specify the conceptual and phenomenological landscape wherein combinations of elements give rise to appearances of various intellectual and physical phenomena. It is to note that a shift in such landscape, which is determined to occur in history, interrupts the progression of knowledge by changing how philosophers and scientists view the world in terms of assumptions and premises, as well as the questions they ask about that world and the instruments they employ to understand it by answering those questions. This brings us again to the link between episteme and scientific paradigm, and thus to data-intensive scientific development (i.e., inductive empiricism and data-driven science) and its use of big data analytics techniques to generate or discover new knowledge.

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Michel Foucault and Thomas Kuhn’s Contribution to the Philosophy of Scientific Knowledge

The two notable products of the interest of many historians, philosophers of science, as well as sociologists of science in the relationship between scientific knowledge and its application were Kuhn’s (1962) study, The Structure of Scientific Revolutions, and Foucault’s (1966, 1969) two studies, The Order of Things: An Archaeology of the Human Sciences and The Archaeology of Knowledge. Both Kuhn and Foucault adhere to the two premises shared by the social constructionist approaches (Bibri 2015). First, the ways we understand, view, and explain the world are subject to constant reconfigurations, perennially changing, as they are historically contingent and socio-culturally specific; and second, our knowledge of the world is not mere reflections of reality/pure representations of nature and thus should not be treated as absolute or objective truths (e.g., Burr 1995; Gergen 1985). In other words, social constructionist worldview posits that we are fundamentally cultural and historical beings and our knowledge about the world is the products of people’s daily making of history, i.e., historically situated interchanges among them (Gergen 1985). As concluded by Bibri 2015, pp. 49–50), ‘social

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constructionist worldviews do not provide a particular view of the world, other that there is no one true view of the world, and also a particular understanding of the world at some point gains dominance, while other understandings become insignificant. To put it differently, they postulate that the world is inherently heterogeneous and that any sense-making (attributing meaning to social constructions) or meaning–production system is not mere reflections of reality, but rather interpretations that are conditioned by their social context and the socially dominant discourses within at a particular time of history. Hence, social constructionist approaches offer numerous readings aiming at deconstructing generally held social assumptions, premises, and values.’ One implication of the above is that scientific discoveries and technological developments will continuously be situated in a volatile and tense relationship with an inherently contingent, heterogeneous, fractured, conflictual, plural, reflexive social world (Bibri 2015). Kuhn and Foucault’s influential works were instrumental in crystallizing a new approach to the historical and social studies of science, in which scientific knowledge and related facts and claims were seen as outcomes of socio-culturally and historically conditioned inquiries rather than mere reflections of reality. Foucault challenges the validity of the absolute truth claims pertaining to the human sciences, contending that they are articulated within the limits or confines of a particular scientific discourse and society’s general politics or regimes of truth. Foucault (1972) asserts that knowledge, whether theoretical or silently invested in practice, is fundamentally culturally contextual and historically situated, as well as a matter of episteme, the rigid understandings of truth that lies beneath all the discourses of knowledge of a particular epoch, which is a subset of historical a priori/positive unconscious of knowledge. This implies that different periods of history constitute different epistemological fields or systems of thoughts, and all social constructions of (scientific) knowledge fall under the episteme of a historical epoch, to reiterate. Likewise, Kuhn (1962) challenges the then-prevalent view of science as a buildup of objective facts toward a more understanding of truth, contending that scientific discoveries are contingent upon the kinds of questions scientists ask, which in turn hinge on their philosophical commitments, among others. One corollary of this is that the prevailing scientific foundations, assumptions, and methods used to probe or look at the world become riddled with issues, which can incite radical scientific revolutions. These are dubbed by Kuhn as paradigm shifts. Paradigm shifts alter the behavioral patterns underlying the evolution of knowledge by changing how scientists view the world in terms of the way they go about to reason about nature or reality, i.e., the questions they formulate about the world as well as the methods they employ to understand it. The above also relates to the concept of episteme introduced by Foucault (1972) in the sense of the conditions of possibility for knowledge. In particular, ‘the distinguishing characteristic of modern science is its methodology: the means and tools by which it achieves results and accumulates knowledge about the world around us. This knowledge cannot be infallible merely because there is no definitive truth, and the quest for it will continue ceaselessly. Besides, our experience and reasoning remains limited to fully comprehend and transcend what is larger than us and contain us, that is, the world. The only truth discovered so far is that there is no absolute truth. Science does not currently, and probably never will, give statements of eternal truth, a timeless form of it. It only provides theories, which should properly be evaluated as beginnings rather than ends. Some of these theories will indeed be refined or expanded, and others may even be completely discarded in favor of alternative theories that might emerge in light of new data generated by scientists’ (Bibri 2015, p. 22). All in all, scientific knowledge is in constant change. This implies that, as Kuhn (1962) concludes, the path of science through paradigm shifts or scientific discoveries is not necessarily (and perhaps won’t be) toward truth but merely away from previous mistakes. Foucault and Kuhn’s works have forced scholars and scientists within the human sciences and the hard sciences, respectively, to reflect on the assumptions that underpin their empirical endeavors, to seriously consider matters of epistemology, e.g., epistemological limits. The implications posed by their theories have been at the center of a plethora of explorations, e.g., dedicated books, by high-profile theorists, writing about and often highlighting their contribution to the common branches of science. Their aforementioned books have given rise to the ongoing disagreements and debates over the specificity and contingency of scientific knowledge and the nature of science and what this entail in terms of the availability of truth as well as the possibility of evolutionary progress. However, Foucault (1972) asserts that the shifts in the configuration of knowledge, from episteme to another, ought not to be conceptualized as a sort of evolutionary progress toward better systems of thought and thus knowledge, reflecting a history of growing perfections, but rather a mere pragmatic understanding, what is socially and historically valued and considered to be knowledge (Bibri 2015). In contrast, Kuhn (1962) vehemently denies the belief that the discovery of paradigm shifts and the dynamic nature of science are a case for relativism: the view that all kinds of belief systems are equal (Sankey 1997), and states that when a scientific paradigm is replaced by a new one, albeit through a complex social process, the new one is always better, not just different. Nonetheless, this claim of relativism is tied to another claim that Kuhn does somewhat endorse: that the theories of different paradigms cannot be translated into one another or rationally evaluated against one another—that they are incommensurable.

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Foucault is regarded as an intellectual icon of the postmodern pantheon and Kuhn as a prophet of postmodernism. They both assert that truth in its various formulations and configurations as social and historical constructs is built on the view of society at a specific point in history. Their works are seen as an examination of the scientific community and society at large. Among the several ramifications of their work was a systematic endeavor by the philosophers of science and the sociologists of science to investigate how scientific discovery and technology (Kuhn’s focus) and scientific knowledge and its discourses (Foucault’s focus), respectively, link up with other societal developments, such as culture, politics, policy, ethics, and jurisprudence. Within the confines of this chapter, it is postulated that S&T is socially situated and mediated: Advances in S&T (specifically big data computing and its technological applications) shape and influence society and vice versa, with a focus on the former in the context of smart sustainable/sustainable smart urbanism. For a detailed account of the social shaping dimensions of such urbanism, the interested reader can be directed to Bibri and Krogstie (2016).

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Scientific, Paradigmatic, and Scholarly Shifts

Big data science and analytics is instigating a radical change in the basic concepts, assumptions, and experimental practices of science-thought patterns or ways of reasoning within the ruling theory of science, marking a paradigm shift from the dominant scientific way of looking at the world. The use of big data analytics as a framework has tremendous potential to advance, or replace, the prevailing scientific method. It has been argued that the unfolding and soaring deluge of data renders the scientific method obsolete, and heralds the end (or decline) of theory resulting from the generalizations obtained from the experiments conducted by scientists as part of the scientific method. Taking everything into consideration, the current model of reality, which has dominated a protracted period of puzzle-solving, is undergoing sudden drastic change—with wide-ranging societal implications.

4.1 On the Old and New Way of Doing Science One of the key questions the philosophers of science are concerned with and actively study is: Why do scientists continue to rely on models and theories which they know are partially inaccurate, among others? Indeed, how many of the theoretical models have been modified, overturned, or disregarded in light of new evidence and novel perspectives? How many of the theoretical models that have constantly been corrected or refined are still useful? And how many of the yet useful theoretical models are able to consistently, if imperfectly, explain the world around us? The attempt to answer these questions prompts us to question the accuracy of such models in the first place. A theoretical model is typically associated with the explanatory power underlying a scientific paradigm, i.e., an intellectual and philosophical framework of concepts, theories, procedures, results, and generalizations within which subsequent work is structured. The history of science is replete with epistemological breaks, what Bachelard (1986) refers to as unthought/unconscious structures that are immanent within the realm of the sciences. It, as asserted by Bachelard (1986), consists in the formation and establishment of these epistemological breaks, and then the subsequent tearing down of the obstacles. The latter stage is an epistemological rupture—where an unconscious obstacle to scientific thought is thoroughly ruptured or broken away from. Indeed, as asserted by Foucault (1970, pp. xxi–xxii), knowledge is a matter of episteme: a precognitive space that determines ‘on what historical a priori, and in the element of what positivity, ideas could appear, sciences be established, experiences be reflected in philosophies, rationalities be formed, only, perhaps, to dissolve and vanish soon afterwards.’ In a nutshell, the history of science has shown that the turbulence that sets in can lead to an epistemological break or paradigm shift that takes place over varied periods of time. This implies that, among others, all theoretical models are flawed, if not wrong, and increasingly we can do better or succeed without them. For example, while quantum mechanics is yet another theoretical model that is flawed, no doubt a caricature of a more complex underlying reality, quantum mechanics based on statistical analysis offers a way better picture of reality. The basic argument is that the more we learn about natural phenomena, the further we find ourselves from a theoretical model that can explain them. Nevertheless, we do not have to settle for theoretical models as we grow up in an era of massively abundant data, a deluge or corpus that is indeed being treated as a laboratory of the human condition, thereby providing the raw material for sifting through the most measured and tracked age in history. In the upcoming Exabyte/Zettabyte Age, the analysis of the deluge of data will generate valuable knowledge and deep insights, which will be good enough to enhance human decisions and thus practices, as well as advance and accelerate progress on science. In the data-intensive approach to scientific discovery, no causal analysis and assumptions about any kind of relationships are required; moreover, such approach applies sophisticated methods (i.e., advanced

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simulation models informed by the science of complexity in terms of the common dynamical properties, processes, and behaviors characterizing complex systems) for predicting outcomes far in the future with unprecedented accuracy, as well as uses rigorous frameworks (e.g., data mining, statistical analysis, etc.) to answer challenging analytical questions (See Bibri 2018a for a detailed account and discussion of complexity science and complex systems in relation to smart sustainable cities of the future). With the deluge of the data available thanks to its new and extensive sources, the numbers (overwhelming data quantities) speak for themselves, and many complex phenomena and theories of human behavior can be tracked and measured with unprecedented fidelity in a world where large-scale computation, novel data-intensive techniques and algorithms, and advanced mathematical models—technologies underpinning big data computing/analytics—replace every other tool that might be brought to bear. The big target is science where the scientific approach is built around testable hypotheses, a way of doing science that has prevailed for hundreds of years. Hypothesized models as systems visualized in the minds of scientists are tested, and subsequently experiments confirm or falsify the models of how the world works. For a scientific hypothesis to be meaningfully tested, it must be falsifiable, implying that it is possible to identify a possible outcome of an experiment or observation that conflicts with the predictions deduced from the hypothesis. In relation to hypothesis testing, it is important for scientists to understand the underlying mechanisms that connect two variables, since correlation does not imply causation, and hence, no conclusions should be drawn simply on the basis of correlation between two variables. A statistical hypothesis is testable on the basis of observing a process that is modeled via a set of random variables, and statistical hypothesis test is a method of statistical inference. In the statistics literature, statistical hypothesis testing plays a fundamental role (Lehmann and Romano 2005) in statistical inference, as well as in the whole of statistics. As concluded by Lehmann (1992) in a comprehensive review, despite its shortcomings, the new paradigm of statistical hypothesis, and the many developments carried out within its framework continue to play a central role in both the theory and practice of statistics. However, significance testing has particularly been the favored statistical tool in some experimental social sciences (Hubbard, Parsa and Luthy 1997), while other fields have favored the estimation of parameters (e.g., effect size). It is used as a substitute for the traditional comparison of predicted value and experimental result at the core of the scientific method. While hypothesis testing is of continuing interest to philosophers (Lenhard 2006; Mayo and Spanos 2006), much of their criticism of it is discussed by statisticians in other contexts, especially correlation does not imply causation. Covering a wide variety of issues, the criticism of statistical hypothesis testing fills volumes (e.g., Chow1997; Harlow 1997; Kline 2004; McCloskey and Stephen 2008; Morrison and Henkel 2006; Oakes 1986). As a bias related to science when performing experiments to test hypotheses, a scientist may have a preference for one outcome over another (Pease 2016; Van Gelder 1999). Nonetheless, eliminating this bias can be achieved by careful experimental design and transparency, as well as a thorough peer review (Krimsky 2003; Shatz 2004). A normal practice for independent researchers after the publication of the results of an experiment is to double-check how the research was carried out, and to follow up by performing similar experiments to determine how dependable the results might be (Bulger et al. 2002). Taken in its entirety, the scientific method allows for highly creative problem solving while minimizing any effects of the confirmation bias and other subjective biases (Backer 2008). The prevailing scientific approach is increasingly becoming obsolete as faced with the unfolding and soaring deluge of data related to a large number and variety of phenomena. Such deluge is available for scientific exploration within many different disciplines and fields. The data collected from various sensors, e.g., remote sensing technologies, are analyzed to extract useful knowledge and valuable insights for societal benefits, where a large number of scientists can collaborate in terms of designing, operating, and analyzing the products of sensor devices and networks for scientific studies. The data produced from several scientific explorations require advanced tools to facilitate the efficiency of their management, processing, analysis, validation, visualization, and dissemination, while preserving the intrinsic value of the data. Further, data-intensive approach to science is a promising epistemological shift, where colossal amounts of data allow us to say that correlation is enough using data science systems, processes, and methods, specifically big data computing and the underpinning technologies. We can analyze the data without hypotheses about what they might show, and accordingly, we can stop looking for models. We can throw the numbers into huge computing clusters (dedicated data processing platforms) and let statistical or data mining algorithms discover patterns and make correlations from these discoveries where science cannot. It comes at no surprise that the application and use of big data analytics is increasingly gaining traction and foothold in many scientific research fields, taking over the prevailing scientific method.

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4.2 Data-Intensive Science as a Paradigmatic/Epistemological Shift and Its Underpinnings Data-intensive science/scientific development as a new paradigm has emerged as a result of the recent advances in data science systems, processes, and methods and thus big data computing and the underpinning technologies. Turing award winner Jim Gray envisions data science as the new paradigm of science and asserts that everything about science is changing because of the impact of advanced ICT and the evolving data deluge (Bell et al. 2009). The Exabyte Age is upon us. Data-intensive scientific discovery is the fourth paradigm of science where science involves the exploration and mining of scientific data and using advanced big data analytics techniques to unify theory, simulation, and experimental verification (Bell et al. 2009). The first paradigm is where science used empirical methods thousands of years ago; the second paradigm is where science became a theoretical field a few hundred years ago, involving the process of generating and testing hypotheses; and the third paradigm is where science used calculation, conducting simulation and verification by computation in recent decades (Bell et al. 2009). Data-intensive science as an epistemological shift involves mainly two positions. The first position is a form of inductive empiricism in which the data deluge, through analytics as manifested in the data being wrangled through an array of multitudinous algorithms to discover the most salient factors concerning complex phenomena, can speak for itself free of human framing and subjectivism, and without being guided by theory (as based on conceptual foundations, prior empirical findings, and scientific literature). As argued by Anderson (2008), ‘the data deluge makes the scientific method obsolete’ and that within big data studies ‘correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all’. This relates to exploratory data analysis, which may not have prespecified hypotheses, unlike confirmatory data analysis used in the traditional way of doing science that does have such hypotheses. The second position is data-driven science, which seeks to generate hypotheses out of the data rather than out of the theory, thereby seeking to hold to the tenets of the scientific method and knowledge-driven science (Kelling et al. 2009, p. 613). Here, the conventional deductive approach can still be employed to test the validity of potential hypotheses but on the basis of guided knowledge discovery techniques that can be used to mine the data to identify such hypotheses. It is argued that data-driven science will become the new dominant mode of scientific method in the upcoming Exabyte/Zettabyte Age because its epistemology is suited to exploring and extracting useful knowledge and valuable insights from enormous, relational datasets of high potential to generate more holistic and extensive models and theories of entire complex systems rather than parts of them, an aspect which traditional knowledge-driven science has failed to achieve (Kelling et al. 2009; Miller 2010). The best practical example of inductive empiricism associated with the recent epistemological shift in science is the shotgun gene sequencing by John Craig Venter, using statistical analysis as a big data analytics technique. Enabled by high-speed sequencers and supercomputers that statistically analyze the data they produce, Venter went from sequencing individual organisms to sequencing entire ecosystems. As an alternative to the costly option of using supercomputers, advanced data processing platforms are designed for handling the storage, analysis, and management of large datasets directed for scientific and academic explorations. As an example of such platforms, Hadoop MapReduce is widely used in this regard due to the suitability of its functionalities with respect to dealing with colossal amounts of data, as well as to its advantages associated with load balancing, cost-effectiveness, flexibility, and processing power. Hadoop allows to distribute the processing load among the cluster nodes, which enhances the processing power; to add or remove nodes in the cluster according to the requirements; to make the homogenous cluster with various group of machines; and to handle unstructured data (Bibri 2018a). Further to the point, in his endeavor of sequencing the air in 2005, Venter discovered thousands of previously unknown species of bacteria and other life-forms. He can tell you almost nothing about the species he found; does not know what they look like, how they live, or much of anything else about their morphology; and does not even have their entire genome. A statistical blip—a unique sequence that, being unlike any other sequence in the database, must represent a new species—is all he has, which would be impossible to achieve with the old way of conducting scientific research or doing science. The point is that by analyzing data with high-performance computing resources, Venter has advanced biology more than anyone else of his generation—thanks to big data computing and the underpinning technologies. The future potential of data-intensive science is so enormous that this kind of thinking is poised to go mainstream, pervading many different scientific and academic fields. While learning to use and mastering data processing platforms or supercomputers may be challenging, the opportunity is tremendous in the sense that the new availability of huge amounts of data, along with the statistical analysis and data mining tools to crunch these numbers, offers a whole new way of explaining and understanding the world around us.

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Following the Kuhnian paradigm shift, data-intensive science is a paradigmatic break with the current paradigm of science. As such, it represents universally recognized scientific achievements that, for the current period of time, provide model problems and solutions for a community of practitioners as associated with both inductive empiricism and data-driven science, that is: • • • • • •

what is to be observed and scrutinized; the kind of questions that are supposed to be asked and probed for answers; how these questions are to be structured; what predictions made by the primary theory within the discipline; how the results of scientific investigations should be interpreted; and how an experiment is to be conducted, and what equipment is available to conduct the investigation.

As to inductive empiricism, the scientific method becomes obsolete due to the massive data available for scientific exploration, correlation supersedes causation, and coherent models and unified are not required for scientific advancement. Concerning data-driven science, hypotheses are generated out of the data rather than out of the theory; the deductive approach is used to test the validity of potential hypotheses on the basis of guided knowledge discovery techniques; useful knowledge is explored and extracted from massive, interconnected datasets; and holistic and extensive models and theories of entire complex systems can be generated. In light of the above, data-intensive science represents a fundamental change in thought patterns, including theories, assumptions, and experimental practices. Kuhn (1962) suggests that the history of science can be divided up into times of normal science and briefer periods of revolutionary science. He characterizes normal science (when scientists add to, elaborate on, and work with a central, accepted scientific theory) as the process of observation and puzzle-solving which takes place within a paradigm, whereas revolutionary science occurs when one paradigm overtakes another in a paradigm shift (e.g., Bird 2013). He asserts that during times of revolutionary science, anomalies refuting the accepted theory have built up to such a point that the old theory is broken down and a new one is built to take its place in a paradigm shift. Each paradigm has its own distinct questions, aims, procedures, and interpretations. The choice between paradigms involves setting two or more depictions against the world and deciding which likeness is most promising. With the above in mind, it has been argued that scientists are currently encountering inconsistencies and anomalies, which partly have brushed away as acceptable levels of error for quite sometime, and partly have been ignored and not dealt with, with different levels of significance to the practitioners of science. These inconsistencies and anomalies have been mounting up toward a point when researchers in many scientific fields are increasingly favoring the data-intensive approach to science, thereby no longer working within the existing framework of science. In short, a significant number of inconsistencies and anomalies are arising, and a data-intensive science is making sense of them. As repeatedly shown by the history of science, there again is a turbulence setting in that is triggering a paradigm shift in science as being driven and shaped by data science and hence the emerging advancements and innovations in big data computing and the underpinning technologies. Indeed, data-intensive science is taking an identifiable form and increasingly gaining its own new followers, which are currently in the phase of intellectual conflict with the hold-outs of the old paradigm of science. In this regard, this new scientific truth is not only making its opponents see the light but eventually die, manifested in the new generation of data science advocates growing up that is familiar with this truth. In addition, the rationale for the choice of the data-intensive approach to science as an exemplar is a specific way of viewing the current reality, where this view and the status of this exemplar are mutually reinforcing. This paradigm shift in science is so convincing that normally renders the possibility of new epistemological alternatives intuitive, thereby not obscuring the possibility of the existence of other imageries hidden behind the current paradigm of science. Arguably, the conviction that the current paradigm of science is reality tends to disqualify evidence that might undermine the paradigm of science itself, which leads to a buildup of unreconciled inconsistencies and anomalies that are determined to accumulate and thus cause a paradigm shift in science. This is responsible for the eventual revolutionary overthrow of the incumbent paradigm of science, and its replacement by a new one (Kuhn 1962). Yet, the acceptance or rejection of a paradigm is a social process as much as a logical process, an argument that relates to relativism: the idea that knowledge and truth exist in relation to culture, society, or historical context, and are not absolute, or that views are relative to differences in perception and consideration. There is no universal, objective truth according to relativism; rather, each point of view has its own truth. Kuhn (1962, p. 170) denies the accusation of being a relativist later in his postscript: ‘scientific development is … a

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unidirectional and irreversible process. Later scientific theories are better than earlier ones for solving puzzles… That is not a relativist’s position, and it displays the sense in which I am a convinced believer in scientific progress.’ The popularity of the term ‘data science’ has exploded in the academia where many critical academics see no distinction between data science and statistics. However, many statisticians envision data science as an increasingly inclusive applied field that grows out of traditional statistics and goes beyond traditional analytics. This implies that data science differs from statistics. One key difference is that statisticians are able by means of data science methods, systems, and processes to develop models for highly complex systems that were unfathomable: incapable of being fully explored or understood, before. In addition, emerged in the wake of big data, data science, as argued by Donoho (2015), does not equate to big data in that the size of the dataset is not a criterion to distinguish data science and statistics. Also, data science is a heavily applied field where academic programs currently do not sufficiently prepare data scientists for the jobs in that many graduate programs misleadingly advertise their analytics and statistics training as the essence of a data science program (Barlow 2013; Donoho 2015). From a technical perspective, while statistics emphasizes models grounded in probability theory to deal with data arising from real-world phenomena, and provides principles and tools for the construction of statistical hypotheses as models that involve such modeling processes as data generation, evaluation and assessment, prediction, and uncertainty quantification, data science brings to statistics large-scale computation (modern computational infrastructures), data-intensive techniques, algorithmic design and analysis, large datasets, and advanced mathematical models. Data science, most often linked to the big data explosion, is the amalgamation of numerous parental disciplines, as mentioned above. As an example of capturing this, Blei and Smyth (2017) describe data science is ‘the child of statistics and computer science,’ where the ‘child’ metaphor appropriately depicts that data science inherits from both its parents, but eventually evolves into its own entity. They further elaborate: ‘data science focuses on exploiting the modern deluge of data for prediction, exploration, understanding, and intervention. It emphasizes the value and necessity of approximation and simplification; it values effective communication of the results of a data analysis and of the understanding about the world and data that we glean from it; it prioritizes an understanding of the optimization algorithms and transparently managing the inevitable trade-off between accuracy and speed; it promotes domain-specific analyses, where data scientists and domain experts work together to balance appropriate assumptions with computationally efficient methods’ (Blei and Smyth 2017). Data science is largely seen as the umbrella discipline that incorporates a number of other disciplines. As an interdisciplinary field, data science employs methodologies and practices from across several academic disciplines while morphing them into a new discipline. Data science is often said to include particularly the allure of big data, the fascination of unstructured data, the advancement of data-intensive techniques and algorithms, and the precision of mathematics and statistics. One implication of this is that data science is different from the existing practice of data analysis across all disciplines, which focuses only on explaining datasets. The practical engineering goal of data science: actionable knowledge and consistent patterns for generating predictive models takes it beyond traditional approaches to analytics. Now the data in those disciplines and applied fields that lacked solid theories, like the social sciences and related disciplines, could be utilized to generate powerful predictive models (Dhar 2013). Cleveland (2001) urges to prioritize extracting applicable predictive tools over explanatory theories from colossal amounts of data. For the future of data science, Donoho (2015) projects an ever-growing environment for open science where datasets used for academic publications are accessible to all researchers. Open science also involves making scientific research available to all levels of an inquiring society, as well as disseminating, sharing, and developing knowledge through collaborative networks. Several research institutes have already announced plans to enhance reproducibility and transparency of research data (Collins and Tabak (2014). Other big journals are likewise following suit (McNutt 2014; Peng 2009). The future of data science not only exceeds the boundary of statistical theories in scale and methodology, but data science will revolutionize current academia and research paradigms (Donoho 2015). The scope and impact of data science will, as concluded by Donoho (2015), continue to expand enormously in the upcoming decades as scientific data and data about science itself become overwhelmingly abundant and ubiquitously available. Already, significant progress has been made within data science, information science, computer science, and complexity science with respect to handling and extracting knowledge and insights from big data and these have been utilized within urban science (e.g., Bibri 2018a; Bibri and Krogstie 2017c; Kitchin 2016). Data science, the new paradigm of science, employs scientific methods, systems, processes, and algorithms to extract useful knowledge and valuable insights from large masses of data in various forms, both structured and unstructured. It uses theories and techniques drawn from many fields within the context of statistics, mathematics, computer science, and information science. Data science (and thus big data analytics) techniques, such as data mining and pattern recognition, statistical analysis, data visualization and visual analytics, and prediction and simulation modeling are largely in the early stages of their development given that the statistical methods that have prevailed over several decades were originally designed to perform data-scarce science, i.e., to identify significant correlations and relationships from small, clean sample

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data sizes with known attributes or properties. These analytics techniques rely on machine learning (artificial intelligence) techniques and huge computational power to process and analyze data. Nonetheless, recent years have witnessed a remarkable progress within computer science, information science, and data science with regard to handling and extracting knowledge from large masses of data and these have been utilized in urban science. The evolving big data computing model represents the challenging task of data organization, processing, and analysis associated with the process of knowledge discovery from voluminous, varied, real-time, exhaustive, fine-grained, indexical, dynamic, flexible, relational data. This approach is at the core of epistemology in terms of the nature of knowledge and how it can be generated, as well as involves the questionability of the existing knowledge claims, the criteria for knowledge and justification, and the sources and scope of knowledge as issues that are at the center of debate in epistemology. Using the process of data mining/knowledge discovery as a systematic framework with well-defined stages for structuring data-analytic thinking and practice is increasingly pervading scientific disciplines in terms of research and innovation (e.g., Bibri and Krogstie 2018). Worth pointing out in this regard is that the best opportunity for using the data deluge is to harness and analyze data not as an end in itself—but rather to develop big theories, e.g., about how smart sustainable/sustainable smart cities can be operated, managed, planned, designed, developed, and governed in ways that overcome the challenges of sustainability and urbanization. In this context, big data analytics can be exploited to reveal hidden and previously unknown patterns and discover meaningful correlations in large datasets pertaining to natural and social sciences so to develop more effective ways of responding to population growth, environmental pressures, changes in socio-economic needs, global shifts/trends, discontinuities, and societal transitions in the form of new processes, systems, designs, strategies, and policies, as well as products and services. In the meantime, to really get a grip on the use of big data analytics to address the challenges of sustainability in an increasingly urbanized world, new theory about big data analytics theory—meta-theory—is necessary. From a general perspective, West (2013) vividly argues that big data require big theories. Specific to smart sustainable/sustainable smart urbanism, discovering patterns and making correlations from the deluge of urban data can only ever occur through the lens of a new kind of theory (Batty 2013; Bibri 2018a). Especially, data-intensive science needs to meet three criteria in order to match the Kuhn’s (1962/1996) notion of paradigm shift: It must be based on and provide a meta-theory, be acknowledged by a scientific community of practitioners, and possess a number of successful practices. The extant literature shows that data-intensive science as a paradigm shift is still evolving in terms of meta-theory—but has a large number of successful practices and is acknowledged by the scientific community. There are varied arguments about whether big data computing will herald the end of theory and hence the extent to which it has the answers, manifested in the number of the emerging epistemological positions pertaining to data-intensive science. This pertains particularly to the ways supercomputers: large-scale computation, data-intensive techniques and algorithms, and advanced mathematical models used in building and performing big data analytics, can potentially generate more useful, insightful, or accurate results than domain experts, scientists, specialists, and researchers who traditionally craft targeted hypotheses and devise research strategies. This revolutionary notion is increasingly entering the research practices of institutions, organizations, and governments. The idea being that the data deluge can reveal secrets to us that we now have the power and prowess to uncover. In other words, we no longer need to postulate and hypothesize; we simply need to let machines lead us to the patterns, correlations, trends, and shifts in social, economic, political, and environmental relationships. There is no denial of the significance of the analytical power of big data. And the huge resources being invested in both the public and private sectors to further investigate and advance big data computing is a testament to this. Having recently, as a research wave and direction, permeated and dominated academic circles industries, coupled with its research status being consolidated as one of the most fertile areas of investigation, big data analytics has attracted researchers, scholars, scientists, experts, practitioners, policymakers, and decision-makers from diverse disciplines and professional fields —given its importance and relevance for generating well-informed decisions and deep insights of highly useful value. Therefore, big data analytics is a rapidly expanding research area and becoming a ubiquitous term in understanding and solving complex challenges and problems in many different domains. The big data movement has been propelled by the intensive R&D activities taking place in academic and research institutions, as well as in industries and businesses—with huge expectations being placed on the upcoming innovations and advancements in the field. In particular, a large part of ICT investment is being directed by giant technology companies, such as Google, IBM, Oracle, Microsoft, SAP, and CISCO, toward creating novel computing models and enhancing existing practices pertaining to the storage, management, processing, analysis, modeling, simulation, and evaluation of big data, as well as to the visualization and deployment of the analytical outcome for different purposes (Bibri 2018a, 2019a). Big data computing is undoubtedly useful for addressing and overcoming many important issues faced by society, including sustainability and urbanization, but it is important to ensure that we are far rom being seduced by the promises and claims of big data computing to render theory unnecessary. There are some projections, though, that most, if not all, of the social questions that are of most concern to us will be answered based

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on sifting through and harvesting sufficient quantities of big data. However, several skeptical views challenge the achievability of this vision or the realization of this prospect, predicated on the assumption that there will always be uneven data shadows and inherent biases in how information is used and technology is produced. Thus, it is equally important not to overlook the important role of expert domains or specialists to offer insights into what the data deluge can do, but perhaps they do not reveal it.

4.3 The Data–Intensive Scientific Approach to Urban Sustainability Science and Related Wicked Problems Cities are full of complex issues that are not easily captured or steered. The problems of cities are primarily about people and their environment and life. Physical, infrastructural, environmental, economic, and social issues in contemporary cities define what planners call ‘wicked problems’, a term that has gained currency in urban planning and policy analysis, especially after the adoption of sustainable development within urban planning and development since the early 1990s. Cities are characterized by wicked problems (Rittel 1969; Rittel and Webber 1973), i.e., difficult to define, unpredictable, and defying standard principles of science and rational decision-making. When tackling wicked problems, they become worse due to the unanticipated effects and unforeseen consequences that were overlooked, because the systems in question were not approached from a holistic perspective, or were treated in too immediate and simplistic terms. The essential character of wicked problems is that they, according to Rittel and Webber (1973), cannot be solved in practice by a central planner. Bettencourt (2014) reformulates some of their arguments in modern form in what is called the ‘planner’s problem,’ which has two distinct facets: (1) the knowledge problem and (2) the calculation problem. The first problem refers to the data needed to map and understand the current state of the smart sustainable/sustainable smart city. It is conceivable that urban life and physical infrastructure could be adequately sensed in several million places at fine temporal rates, generating huge but manageable rates of information flow by advanced ICT. It is not impossible, albeit still implausible, to conceive and develop advanced technologies that would enable access to detailed information about every aspect of the infrastructure, services, social lives, and environmental states in a smart sustainable/sustainable smart city. The second problem refers to the computational complexity to carry out the actual task of planning in terms of the number of steps necessary to identify and assess all possible scenarios and choose the best possible course of action. Unsurprisingly, the exhaustive approach of assessing all possible scenarios in such city is impractical due to the fact that it entails the consideration of impossibly or unreasonably large spaces of possibilities. As a scientific discipline, urban sustainability science integrates urban sustainability, urban science, and sustainability science. The notion of sustainability has been applied to urban planning and development since the early 1990s. This was marked by the emergence of the notion of urban sustainability. Urban sustainability science has theoretical foundations and assumptions from which it has grown that have solidified into a defined science after the establishment of sustainability science, which emerged in the early 2000s. As such, it can serve as a theoretical basis for urban planning and development under what is labeled ‘sustainable urbanism’ that can effectively engage with the wicked problems presented by cities and their sustainability. The objective of urban sustainability is to uphold the changing dynamics and hence reciprocal relationships (within and across levels and scales) that maintain the ability of the city to provide not just life-supporting but also life-enhancing conditions as exhibited by its collective behavior. To achieve this, the city should work toward enhancing the underlying environmental, physical, social, and economic systems over the long run by means of sustainable interventions and programs using advanced technologies and their novel applications, with the primary purpose of maintaining predictable patterns of behavior and hence stable reciprocal relationships responsible for generating such patterns. Typically, such relationships cycle to produce the behavioral patterns that the city exhibits as a result of its operational functioning, planning, design, and development in the context of sustainability. In particular, as the positive adaptation of the city depends upon how well it is adjusted with the environment, it needs to make changes to protect itself and grows to accomplish its goals in terms of achieving the ultimate goal of sustainability. One way of doing this is to self–correct itself based on reactions from the natural/environmental system with respect to climate change and related hazards and upheavals. This feature relates to the adaptive nature of complex systems in that they have the capacity to change and learn from experience. To put it differently, the objective of urban sustainability can be accomplished by rendering the city dynamic in its conception, scalable in its design, efficient in its operational functioning, and flexible in its planning, which is of crucial importance for dealing with population growth, environmental pressures, changes in socio-economic needs, global shifts, discontinuities, and societal transitions (Bibri 2018a, 2019b). This involves maintaining the critical structures, key dependencies, functional integrity, resource availability, well-being, and capacity for regeneration and evolution of the city. What is important with respect to ensuring the persistence of structures and conditions necessary for keeping the city system within a

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preferred stability state is the need for continuous reflection as an effective way to learn from both failures and successes, as well as to achieve a deep understanding of how socio-ecological systems function to be able to work with, anticipate, and harness the dynamics within such systems. The quest for finding an urban planning and development paradigm that can accommodate the wicked problems of cities and their sustainability and overcome the complexity and unpredictability introduced by social factors is increasingly inspiring scholars to combine urban sustainability and sustainability science under what has recently been termed as ‘urban sustainability science.’ This is in turn being informed by urban science and thus big data science and analytics. While the introduction of sustainability to the goals of urban planning and development added another layer of complexity brought about by the consideration of environmental externalities and social and economic concerns, the new urban science has opened new windows of opportunity to deal with such issues in cities on the basis of modern computation and data abundance. Indeed, sustainability as entailing complex dynamics of human–natural system interactions requires a decisive, radical change in the way science is undertaken and developed. This change is what data-intensive scientific development is about—as enabled and driven by big data science and analytics. The great innovation of big data science and analytics and the underlying technologies is that the urban problems should be approached in full knowledge, which supposes a new approach to scientific development based on massive-scale data. As an evolving, systematic enterprise building and organizing knowledge in the form of explanations and predictions about the world, data-intensive scientific development entails using data-driven inductive empiricism and data-driven science. These recent epistemological approaches are at the core of urban science (Kitchin 2016), which informs urban sustainability science. This is due to their critical importance and relevance to such urban practices as operational functioning, planning, design, and development with regard to sustainability. There are various reasons that justify the adoption of data-intensive scientific development in urban sustainability science, which indeed has theoretical foundations and assumptions from which it has grown that have solidified it into a defined science whose focus is on general models, theories, laws, and experimental practices. It is imperative for urban sustainability science, a field that focuses on understanding the dynamic interactions of the social and ecological systems of the city, to develop and apply an advanced approach to scientific inquiry and exploration for dealing with the kind of wicked problems and intractable issues pertaining to urbanism as a set of multifaceted, contingent practices. Also, urban sustainability science should embrace data-intensive scientific development in order to be able to transform knowledge on how the natural and human systems in cities interact in terms of the underlying (changing) dynamics for the purpose of designing, developing, implementing, evaluating, and enhancing human engineered systems as practical solutions and interventions that support the idea of the socio-ecological system in balance. This embrace is additionally aimed at nurturing and sustaining the linkages between scientific research and technological innovation and policy and public administration processes in relevance to sustainability. To put it differently, the data-intensive approach to urban sustainability science is of high relevance to the cultivation, integration, and application of knowledge about natural systems gained especially from the historical sciences, and its coordination with knowledge about human interrelationships gained from the social sciences and humanities. This is of crucial importance for evaluating, mitigating, and minimizing the intended and untended consequences of anthropogenic influence on social and ecological systems across the globe and into the future. More to the appropriateness of data-intensive scientific development, urban sustainability science mixes and fuses disciplines across the natural sciences, social sciences, formal sciences, and applied sciences. The philosophical and analytical framework of urban sustainability science draws on and links with numerous disciplines and fields, and is studied and examined in various contexts of environmental, social, economic, and cultural development and managed over many temporal and spatial scales. The focus ranges from macrolevels starting from the (sustainability) of planet Earth to the sustainability of societies, regions, and cities, as well as economies, ecosystems, and communities, and to microlevels encompassed in streets, buildings, and individual lifestyles (Bibri 2018a). In view of that, big data computing can perform more effectively with respect to achieving the desired outcomes expected from the application of interdisciplinarity and transdisciplinarity as scholarly enterprises due to the underlying analytical power, coupled with the data deluge available for scientific inquiry and exploration. This is particularly important in the context of urban science for gaining new interactional and unifiable knowledge necessary for exploring and exploiting the opportunity of using advanced technologies to solve real-world problems and challenges, especially those associated with sustainability and urbanization. The solutions to the kind of wicked problems and intractable issues associated with urban sustainability are anchored in the recognition that the urban world has become integrated, complex, intricate, contingent, and uncertain. The data-intensive approach to urban sustainability science is primarily meant to facilitate the link of such problems and issues to the type of problems and issues explored and probed by sustainability science, as well as demonstrates how the understanding of cities as instances of socio-ecological systems provides a conceptual and analytical framework for addressing and overcoming some of the challenges

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characteristic to such problems and issues. There is a host of new practices that sustainability science could bring to urban sustainability under the umbrella or sphere of data-intensive science, an argument that needs to be developed further and to become part of mainstream debates in urban research and practice. This argument is being stimulated by the ongoing discussion and development of the new ideas about the untapped potential of big data computing and the underpinning technologies for advancing both sustainability science and urban sustainability as well as merging them into a holistic framework informed by the new urban science. This kind of integrated and holistic framework should focus on probing the complex mechanisms involved in the profound interactions between environmental, social, economic, and physical systems to understand their behavioral patterns and changing dynamics so as to develop upstream solutions for tackling the complex challenges associated with the systematic degradation of the natural and environmental systems and the concomitant perils to the human and social systems. Urban sustainability science as a research field seeks to give the broad-based and crossover approach of urban sustainability a solid scientific foundation. It also provides a critical and analytical framework for urban sustainability and, to draw on Reitan (2005), must encompass different magnitudes of scales (of time, space, and function), multiple balances (dynamics), multiple actors (interests), and multiple failures (systemic faults). In addition, it should be viewed as a field defined more by the kind of wicked problems and intractable issues it addresses rather than by the scientific and academic disciplines it employs, thereby being neither basic nor applied research; it serves the need for advancing both knowledge discovery and actionable decisions by creating a dynamic bridge between the two thanks to new big data analytics techniques. On the whole, the link between sustainable urban development and urban data science stems from the idea that the former is an aspiration that should, as realized by many scholars over the past two decade or so, be achieved only on the basis of advanced scientific knowledge and thus the approach to producing it, thereby the relevance of big data science and analytics. This has justified the establishment of a new branch of science: urban sustainability science, due to the fact that the city is, arguably, confronted at an ever unprecedented rate and larger scale with the ramifications of its own success as a product of social revolution. The way things have changed in recent years (and the attempts being undertaken to take this into account) calls for a novel approach to science for explaining, predicting, and understanding the underlying web of the ongoing, reciprocal relationships that are cycling to generate the patterns of behavior that the complex city system is exhibiting, and for figuring out the mechanisms such system is using to control itself. The point is that the complexities, uncertainties, and hazards of the human adventure are triggering drastic changes increasingly requiring insights from all the sciences to tackle them if there is a shred of seriousness about the aspiration to improve sustainability, resilience, and the quality of life, i.e., sustainable urban development. The essential opportunities and challenges of the use of big data computing and the underpinning technologies in smart sustainable/sustainable smart urbanism as a practical application of urban sustainability science have, despite their appeal, not been sufficiently systemized and formally structured. In particular, the necessary conditions for the strategic application of big data science and analytics in such urbanism need to be spelled out, and their limitations must also be anticipated and elucidated (Bibri 2018a). There are different ways of addressing these and other important questions considering the available interdisciplinary and transdisciplinary knowledge of such urbanism (see Bibri 2018a, c for an overview) in the Big Data Age. In this line of thinking, Bettencourt (2014) attempts to answer some of these questions by formalizing the use of big data analytics in urban planning and policy in light of the conceptual frameworks of engineering, and shows that this formalization enables to identify the necessary conditions for the effective use of big data in urban policies that address a large array of urban issues. This is intended to demonstrate that big data computing and the underpinning technologies as an instance of ICT of pervasive computing are providing new opportunities for the application of advanced engineering solutions to smart sustainable/sustainable smart cities. It is conspicuous now that big data science and analytics may offer radically novel solutions to the wicked problems of urban sustainability related to planning, design, and development. Big data computing is so fast in comparison to most physical, environmental, social, and economic phenomena that myriads of key urban planning and policy problems are falling within this window of opportunity (Bibri 2018a). In such circumstances, models of system response enabled by big data analytics can be very simple and crude and typically be linearized (see Astrom and Murray 2008). Thus, the analytical engineering approach conveniently bypasses the complexity that can arise in the nested systems of smart sustainable/sustainable smart cities at longer temporal or larger spatial scales (Bibri 2018a). Bibri (2018a) summarizes some key urban sustainability problems with their typical temporal and spatial scales and the nature of their operating outcomes. The potential miracle of big data science and analytics in this regard lies in essentially advancing urban sustainability and solving related complex issues without coherent models, unified theories, or any mechanistic explanation at all thanks to the data deluge that makes the scientific method obsolete, and within big data studies correlation supersedes causation. Many examples of planning, design, management, and policy practices in those cities that use data successfully can have this flavor, irrespective of whether their development and implementation involve organizations or computer algorithms (Bibri 2018a).

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4.4 Building the New Urban Science and Establishing the Related Research Domain 4.4.1 Research Status As an interdisciplinary research field, urban science is concerned with the study of diverse urban issues and problems, and thereby aims to produce both theoretical and practical knowledge that contributes to understanding and solving them in contemporary society. Positioned at the intersection of science and design, it draws on new disciplines in the natural science and information science, and seeks to exploit the development of modern computation and the growing abundance of data. As a field within which data science is practiced to inform and sustain data-driven urbanism and urban big data development, it entails making sense of cities as they are by identifying relationships and urban laws, as well as predicting and simulating likely future scenarios under different conditions, potentially providing valuable insights for planning and development decision-making and policy formulation (Kitchin 2015). As such, it involves data-analytic thinking and computational modeling and simulation approaches to exploring, understanding, and explaining urban processes, and also addressing several challenges posed by urban data. The two fundamental ones are: (1) how to handle and make sense of billions of observations that are being generated on a dynamic basis (Batty et al. 2012) and (2) how to translate the insight derived into new urban theory (fundamental knowledge) and actionable outcomes (applied knowledge) (Batty 2013; Foth 2009; Ratti and Offenhuber 2014). In view of the above, urban science is associated with scientific research as applied research: the search for solutions to practical problems using the knowledge and applied research as the aim of basic research, which is another form of scientific research. A great deal of our understanding comes from the curiosity-driven undertaking of basic research, although some scientific research is applied to specific problems. This leads to options for technological advancements that were not planned or sometimes even imaginable, i.e., big data computing and the underpinning technologies. However, the new urban science, a field in which data science and big data analytics are practiced, aims to make cities more sustainable, resilient, livable, and transparent by rendering them more measurable, knowable, and controllable in terms of their operational functioning, planning, design, and development. Indeed, cities are becoming highly responsive to a form of data-driven urbanism that is the key mode of production for what have widely been termed smart sustainable/sustainable smart cities whose monitoring, understanding, and analysis are increasingly relying on the core enabling technologies of big data analytics. Research on such cities, the leading global paradigm of urbanism, is garnering growing attention and rapidly burgeoning, and its status is consolidating as one of the most enticing and fanciest areas of investigation today, making the relevance and rationale behind the smart sustainable/sustainable smart city debate of high significance and value with respect to the future form of urbanism. This area is typically concerned with addressing a large number and variety of issues related to both sustainable cities and smart cities in the context of sustainability, as well as to the amalgamation of these two classes of cities as landscapes and approaches in the context thereof (Bibri 2018a, 2019b). A large part of research in this area focuses on exploiting the potentials and opportunities of advanced technologies and their novel applications, especially big data computing/analytics, as an effective way to mitigate or overcome the issue of sustainable cities and smart cities being extremely fragmented as landscapes and weakly connected as approaches, particularly at the technical and policy levels (Bibri 2019a, b). Further, at the heart of data-driven smart sustainable/sustainable smart urbanism is a computational understanding of city systems that reduces urban life to logic, calculative, and algorithmic rules and procedures, while also drawing together and interlinking urban big data to provide a more holistic and integrated view and synoptic intelligence of the city directed primarily for improving, advancing, and maintaining the contribution of both sustainable cities and smart cities to the goals of sustainable development in an increasingly urbanized world. This is underpinned by epistemological realism and instrumental reality, which are informed by and sustain urban science that in turn seeks to make cities more sustainable, resilient, and efficient. In view of that, urban science pursues deeply quantitative and computational approaches to understanding and dealing with cities. It involves a combination of two scientific inquiry methods: (1) a deductive scientific approach aimed at uncovering the common processes that influence the structure and dynamics of all cities (based on data-driven science), and (2) a descriptive approach based upon field work and surveys (or inductive empiricism using big data analytics techniques) aimed at understanding the specificity of cities. In a nutshell, with the world in a phase of unprecedented urbanization, coupled with the mounting challenges of sustainability, the new urban science is emerging as a coherent body of theory or a systematically organized body of knowledge driven by data-intensive science that can contribute to a more sustainable urban world. Across the globe, many research institutions are, as supported by governments, exploring the rapidly expanding scholarly movement of urban science. As demonstrated in a series of scientific and academic publications, new waves of scientific

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interest in cities are emerging, and urban research is increasingly being colonized by other disciplines, i.e., appropriated as a domain for their own use, and the projected scale and extension of urban science research institutions is growing and predicted to grow to huge investment in the next decade. There is a quest for new research models that leverage digital platforms for wide participation in urban research, and that mainstream its benefits for sustainability in an increasingly urbanized, technologized, and computerized world. In this regard, there are many endeavors developing several visions of how urban science might unfold in the coming decades (see, e.g., Batty 2013; Batty et al. 2012; Bibri 2018a, b, 2019b; Kitchin 2014a, b, 2015, 2016) for the purpose of informing policy, supporting research and innovation, and mainstreaming institutional research. Foresight is of crucial importance for informing strategic planning for effectively advancing urban science over the critical coming phase of evolution of this nascent movement as pushed by big data computing, especially in the sphere of smart sustainable/sustainable smart cities of the future (Bibri 2018a, 2019b). Such cities are indeed seen as the most important arena for sustainability transitions and thus central to ensuring a sustainable future because they constitute the hubs and sites of innovation within different, yet related, innovation systems, namely national, regional, sectoral, technological, and quadruple helix of university–industry–government–citizen relations. Besides, any advancement in the science of cities in support of sustainability requires long-term strategic planning, and foresight studies can serve as a basis for inspiration in the discussions and decision-making processes (Bibri 2019b). In addition, in each of the three main pillars of sustainability—environmental, social, and economic—urbanization now plays a key role. In this respect, urban science is required to draw from the natural, engineering, and social sciences, as well as the arts and humanities, while linking directly into practice. This is crucial for urban science to be collectively greater than the sum of its parts. Especially, smart sustainable/sustainable smart cities are complex systems par excellence, more than the sum of their parts, and consequently, the underlying urbanism has become ever more complex with the very technologies being used to make sense of and deal with it as involving special conundrums, wicked problems, intractable issues, and complex challenges associated with sustainability and urbanization. This is well reflected in the operational functioning, planning, design, and development of smart sustainable/sustainable smart cities as a leading paradigm of urbanism. Consequently, to tackle such cities as complex systems and dynamically changing environments requires innovative solutions and sophisticated approaches as to the way they can be monitored, understood, and analyzed so as to be effectively operated, managed, planned, designed, developed, and governed in line with the long-term goals of sustainability, thereby strategically improving, advancing, and maintaining their contribution to the objectives of sustainable development. This can be accomplished, by developing and applying new urban intelligence functions as new conceptions of the way such cities function and utilize complexity science, data science, and urban science in fashioning new powerful forms of urban simulation models and optimization and prediction methods on the basis of big data analytics that generate urban forms and structures that improve sustainability, efficiency, resilience, and the quality of life.

4.4.2 Challenges and Prospects There are numerous challenges and opportunities pertaining to the development of urban science that addresses and overcomes the pressing issues of sustainability and urbanization and enables more effective science–policy interfaces (Batty et al. 2012; Bibri 2018a, b, 2019a; Kitchin 2014a, b, 2015, 2016). In terms of science in the big data era, there are major shifts in the human condition (e.g., urbanization, sustainability, etc.) that require new changes in science, and vice versa (e.g., data science, pervasive computing, etc.). Today, the increasing rate, scale, and speed of urbanization, coupled with the mounting challenges of sustainability are pushing again the scientific frontier. In the meantime, policy bodies across governance scales, from the local to the multilateral, often struggle to gather an adequate response to the pace or specificity of urban change in terms of evidence base and the capacity to grasp it as a drastic shift from a holistic perspective. In light of such growing spotlights on cities and their sustainable transformation and other dynamic changes, developing more effective knowledge for actionable and successful interventions guided and supported by policy has become more imperative than ever. Smart sustainable/sustainable smart cities are increasingly seen as a place of high potential for scientific inquiry and professional training that will provide the raw material for generating adequate scientific knowledge for responding to the challenges of sustainability and urbanization. The ultimate aim is to fill the shortage urban research and education is facing nowadays in key respects, especially data science, big data computing, urban science, urban informatics, sustainability science, and data-driven urbanism in the ambit of such cities. It is of crucial importance and strategic value to invest in research activities and education programs focused on these domains. In this context, the emergence of such cities should be approached from the lens of hospitality, one through which we can alien their affordances as well as embrace and realize their advantages and better understand their disadvantages. It is in research institutions and universities where this hospitality can best be achieved, and which can use and explore such cities as laboratories of innovation (Bibri 2018a). These research and educational entities are well positioned to readily host the emergence of such cities, and seek innovative solutions and

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sophisticated approaches of relevance to the challenges of sustainability and urbanization with regard to developing new metrics for measuring, and new methods for assessing, the urban progress toward achieving the long-term goals of sustainable development (Bibri 2018a). Another strategic value of investing in research and education lies in training and educating a new generation of interdisciplinary and transdisciplinary researchers, scholars, and practitioners within the domain of urban sustainability science, as well as in gaining new knowledge to explore and exploit the opportunity of using and applying advanced technologies to solve real-world problems and challenges in the realm of such cities. Across academia, urban knowledge is outdated and underfunded (McPhearson et al. 2016). Much of what we know about cities to date has been gleaned from studies that are characterized by data scarcity (Miller 2010), and thus involve the use of traditional data collection and analysis methods with inherent limitations and constraints. In other words, urban research tends to rely on selective samples, so we still know very little about the majority of urban settlements and challenges around the world. As widely acknowledged in the domain of smart and sustainable urbanism in relation to academic and scientific research, ‘small data’ studies are associated with high cost, infrequent periodicity, quick obsolescence, incompleteness, inaccuracy, as well as subjectivity and biases, adding to capturing a relatively limited sample of data that is tightly focused, less representative, restricted in scope and scale, time and space specific, and relatively expensive to generate and analyze (Bibri and Krogstie 2018). Moreover, urban research grapples today with the shortcomings of studies as they focus only on selective categories of subjects, and neglect important questions of environmental sustainability, inequality, and vulnerability. There is an urgent need for scientific research endeavors for developing systematic frameworks for city analytics and ‘big data’ studies in relation to the domain of smart sustainable/sustainable smart urbanism (Bibri 2018a). This is in response to the emerging model of big data computing and the increasing influence of big data analytics and its application on enabling, operating, organizing, and planning the processes of smart sustainable/sustainable smart cities as a leading paradigm of urbanism (Bibri and Krogstie 2018). The intention is to utilize and apply well-informed, knowledge-driven decision-making and enhanced insights to improve and optimize urban operations, functions, services, designs, strategies, and policies in line with the vision of sustainability. Further, small pockets of well-funded research domains are often aligned to opportunistic themes driven by industry, policy, and market dynamics beyond academia, such as climate change, resilient cities, smart cities, and cyberphysical cities —rather than offering the wider coverage necessary for balanced interventions by practitioners from a variety of professional fields, especially in relation to sustainability dimensions. Overall, research outcomes remain patchy in the sense of being both inconsistent and not of the same quality as well as happening in small, isolated areas due to the complex nature of urban scholarship as being mismatched by the adequacy of such outcomes. Significant issues like new urban intelligence functions and related simulations models and prediction and optimization methods, which involve control, automation, management, efficiency, and enhancement as related to urban operations, functions, designs, services, strategies, and policies have made minimal headway into urban research in the context of sustainability (see Chapter 10 for further discussion), although the phenomenon associated with the escalation of scientific work on sustainability is set to accelerate some progress on urban science. Smart sustainable/sustainable smart cities should evolve new urban intelligence functions for monitoring, planning, and designing the operating and organizing processes of urban life, which relates to what has been termed laboratories for innovation (Batty et al. 2012; Bibri 2018a, 2019b). Current urban research on the pressing global issues pertaining to sustainability and urbanization is rudimentary and fragmented at a time when the window of urban transformation demands robust and sophisticated urban research along with focused and strategic technological innovation and advancement. It is in large part trapped in the twentieth-century tradition of the systematic study of cities, particularly in terms of planning, engineering, and design. This consequently keeps us in a long distance from understanding the fabric of city systems that shape the way urban areas impact humanity with respect to sustainability dimensions, and vice versa. There is a need for urban research to become a coherent urban science, and for urban scholarship to become well-informed in the ways it can convey the full spectrum of major urban changes—sustainability transitions. Urban research also needs to be adequately directed to real-problem applications associated with sustainability and urbanization beyond the need for a stronger interdisciplinary and interdisciplinary lens. At present, like many other city-related fields, urban science is segmented by disciplinary boundaries when the urban transformation related to sustainability and urbanization demands truly holistic urban research, and whereas solutions to related problems require integrated (cross-disciplinary) knowledge. The new urban science is multidisciplinary and draws upon theoretical ideas from across the contributing disciplines and hence bring together architects, engineers, ecologists, social scientists, computer scientists, data scientists, and built and natural environment specialists. All these actors are undertaking research and developing strategies and programs to tackle the challenging elements of urbanism, e.g., investigating the metabolism of cities as ecosystems characterized by stocks and flows of resources, including energy, material, water, capital, and information, and urban infrastructure systems as complex socio-technical systems composed not only of engineered structures, but also the people who make up the subsystems of

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urbanites and administrators. However, discrete research communities are unable to jointly advise urban planners on the complex multidimensional nature of urban sustainability problems, or indeed on the most appropriate prioritization of urban solutions. While smart sustainable development in which effective policy frameworks and measures and relevant institutional structures and practices are needed for integrating the research and innovation agenda of advanced ICT with the agenda of sustainable development, while aligning and mobilizing strategic urban planning and development actors in this direction (Bibri 2018a), urban science requires institutional, political, and managerial expertise alongside academic skills for its establishment, as well as injecting specialist expert knowledge into practice for the purpose of establishing data-driven smart sustainable/sustainable smart urbanism. There is a need for a close collaboration between urban scientists and urban planners as communities of research and practice, while converging on the places where knowledge is most needed to solve sustainability and urbanization problems and challenges, and where the best scholarship has to be produced. In this respect, it is important to keep in mind that rectifying the parlous state of urban knowledge production and dissemination in the sphere of smart sustainable/sustainable smart cities is not just about scaling up and undertaking more research endeavors on more topics, but much urban knowledge related to the operational aspects of sustainability remains context specific, despite the likely universality of urban conditions. And urban scholars might not reveal their thoughts readily as to offering advice to policymakers and practitioners if they are unsure about how their research applies to context-specific problems they have not directly dealt with.

4.4.3 The Need for Recasting and Reforms Urban science needs to be recast in several ways, and also important reforms are required, which present particular challenges, to provide a step change in the way urban knowledge is produced, enhanced, and advanced, and ultimately to secure sustainability transition as a major urban change. The need for recasting and reforms pertains to the following: • A re-orientation in how cities are conceived. • A reconfiguration of the epistemology of urban science to openly recognize the contingent and relational nature of urban science as well as urban systems and processes. • Effectively linking scholarly contributions with practice. • Pushing new research agendas that are adequate to the scale of the challenges cities face today in relation to sustainability and urbanization. • A fundamental shift of scientific and research paradigms in urban science, together with a reorganization of the institutional forms that bring scientists, experts, and researchers on common grounds. • A restructuring of urban data systems, urban research, and the scientific research-to-practical applications continuum. • A transformation of urban research in ways that respond to the requirements of sustainability and the demands of urbanization, which necessitates a fundamental restructuring of the scale, location, and mandate of the research institutions and universities that generate urban knowledge and sustain its production. • Prompting the urban scientific inquiry and exploration that zooms in and focuses on specific urban dimensions or opportunistic themes driven by industry and market drivers, and concurrently strengthening the global assessments that enable a high level of granularity, which is necessary for understanding sustainability and urbanization issues. • Developing an urban science that can grapple with issues of the policy relevance, prioritization, and codeterminants of sustainability transition in the context of the increasingly urbanized world. It is deemed useful to elaborate on the first three of the above for clarification purposes. Concerning the re-orientation in the way cities are conceived, instead of ‘being cast as bounded, knowable and manageable systems that can be steered and controlled in mechanical, linear ways, cities need to be framed as fluid, open, complex, multilevel, contingent and relational systems that are full of culture, politics, competing interests and wicked problems and often unfold in unpredictable ways. Reducing this complexity into models and then using the outcomes to drive urban management produces a reductionist and limiting understanding of cities and overly technocratic forms of governance. Rather these models need to be complemented with other forms of knowledge… In other words, city analytics and its instrumental rationality should not be allowed to simply trump reason and experience, or other sources of information and insight such as those based on ‘small data’ studies, in shaping and driving urban governance. Instead, they should be used contextually and in conjunction with each other’ (Kitchin 2016, p. 11). From a philosophical perspective, instrumental rationality as a specific form of rationality focusing on the most efficient means to achieve a specific end is not in itself reflecting on the value of that end. Indeed, while it is a crucial, and presumably

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indispensable, component of practical rationality, it is partly constitutive of intention, need, desire, or action. In particular, expressing a means to an end, it means doing whatever it takes to achieve a goal so long as it aligns with certain ultimate objective. The idea here is that only the end really matters. As such, it can be highly problematic in terms of the increasing rationalization of the city and the unending drive for efficiency, and thus should be combined with values as to guiding our choices and decisions. In fact, we humans possess two independent rational capacities: operational reasoning to acquire technical knowledge of means that are functional (instrumental rationality) and ethical reasoning to acquire moral knowledge of ends that are right (value rationality). Nevertheless, the negative connotation of instrumental rationality might not be of relevance in this context so long as the specific end is achieving the long-term goals of sustainability as a form of environmental and economic efficiency for improving societal outcomes. Originally, the philosophical concept of rationality emerged in the broader context of major developments in science, technology, philosophy, politics, and society when people started to focus more on themselves to guide their lives and make rational choices. As regards the need for recasting the epistemology of urban science, the main argument relates to the social shaping of science-based technology or the social construction of scientific knowledge and its practical application, which relate to the analytical and philosophical framework of STS (see Bibri 2015 and Bibri and Krogstie 2016 for a detailed discussion). In light of this, the re-casting in question involves recognizing that the realist assumptions, which posit that urban science can reveal fundamental truths about the city, are flawed. Urban science can only produce a particular view through a specific lens, and cannot provide neutral, objective, God’s eye views of the city (Kitchin 2016). On the one hand, the data used do not exist independently of the ideas, instruments, systems, practices, and knowledge employed—and embedded within a multidimensional context (e.g., local, national, social, political, cultural, organizational, regulatory, etc.)—to generate and process them (Bibri 2019b; Ribes and Jackson 2013). To put it differently, data are never raw, but always already cooked to a particular recipe for a particular purpose (Bowker 2005; Gitelman 2013). On the other hand, big data computing and the underpinning technologies are socio-technical in nature. As such, they are not neutral, purely technical means of assembling and making sense of data; instead, they are shaped by philosophical ideas, socio-political frameworks, and ideological means (Bibri 2018a; Bibri and Krogstie 2016; Kitchin 2016). In particular, big data technology is ‘cultural’ since it can be conceptualized as a discourse prioritizing specific concepts, ideas, claims, assumptions, and visions about the nature and practice of science and technology in society and the role of diverse actors in shaping them, to draw on Bibri (2015). There is potential for realizing that the big data-driven technologized nature of the city is neither apolitical nor inevitable. Furthermore, when engaging in a discursive-material analysis, the politics of this science-based technology does not become the result of the unconditioned agency of the involved actors, e.g., scholars, scientists, experts, engineers, and technologists. Rather, such technology can be conceived as specific techno-socio-political practice which depends on the agency of various actors promoting it and forming coalitions on particular technological innovations and on the political regulation of science and technology in society. All in all, big data technology is the outcome of social processes involving diverse intertwined factors and many stakeholders with a vested interest. Accordingly, urban science as a field in which data science and big data computing are practiced needs to recognize that it does not reflect the world as it actually is and to openly acknowledge its contingencies, limitations, and inherent politics, but rather actively frames and produces the world (Kitchin 2016; Kitchin, Lauriault and McArdle 2015). This is, though, not to say: ‘the fundamental approach of analytics, modeling, and simulation is radically altered, but rather that how these approaches work in messy practice is detailed and grand claims as to their veracity or validity is tempered. This would include detailing how ethical issues were considered and the research design altered appropriately’ (Kitchin 2016, p. 11). Furthermore, in contrast to what some urban or data scientists argue for: The way the city (urban systems) is managed or steered is less open to political influence or not politically inflicted but rather is driven by objective, neutral views in a technocratic, pragmatic way, all technical systems and the data they produce are far from being based on value-free facts and thus non-influenced by politics or politically benign. This carries over its effects to scientific knowledge and scholarly discourses in the sense of being socio-politically situated: inherently part of and influenced by social structures, and produced in social interactions. Thus, the polity, the organized society or the state as a political entity, becomes an inherent part of the scientific and scholarly enterprise. One implication of this is that a data-driven approach to urban governance tends to be anecdotal, politico-economically driven, or localist, rather than based on evidence-informed decision-making, system control, and policy formation. Therefore, the core enabling technologies of big data computing do have inherent politics—they measure values and communicate them as well as process, analyze, interpret, and display the data with biases and limitations, despite the claim of applying scientific principles (framed) and generating information that reflect the truth about cities. Indeed, the God’s view of the city claimed to be provided by the data deluge and its analytics is partial and subject to caution due to the technical issues pertaining to data coverage, access, quality, and veracity, among others.

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The above two dimensions along which the respective recasting needs to proceed relate to the numerous ethical issues that arise from the development and deployment of smart sustainable/sustainable smart city technologies and accompanying urban science. This has led to a number of critiques concerning the underlying concepts, ethos, and practices of urbanism as underpinned by such technologies (e.g., Bibri 2018a, 2019a; Greenfield 2013; Kitchin 2014a; Mattern 2013; Marvin, Luque–Ayala and McFarlane 2016). ‘One response to these critiques is to call for a fundamentally different approach to urban development and the practice of other forms of urban studies rather than urban science. Another is to argue that… urban science needs to be re-imagined and recast’ (Kitchin 2016, p.11). It needs to be recognized that smart sustainable/sustainable smart city technologies do provide many benefits to urban planners, managers, administrators, and citizens (see Bibri 2018a for a detailed account). Similarly, urban science does provide novel and useful insights into cities, their systems, and citizens (Kitchin 2016). As to linking scholarly contributions with practice, it is argued that there is a need for strong collaboration between academics and practitioners. Especially, the contribution of science to shaping the future of smart sustainable/sustainable smart urbanism tends to be poor in terms of interlinking academia and practice, despite the critical juncture at which urban scholarship and practice is positioned. One way to redress these shortcomings and develop more urban cross-disciplinary and policy-engaged research is to focus on the state of urban research and its science–policy nexus. In addition, a renewed urban research agenda should be based on a stronger interdisciplinarity and transdisciplinarity character with respect to all the natural, social, and human sciences and practices—against the backdrop of the rising challenges of sustainability and urbanization and their complexity. This scholarly spirit signifies that urban research must embrace the diversity, integration, and fusion of many relevant disciplines, which allows for holistic and novel insights into various social, environmental, and economic issues (Bibri 2018a, c), as well as for a prioritization of effective advice to urban policymakers (Robinson and Parnell 2017). This is also of importance for building effective science–policy interfaces for addressing and overcoming the challenges of sustainability and urbanization in terms of both up-skilling scientists to speak to politics and policy and decision and policymakers to read science—rather than simply professionalizing urban science for the primary purpose of effectively operating, managing, planning, and designing cities in line with the vision of sustainability, thereby lacking key codeterminants in the social process shaping major societal transitions. It is of equal importance not to hinder efforts toward reproducibility in urban research if ultimately we want to produce a balanced and comprehensive knowledge through the collection of diverse kinds of data within urban science (Munafò et al. 2017). Reproducibility measures whether an experiment/study can be reproduced in its entirety, either by the same researcher or by someone else working independently. Reproducibility is one of the main principles of the scientific method and is often associated with threats. Indeed, if an experiment cannot be repeated to produce the same results, this implies that the original results might have been flawed or in error. Hence, it is common for an experiment to be performed multiple times. For significant results, some scientists are also inclined to replicate the results for themselves, especially if these results would be of particular importance to their own work. Figure 2 illustrates the threats to reproducible science. Figure 2 shows an idealized version of the hypothetico-deductive model of the scientific method. Various potential threats to this model exist (indicated in red), including:

Fig. 2 Threats to reproducible science. Source Munafò et al. (2017)

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lack of replication; hypothesizing after the results are known; poor study design; low statistical power, analytical flexibility; P-hacking; publication bias; and lack of data sharing.

Together the above threats will serve to undermine the robustness and reliability of published research and may also impact on the ability of science to self-correct (Munafò et al. 2017). Nevertheless, the data-intensive approach to science driven by big data science and analytics holds great potential to mitigate such threats thanks to the underlying scientific systems, processes, and methods as well as the systematic framework and analytical power pertaining to big data computing, coupled with the massive data involved in scientific inquiries and explorations. Indeed, reconsidering the foundation of building a focused urban science will need cross-topic, cross-scale, and cross-location studies that require novel analytical methods and vastly different competencies (especially data analysts, data scientists, computer scientists, urban scientists, social scientists, engineering scientists, big data developers and engineers, environmental scientists, technical planners, etc.), as well as abundant data. However, the colossal amounts of the data being routinely sensed in real time, coupled with the emergence and adoption of data-intensive approach to scientific and academic research, will require sophisticated tools, techniques, algorithms, and models for data management, processing, analysis, and interpretation as part of big data analytics processes, such as data mining and pattern recognition, statistical analysis, and simulation and prediction modeling. If we can fashion novel ways of handling the unfolding and soaring deluge of urban data for extracting useful knowledge and valuable insights for enhanced decision-making and scientific uses, which works very well in the new big data era where the numbers speak for themselves, we will move the field of urban science forward in great leaps.

4.4.4 On the Data Avalanche and Its Potential Many of the questions related to sustainability and urbanization and how they intertwine and interrelate at several levels that the new urban science attempts to answer have only become tractable thanks to the increasing availability of the deluge of urban data. The data avalanche is here: a sudden arrival of data in overwhelming quantities. There has recently been much enthusiasm about the immense opportunities and fascinating possibilities created by the unfolding and soaring deluge of varied, real-time, exhaustive, fine-grained, indexical, dynamic, flexible, evolvable, relational data and its new and extensive sources to understand, analyze, and plan smart sustainable/sustainable smart cities in a transformative way so as to improve, advance, and maintain their contribution to the goals of sustainable development (Bibri 2018a; Bibri and Krogstie 2018). This is owing to thinking data-analytically about urban sustainability for the purpose of finding answers to challenging analytical questions so as to address the wicked problems and disentangle the intractable issues related to the practice of urbanism, especially urban planning and development. Especially, new approaches to city analytics in the domain of smart sustainable/sustainable smart urbanism are needed to provide an additional depth and insight with respect to urban phenomena and dynamics and the underlying complexities and intricacies, as well as to bring robustness to the research results within this domain as a key objective of the new urban science (Bibri and Krogstie 2018). Using the data deluge, city analytics and ‘big data’ studies involve the application of various techniques based on data science fundamental concepts— i.e., data-analytic thinking and the principles of extracting useful knowledge from large masses of data. Data mining as one of the most applied techniques in the domain of smart sustainable/sustainable smart urbanism provides some of the clearest illustrations of the principles of data science. While this technique is gaining a strong footing in this domain, its application is associated with significant challenges due to the interdisciplinary and transdisciplinary nature of urban data, and what this entails in terms of all kinds and levels of complexity. Nevertheless, as argued by Bibri and Krogstie (2018), there is tremendous potential for advancing such urbanism through creating a data deluge whose analysis can provide much more sophisticated, finer-grained, wider-scale, real-time understanding and control of various aspects of urbanity in the undoubtedly upcoming Exabyte/Zettabyte Age. This is one of the key goals of the new urban science. As a corollary of this, as uncovered and documented by Bibri (2018a, b, 2019a), enormous opportunities are available for—in addition to finding answers to challenging analytical questions and transforming urban knowledge—utilizing big data technology and its novel applications to improve and maintain the contribution of both sustainable cities and smart cities to the goals of sustainable development through optimizing and enhancing urban operations, functions, services, designs, strategies, and policies across multiple urban domains in line with the vision of sustainability. However, as also demonstrated by the author, just as there

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are immense opportunities ahead to embrace and exploit, there are enormous challenges and open issues ahead to address and overcome in order to achieve a successful implementation of big data technology and its novel applications in such cities. See Chap. 7 for an overview of these challenges and open issues.

4.5 Urban Knowledge Discovery/Data Mining and Big Data Studies and Related Issues In contrast with urban knowledge derived from longer standing, more traditional urban studies, data science as practiced within the field of urban science offers the potential for the kind of urban knowledge that is inherently longitudinal, and has greater breadth, depth, scale, and timeliness (Batty et al. 2012; Kitchin 2016; Lazer 2009) in the context of smart sustainable/sustainable smart urbanism. This is being enabled and afforded by the unfolding and soaring deluge of urban big data. With respect to the data-driven urban knowledge, the emphasis has been on the development of new big data analytics that utilize sophisticated techniques and advanced mathematical models designed to process and analyze enormous datasets (e.g., Batty 2013; Bibri 2018a; Kitchin 2014a, 2016; Miller 2010) containing varied, real-time, exhaustive, fine-grained, indexical, flexible, evolvable, relational type of data. This pertains to the process of knowledge discovery, which involves carefully choosing variable selection mechanisms, encoding schemes, preprocessing, reductions, and projections of the data prior to discovering the intended patterns and building the relevant models, as well as their evaluation, interpretation, and visualization (Bibri 2018a). The pursuit of mastering the complexity of the process of knowledge discovery for smart sustainable/sustainable smart cities requires building an entirely new holistic system for big data analytics involving their operational functioning, planning, design, and development in terms of applied intelligence functions directed primarily for improving, advancing, and maintaining their contribution to the goals of sustainable development through continuously optimizing and enhancing their operations, functions, services, designs, strategies, and policies in line with the vision of sustainability. The entire analytical process able to create the needed knowledge services or associated with extracting useful knowledge and valuable insights in the form of such functions pertaining to decision-making processes should be expressible within a system that supports the following: • • • • • • • • • • •

the acquisition of data from multiple distributed sources; the management of data streams; the integration of heterogeneous data into coherent databases; the definition of observables to extract relevant information from available datasets; data transformation and preparation; methods for distributed data mining and network analytics; the organization and composition of the extracted models and patterns as well as the evaluation of their quality; tools for visual analytics to study the behavioral patterns and models; the availability of visualizations to planners, strategists, and decision-makers; methods for the simulation and prediction of the mined patterns and models; mining strategies for overcoming the scalability issues associated with big data in distributed environments.

Recent years have witnessed a remarkable progress within computer science, information science, and data science with regard to handling and extracting valuable knowledge and deep insights from large masses of data, and these have been utilized in urban science, to reiterate. In parallel, several projects of knowledge discovery across the globe as precursors in mining data related to different urban domains have developed various analytical and mining methods for spatiotemporal and spatial data (Batty et al. 2012; Bibri 2018a). They have shown to support the complex knowledge discovery process from the raw urban data, capable of supporting the decisions of different urban planners, administrators, and managers, thereby revealing the striking analytical power of big data. As an advanced form of decision support, the complex process of knowledge discovery/data mining is by far the most applied big data analytics technique or widely used framework for automatically extracting useful knowledge and valuable insights from large masses of data for enhanced decision-making in the domain of smart sustainable/sustainable smart urbanism (see, e.g., Batty et al. 2012; Bibri 2018a). Data mining (also known as knowledge discovery) is the computational process of probing colossal datasets in order to find frequent, hidden, and previously unsuspected and unknown patterns and subtle relationships; to make useful, meaningful, and valid correlations from these discoveries; and to summarize the results in novel ways and then visualize them in understandable formats prior to their deployment for decision-making purposes (Bibri 2018a; Bibri and Krogstie 2018). According to several codifications of the process of data mining, this process consists of well-defined stages, namely problem understanding, data understanding, data preparation, model building, result evaluation, and result deployment (see Chap. 9 for an illustration).

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A data mining framework for urban analytics and big data studies is developed, illustrated, and discussed in Chap. 9. It consists of six steps, namely: 1. 2. 3. 4. 5. 6.

understanding and specifying urban sustainability problems; understanding urban data; preparing and combining urban data from diverse sources; building models and generating patterns as true regularities; evaluating and interpreting the obtained results; deploying the results for urban operations, functions, services, strategies, and policies.

Chapter 9 provides a detailed description of these steps in the context of smart sustainable/sustainable smart urbanism. Some views argue that knowledge discovery and data mining are slightly two different processes in that the former emphasizes the high-level application of particular techniques and algorithms of data mining. This implies that data mining involves the application of these techniques and algorithms for extracting patterns and building models from data without the additional steps of the process of knowledge discovery (Bibri 2018a). This process consists of five steps, namely: 1. Data selection involves retrieving the relevant data from the databases for analysis. Creating a target dataset entails focusing on a subset of variables or data samples on which knowledge discovery is to be performed. The server of the databases fetches the relevant data on the basis of the data mining request. Prior to selection, urban data are collected, stored, and integrated from multiple sources. These techniques are performed on the data contained in the databases, data warehouses, or other data repositories. 2. Preprocessing involves collecting necessary information to account for noise, as well as handling missing data fields through de-noising, filtering, fusing, and standardizing, thereby removing redundant, unnecessary, and irrelevant data. This is intended to make the dataset ready for processing. 3. Data transformation is about data reduction and projection, that is, finding useful features/attributes to represent the data depending on the objective of the data mining task to be performed, and using dimensionality reduction to decrease the effective number of variables under consideration. In other words, the data in this stage are consolidated into forms relevant for mining by performing summary operations. 4. Data mining is where the data mining task is selected in order to build models (decision tree, neural network, linear or nonlinear equation, etc.), or to search for patterns of interest in the data in a particular representational form based on the algorithms being applied, in addition to deciding which models and parameters may be appropriate and matching the applied data mining algorithms with the overall criteria of the whole process. 5. Patterns evaluation entails assessing the data mining results and gaining confidence that the resultant models are valid and reliable, e.g., identifying the truly interesting patterns capturing regularities in the data and not just sample anomalies, odds, or idiosyncrasies. In other words, the identified patterns should represent knowledge based on some interestingness measures, according to the domain knowledge used to assess the interestingness of the resulting patterns. In employing interestingness thresholds or constraints, the step interacts with the data mining module (or may access such thresholds or constraints stored in the knowledge base) in order to focus the search toward interesting patterns. As to the knowledge presentation, visualization techniques are used to present the mined knowledge in an understandable format for human interpretation. This entails graphical user interfaces between the data mining system and the data analyst to allow them to interact with each other for different kinds of simple and complex tasks, including carrying out exploratory data mining, browsing database and data warehouse schemas, and visualizing the mined patterns in different forms. Further, other views argue that knowledge discovery and data mining as processes can be used interchangeably. This argument emanates from the illustration of how the two processes converge on most of the stages for extracting useful knowledge from data. For example, the selection step in knowledge discovery includes developing an understanding of the application domain, the relevant prior knowledge, and the objectives of the project (Bibri 2018a). These represent the problem understanding phase of the process of data mining. Table 1 illustrates the link between the two processes. However, conducting scientific and academic research using advanced big data analytics techniques has positive implications for sustainability that are wide-ranging, spanning such urban processes as operations, functions, services, designs, strategies, and policies pertaining to multiple urban domains in terms of advancing various forms of knowledge and

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Table 1 Link between the data mining and knowledge discovery processes

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Data mining process

Knowledge discovery process

Data understanding

Selection

Data preparation

Preprocessing and transformation

Model building

Data mining

Results evaluation

Interpretation/evaluation

Source Bibri (2018a)

related practices within the field of smart sustainable/sustainable smart urbanism (Bibri 2018a). See Chap. 8 for a list of such implications as a series of bullet points. Therefore, using the process of knowledge discovery/data mining is increasingly gaining traction and foothold in many academic and scientific research fields, taking over the scientific method that has prevailed for centuries. Here, the use of this process is seen as an important and effective way to, in addition to conducting scientific exploration and discovery based on big data, solve complex problems within a wide number and variety of domains, including smart sustainable/sustainable smart urbanism. By mining urban data, it is possible to discover laws and principles of sustainable development pertaining to environmental and socio-economic aspects of the city (Bibri 2018a). This development will allow an inference of the varied city stakeholders’ responses to operations, functions, services, designs, strategies, and policies in relation to multiple urban domains with regard to sustainability. Indeed, data-analytic and sustainable thinking and practice as an integrated approach into urbanism connects the best elements of data science technologies and urban sustainability practices. Data science has brought a novel approach to the way problems can be conceived of, understood, and tackled within a wide variety of domains. Accordingly, big data computing is changing the paradigm of scientific development, shifting from mainly formulating and testing hypotheses as well as collecting data manually and examining and reflecting on them to relying more and more on data generation, organization, processing, analysis, modeling, simulation, and verification (Bibri 2018a). This paradigm shift obviously spans many major academic and scientific research domains. In this context, it will help make decisions easier to judge, knowledge-driven, and strategic, and hence support and enhance existing, and create new, practices, strategies, and policies. For instance, big data analytics and related simulation models and optimization and prediction methods hold great potential to completely redefine urban problems, as well as offer entirely innovative opportunities to tackle them as part of new urban intelligence and planning functions, thereby doing more than merely enhancing existing urban practices. Further, experiences have shown that traditional scientific and academic research paradigms lead to questionable and challengeable assumptions about the evolution of social practices. Therefore, it is more beneficial and effective to search for new practices by rather using data-driven research approaches (as part of data-driven science and inductive empiricism) and thus the wider application of big data analytics techniques in the domain of smart sustainable/sustainable smart urbanism. In this sense, new practices can develop around big data technology, which can in turn be adapted and integrated into these practices, thereby advancing further its use in a way that fits into a wider strategy or formula that makes this technology more meaningful and relevant at the practical level (Bibri 2018a).In a nutshell, big data analytics is becoming increasingly a salient factor for academic and scientific innovation with regard to addressing complex challenges, wicked problems, and pressing issues, i.e., responding to major environmental concerns and socio-economic needs, mitigating the risks posed by ICT itself to environmental and social sustainability, and containing the potential effects of urbanization. There has recently been much enthusiasm in the domain of smart sustainable/sustainable smart urbanism about the immense possibilities and fascinating opportunities created by the deluge of urban data and its extensive sources with regard to improving urban operational functioning, management, planning, development, and governance in line with the goals of sustainable development as a result of thinking about and understanding sustainability and urbanization and their relationships in a data-analytic fashion for the purpose of generating and applying knowledge-driven, fact-based, strategic decisions in relation to such urban domains as transport, traffic, mobility, energy, environment, education, healthcare, public safety, public services, governance, economy, and science and innovation (Bibri 2018a, 2019a, b). This emanates from the ability of big data computing and the underpinning technologies to effectively monitor, understand, and analyze smart sustainable/sustainable smart cities to improve and maintain their sustainability performance through continuously optimizing their operations, functions, services, designs, strategies, and policies across multiple urban domains in line with the vision of sustainability (e.g., Bibri 2018a; Bibri and Krogstie 2017b). The use of big data computing and the underpinning technologies offers the prospect of cities in which natural resources can be managed sustainably and efficiently to enhance societal and economic outcomes by means of data-driven methods. This in fact epitomizes what smart sustainable/sustainable smart cities of the future entail and aim for: a set of transformative, innovative urban processes and

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approaches that amalgamate technological capabilities and strategic, data-driven decisions for boosting the performance of urban systems on the basis of a quest for promoting the health of individual citizens, communities, and natural ecosystems; conserving resources; and fostering economic development (Bibri 2018a). In light of this, the prospect of developing and implementing such cities based on big data analytics and its novel applications is fast becoming the new reality as manifested in the ever-growing embeddedness of advanced data sensing, data processing platforms, cloud and fog computing infrastructures, and wireless communication technologies as core enabling technologies of big data analytics into the fabric of urban environments for the purpose of solving the challenges of sustainability (e.g., Al Nuaimi et al. 2015; Batty et al. 2012; Bibri 2018a, 2019a). Unsurprisingly, big data have become the fundamental ingredient for the next wave of urban analytics and big data studies (Bibri 2018a, 2019a, b). As a result, many governments have started to exploit and harness urban data to reap their numerous benefits to support the development of their cities with regard to sustainability, efficiency, resilience, equity, and the quality of life. In the meantime, it has become of critical importance for urban professionals, analysts, and researchers to understand the fundamental concepts of data science and thus data mining/knowledge discovery even if they never intend to approach urban sustainability or sustainable urbanism problems from a data-analytical perspective merely because data analysis has now become so critical to urbanism practices. Both sustainable cities and smart cities are increasingly driven by big data analytics (e.g., Al Nuaimi et al. 2015; Batty et al. 2012; Bibri 2018a, b, 2019a, b; Bibri and Krogstie 2017a, b; Kitchin 2014, 2015, 2016), so there is a great professional and academic advantage in terms of interacting data-analytically with smart sustainable/sustainable smart cities as an emerging holistic approach to urbanism in an efficient and capable way. Understanding the fundamental concepts of data science and making an effective use of the available frameworks for organizing data-analytic thinking, especially the process of data mining/knowledge discovery, not only will allow urban professionals, analysts, and researchers to interact competently with such cities, but will help to envision tremendous opportunities for improving urbanism practices in the context of sustainability based on data-driven decision-making in the sphere of smart sustainable/sustainable smart urbanism. The emerging cities badging or regenerating themselves as smart sustainable/sustainable smart are exploiting new and existing data resources for environmental and socio-economic gains and benefits. They gather data science teams and urban scholars and practitioners on common ground to bring big data computing and the underpinning technologies as well as sustainability practices to bear to increase the contribution of such cities to the goals of sustainable development. Increasingly, urban administrators need to oversee analytics teams and analysis endeavors across multiple urban domains, local city governments must be able to invest wisely in urban projects and initiatives with substantial data assets directed for improving the different aspects of sustainability, and urban strategists and policymakers must be able to devise plans and design regulatory policies, respectively, that exploit and leverage data in the needed transition toward sustainability and its advancement (Bibri 2018a). It is worth pointing out that several computational and scientific approaches to cities, such as knowledge discovery/data mining, digital mapping and geographical information systems, quantitative geography and urban modeling, and urban cybernetics theory and practice, are based on realist epistemology. This approach postulates ‘the existence of an external reality which operates independently of an observer and which can be objectively and accurately measured, tracked, statistically analyzed, modeled and visualized to reveal the world as it actually is. In other words, urban data can be unproblematically abstracted from the world in neutral, value-free, and objective ways and are understood to be essential in nature; that is, fully representative of that which is being measured (they faithfully capture its essence and are independent of the measuring process)… And these data when analyzed in similarly objective ways reveal the truth about and a ‘God’s eye’ view of cities. As such, they promote an instrumental rationality that underpins the notion that cities can be steered and managed through a set of data levers and analytics and that urban issues can be solved through a range of technical solutions’(Kitchin 2016, p. 4). One of the implications of such a framing as to the criticism of urban science is that the scientific and informatics approaches willfully ignored the role of politics, social norms, social structures, ideology, and culture, as well as the metaphysical aspects of human life, in shaping urban relations, governance, planning, and development (Harvey 1973). Another implication of such a framing associated with urban science being roundly criticized within the social sciences is that it is too atomizing, reductionist, mechanistic, essentialist, deterministic, parochial, and closely aligned with positivist thinking, collapsing diverse complex, multidimensional social structures and relationships to abstract data points and universal formulae and laws (Buttimer 1976). In addition, it produces the kind of policy interventions that both did much damage to city operations as well as failed to live up to their promises (Flood 2011). Therefore, computational and scientific approaches to cities have been perceived as inadequate to solve urban problems due to their wicked nature. It is argued that such problems are often best solved through political/social solutions, citizen participation, and deliberative democracy, rather than technocratic forms of governance (Greenfield 2013; Kitchin et al. 2015). Moreover, such approaches are claimed to produce a limited and limiting understanding of how cities work and how

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they should be managed. The former pertains to foreclosing what kinds of questions can be asked and how they can be answered (Kitchin 2016), and the latter is associated with foreclosing other forms of urban knowledge, such as knowledge derived from practice and deliberation and based on experience (Parsons 2004). Nonetheless, while such approaches have been criticized for failing to recognize that cities are complex, intricate, multifaceted, and contingent systems, full of contestations and intractabilities that are not easily captured or steered, a view which undoubtedly still holds (Kitchin 2016; Kitchin et al. 2015), advocates of computational social and urban science counter that in the age of big data the variety, exhaustivity, resolution, flexibility, evolvability, and relationality of data, coupled with the growing power of big data computation and analytics, address some of the raised critiques, especially those related to reductionism and universalism, by providing more finely grained, sensitive, and nuanced analysis that can take account of context and contingency (Kitchin 2014b).

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Discursive, Epistemic, Historical a Priori, Institutional, Non-paradigmatic, Preparadigmatic, and Postparadigmatic Dimensions

5.1 Discursive Dimensions Smart sustainable/sustainable smart urbanism is an academic discourse (Bibri 2019b). It represents a discursive field, i.e., an amalgam of various discourses (e.g., smart cities, sustainable cities, smart urbanism, sustainable urbanism, sustainable development, smart growth, etc.) that are socially constructed and thus materially produced. In academic terms, it is an integrated field involving several disciplines from the social, natural, formal, and applied sciences, including data science, urban science, urban informatics, engineering science, sustainability science, environmental science, urban planning and development, ecology, economy, sociology, and politics and policy.

5.1.1 On the Discursive Genesis of Smart Sustainable Cities as a Leading Paradigm of Urbanism The debate focusing on the untapped potential of big data computing and the underpinning technologies for catalyzing and boosting the process of sustainable development toward achieving the long-term goals of sustainability in relation to smart sustainable/sustainable smart cities, which in turn represent an academic discourse (e.g., Bibri 2018a, 2019a, b), relates to the academic discourse of ICT for urban sustainability. This discourse has emerged and gained popularity after the prevalent ICT visions of pervasive computing have become deployable and achievable computing paradigms, coupled with the recent advances in data science, urban science, and urban informatics. Moreover, the amalgamation of these computing paradigms and sciences in terms of the underlying core enabling technologies as well as scientific methods, processes, and systems in relation to many urban domains as regards sustainability has contributed to the emergence of the discourse of smart sustainable/sustainable smart urbanism. In this regard, concrete language use can change the urban world as an instance of the social and cultural world by combining elements from different discourses. However, the above discourses metonymically represent the discourse of sustainable information society, a meta-discourse which regulates the discourse of such urbanism. Sustainable information society denotes ‘a society in which new…ICT…and knowledge are used in order to advance a good-life for all individuals of current and future generations. This idea is conceived in a multidimensional way, identifying ecological, technological, economic, political, and cultural aspects and problems’ (Fuchs 2005, p. 219). Smart sustainable/sustainable smart cities are the leading paradigm of urbanism (Bibri 2019b). Bibri (2018a, p. 299) conceives of them ‘as a social fabric and web made of a complex set of networks of relations between various synergistic clusters of urban entities that, in taking a holistic or systemic perspective, converge on a common approach into using and applying smart technologies to create, develop, disseminate, and mainstream the innovative solutions and sophisticated methods that help provide a fertile environment conducive to improving advancing sustainability. This can occur through strategically assessing and continuously enhancing the contribution of such cities to the goals of sustainable development. Here, ICT can be directed toward, and effectively used for, collecting, processing, analyzing, and synthesizing the data on every urban system and domain as involving forms, structures, infrastructures, networks, facilities, processes, activities, and citizens.’ The whole idea revolves around leveraging the convergence, ubiquity, advance, and potential of ICT of pervasive computing and its prerequisite enabling technologies, especially big data computing and the underpinning technologies, in the transition toward the needed sustainable development and sustainability advancement in an increasingly urbanized world (Bibri 2018a, c). However, ICT as a set of smart urban technologies in this context represents social constructions, whereby a seamless web of societal factors (scientific, cultural, social, historical, political, economic, legal, and institutional) and thus actors shape the emergence, production, uptake, and evolution of such technologies (Bibri 2015; 2018a).

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Furthermore, Bibri (2018c) looks at the discursive dimensions of a foundational framework for smart sustainable/sustainable smart urbanism as a form of social practice, and in doing so, he discusses various aspects and issues of discourse and its relation to such practice, as well as identifies the main discourses underlying and shaping this practice. Bibri (2018a) provides an account of how these discourses interrelate along with a variety of the discursive and social dimensions of smart sustainable cities. In a more comprehensive study, Bibri and Krogstie (2016) analyze the nature, practice, and impact of ICT of pervasive computing for urban sustainability as a form of S&T within the defining context of smart sustainable cities. Specifically, they probe the ways in which this form has emerged from different perspectives, why it has become institutionalized and interwoven with politics and policy-urban dissemination, as well as the risks it poses to environmental sustainability. To achieve this aim, an analytical and philosophical framework of Science, Technology, and Society (STS) is adopted, which supports analyzes and evaluations whose approaches are drawn from a variety of disciplinary and theoretical perspectives. Generally, STS employs diverse qualitative approaches, such as discourse analysis, comparative historical analysis, and cases and controversies, depending on the topic under investigation. Bibri and Krogstie (2016) use a discourse analysis as a research methodology in relevance to the nature and scope of the topic of smart sustainable cities. Therefore, the analytical focus of this STS study is on the discursive-material facets and dialectics of such cities as a techno-urban transformation. Another reason for adopting discourse analysis is that this STS study deals with knowledge constructions and the societal context—ecologically technologically advanced societies—in which such constructions are given meaning and ultimately applied in the form of social practices, to draw on Bibri (2015). Furthermore, discourse analysis as a transdisciplinary analytical strategy is employed here to probe the techno-urban vision of such cities and its role alongside material mechanisms and practices associated with the translation of such vision into hegemonic techno-urban projects, initiatives, and strategies and hence its institutionalization in urban structures and practices, i.e., in transforming urban domination. In fact, discourse analysis is one of the most used analytical approaches in social constructionism for discourse is of fundamental importance for constructing social phenomena, categories, and mechanisms as shared understandings of the social world. Social constructionism deals with the ways in which such understandings are jointly constructed, reconstructed, institutionalized, and conventionalized by society, thereby constituting the basis for the shared assumptions about reality. Accordingly, discourse analysis is underpinned by a social constructionist approach to knowledge, which rests on several premises, including the historical contingency and cultural specificity of knowledge, a critical approach to taken-for-granted (scientific) knowledge, and the relationship between knowledge (and its discourses) and social practices as well as social processes (Gergen 1985; Burr 1995). For a descriptive account of the premises of social constructionism, the reader can be directed to Bibri (2015). Its main philosophical assumptions entail that multiple categories of reality are legitimate, written works are open to multiple readings, and language is not a representation of reality. Both Kuhn (1962) as a philosopher of science and Foucault (1966, 1972) as a sociologist of science adhere to the premises shared by social constructionist approaches, and their works were instrumental in establishing that scientific knowledge and its related facts are not mere reflections or pure representations of reality—but rather outcomes of historically and socio-culturally conditioned investigations (Bibri 2015; Bibri and Krogstie 2016), to reiterate. In regard to the findings of the study conducted by Bibri and Krogstie (2016), smart sustainable cities are discursively construed and materially produced on the basis of the socially constructed understandings and socially anchored and institutionalized actions pertaining to ICT of pervasive computing for urban sustainability, of which big data computing and the underpinning technologies are a subset. Therefore, such cities (and related urbanism) are mediated by and situated within ecologically technologically advanced societies. And as an urban manifestation of scientific knowledge and technological innovation, they are shaped by, and also shape, socio-cultural and politico-institutional structures and practices as urban determinants. In addition, the study demonstrates that the success and expansion of such cities (and related urbanism) emanates from the transformational power, knowledge/power relation, productive and constitutive force, and legitimation capacity underlying ICT of pervasive computing (especially big data computing and the underpinning technologies) for urban sustainability due to its association with the scientific discourse and its societal entailments.

5.1.2 Discursive Hegemony The discourse of big data science and analytics for sustainable development and that of smart sustainable/sustainable smart urbanism depart from and build on the same assumptions and claims pertaining to the link between smartness and sustainability. Put differently, the latter represents the defining context and applied domain for the innovative engineering analytical solutions being suggested or offered by the former. Achieving the goals of sustainable urban development with support of big data science and analytics occurs through such urbanism and within smart sustainable/sustainable smart cities as a leading paradigm of urbanism. However, the focus in the discussion here is only on the discourse of smart

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sustainable/sustainable smart urbanism in terms of hegemony. This discourse becoming more powerful and established as a scholarly discourse is demonstrated by the contemporary scholars and practitioners from many disciplines and professional fields, respectively, relating to it in a structured way in many contexts of urban practices. In a nutshell, it is a ‘hegemonic discourse’ (see, e.g., Sum 2004; Hajer 1995). The discursive hegemony of big data science and analytics-driven smartness and sustainability over the current form of urban planning and development is manifested in this discourse becoming so embedded in the information society that it appears of uttering nonsense to ask about its assumptions. The big data science and analytics orientation of smart sustainable/sustainable smart urbanism has gained dominance in that society under its discourse, which as a separate discursive field represents a cluster of several scholarly and scientific discourses that revolve around the relationship between data science, information science, urban science, sustainability science, urban sustainability science, sustainable development, and urban planning and design. This implies that this discourse has gained legitimacy as an academic discourse and thus urban pursuit—though diverse urban policies, strategies, projects, and initiatives supported by scholarly research and scientific innovation endeavors. In particular, the growing academic interest in this discourse is such that it has become part of mainstream debate in city-related disciplines. This is because of the (perceived) potential of the innovative engineering analytical solutions being proposed by big data science and analytics for catalyzing and boosting sustainable urban development processes and, thus, for advancing urban sustainability. In addition, this discourse and its translation into hegemonic techno-urban projects and strategies and their ongoing institutionalization in urban structures and practices are anchored in the postulation that future visions of noteworthy advances in S&T (computing and ICT) bring with them wide-ranging visions of the future on how cities will evolve and the opportunities such future will bring, e.g., sustainability, resilience, efficiency, and the quality of life, to draw on Bibri (2018a). The importance of techno-urban visions of the future which materialize subsequent to new scientific innovation and its technological applications (i.e., big data science and analytics) lies in that such visions ‘have the power not only to catch peoples’ minds and imaginations, but also to inspire them into a quest for new possibilities and untapped opportunities and to challenge them to think outside common mindsets’ (Bibri 2015, p. 3). This is of relevance as to the innovative ways that are mostly needed to address the challenges of sustainability and urbanization in modern and future cities (Bibri 2018a, 2019a, b). In relation to this, as with other academic discourses, the discourse of smart sustainable/sustainable smart urbanism, which is constructed in light of new conceptions about the scientific, technological, environmental, economic, institutional, social, and cultural changes over the past decade—contains an all-embracing understanding of the problems cities are facing and is also the defining context and applied domain for the suggested or offered big data science and analytics solutions, which represent future possibilities for overcoming the challenges sustainability and urbanization.

5.1.3 Discursive-Material Dialectics, Construal, and Construction With being a hegemonic semantic order, smart sustainable/sustainable smart urbanism as a techno-urban vision has been construed and constructed (see, e.g., Bibri 2018a; Jessop 2004). That is to say, it has resonated with material mechanisms and practices. Constituting techno-urban objects and their related subjects with specific material and ideal interests (discursive constructions), such urbanism has a pivotal role alongside material mechanisms and practices in reproducing and/or transforming urban domination (see Sum 2006). As a set of representations, it has been discursively construed in different spatial contexts (regions, cities, districts, and neighborhoods within ecologically or technologically advanced nations) and reproduced materially through institutional and organizational apparatuses and their techniques, i.e., actors, subjects, rules, objects, frameworks, and knowledge (see, e.g., Bibri 2018a; Jessop 2004). This material reproduction entails the translation of the underlying techno-urban vision into hegemonic techno-urban strategies, projects, and initiatives as well as their institutionalization in city structures and urban practices, to reiterate. With regard to the construal of smart sustainable/sustainable smart urbanism, Jessop (2004, p. 164) asserts that the relative success of discursive construals, which ‘can be durably constructed materially,’ ‘depends on how… [it] and any attempts at construction correspond to the properties of the materials…used to construct social reality.’ This supports the argument about the discursive-material dialectics and the importance of discursivity and materiality to an adequate account of the reconstruction of smart sustainable/sustainable smart urban transformation. Specifically, focusing on how urban politics in relation to such urbanism is done in a dialectic interplay between ‘discursive selectivity (discursive chains, identities, and performance) and material selectivity (the privileging of certain sites of discourse and strategies of strategic actors and their mode of calculation about their “objective interests,” and the recursive selection of these strategies)’ (Sum 2006, p. 8) in different spatial contexts is crucial to understand why this new discourse has been translated into concrete projects, initiatives, and strategies and, thus, policy orientation has been legitimated with references to it, to draw on Bibri (2015, 2018a). On the whole, there is a mutual dependence between semiosis and the material world, a dialectic interplay in which smart sustainable/sustainable smart

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urbanism is constructed as an urban reality from an ontological standpoint. Semiosis refers to ‘the intersubjective production of meaning’ and can be viewed as an umbrella concept for discourse and language (Jessop 2004, p. 161). The dialectic of discursivity and materiality is in turn crucial to the social construction of smart sustainable/sustainable smart urbanism. This involves developing, institutionalizing, and conventionalizing this techno-urban phenomenon by the information society (i.e., ecologically or technologically advanced nations) through social constructs or cultural frames. Such constructs or frames represent models of the urban world that are created, shared, and reified through language in the form of scientific and academic publications. Social constructionism posits that people rationalize their experiences through models and language (Leeds-Hurwitz 2009), i.e., a concrete use of language through combining elements from different discourses (Bibri 2018a). Further, social constructs or cultural frames are produced by and depend on contingent aspects of people as social selves through social practices which form objects that an array of previous and current academic discourses on urbanism talk about. Accordingly, the constitution and reconstitution of urban life as a form of social and cultural change occurs through discursive practice. In light of this, recent years have witnessed a proliferation of scientific and scholarly writings on the growing role of big data science and analytics in advancing urban sustainability (e.g., Bibri 2018a, b, c, 2019a, b; Bibri and Krogstie 2017a, b, c; Bibri and Krogstie 2018), a form of semiosis that has generated a plethora of discursive constructions in support of big data science and analytics in terms of its contribution to urban sustainability. The related magnitude and diversity of scientific and scholarly research has in turn given rise to data-driven smart sustainable/sustainable smart urbanism as an integrated and holistic approach into urban planning and development. This body of work continues to flourish and rapidly burgeon and is consequently instigating drastic transformations pertaining to the way the city can be operated, managed, planned, designed, developed, and governed. This is being fueled by the academic and scientific debates on urban sustainability science and its big data science and analytics orientation as informed by the new urban science with respect to its role in evaluating and mitigating the unintended consequences of urban activities on the socio-ecological systems of cities across the world. Sustaining the scholarly and scientific momentum can also be explained by the resonance of data-driven smart sustainable/sustainable smart urbanism as a new intellectual trend with the practices of local city governments, urban policy bodies and networks, research institutions, universities, sustainable development institutes, and ICT industry consortia. These corroborating aspects are being demonstrated in the ongoing urban studies, projects, initiatives, strategies, and policies taking place within ecologically or technologically advanced societies across the globe in relation to the development and implementation of smart sustainable/sustainable smart cities on the basis of big data science and analytics and the underlying core enabling technologies.

5.2 Historical a Priori, Epistemic, and Institutional Dimensions As a discourse, smart sustainable/sustainable smart urbanism articulates ways of thinking, seeing, talking, and acting, which represent historical events, cultural manifestations, and socio-political practices. The importance of the wider social context of such discourse is paramount. The rationale of the analysis here is to provide explanations that are valid beyond society and culture and the current period of history by examining the commonalities in the cultural and material resources and practices brought to bear on the construction and stabilization of smart sustainable/sustainable smart urbanism. This involves identifying the dominant formations of knowledge, the orderly structures underlying the production of knowledge, societal and institutional processes, and other material forms shaping actions. It is important to emphasize the historical contingency, cultural specificity, and the actions of multiple historic and societal actors that provide the infrastructure for such urbanism— the conditions for it to emerge, function, and evolve within ecologically and technologically advanced nations. In terms of historical situativity, the discourse of smart sustainable/sustainable smart urbanism has materialized in more recent years. It is increasingly becoming established and powerful as social practices—institutionalized and socially anchored actions—relate to it in a structured way in different contexts. It is thus circulating freely in ecologically and technologically advanced societies as a valid way of thinking about urban practices. Nonetheless, in consideration of the very nature of discourses, it may become more powerful or cease to exist depending on the states of stability: long-lasting or temporary, emerging from the dynamics of change in urban strategies, social and institutional structures, and technological systems, to draw on the coevolutionary transition approach (Bibri 2015). As such, it is the result of people’s daily making of history; it changes over time, can persist or vanish as a way of thinking and acting. At present, urban and institutional structures and practices continue to be transformed in ways that embody this discourse, by spurring and promoting related projects, strategies, and policies. Big data computing and the underpinning technologies applied within the field of urban science are breaking through to the mainstream due to their potential to address the environmental, social, and economic challenges of sustainability and urbanization.

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The discourse of smart sustainable/sustainable smart urbanism is not a timeless, ideal form but rather a fragment of history and an integral part of a major shift in knowledge configuration posing its own limits and transformations, as well as the specific modes of its temporality and of being rule-bound sets of statements. It has resulted from a specific episteme, what modern society considers and values to be knowledge in terms of the fundamental body of ideas and shared presuppositions, claims, and premises that define the nature and sets the bounds of what is accepted as true knowledge in the current epistemic epoch. This relates to how knowledge can be created and developed—through different types of scientific inquiry and scientific methodologies. From a social constructionist epistemology approach, researchers in the field of smart sustainable/sustainable smart urbanism share some assumptions about urban reality which permeate the process of research in the form of methodological frames and theories. Thus, the dominant episteme in the current period of history represents the primary system of fundamentals that underlie the making of knowledge which causes to emerge smart sustainable/sustainable smart urbanism as a new discourse, and also define its associated material mechanisms in terms of institutional apparatuses and their various techniques. In a sense, the idea of smart sustainable/sustainable smart urbanism helps to both characterize and classify the respective episteme. As a precognitive space, the prevailing episteme determines not only the appearance of this idea and many other related ideas, but also the establishment of a number of new sciences, philosophies, and rationalities in the current historical period that are related to this idea, such as urban science, urban informatics, urban big data analytics, data-driven urbanism, and so on. These epistemological products are conditioned by what Foucault terms historical a priori as manifested in the scientificity and objectivity underlying the positivities that constitute the scientific disciplines and sub-disciplines. Put differently, the historical a priori involves the scientific discourse as the ultimate form of rational and objective thought and the basis for legitimacy in knowledge-making and decision-making. Foucault (1972) argues that scientific knowledge is not inherently more ‘true’ than other forms of knowledge. However, one can relatively easy discern the function and meaning of historical a priori as situated and reconfigured in what modern society considers and values to be true knowledge, as it tends to be stable enough to be detected yet flexible enough to move through. The discourse of smart sustainable/sustainable smart urbanism should evidently appear as part of the configurations within the space of knowledge privileged by modern society in the current historical epoch. These configurations are giving rise to diverse forms of research on smart sustainable/sustainable smart urbanism, thereby forming the conditions of possibility for urban knowledge, the mutually constitutive relationship between scientific discourse and technology. Scientific knowledge, principled systems of understanding, has been contrasted with empiricism. The main argument is that the historical a priori and thus episteme underlying the discourse of smart sustainable/sustainable smart urbanism are exhibiting some shifting patterns—within a single period, so too are what they underlie and determine as ideas, sciences, philosophies, rationalities, hypothetical concepts, and so on. In other words, the historical a priori that is the positivity of scientific discourse is not an immutable law or a level of existence, but is transformable along with specific discourses, e.g., smart sustainable/sustainable smart urbanism as involving urban science, urban informatics, data science, sustainability science, complexity science, and urban planning and development. Under this assumption, the positivity in the form of statements and their relations define a limited range of things to be said about the discourse of such urbanism, although the underlying specificity of positivity that this discourse has as a kind of historical a priori provides it with its condition of reality, defining the range of statements that are made within it. This discourse denotes a coherent body of statements, a collection of utterances governed by the rules of construction, that are organized in a systematic way to create a self‐confirming account of urban reality and to attempt to make it true. The conditions of existence for meaning production in this discourse entail that statements emerge on the basis of historical rules, which delimit what can be uttered. In this respect, this discourse consists of a limited number of statements for which a group of conditions of existence for meaning can be defined. Accordingly, it creates a network of rules as preconditions for statements to exist and to be meaningful. In this sense, these rule-bound sets of statements impose limits on what gives meaning in it, and as a consequence, innumerable statements are not articulated and would never be accepted as meaningful. This constraining force limits the ways of talking about and acting in the urban world, ruling out alternative ways of talking and acting. From a somewhat different perspective, like all discourses, characteristic to the discourse of smart sustainable/sustainable smart urbanism as an interlacing of knowledge and practice are frame and framing in terms of its structuration—the domination of the terms of the debate—and meaning construction—the operation of inclusion and exclusion of facts and topics, respectively. In this regard, through a forming pattern that directs the construction of discursive fragments (texts belonging to discourse), this discourse holds together and gives meaning and coherence to a diverse set of representations. Also, through selection and salience, this discourse selects some aspects of reality and makes them more salient to achieve certain intentional effects, such as causal interpretation and ethical evaluation. Moreover, by means of framing as a discursive strategy, how this discourse operates to construct meaning in the broader relation to social, cultural, and political contexts, it alters reality in order to achieve a certain goal. One implication of framing in terms of the inclusion process is to overvalue

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certain aspects (e.g., topics, facts, benefits, value judgment, etc.) pertaining to the bright side of smart sustainable/sustainable smart urbanism, and in terms of the exclusion process is to undervalue, conceal, or ignore other aspects relating to the dark side and empty promises of it. This exclusionary aspect entails leaving out a number of issues related to social, ethical, political, cultural, and environmental concerns in the construction process of such urbanism. Therefore, a key feature of framing reality in the discourse of smart sustainable/sustainable smart urbanism is to promote its goodness and godliness or paint its promises in sunny colors in terms of advancing sustainability. Framing is inherent to the construction of all types of discursive fragments. For what it entails, framing relates to the concept of positivity of discourse in the sense of excluding anything hidden within it, missing from it, or lying beneath it—but focusing only on its visible, traceable relations. From a conceptually different approach, the discourse of smart sustainable/sustainable smart urbanism has grounds—impacted by the earlier constructions of urban reality and how they were reproduced in relation to the significance of the discursive constructions pertaining to the role of advanced ICT in the city—from which it has emerged and evolved, building on a set of established discourses and, thus, engendering changes in urban reality. Overall, a common aspect of discourses as ways of talking (and thus framing) is that they do not actually reflect the world as it actually is, but tend to change the meaning of reality. And since people who produce the discourse of smart sustainable/sustainable smart urbanism exist within the social, cultural, and historical contexts of modern society, this discourse has socio-cultural and historic aspects. More to the limits of the discourse of smart sustainable/sustainable smart urbanism, Derridean philosophy, in relation to deconstruction as a form of philosophical analysis that has been deployed in the analysis of scientific writings, posits that intrinsic meaning is accessible by virtue of pure presence, and denies the possibility of an intrinsic meaning and of a pure presence. In this respect, the relevant question to raise is: What can be overvalued and undervalued in discourses as a form of meaning production and system of understanding? Indeed, a critical deconstruction of the discourse of smart sustainable/sustainable smart urbanism would employ strategies for analyzing the silences and absences in scientific and academic publications on such urbanism. Especially, it is constructed as something positive and profound, associated with radical urban transformation that one can hardly be against. It fits nicely into the grand narratives of modern society where development is both valued and inevitable. In addition, the discourse of smart sustainable/sustainable smart urbanism is linked to the regimes of truth, which are infused with power relations and ways of seeing (like all social knowledge constructions) that impact on the human subjects producing this discourse. One of Foucault (1972) arguments is that subjects are created in discourses and thus not really free to think and act, as their ideas and actions are generated and shaped by social, cultural, political, and institutional structures (see Bibri 2015 for a detailed discussion of subject positioning in relation to ICT of pervasive computing). Moreover, the mechanisms behind the production of this discourse have been shaped and supported by political and institutional apparatuses and their techniques, such as rules, regulations, and objects that play a fundamental role in the application of this discourse to the urban world in modern society (see, e.g., Bibri 2015, 2018a, 2019b; Bibri and Krogstie 2016). Governmental institutions are postulated to shape but also operate within the parameters generated by the dominant configuration of knowledge and the ensuing general structures and practices. Thus, it is in the remit of such institutions to facilitate the use of smart sustainable technologies and infrastructures through regulatory frameworks as well as the social norms that normalize the use of such technologies and infrastructures. The basic idea is that a favorable institutional framework reduces the uncertainties for smart sustainable/sustainable smart urbanism and thus chances for its failure. In general, institutions entail the kind of actions, rules, and social structures and practices that persist over time and represent characteristics of social aggregate that are larger than a single organization. They are identified with a societal purpose and facilitate coordination between various actors and networks, transcending individuals by mediating the rules that govern the behavioral patterns that are of importance to society, e.g., compliance with sustainability values. In this context, institutions involve policy networks, legal bodies, universities, public research institutes, industry associations, and research communities involved in smart sustainable/sustainable smart urban projects, initiatives, or programs. In particular, research and practice pertaining to the domain of smart sustainable/sustainable smart urbanism are regarded legitimate due to the high degree to which they conform to these larger institutional frameworks. Legitimacy has both cognitive and socio-political aspects (Aldrich and Fiol 1994). The cognitive aspects of legitimacy refer to the degree to which activities, organizations, and technologies are understood by actors and, in the extreme, completely taken for granted (frames of mind). The socio-political aspects of legitimacy denote the degree to which societal actors accept activities, organizations, and technologies as desirable or appropriate under socially constructed system of rules, norms, values beliefs, and sensibilities. The (pre)cognitive and socio-political legitimacy conditions the activities of urban actors, as well as the ways in which these actors’ strategic actions can work to reshape the institutional bases of this legitimacy, to draw on Suchman (1995), Based on the above reasoning, the governmental institutions promoting smart sustainable/sustainable smart urbanism and supporting urban scientists and big data technologists across the globe depend on changes to the social norms, behaviors,

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values, and beliefs of people. This implies that the claimed numerous benefits of such urbanism only remain inadequate to induce a deep structural change in this direction. The basic premise is that institutional recommendations are not assumed to be optimal, especially in relation to the high levels of uncertainty characteristic of technological change and innovation. And they are contingent upon the wider institutional frameworks in which they are embedded. Subsequently, the institutional endeavors being undertaken to further reinforce the development and assimilation of smart sustainable/sustainable smart urbanism are likely to be lessened, if not abandoned, due to societal, market, and global factors and shifts, and consequently, the level of investment being considered in this regard and the support provided to promote big data technologies decrease. In discursive terms, the discursive positions pertaining to such urbanism may overtime become recurrent and start sedimenting, and hence, changes to arguments and/or new alternative views will emerge and the established discursive positions will be questioned and challenged. Similarly, it is possible that urban knowledge may induce more effects of power by undergoing some fundamental reconfiguration—within the limits of the episteme of modern society in the current historical period. Regardless, the future is not predetermined, but open to alternatives which can be shaped by social practices, where people act upon new objects of knowledge over and over and transform and challenge discourses (Bibri 2015).

5.3 Non-paradigmatic Aspects Smart sustainable/sustainable smart urbanism is about the use of big data technology and its novel applications by city stakeholders as human actors to advance sustainability by enhancing and optimizing urban operational functioning, planning, design, and development in response to the goals of sustainable development and the challenges of urban growth. This is based on new perspectives and insights pertaining to the way urban actors understand, value, and aspire to apply and use big data computing and the underpinning technologies. Smart sustainable/sustainable smart cities as urban environments can be generated which improve sustainability, resilience, efficiency, and the quality of life for citizens. Further, if the people are the main actors in this regard, the relevant techno-urban/socio-technological reality must be only of the people’s own construction. Following this reasoning, how can there be general theories about smart sustainable/sustainable smart urbanism, let alone paradigms? There can only be an archipelago of local techno-urban perspectives on the incorporation of big data computing and the underpinning technologies into urban life and environments, and on how this can make urban living smarter and more sustainable. In addition to this argument, such urbanism travels under many aliases. This scattering of techno-urban trends does not facilitate, or provide the conditions for, establishing a coherent body of theory. Too often, such trends are characterized by high specialization, representing dedicated fields that often fail to connect with, or refer in any systematic way to, each other. They keep on, since the advent of big data science and analytics, generating alternative names with some of them even from the ground up, in the process of starting from scratch or reinventing the wheel, as shown by recent studies, without endeavoring to generate expert opinion or zero in on ground rules. The lack of interdisciplinary and transdisciplinary research endeavors is a major concern in the field of smart sustainable/sustainable smart urbanism (Bibri 2018a, 2019a). There are further reasons why the notion of paradigm (shift) does not apply to such urbanism. First, such urbanism concerns normative values and, thus, involves various policy frameworks rather than explanatory and meta-theoretical frameworks. Second, it represents more a vision of the future city than a reality. By virtue of its very definition, it is normative, signifying a certain desired view on the techno-urban world (see Bibri 2019b). Third, it is promoted by certain companies, organizations, institutions, and policymakers for other underlying purposes than sustainability, e.g., serving economic and political end goals. The main argument is that smart sustainable/sustainable smart urbanism is such urbanism is not necessarily anti-theoretical but intellectually fragmented. The work of many authors within its domain can be contextualized in terms of their institutional belonging, scholarly affiliation, social location, cultural inclination, ideological commitment, and socio-political status. In particular, the institutional belonging of urban scholars and scientists may, in some cases, be associated with promoting such urbanism to primarily achieve economic and political objectives and not only environmental and social goals.

5.4 Preparadigmatic and Postparadigmatic Aspects The set of experiences, beliefs, and values that affect the way people perceive reality and respond to that perception in society is referred to as worldview, which equates to paradigm in the social sciences. Handa (1986) introduced the idea of ‘social paradigm’ in relation to the social sciences, identifying the basic components of a social paradigm and addressing the

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issue of paradigm shift. As with all paradigms in the social sciences, smart sustainable/sustainable smart urbanism being non-paradigmatic and postparadigmatic in nature has to do with not being grounded in a meta-theoretical foundation that transcends contingent human actions. Such urbanism lacks a theoretical model with an explanatory power of a universal character, and as taken to assume a paradigmatic shift in the way the city is operated, planned, designed, and developed, it does not demonstrate a drastic break in intellectual and thus socio-political practice with respect to urban institutionalized and socially anchored actions. Smart sustainable/sustainable smart urbanism is preparadigmatic because there is no scholarly consensus available in the field, or in the social sciences from which this field derives. The social sciences involve volatile theories, pluralism of theoretical models, multiplicity of research methods, and numerous unsolved issues. Adding to this is the generally understood extraordinary complexity of the social sciences, in that they involve social and political processes which are reflexive in nature, i.e., social actors act upon theories themselves, which are adapted in action (see Bourdieu’s (1988) analyzes of the social sciences in action and Bourdieu and Wacquant (1992) for reflexive sociology). This is assumed to carry over its effects to the production and application of knowledge about such urbanism. Moreover, related disciplinary synergies further complicate the matter further through identifying limitations, complications, and new possibilities. Further in this regard, there is a propensity across the globe for hugely investing in research and education to train and educate a new generation of interdisciplinary and transdisciplinary researchers, scholars, and scientists within the field of urban sustainability science, as well as to gaining new knowledge needed for exploring and exploiting the opportunity of using and applying big data computing and the underpinning technologies to solve real-world problems, especially in the sphere of smart sustainable/sustainable smart cities. Also, big data analytics is associated with significant challenges due to the interdisciplinary and transdisciplinary nature of deluge of urban data, and what this entails in terms of all kinds and levels of complexity. Smart sustainable/sustainable smart urbanism is postparadigmatic because the state of research or inquiry within the field reflects and acknowledges the gaps, risks, paradoxes, limits, deficiencies, fallacies, misunderstandings, and discontinuities that such urbanism fails to notice. See Bibri (2018a, 2019a, b) for a detailed account of these issues. In addition, while there is a growing consensus among urban scholars and applied urban science experts that big data science and analytics and the underlying technologies and their novel applications will be a salient factor in the operational functioning, management, planning, design, and development of smart sustainable/sustainable smart cities of the future, there still are significant scientific and intellectual challenges that need to be addressed and overcome for building such cities based on big data computing, and then for accomplishing the desired outcomes related to sustainability and urbanization (Bibri 2019a). Such challenges pose complex research questions and constitute fertile areas of investigation awaiting interdisciplinary and transdisciplinary teams of scholars, scientists, experts, and researchers working or involved in the field of data-driven smart sustainable/sustainable smart urbanism. Furthermore, the prospect of such urbanism is increasingly being complicated by subjecting its development to an ever-growing number of disciplines and methodological approaches, which require continuous experimentation, monitoring, and reporting. However, even new trends of such urbanism are inherently subject to future interrogation, predicated on the assumption that the configuration of social knowledge is subject to constant change. As argued by Bibri (2019b), a well-established fact is that cities evolve and change dynamically as urban environments, so too is the underlying planning knowledge that perennially changes in response to new emergent factors and changes. Explicitly, cities need to be dynamic in their conception, scalable in their design, efficient in their operational functioning, and flexible in their planning in order to be able to deal with population growth, environmental pressures, changes in socio-economic needs, global shifts/trends, discontinuities, and societal transitions (Bibri 2018a, 2019b). It is contended that open, indeterminate planning is associated with several advantages, including the tolerance and value of topographic, social, and economic discontinuities; continuous adaptation; and citizen participation, and these are common to human settlements. All in all, the usage of paradigm and paradigm shift as related to such urbanism can be used in a loose sense of an ‘intellectual framework and trend,’ similar to discourse and discursive shift.

5.5 Paradigm and Paradigm Shift in the Social Sciences Due to the inherent reflexive and condition-changing character associated with all kinds of techno-urban/socio-technological phenomena, it is appropriate to use a discourse and discursive shift as loose senses of a paradigm and paradigm shift concerning smart sustainable/sustainable smart urbanism. Indeed, the social sciences, a major category of academic disciplines and a branch of science, and thus city-related academic disciplines are not based on a Kuhnian paradigm. The underlying assumption is that paradigm and paradigm shift do not hold in, or are not of relevance to, the social sciences. Kuhn (1962) explains that he developed the concept of paradigm precisely to distinguish the social sciences from the natural

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sciences. Kuhn’s (1962/1996) position is that the social sciences are ‘preparadigmatic’ because there is no scholarly consensus available but pluralism. Mattei (2001), in his article ‘Paradigms in the Social Sciences,’ develops Kuhn’s original thesis that there are no paradigms at all in the social sciences since the concepts are polysemic, involving the deliberate mutual ignorance between scholars and the proliferation of schools in these disciplines. The social sciences are characterized by a ‘tradition of claims, counterclaims, and debates over fundamentals’ (Kuhn 1972). Moreover, it has widely been recognized that the social sciences are of an extraordinary complexity due to the reflexive nature of social processes as well as the changing social conditions. The latter relates to the postparadigmatic character of the social sciences. They are moreover articulated within the confines of particular discourses (Foucault 1972), which are socioculturally conditioned and historically restricted. While this argument does not extend to include the hard sciences, which involve methodological rigor and objectivity and thus legitimation capacity stemming from the scientific method and discourse, contemporary sociologists of scientific knowledge claim that the hard sciences are also socially constructed and have historical and contingent factors woven into them (Bibri 2015). However, the assumption of social theories providing a window onto the inner characteristics of phenomena and discovering and establishing a discursive truth on the grounds that all knowledge is socially and historically situated has been often dealt with as unproblematic (Bibri 2015). Given their nature, the social sciences tend to create the kinds of knowledge that involve subjectivities and pluralisms. Objective viewpoints and universal laws are what the social sciences have constantly been striving to create and discover. This has been viewed by some philosophers as ‘an intellectual crisis’ in the social sciences. Meta-theory in smart sustainable/sustainable smart urbanism, as involving the social sciences, offers many challenging arguments and analyzes of basic conceptual and theoretical frameworks for the study of human behavior and functioning and their implementation and simulation into big data technologies and its applications and systems as part urban science. However, some argue for the role of knowledge produced by the social sciences in meeting the demands and needs of society and its persistent problems and issues as well as for the role of subjectivity in science, which, as some scholars argue, cannot be studied scientifically, and as others contend, can and should be if progress in (social–scientific) knowledge is to be made. However, from the perspective of historical studies of science, a new approach crystallized by the influential work of Kuhn (1962), even scientific facts are viewed as products of socially conditioned investigations rather than mere reflections or objective representations of reality, to reiterate. Based on the above reasoning and assumptions, in current usage, smart sustainable/sustainable smart urbanism can be used in a broad and loose sense of ‘an intellectual trend,’ similar to discursive shift in the information society, as it is not very possible and even eye-catching to see beyond the current approach to urbanism. It has become of high relevance and usefulness to carry out critical examinations of different perspectives on and disciplinary approaches to smart sustainable/sustainable smart urbanism: data-driven (open, transparent, controlled, etc.), smart (efficient, attractive, etc.), sustainable (green, equitable, resilient, etc.), and how they interact to produce specific types of solutions to complex challenges and problems. In this, regard, junctures of synergies between these approaches remain worthy of investigation to broaden and deepen the scope of smart sustainable/sustainable smart urbanism knowledge for the scientific community and the scope of smart sustainable solutions available for urban planners and decision-makers. This is intended to foster multidisciplinary cross-fertilization within the integrated frameworks pertaining to such urbanism, thereby exploring the relationships, conflicts, and connections between these different approaches to urbanism and begin to build a shared understanding of the roles, synergies, and trade-offs between them.

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Discussion and Conclusion

This chapter examined the unprecedented paradigmatic, scientific, scholarly, epistemic, and discursive shifts the field of smart sustainable urbanism is undergoing in light of big data science and analytics and the underlying advanced technologies, as well as highlighted and discussed how these shifts intertwine with and affect one another, and their sociocultural specificity and historical situatedness. It introduced and discussed the main concepts and theories necessary for gaining a deep and broad understanding of the multifaceted topic under examination. The conceptual and theoretical underpinnings identified and distilled with respect to the scientific, paradigmatic, and scholarly shifts included: science and philosophy, scientific method, hypothesis and hypothesis testing, scientific models, scientific theories, scientific laws, theoretical models, the philosophy of science, and paradigm and paradigm shift. And with respect to the discursive and epistemic shifts, such underpinnings encompassed: discourse; academic discourse; discursive truth; power as a productive and constitutive force; the relationship between power, knowledge, and truth; discursive and social practices; as well as epistemology, episteme, historical a priori, and their interrelationships.

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The conceptual and theoretical underpinnings as a collection of interrelated concepts and theories are intended to support and guide this study, as well as to elucidate why the issues under examination exist and how they should be, or are being, dealt with. In short, they represent a sort of lens through which to look at this study. As such, they can explain, predict, and help understand the phenomenon of smart sustainable/sustainable smart urbanism and the associated emerging shifts, as well as challenge, interrogate, reconfigure, and extend existing knowledge within the confines of critical bounding assumptions. They serve additionally to construct new knowledge by validating or evaluating explicitly stated assumptions. The overall intention is to strengthen this study by allowing the reader to reflectively and critically assess the assumptions explicitly enunciated, to connect to existing and dominating knowledge, and to transition from portraying the topic of smart sustainable/sustainable smart urbanism to uncovering and generalizing about its various dimensions—while identifying the limits to this generalization process. This is predicated on the assumption that the conceptual and theoretical underpinnings may determine the specific assumptions that this study involves with regard to analyzing and interpreting the scientific literature. On the whole, the value of such underpinnings lies in fulfilling one primary purpose: to explain the nature, meaning, implications, and challenges associated with the multifaceted phenomenon of smart sustainable/sustainable smart urbanism in terms of the related shifts as being instigated by big data science and analytics. Furthermore, this chapter looked at the paradigmatic, scientific, and scholarly shifts in question. In doing so, with respect to the paradigmatic and scientific shifts, it shed light on the old and new way of doing science while providing insights into how big data science and analytics is reshaping the approach to scientific discovery and development. It also elucidated why and how data-intensive science makes a paradigmatic shift from, or an epistemological break with, the current scientific paradigm. Additionally, the dilemma of wicked problems in planning was elaborated, and the relevance of data-intensive approach to urban sustainability science was highlighted and discussed. However, characteristic to science is a worldview that dominates science for a period of time during which that worldview as a reigning single paradigm is extended. Worth noting in this regard is that the acceptance or rejection of a scientific paradigm is a complex process involving societal structures. Also, scientific knowledge operates within the limits of the regime of truth and institutional apparatuses of society; hence, it is only one kind of truth and should not be especially privileged. To put it differently, truth in various formulations and configurations is built on the view of society at a specific point in history, and as such, it is not absolute, objective, universal, and value-free. Furthermore, as with scientific facts which are never really more than opinions whose dominance is transitory and far from conclusive, theories should properly be evaluated not as solutions but as beginnings. Therefore, theories cannot tell us how things should be because they are not—yet continue to strive for—a value-free view of reality (Bibri 2015). It is safe to argue that scientific and scholarly discourses are inherently part of and influenced by societal structures, and that theory formation, explanation, and description are socio-politically and thus culturally situated. Reflection on the role of scientists and scholars in society and the polity therefore becomes an inherent part of the scientific and scholarly enterprises. The dynamic nature of science and the limits of scientific knowledge are a case for cultural relativism. In this context, it has been posited that it is not possible to gain access to universal truth, as there is no escape from social representations and historical contingencies, and that truth effects are created within the discourse of knowledge itself. However, a common argument (e.g., Keith 1977; Dawkins 2007, 2016) against relativism suggests that it inherently contradicts, refutes, or stultifies itself. That is to say, the statement ‘all is relative’ is categorized either as an absolute statement or a relative one. If it is absolute, then this statement provides an example of an absolute statement, proving that not all truths are relative. If it is relative, on the other hand, then this statement does not rule out absolutes. Philosopher Hilary Putnam in (Baghramian 2004) states that some forms of relativism make it impossible to believe one is in error. If there is no truth beyond an individual’s belief that something is true, then an individual cannot hold their own beliefs to be false or mistaken. Regardless, characteristic of science is striving for a value-free, objective view of reality or seeking to ascertain what goes on in the world and to understand why, thereby attempting to achieve truthfulness, validity, and certainty. Moreover, the agreement of the knowledge with its object (Heidegger 1962) as a traditional conception of truth in which explanations about how the world works can be sought varies in different sciences. The truth in physics is absolute and universal. Physical laws are valid in the sense of being logically or factually sound always and anywhere. The physical world is ruled by a system of physical uniformities invariable through time and space (Popper 1986). Social relations and behaviors may not produce true regularities but stable ones. In sociology, the truth is not absolute. The laws cannot be fully rejected or fully accepted. The laws of social life differ in different places and periods. They depend on a particular cultural context and historical situation (e.g., Foucault 1972; Popper 1986). The idea of social laws relates to social physics, a field of science which uses mathematical tools inspired by physics to understand human behavior and social relations. Also known as the science of social phenomena, it is subject to invariable natural laws—compare social dynamics: social statics, as well as involves the quantitative study of human society: social statistics. It revolves around the idea of studying political and social phenomena as if they

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were natural forces. Recently, social physics has become a new way of understanding human behavior and social phenomena based on big data analytics. Current urban science draws on positivistic ideas emanating within social physics which seeks to identify the social determinates and ‘laws’ of cities using big data computing. With regard to the scholarly shifts, the focus was on the issues, challenges, prospects, reforms, and the potential of data avalanche pertaining to the development of the new urban science and the establishment of the related research domain, in addition to big data studies and the process of knowledge discovery/data mining as a new approach to data collection and analysis, which is being increasingly adopted in scholarly research practice within the domain of smart sustainable/sustainable smart urbanism. The new urban science as informed by big data science and analytics depends heavily upon advanced technologies, which provide not only previously inconceivable analytical power and thus answers to challenging analytical questions, but also access to huge amounts of data ripe for exploitation with respect to scientific explorations and scholarly investigations. Indeed, many of the questions related to sustainability and urbanization and how they intertwine at several levels that urban science attempts to answer have only become tractable because of the increasing availability of urban data. What will be exciting to witness in the near future is how data science will evolve and affect urban science and sustainability science; what new techniques will be invented that would not have come into existence if not for the amalgamation of the parental disciplines of data science, as well as the extent to which they will radically change urban sustainability science; and what new kinds of urban problems will urban sustainability science, using more advanced big data computing and the underpinning technologies, be able to solve. With respect to the latter, there is currently a need for sophisticated digital laboratories, which can be equipped with state-of-the-art tools for modeling multidimensional data and simulating urban phenomenon pertaining to sustainability and urbanization. In addition, this chapter analyzed the discursive, epistemic, historical a priori, institutional, non-paradigmatic, preparadigmatic, and postparadigmatic dimensions of smart sustainable/sustainable smart urbanism. The themes ‘discursive hegemony’ and ‘discursive-material dialectics, construal, and construction’ are particularly intended to help the reader gain better insights into how the discursive shifts in such urbanism take place from a societal perspective. However, the link between such urbanism and big data science and analytics under what has come to be identified as the discourse of ‘data-driven smart sustainable/sustainable smart urbanism’ is empirically under-researched, theoretically underdeveloped, and insufficiently practiced, adding to what the state of research within this field acknowledges in terms of the associated gaps, risks, paradoxes, limitations, deficiencies, fallacies, misunderstandings, and challenges and open issues. It is also associated with invisible social life that originates from the social shaping and construction of such urbanism within the wider socio-technical landscape where it is embedded, and that propels it across ecologically or technologically advanced societies and their institutional apparatuses, socio-technical constellations, and governance arrangements through diverse research institutes, universities, industry consortia, business communities, technical research laboratories, government agencies, and policy bodies and networks, to draw on Bibri and Krogstie (2016). Such urbanism as driven by big data computing and the underpinning technologies represents techno-urban/socio-technical imaginaries that involve an active exercise of political power or state influence and the management of political dissection, and hence is not principally determined by the scientific discourse, in addition to being less goal-oriented and less grounded in realism. Political action and power are in constant interaction with discourses as objects of knowledge. They are the basis elements of the creation and use of knowledge— produced by discourses. Foucault (1991) posits that while political action does not alter the meaning and form of discourses, it does shape the conditions of their emergence, insertion, functioning, and evolution. Accordingly, as a consequence of its interaction with the discourse of data-driven smart sustainable/sustainable smart urbanism, politics forces its production, uptake, and dissemination. In a recent study, Bibri and Krogstie (2016) probe the ways in which big data-driven smart sustainable cities as a leading paradigm of urbanism have emerged from different perspectives, why they have become institutionalized and interwoven with politics and policy-urban dissemination, as well as the risks they pose to environmental sustainability. To achieve the aim of this study, the authors adopt an analytical and philosophical framework of STS, which supports analyzes and evaluations whose approaches are drawn from a variety of disciplinary and theoretical perspectives. With the above in mind, smart sustainable/sustainable smart urbanism as a manifestation of (a set of historical events, actions, and objects embodying) socio-scientific knowledge and its practical application (sustainability and science as applied to the planning, design, and development of cities) is a matter of episteme, a subset of the order underlying the Western culture in the current period of time and within which new sciences (e.g., data science, data-intensive science, urban science, sustainability science, and urban sustainability science) are established as well. Thus, such urbanism, as with all these sciences, is episteme-conditioned and historically restricted; hence, the necessity to be open to future interrogations that may lead to abandon or fundamentally reconfigure some of the currently prevailing beliefs, assumptions, and claims of such urbanism. This applies to data-intensive science itself since characteristic of the sciences is the existence of a single (historically contingent) reigning paradigm, a worldview that dominates science for a period of time during which that

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worldview is (determined to be) extended, recast, or overturned. This relates to scientific revolution which involves science to be thrown into a state of crisis, during which new ideas appear and eventually a new paradigm is formed. Foucault (1984) supports a critical ethos via a historical ontology of humans, which stems from his interest in exploring their epistemological limits. His concern with such limits has to do with espousing an ongoing, permanent ethos of questionings in the analysis of what ‘is given to us as universal, necessary, and obligatory’ under a particular precognitive space and socio-political legitimacy. This is to see whether the so-called transhistorical absolutes (i.e., limits) are perhaps specific, contingent, and produced by virtue of arbitrary forms of constraint in the form of scholarly and scientific discourses as historical events and cultural productions that function as true in articulating particular ways of thinking, seeing, and acting in the world for they form what is held as knowledge (truth) according to the regimes of truth of society or culture (Foucault 1984), i.e., the historically specific social mechanisms which produce discourses that function as true in particular times and places and that are made true through discursive practice. Foucault (1972) asserts that it is not possible to gain access to universal truth since it is impossible to talk from a position outside discourse (whether scholarly or scientific), to have access to holistic knowledge of what may represent our historical limits, or to transcend historical contingency and thus see things from an ahistorical perspective due to the fact that, considering our rejection of impartial and universal analyzes, we are not shaped significantly by grander, more general structures, and subsequently, the possibility of transcending the experience we possess of our limits is always restricted and determined, thereby the inescapability of starting all over again. The whole premise is that smart sustainable/sustainable smart urbanism entails knowledge claims that are associated with biases, confines, and perspectives that need to be challenged, questioned, dismantled, broadened, and/or corrected in the quest for a form of holisticised knowledge—though not a timeless, ideal form of it. Indeed, the contingency and specificity underlying our understanding of the social world has implications for ruling out alternatives of thinking and acting in that world. Defining human’s sociological nature is not grounded in universal evolutionary knowledge but rather on the plural, incompatible sources of knowledge (Bibri 2015). What is needed—as an optimistic note—is an ‘epistematic’ understanding by attempting to contrast, learn from, and harness different epistemes (historical epistemological fields or thought systems). This is necessary for the achievement of a (macro)evolutionary goal by shifting modern science and philosophy to an evolutionary higher (in complexity of organization) epistematic level, by creating a system of basic principles (foundations for science and philosophy) capable of overcoming the mounting challenges of our time and rendering them unaffected by or independent of time, including the universalization of scientific knowledge with respect to people’s wellness (Bibri 2015). This pertains, for example, to urban sustainability science. Otherwise, we will open up a new space for constituting ourselves anew in light of new historical contingencies that will shape us, and thereby, new scholarly discourses of urbanism as driven by new sciences will emerge—and hence be constructed, reconstructed, and transformed in social practices. Thus far, the future remains open to alternatives which can be shaped by discursive and social practices, where people act upon new objects of knowledge over and over and challenge discourses or combine elements of different discourses (concrete language use) to form new ones and then change the social and cultural world (Bibri 2015).

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On the Sustainability and Unsustainability of Smart and Smarter Urbanism and Related Big Data Technology, Analytics, and Application

Abstract

There has recently been a conscious push for cities across the globe to be smart and even smarter and thus more sustainable by developing and implementing big data technologies and their applications across various urban domains in the hopes of reaching the required level of sustainability and improving the living standard of citizens. Having gained momentum and traction as a promising response to the needed transition toward sustainability and to the challenges of urbanization, smart and smarter cities as urban planning and development strategies (or urbanism approaches) are increasingly adopting the advanced forms of ICT to improve their performance in line with the goals of sustainable development and the requirements of urban growth. One of such forms that has tremendous potential to enhance urban operations, functions, services, designs, strategies, and policies in this direction is big data computing and its application. It was not until recently that the realization grew about the benefits of exploiting the big data deluge and its extensive sources to better monitor, understand, analyze, and plan smart and smarter cities to improve their contribution to sustainability. However, topical studies on big data applications in the context of smart and smarter cities tend to deal largely with economic growth and the quality of life in terms of service efficiency and betterment, while overlooking and barely exploring the untapped potential of such applications for advancing sustainability. In fact, smart and smarter cities raise several issues and involve significant challenges when it comes to their development and implementation in the context of sustainability. This chapter provides a comprehensive, state-of-the-art review and synthesis of the field of smart and smarter cities in regard to sustainability and related big data analytics and its application in terms of the underlying foundations and assumptions, research issues and debates, opportunities and benefits, technological developments, emerging trends, future practices, and challenges and open issues. This study shows that smart and smarter cities are associated with misunderstanding and deficiencies as regards their incorporation of, and contribution to, sustainability, respectively. Nevertheless, as also revealed by this study, tremendous opportunities are available for utilizing big data applications in smart cities of the future or smarter cities to improve their contribution to the goals of sustainable development through optimizing and enhancing urban operations, functions, services, designs, strategies, and policies, as well as finding answers to challenging analytical questions and advancing knowledge forms. However, just as there are immense opportunities ahead to embrace and exploit, there are enormous challenges ahead to address and overcome in order to achieve a successful implementation of big data technology and its novel applications in such cities. These findings will help strategic city stakeholders understand what they can do more to advance sustainability based on big data applications, and also give policymakers an opportunity to identify areas for further improvement while leveraging areas of strength with regard to the future form of sustainable smart urbanism. Keywords











Smart cities Smarter cities ICT of pervasive computing Intelligence functions Simulation models Sustainability








Big data analytics Big data applications Urban systems and domains

© Springer Nature Switzerland AG 2019 S. E. Bibri, Big Data Science and Analytics for Smart Sustainable Urbanism, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-17312-8_7




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1

Introduction

Cities have a central and defining role in strategic sustainable development; therefore, they have increasingly gained a central position in operationalizing and applying it. This is clearly reflected in the Sustainable Development Goals (SGDs) of the United Nations’ 2030 Agenda for Sustainable Development, which entails, among other things, making cities more sustainable and resilient (UN 2015a), as well as well documented by European Commission (2011). This is anchored in the recognition that cities as the engines of, and the hubs of innovation that drive, economic development are the world’s major consumers of energy resources and significant contributors to GHG emissions. It is estimated that they consume about 67% of the global energy demand and generate up to 70% of the harmful GHG emissions. Accordingly, they represent the key generators of environmental pollutants and the main hotspots of vulnerability to climatic hazards and related upheavals, in addition to social inequality, disparity, vulnerability, and insecurity (Bibri 2018a). In view of that, they are seen as the most important arena for instigating major sustainability transitions, adding to being the key sites of economic, environmental, and social dynamism and innovation and thereby holding great potential for making significant contributions to social transformation and thus sustainable development (Bibri 2018c). As such, they provide ideal testing grounds and operating environments for innovative ICT solutions pertaining to diverse urban systems and domains. In this regard, the UN’s 2030 Agenda regards ICT as a means to promote socio-economic development and protect the environment, increase resource efficiency, achieve human progress and knowledge in societies, upgrade legacy infrastructure, and retrofit industries based on sustainable design principles (UN 2015a, c). Hence, the multifaceted potential of the smart city approach as enabled by ICT has been under investigation by the UN (2015b) through their study on ‘Big Data and the 2030 Agenda for Sustainable Development.’ Unprecedented in their magnitude and influence in history, the spread of urbanization and the rise of ICT are among the most important global shifts at play across the world today and will undoubtedly change urbanism in a drastic and irreversible way. As widely estimated, the urban world will become largely technologized, computerized, and urbanized within just a few decades, and ICT as an enabling, integrative, and constitutive technology of the twenty-first century will accordingly be instrumental, if not determining, in addressing many of the conundrums posed, the issues raised, and the challenges presented by urbanization (Bibri 2018a). It is therefore of strategic value to start directing the use of emerging ICT into understanding and proactively mitigating the potential effects of urbanization, with the primary aim of tackling the many intractable and wicked problems involved in urban operational functioning, management, planning, and development, especially in the context of sustainability which is another macro-shift at play across the world today. Indeed, the rapid and anticipated urbanization of the world pose significant and unprecedented challenges associated with sustainability (e.g., David 2017; Han et al. 2016; Estevez et al. 2016) due to the issues engendered by urban growth in terms of resource depletion, environmental degradation, intensive energy usage, air and water pollution, toxic waste disposal, endemic traffic congestion, ineffective decision-making processes, inefficient planning systems, mismanagement of urban infrastructures and facilities, poor housing and working conditions, public health and safety decrease, social vulnerability and inequality, and so on (Bibri 2018a). These accordingly affect the quality of life and well-being of citizens as well as the efficiency of urban operations and functions (Degbelo et al. 2016). In short, the multidimensional effects of unsustainability in modern and future cities are most likely to exacerbate with urbanization (Bibri 2018a). Urban growth will jeopardize the sustainability of cities (Neirotti et al. 2014). Therefore, ICT has come to the fore and become of crucial importance for containing the effects of urbanization and facing the challenges of sustainability. ICT becoming part of mainstream debate in this regard emanates from the increasing ubiquity presence of, and new discoveries in, computing, coupled with the massive use of its technological applications across various urban systems and domains. In fact, advanced sophisticated technologies and novel complex approaches are now more needed than ever to address and overcome the challenges and issues facing modern and future cities. This pertains to the way these cities should be monitored, understood, analyzed, and, hence, operated, managed, organized, and planned to improve and maintain their contribution to the goals of sustainable development. There is an increasing recognition that emerging and future ICT constitutes a promising response to the challenges of urban sustainability due to its tremendous, yet untapped, potential to catalyze and boost sustainable development processes (see, e.g., Angelidou et al. 2017; Batty et al. 2012; Bibri 2018a, b; Bibri and Krogstie 2016, 2017a; Kramers et al. 2014). Many urban development approaches reference the role of ICT in achieving the goals of sustainable development (e.g., Angelidou et al. 2017; Al-Nasrawi et al. 2015; Bibri 2018a; Taghavi et al. 2014). As pointed out by Bibri (2018a), ICT constitutes an effective approach to decoupling the health of the city and the quality of life of citizens from the energy and material consumption and concomitant environmental risks associated with urban operations, functions, services, designs, and policies.

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Introduction

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In the wake of the rapid advancement of ICT of various forms of pervasive computing, recent research has started to focus on incorporating sustainability in the smart city concept and approach (e.g., Al Nuaimi et al. 2015; Angelidou et al. 2017; Neirotti et al. 2014). The underlying assumption is that, as ICT becomes spatially omnipresent across urban environments, i.e., data sensing, data processing, cloud/fog computing, and wireless communication, networking becomes more and more combined with infrastructure, architecture, ecosystem services, human services, and even citizens’ bodies, smart cities can become smarter and also so as to solving environmental problems and responding to socio-economic needs (see, e.g., Batty et al. 2012; Bibri 2018a; Piro et al. 2014; Shepard 2011; Townsend 2013). Therefore, most of the prospects and opportunities in this regard relate to what is labeled ‘smarter cities’, a class of cities which is viewed as future visions of smart cities, and is characterized by an ever-growing embeddedness and pervasion of ICT into the very fabric of the city (Bibri 2018a). They include ubiquitous cities, sentient cities, ambient cities, real-time cities, and cities as Internet of everything. For these cities, big data analytics is seen as a critical enabler and powerful driver as to the transformation of their ecosystem on several scales, including the way sustainability can be understood, applied, and planned. Undoubtedly, the main strength of the big data technology is the high influence it will have on smart cities of the future or smarter cities and their citizens’ lives (see, e.g., Al Nuaimi et al. 2015; Batty et al. 2012; Bettencourt 2014; Bibri 2018a; Hashem et al. 2018; Khan et al. 2015; Kitchin 2014; Kumar and Prakash 2014; Pantelis and Aija 2013; Townsend 2013). Thereby, the notion of big data analytics and its application in sustainable urban development has gained traction and foothold among urban scholars, scientists, practitioners, and policymakers over the past few years. Indeed, big data computing as a new paradigm is fundamentally changing the way modern cities can sustainably be operated, managed, planned, and developed, shaping and driving decision-making processes within many urban domains (Bibri 2018a), especially with regard to optimizing resource utilization, mitigating environmental risks, responding to socio-economic needs, and enhancing the quality of life and well-being of citizens in an increasingly urbanized world. This paradigm is clearly on a penetrative path across all the systems and domains of smart and smarter cities that rely on advanced ICT in relation to operational functioning, management, planning, and development. This is manifested in the proliferation and increasing utilization of the core enabling technologies of big data analytics across those cities badging or regenerating themselves as both smart and smarter for storing, managing, processing, analyzing, and sharing colossal amounts of urban data for the primary purpose of extracting useful knowledge in the form of applied intelligence functions and simulation models. Big data are regarded as the most scalable and synergic asset and resource for smart and smarter cities to enhance their performance on many scales, as they have become the fundamental ingredient for the next wave of urban analytics (Bibri 2018a). As a result, many governments have started to exploit urban data and their numerous benefits to support the development of smart and smarter cities across the globe with regard to sustainability, efficiency, resilience, equity, and the quality of life. However, to facilitate big data analytics and achieve a successful implementation of the associated applications and services toward reaching this goal, huge investments in the underlying core enabling technologies are needed, among other things. In light of the above, when discussing ICT solutions for sustainability reference is made to the concept of sustainable smart cities. The use of this concept serves to substantiate the growing role and significance of advanced forms of ICT, especially big data analytics, in enabling modern cities to realize their full potential by getting smarter in overcoming the challenges of sustainability and the pressures of urbanization (Bibri and Krogstie 2016). This is predicated on the assumption that the advanced forms of ICT offer tremendous potential for monitoring, understanding, and analyzing various aspects of urbanity for better operating, managing, and planning urban systems, which can be leveraged in the needed transition toward sustainability. Therefore, the development of smart and smarter cities based on big data analytics constitutes a promising response to the challenges of sustainability and urbanization, and, to note, the expansion of this advanced technology has played an important role in the feasibility of smart and smarter city initiatives (see, e.g., Al Nuaimi et al. 2015; Batty et al. 2012; Bibri 2018a; Hashem et al. 2018). However, according to a recent literature review (Bibri and Krogstie 2017a), while smart and smarter cities have played a key role in transforming different areas of human life, they are still associated with misunderstanding and deficiencies with regard to incorporating the goals of sustainable development. Also, there is a weak connection between smart targets and sustainability goals (Angelidou et al. 2017; Bibri 2018a; Bifulco et al. 2016), despite the proven role of ICT in supporting modern cities in moving toward sustainability (Bibri and Krogstie 2017a). On this note, Angelidou et al. (2017) conclude that the smart city and sustainable city landscapes are extremely fragmented both on the policy and the technical levels, and there is a host of unexplored opportunities toward sustainable smart city development. In a nutshell, smart and smarter city approaches raise many issues and present significant challenges in the context of sustainability (e.g., Ahvenniemi et al. 2017; Bibri 2018a).

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Concerning big data analytics and its application, while research has recently been active in the realm of smart and smarter cities, the bulk of work tends to deal largely with economic growth (management, efficiency, innovation, productivity, etc.) and the quality of life in terms of service efficiency and betterment (e.g., Batty 2013; Khan et al. 2015; Kitchin 2014; Kumar and Prakash 2014; Hashem et al. 2018; Rethore 2018), while overlooking and barely exploring the untapped potential of big data applications for advancing the different aspects of sustainability. Indeed, many of the emerging smart solutions are not aligned with sustainability goals (Ahvenniemi et al. 2017). In view of that, smart and smarter cities need to direct their focus toward utilizing big data applications for improving their contribution to the goals of sustainable development across urban domains (Bibri 2018a). This chapter provides a comprehensive, state-of-the-art review and synthesis of the field of smart and smarter cities in regard to sustainability and related big data analytics and its application in terms of the underlying foundations and assumptions, research issues and debates, opportunities and benefits, technological developments, emerging trends, future practices, and challenges and open issues. This extensive interdisciplinary and transdisciplinary review and synthesis endeavors to present a detailed analysis and synthesis and critical evaluation and discussion of the available qualitative and quantitative research covering the topic of smart and smarter cities, with a particular emphasis on cross and beyond disciplinary forms of knowledge. It is deemed important to identify and stimulate new research opportunities in the field. The added values of this review involve thoroughness, comprehensiveness, topicality, and original contribution in the form of novel insights as a result of analyzing, synthesizing, and critically evaluating a large body of recent works. The main motivation for this chapter is to capture further and invigorate the application demand for the urban sustainability solutions that big data analytics can offer in the context of smart and smarter cities. The remainder of this chapter is structured as follows. Section 2 outlines the literature review and synthesis methodology in terms of approach, search, selection, organization, and purpose. In Sect. 3, the relevant conceptual, theoretical, and discursive foundations and assumptions are presented, described, and discussed. Section 4 provides a detailed, two-part survey of the relevant work in terms of issues, debates, gaps, benefits, opportunities, and prospects. The first part addresses smart cities in terms of general and particular research strands, deficiencies, and potentials with regard to sustainability, as well as smarter cities in terms of characteristic features, social shaping dimensions, and the current issues of and future potentials for sustainability. The second part covers big data analytics and its application in smart and smarter cities in terms of research status and data growth projection, research issues and future prospects, core enabling technologies, and big data applications and their sustainability effects and benefits (specifically covering a critical evaluation of topical studies, and analytical and practical applications in urban domains). Section 5 identifies the key scientific and intellectual challenges and sheds light on the common open issues associated with the use of big data analytics and related applications in (enabling, operating, managing, and planning) smart and smarter cities. This chapter ends, in Sect. 6, with concluding remarks, findings, thoughts and reflections, and contributions.

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Methodical–Topical Literature Review Methodology

This extensive review and synthesis involves the exploration of a vast and diverse array of literature on the topic (including journal articles, conference proceedings, books, reports, and dissertations), and integrates and fuses various disciplinary, scientific, and technological areas at the core of this study, with an emphasis on the qualitative research in the field. In light of this, and given moreover the nature of this topic, adopting a topical approach to this review and synthesis is deemed more relevant than a systematic one. Indeed, this chapter determines the usefulness of this substantive category of review and synthesis to this endeavor. In addition, this review and synthesis is methodical in the sense that it is arranged according to, characterized by, or performed with a method or order. Also, it is done based on a loose coupling of technical and social perspectives (e.g., Levy and Ellis 2006; Webster and Watson 2002). In view of that, a review and synthesis method is developed as a means to indicate the issues (concepts, theories, academic discourses, themes, and topics) to be addressed, search strategy for retrieving the sought articles and other documents, inclusion and exclusion criteria for identifying and selecting the relevant ones, and abstract review protocols. Prior to delving into such method, it is useful to elucidate what the interdisciplinary and transdisciplinary approach entails in the context of this chapter.

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2.1 Interdisciplinary and Transdisciplinary Approach Interdisciplinarity and transdisciplinarity have become a widespread mantra for research within diverse fields, accompanied by a growing body of academic publications. The field of sustainable smart and smarter cities is profoundly interdisciplinary and transdisciplinary in nature, so too is research within, and thus literature on, it. This also applies to any review and synthesis of this literature, which is accordingly multidisciplinary as well in the sense of using insights and methods from several disciplines, or involving several disciplines in an approach to a problem or topic. These disciplines include, but are not limited to: urban planning, urban development, geography, sustainable development, sustainability science, environmental science, data science, computer science, ICT, systems thinking, complexity science, policy, and innovation. However, multidisciplinary efforts remain limited in impact on theory building for coping with the changing human condition (Morinière 2012). Clearly, sustainable smart and smarter city research naturally lends itself to multidisciplinary, interdisciplinary, and transdisciplinary approaches and strategies (e.g., Bibri 2018a; Warleigh-Lack 2011). For a descriptive account of the interdisciplinary and transdisciplinary approaches to research, the interested reader can be directed to Bibri (2018a, c). However, they all require conceptual precision in order for research outcomes to be valid and usable (e.g., Lytras and Visvizi 2018). This interdisciplinary and transdisciplinary literature review and synthesis is a topical, analytical, and organizational unit that is justified and determined by the essence and orientation of the research field of sustainable smart and smarter cities in terms of the underlying scholarly approach. As such, it is an opportunity to situate the researcher in an ecology of ideas, a process which can be approached from the perspective of complexity and intricacy. In this respect, the key dimensions that can be considered, especially in relation to transdisciplinarity, include: integrating rather than eliminating the researcher from the research, metaparadigmatic rather than intra-paradigmatic, research-grounded rather than discipline-grounded, and applying systems and complexity thinking rather than reductionism.

2.2 Hierarchical Search Strategy and Scholarly Sources A literature search is the process of querying quality scholarly literature databases to gather applicable research documents related to the topic under review. A broad search strategy was used, covering several electronic search databases, including Cristin, NTNU Open, Scopus, ScienceDirect, SpringerLink, ACM digital library, and Sage Journals, in addition to Google Scholar. The main contributions came from the leading journal articles in relevance to the topic on focus. The hierarchical search approach to searching for literature involved the following: • Searching databases of reviewed high-quality literature; • Searching evidence-based journals for review articles; and • Routine searches and other search engines. In addition, the collection process is based on Scott’s (1990) four criteria for assessing the quality of the sought material, namely: 1. 2. 3. 4.

Authenticity: the evidence gathered is genuine and of unquestionable origin. Credibility: the evidence gathered is free from error and distortion. Representation: the evidence obtained is typical. Meaning: the evidence gathered is clear and comprehensible.

2.3 Selection Criteria: Inclusion and Exclusion To find out what has already been written on the topic of this multifaceted study, the above search approach was adopted, whose objective was to identify the relevant studies addressing the diverse research strands that constitute this interdisciplinary and transdisciplinary review and synthesis. The preliminary selection of available material was done in line with the issues being investigated as pertaining to those strands, using a variety of sources that are up-to-date and authoritative. The selection was initially bounded with the issues intended to be investigated in relation to the topic of this study. This is underpinned by the recognition that once the research issues are set, it becomes possible to refine and narrow down the scope

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of reading, although there may seem to be hundreds of sources of information that appear pertinent (Bibri and Krogstie 2017a). With that in mind, for an article or document to be considered of relevance for providing information or evidence on the issues in question, it should cover one of the conceptual/theoretical subjects or thematic/topical categories intended to be examined, as demonstrated by the sections and subsections of this chapter. The focus was on the articles and documents that provided definitive primary information or evidence from an interdisciplinary and/or transdisciplinary perspective. While certain methodological guidelines were deemed essential to ensure the validity of the review, it was of equal importance to allow flexibility in the application of the topical literature review and synthesis methodology to capture the essence of research within the interdisciplinary and transdisciplinary field on focus. The whole idea was to ‘accumulate a relatively complete census of relevant literature’ (Webster and Watson 2002, p. 16). On the whole, scoring the articles and documents was based on the inclusion of issues relating to the topic on focus. Conversely, the articles and documents excluded were those that did not meet specific criteria in terms of their relevance to the issues being addressed. As to abstract review, the abstracts were reviewed to assess their pertinence to the review and to ensure a reliable application of the inclusion and exclusion criteria. Inclusionary discrepancies were resolved by the re-review of abstracts. The process allowed to further refine and narrow down the scope of reading. The keywords searched included ‘smart cities’, ‘smart cities AND sustainability’, ‘smart cities AND big data analytics’, ‘smart cities AND big data applications’, ‘smart cities AND sustainability AND big data applications’, ‘smart cities AND sustainable cities’, ‘smarter cities AND sustainability’, ‘smarter cities’, ‘ambient cities’, ‘sentient cities’, ‘ubiquitous cities’, ‘real-time cities’, ‘data-driven cities’, ‘smart cities and the IoT’, ‘smarter cities AND big data applications’, and ‘smarter cities AND sustainability AND big data applications’, and ‘urban sustainability AND big data applications’, and ‘sustainable urban development and smart applications’, in addition to the derivatives of these keywords. These were used to search against such categories as the articles’ keywords, title, and abstract to produce some initial insights into the topic. To note, due to the potential limitations associated with relying on the keyword approach, backward literature search (backward authors, backward references, and previously used keywords) and forward literature search (forward authors and forward references) were additionally used to enhance the search approach (Webster and Watson 2002).

2.4 Combining Three Organizational Approaches This literature review and synthesis is structured using a combination of three organizational approaches, namely thematic, inverted pyramid, and the benchmark studies. That is to say, it is divided into a number of sections representing the conceptual and theoretical subjects and the thematic and topical categories for the topic of sustainable smart and smarter cities. The examination and discussion of relevant issues is organized accordingly while, when appropriate, starting from a broad perspective and then dealing with a more and more specific one in terms of studies. In doing so, the focus is on the major writings and publications considered as significant in the field.

2.5 Purpose This literature review is typically performed to serve many different purposes, depending on whether or not it is motivated by, or an integral part of, a research study, as well as on its nature and intent. Within the scope of this chapter, however, it was carried out with the following specific objectives in mind: • To examine and discuss the underlying conceptual and theoretical foundations and their integration and fusion from an interdisciplinary and transdisciplinary perspective, respectively. • To analyze, evaluate, and synthesize the existing knowledge in line with such foundations as set for this study. • To highlight the strengths, weaknesses, omissions, and contradictions of the existing knowledge, thereby providing a critique of the research that has been done within the field and related subfields. • To discuss the identified strengths and weaknesses, with an emphasis on the performance of smart and smarter cities with respect to sustainability and the untapped potential of big data applications for its advancement in the future. • To identify and discuss the knowledge gaps and opportunities within the field with regard to sustainability and related big data applications. • To identify the key relationships between the research findings by comparing various studies addressing the different topics of the study, with a particular focus on sustainability and related big data applications.

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Conceptual, Theoretical, and Discursive Foundations and Assumptions

3.1 Smart Cities According to a recent review conducted by Bibri and Krogstie (2017a), the roots of the smart city concept date back to the 1970s under what is labeled the ‘cybernetically planned cities’, and then in urban development and planning proposals associated with networked or wired cities since the 1980s. Several views claim that the concept was introduced in 1994 (Dameri and Cocchia 2013), and that it is only until 2010 that the number of publications and scientific writings on the topic increased considerably, after the emergence of smart city projects as supported by the European Union (Jucevicius et al. 2014). As echoed by Neirotti et al. (2014), the smart city concept’s origin can be traced back to the smart growth movement during 1990s. Yet it is not until recently that this movement led this concept to be adopted within urban planning and development (Batty et al. 2012). However, regarding its early conceptualization and connotation, the concept was mostly associated with the efficiency of technological solutions with respect to the operational functioning, management, and planning pertaining to energy, transport, physical infrastructure, distribution and communication networks, economic development, service delivery, and so forth. Smart growth implies the ability of achieving greater efficiencies through coordinating the forces that lead to laissez-faire growth: transportation, land use speculation, resource conservation, and economic development, rather than letting the market dictate the way cities grow (Batty et al. 2012). At present, however, many cities across the globe compete to be smart cities in the hopes of reaping the efficiency benefits economically, socially, or, more recently, environmentally by taking advantage from the opportunities made possible by big data analytics and its wider application across urban domains. It is also in this context that it has increasingly become attainable to achieve the required level of sustainability, resilience, and equity, in addition to improving the quality of life and ensuring higher levels of transparency and openness and hence democratic and participatory governance, citizenry participation, and social inclusion. Achieving all these benefits requires sophisticated approaches, advanced technologies and their novel applications and services, resources, financial capabilities, regulatory policies, and strategic institutional frameworks, as well as an active involvement of citizens, institutions, and organizations as city constituents. Worth noting is that the growing interest in building smart cities based on big data analytics as an advanced technology is increasingly driven by the need for addressing the challenges of sustainability and to contain the effects of urbanization. Besides, the main features of smart cities have become a high degree of information and technology integration and a comprehensive application of computing resources. In recent years, the smart city as a catchphrase and phenomenon has drawn increased attention and gained traction among universities, research institutes, governments, policymakers, businesses, industries, and consultancies across the globe. Notwithstanding this prevalence worldwide, the smart city concept is still without a universally agreed definition. In other words, a shared definition of smart city is not yet available or offered. It is difficult to identify common trends of smart cities at global level (Neirotti et al. 2014). Moreover, despite the wide use of the concept and its operationalization today, there is still obscure and inconsistent understanding of its meaning (e.g., Ahvenniemi et al. 2017; Al Nuaimi et al. 2015; Angelidou 2015; Batty et al. 2012; Bibri 2018a; Bibri and Krogstie 2017a; Chourabi et al. 2012; Marsal-Llacuna et al. 2015; Song et al. 2017; Wall and Stravlopoulos 2016). Consequently, multiple meanings have been, and continue to be, adopted by different people within different contexts. The concept having different connotations and being approached from a variety of perspectives is clearly manifested in the various ways in which many governments set initiatives or implement projects to enable their cities to become, badge, or regenerate themselves as, or manifestly plan to be, smart. Accordingly, a large number and variety of definitions (e.g., Al Nuaimi et al. 2015; Albino et al. 2015) have been suggested with different foci and orientations. A set of additional definitions are listed in Table 1. The smart city continues to be a difficult concept to pin down or strictly delineate. The best way of looking at it is by the context within which it can be applied, as hinted at above. This implies that smart city projects, programs, and initiatives tend to be based on specific objectives, technological capabilities, financial abilities, human and social resources, regulatory policies, institutional frameworks, political mechanisms, governance arrangements, and so on (Bibri 2018a). They can also be determined or driven by the state-of-the-art research, development, and innovation in the area of ICT and related applications, infrastructures, platforms, systems, models, methods, computational analytics, and so forth. However, in relation to the objectives, for example, Batty et al. (2012) identify a number of smart city projects, including modeling urban land use; modeling network performance; sensing, networking, and the impact of social media; mobility and travel behavior; transport and economic interactions; integrated databases across urban domains; participatory governance and planning structures; and decision support as urban intelligence. Concerning the financial abilities, many governments are funneling

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Table 1 Definitions of smart cities Different foci and orientation of smart city definitions ‘A smart city is … a city which invests in ICT enhanced governance and participatory processes to define appropriate public service and transportation investments that can ensure sustainable socio-economic development, enhanced quality-of-life, and intelligent management of natural resources’ (Al Nuaimi et al. 2015, p. 3) ‘A smart city is a very broad concept, which includes not only physical infrastructure but also human and social factor’ (Neirotti et al. 2014, p. 27) ‘Connecting the physical infrastructure, the IT infrastructure, the social infrastructure, and the business infrastructure to leverage the collective intelligence of the city … A city striving to make itself “smarter” (more efficient, sustainable, equitable, and livable’ (Chourabi et al. 2012, p. 2292) ‘Smart cities is a term … that describe cities that, on the one hand, are increasingly composed of and monitored by pervasive and ubiquitous computing and, on the other, whose economy and governance is being driven by innovation, creativity and entrepreneurship, enacted by smart people’ (Kitchin 2014, p. 1) A smart city is ‘a city in which ICT is merged with traditional infrastructures, coordinated and integrated using new digital technologies’ (Batty et al. 2012, p. 481) ‘As presently understood, a smart city is one that strategically uses networked infrastructure and associated big data and data analytics to produce a: • smart economy by fostering entrepreneurship, innovation, productivity, competitiveness, and producing new forms of economic development such as the app economy, sharing economy and open data economy; • smart government by enabling new forms of e-government, new modes of operational governance, improved models and simulations to guide future development, evidence-informed decision-making, better service delivery, and making government more transparent, participatory and accountable; • smart mobility by creating intelligent transport systems and efficient, interoperable multi-modal public transport; • smart environments by promoting sustainability and resilience and the development of green energy; • smart living by improving quality of life, increasing safety and security, and reducing risk; and • smart people by creating a more informed citizenry and fostering creativity, inclusivity, empowerment and participation’ (Kitchin 2015, p. 8)

huge expenditures (colossal investments) into ICT research, development, and innovation, which is manifested in the high number of jointly funded research endeavors as well as smart initiatives and implementation projects (e.g., Ahvenniemi et al. 2017). Yet, scholars, academics, planners, ICT experts, and policymakers converge on the idea that the use of ICT pertains to all domains of smart cities, and hence on considering it as an inseparable facet thereof (Bibri 2018a). In this line of thinking, a common thread running in most of the definitions of smart city is its characteristic features and technological components, which are usually observed in smart city proposals, projects, and initiatives, irrespective of their scale, scope, national context, and available resources. In the context of this chapter, however, a smart city can be described as a city that is increasingly composed of, and monitored and operated by, various forms of pervasive computing, as well as whose planning and governance are being driven by innovation as enacted by various stakeholders that capitalize on and exploit cutting-edge technologies in their endeavors and practices. In this light, being instrumented and pervaded with digital devices, systems, and platforms that generate big data, smart cities can enable real-time analysis of urban life, environment, and dynamics as well as new modes of urban planning and governance, and also provide the conditions that are conducive to envisioning and enacting more sustainable, efficient, resilient, transparent, and open human and urban environments. Accordingly, a smart city can also be taken to mean a technologically and data-analytically advanced city that is able to understand its environment and citizens and explore and analyze various forms of urban data to generate useful knowledge in the form of applied intelligence that can immediately be used to solve different kinds of problems, or to make changes to improve the quality of life and the health of the city in terms of sustainability, efficiency, and resilience. In this line of conceptualization, Batty et al. (2012, p. 482) describe smart cities as ‘constellations of instruments across many scales that are connected through multiple networks which provide continuous data regarding the movements of people and materials in terms of the flow of decisions about the physical and social form of the city.’ However, the financial abilities, human/social resources, and regulatory policies required to develop, implement, and sustain smart cities are the most significant challenges governments around the world are concerned about and are dealing with. Positively, the emerging technologies such as big data analytics hold great potential to transform such challenges into opportunities. Furthermore, based on a recent survey of the field of smart cities (Bibri and Krogstie 2017a), there are two main approaches to smart city: (1) the technology-oriented approach, i.e., infrastructures, architectures, platforms, systems, applications, and models and (2) the people-oriented approach, i.e., stakeholders, citizens, knowledge, services, and related data (Bibri 2018a). In other words, there are smart city strategies that focus on the efficiency and advancement of hard infrastructures in terms of transport, energy, communication, and distribution networks, and so on (e.g., Ersue et al. 2014; Gubbi et al. 2013; Khan et al. 2013; Kitchin 2014; Kumar and Prakash 2014; Marsal-Llacuna et al. 2015) and those that

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prioritize the soft infrastructures in terms of social and human capital, participation, equity, safety, cultural heritage, and so forth (e.g., Angelidou 2014; Belanche et al. 2016; Khan and Kiani 2012; Khan et al. 2014; Lombardi et al. 2011; Neirotti et al. 2014). There are also smart city strategies that combine these two perspectives (e.g., Batty et al. 2012; Khan et al. 2015). To gain a broad understanding of the concept of smart city, the interested reader can be directed to Song et al. (2017) who provide a detailed overview of the foundations, principles, and applications of smart cities. Also, Nam and Pardo (2011) conceptualize smart city with the dimensions of technology, people, and institutions. It is of particular relevance in this chapter to highlight the body of the literature focusing on the defining role of ICT (e.g., big data analytics and its application) as well as human and social capital in smart cities in terms of the dimensions of sustainability (e.g., Al Nuaimi et al. 2015; Angelidou et al. 2017; Anthopoulos 2017; Batty et al. 2012; Kramers et al. 2014; Neirotti et al. 2014). This strand of research is concerned with smart cities as urban innovations whose focus is on advancing, harnessing, and integrating physical, human, and social infrastructures for environmental protection, economic regeneration, and enhanced public and social services (Bibri 2018a). The most cited definition in this regard is provided by Caragliu et al. (2009, p. 6): A city is smart city ‘when investments in human and social capital and traditional (transport) and modern (ICT) communication infrastructure fuel sustainable economic growth and a high quality of life, with a wise management of natural resources, through participatory governance.’ This definition is linked to a model that has been used as a ranking system—developed based on six smart dimensions, namely economy, environment, mobility, living, people, and governance —against which smart cities can be assessed in terms of their development and implementation. However, this model neither specifies how these dimensions can be prioritized as to the contribution to sustainability, nor how they can, combined, add value to sustainable development. However, as an extension of this definition, Pérez-Martínez et al. (2013, cited in Ahvenniemi et al. 2017) describe smart cities as ‘cities strongly founded on ICT that invest in human and social capital to improve the quality of life of their citizens by fostering economic growth, participatory governance, wise management of resources, sustainability, and efficient mobility, while they guarantee the privacy and security of the citizens.’ In this line of thinking, Batty et al. (2012, pp. 481–482) describe smart cities as cities ‘in which ICT is merged with traditional infrastructures, coordinated and integrated using new digital technologies,’ and where ‘intelligence functions … are able to integrate and synthesize … [urban] data to some purpose, ways of improving the efficiency, equity, sustainability, and quality of life in cities.’ This view of smart cities highlights—at the level of discourse though—the potential of ICT in catalyzing and improving sustainable development processes. In this context, a sustainable smart city is an innovative city that uses ICT and other means to improve the efficiency of urban operations, functions, and services as well as enhance the quality of life of citizens, ‘while ensuring that it meets the needs of present and future generations with respect to economic, social, environmental as well as cultural aspects’ (UNECE 2015). In view of the above analytical account, the available definitions of smart cities have several commonalities as well as distinctions, i.e., converging on some dimensions and diverging on others apart from technological aspects, including economic, environmental, physical, political, social, cultural, institutional, organizational, and futuristic, in addition to the extent of different sustainability dimensions and their integration. Yet, the majority of these definitions tend to focus on integrated solutions for achieving a sustainable utilization of resources, efficient operation of infrastructures and facilities, high quality of life, and effective urban planning and governance. In more detail, as an attempt to provide a comprehensive definition of smart city from a generic perspective that combines the core features of smart cities as a broad concept, (Bibri 2018a) describes smart city as a city that badges or regenerates itself as smart, or manifestly plans to be so, in terms of achieving efficiency, sustainability, resilience, equity, and livability by investing in, and hence enhancing and continuously advancing, the ICT infrastructure, physical infrastructure, economic infrastructure, and social infrastructure to leverage collective intelligence for the purpose of integrating urban systems and coordinating urban domains in ways that these components exceed their sum as to the collective behavior of the whole city. In other words, it is an innovative city that focuses on developing, implementing, and applying advanced ICT to all its systems and domains, and accordingly perform in an innovative, forward-looking, strategic, and participatory way to enhance its key features: environment, economy, people, mobility, living, and governance, on the basis of the intelligent combination of endowments and activities of independent and aware citizens together with other urban stakeholders (organizations, institutions, industries, enterprises, etc.), thereby ensuring and maintaining socio-economic development, the quality of life, the efficiency of service delivery, the intelligent management of natural resources, and the optimized operation of infrastructures and facilities—ideally in line with the fundamental goals of sustainable development.

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3.2 Smarter Cities and Other Single and Hybrid Faces Smart cities come in many faces depending on the way ICT is applied, the extensiveness of its use, the degree and form of its ubiquity, and/or the focus of its orientation, as well as the kind of digital technology by which it is coordinated and integrated (Bibri 2018a). The common faces that emerged before, or in parallel with (only for a few of them), the adoption of the concept of smart city in urban planning and development around the mid-1990s include: networked cities, wired cities, cyber cities, digital cities, virtual cities, intelligent cities, knowledge cities, and cyber cities, among other nomenclatures. For example, digital cities tend to focus on the hard infrastructure whereas intelligent cities on the way such infrastructure is used (Batty 1989, 1990, 1997), and wired cities embrace ICT as a development strategy, pioneer in embedding digital infrastructure and systems into their urban fabric and utilize them for entrepreneurial and regulatory effect (Dutton et al. 1987). However, they all share a focus on the effects of ICT on urban forms, processes, and modes of living and have largely been subsumed within the label ‘smart cities’ in recent years, although each of those terms is used in a particular way to conceptualize the relationship between ICT and contemporary urbanism (Kitchin 2014). There are also hybrid cities which merge two faces of smart cities or one from smart/smarter cities and one from sustainable cities, such as cyberphysical cities, ubiquitous eco-city, knowledge eco-city, and smart compact city, and so forth. In addition to these faces are the ones that are inspired by the prevalent ICT visions of pervasive computing, including ambient cities, sentient cities, ubiquitous cities, real-time cities, and cities as Internet of everything (e.g., Kitchin 2014; Kyriazis et al. 2014; Perera et al. 2014; Rathore et al. 2018; Shepard 2011; Shin 2009; Thrift 2014; Zanella et al. 2014). These cities have materialized as a result of the advance of ICT of pervasive computing, or rather the evolvement of the dominant ICT visions into achievable and deployable computing paradigms. Seen as future forms of smart cities, they are quite different from what has been experienced hitherto in terms of smartness and its effects on human life at several levels. They came to be identified or labeled as ‘smarter cities’ due to the magnitude of ICT and the extensiveness of data with regard to their application and use across urban systems and domains. The prospect of smarter cities is increasingly becoming the new reality with the massive proliferation of the core enabling technologies underlying ICT of pervasive computing, namely sensor networks, data processing platforms, wireless communication networks, and cloud and fog computing models across different spatial scales (Bibri and Krogstie 2017a). The initiatives of smarter cities in several countries across Europe, the USA, and Asia are considered as national urban development projects epitomising the increasing significance and role of advanced ICT, especially big data analytics, in enhancing the operations, functions, services, strategies, and policies of smart cities of the future associated with planning, management, development, and governance (Bibri 2018a). The conceptualization of smarter cities is built upon the core features of the prevalent ICT visions in terms of the pervasion of technology into the very fabric of the city, the omnipresence and always-on interconnection of computing resources, applications, and services across several spatial and temporal scales. The emerging connotations of smart cities of the future or smarter cities are numerous. Townsend (2013, p. 15) defines a smart city as an urban environment where ICT ‘is combined with infrastructure, architecture, everyday objects, and even our own bodies to address social, economic and environmental problems.’ Piro et al. (2014, p. 169) conceive of it ‘as an urban environment which, supported by pervasive ICT systems, is able to offer advanced and innovative services to citizens in order to improve the overall quality of their life.’ Su et al. (2011) describe it as city which mainly focuses on embedding the next generation of ICT into every conceivable object or all walks of life, including roads, railways, bridges, tunnels, water systems, buildings, appliances, hospitals, and power grids, in every corner of the world, and constituting the IoT. In addition, the concept of smarter cities has been associated with the orientation of smart cities toward achieving the goals of sustainability in the future. In this line of thinking, Chourabi et al. (2012) describe a smart city as a city which strives to become smarter as to making itself more sustainable, equitable, efficient, and livable. This is also consistent with what smart cities of the future entail according to Batty et al. (2012). The underlying assumption is that smarter cities or smart cities of the future have tremendous potential compared to current smart cities as to advancing sustainability. Indeed, there has recently been a conscious push for cities in Europe to be smarter and thus more sustainable, leading to the need to benchmark these cities’ efforts using advanced assessment frameworks to rank them based on how smarter and more sustainable they are. For a detailed account of smarter cities, the interested reader can be directed to Bibri (2018a) where there is a whole chapter about the transition of smart cites to smarter cities and the future potential of the underlying ICT of pervasive computing for advancing environmental sustainability. This is projected to happen because of the prospective advancements and innovations pertaining to big data analytics as an advanced form of ICT (e.g., Al Nuaimi et al. 2015; Batty et al. 2012; Bibri 2018a). In light of the above, a smarter city can be understood as a city where advanced ICT is combined with physical, infrastructural, architectural, operational, functional, and ecological systems across many spatial scales, as well as with urban

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planning processes and governance models, with the primary aim of improving sustainability, efficiency, equity, and livability. Here, smartness should go beyond the technological advancement and efficiency of solutions to include a focused orientation toward incorporating, considering, and achieving the goals of sustainable development. Of relevance to underscore here is that current smart cities strive for smartness targets instead of sustainability goals (e.g., Ahvenniemi et al. 2017; Marsal-Llacuna et al. 2015). Overall, common to all smart cities of the future or smarter cities as urban development approaches is the idea that ICT is, and will be for many years yet to come, central to urban operations, functions, services, strategies, and policies. Irrespective of which ICT vision smart cities of the future or smarter cities instantiate, whether be it sentient computing, ambient intelligence, ubiquitous computing, the IoT, or a combination of two or more of these technological visions, such cities are taken to mean urban spaces loaded with clouds of data intended to shape the life and experience of citizens and bring about major transformations to their environments. Here, big data analytics is given a prominent role, as all over the city, the underlying core enabling technologies can monitor urban areas (in terms of activities, citizen behaviors, events, social dynamics, locations, spatiotemporal settings, environmental states, etc.); analyze, interpret, evaluate, model, and simulate the continuously collected steams of data; and then deploy the obtained results in the form of intelligence and planning functions applicable to various urban domains across several spatial scales. While the current notion of smart cities can ‘be understood as a collection of plural research traditions, performed and commissioned by divergent actors all with their own motivation and implicit understanding of what a city is or should be’ (Shepard 2011), the impetus behind the concept of smarter cities or smart cities of the future (Batty et al. 2012; Bibri 2018a) based on big data analytics and its application is to mobilize and align urban stakeholders through research and development endeavors for the purpose of promoting and advancing sustainability by using advanced ICT to continuously evaluating and strategically planning the contribution of such cities to the goals of sustainable development (Bibri 2018a). Indeed, this goal of big data analytics and its application in smarter cities is more in conjunction with the aspiration and intention of the diverse stakeholders that support the integration of big data technology and the associated information sources.

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A Detailed Survey of Relevant Work: Issues, Debates, Gaps, Challenges, Opportunities, and Prospects

4.1 Smart and Smarter Cities 4.1.1 Research Strands from a General Perspective In the field of smart and smarter cities, research in its various forms is inherently interdisciplinary and transdisciplinary and remarkably heterogeneous in terms of programs and endeavors. As such, it involves a plethora of issues, debates, challenges, risks, impacts, benefits, opportunities, prospects, trends, global shifts, and practices, or an amalgamation of these. In this respect, the topic of smart and smarter cities brings together a wide variety and large number of studies, including research directed at conceptual, theoretical, applied theoretical, analytical, empirical, practical, discursive, futuristic, visionary, socio-technical, and so on with such directions as computational, technical, technological, architectural, environmental, spatial, social, political, cultural, institutional, economic, and overarching. Indeed, the recent years have witnessed a great interest in and a proliferation of publications and scientific writings on, the topic of smart and smarter cities from diverse multiperspectival approaches, reflecting the magnitude, breadth, depth, and heterogeneity of research within the field (Bibri 2018a; Bibri and Krogstie 2017a). This continues to rapidly and dynamically evolve with varied and new emphases and aims, as well as with more integrated and holistic approaches, manifested in the miscellaneous contributions being, and will continue to be, made or produced by a great deal of researchers, scholars, academics, planners, and experts to the conceptualization, design, development, and implementation of smart and smarter cities and related future visions. On the whole, the field of smart and smarter cities merges broad streams of scholarship, which entail many research strands, and as the body of literature on smart and smarter cities has evolved remarkably over the past 10 years or so, new social issues and concerns have been brought to the analysis, and new uses of technology and their ends have been proposed and criticized, respectively. Speaking of such issues and concerns, to note, the challenge is that, as pointed out by Lytras and Visvizi (2018), research originating in the social sciences tends to reduce the centrality of ICT in smart city research, and hence, the depth and breadth of implications that emerge at the intersection of ICT in urban spaces and innate social problems remain underexplored. However, in a recent extensive interdisciplinary literature review, Bibri and Krogstie (2017a) provide a comprehensive review of the field of smart and smarter cities in terms of the underlying foundations and assumptions, state-of-the art

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research and development, research opportunities and horizons, emerging scientific and technological trends, and future planning practices. Among the research strands, they address in their review, which can be seen from a general perspective in the context of this chapter but is deemed of relevance to it as well, and include the following: • The theory and practice of urban computing, urban ICT, and urban science and related sub-areas (e.g., data sensing, big data analytics, context-aware computing, urban informatics, cloud computing, fog/edge computing, middleware infrastructures, intelligence functions, simulation models, database management and integration, wireless communication networks, decision support systems, etc.) and their relation to the operational functioning, management, planning, development, and implementation of smart and smarter cities with respect to such diverse urban domains as energy, natural environment, built environment, transport, mobility, traffic, water, waste, design, education, health care, public safety, governance, economy, and science and innovation. The main focus of this strand of research is on the advancement, use, and application of ICT of ubiquitous computing for optimization, control, automation, management, and assessment purposes, particularly in relation to economic development, service delivery, and the quality of life. Owing to its origins, smart and smarter city research remains dominated by analytical perspectives and applicable insights from broadly conceived ICT of pervasive computing. Even though smart and smarter city research has, over the past few decades, transformed into a multidisciplinary, interdisciplinary, and transdisciplinary field, housing, integrating, and fusing a variety of domains and disciplines, it is still heavily based on computer science and engineering, with an explicit focus on how advanced technologies and their applications and services may be applied in urban environments (Bibri 2018c; Lytras and Visvizi 2018). • The body of the conceptual and theoretical work focuses on developing and examining the existing definitions and theoretical models to provide both a shared conceptualization and understanding of smart and smarter cities as well as a basis for discussions or debates on what the smart and smarter city approach aspires or claims to deliver with respect to smartness and sustainability and their integration and synergy. The second part of this strand of research focuses further on the theories and academic discourses underpinning the thinking about and the conception of the subject and phenomenon of smart and smarter cities. It is concerned with analyzing the discourse of smart urban development and discussing how diverse political mechanisms and policy measures are devised and implemented to institutionalize this discourse and therefore make it function and culturally and publicly disseminate it, as well as how the ensuing decisions are made in relation to the implementation of ICT and its use for operationalizing smart urban development. Among the issues related to this strand of research involve the definition of theoretical terms and models and the creation of discursive notions and constructions along with different understandings, adding to how these underlying issues are germane to the subject of smart and smarter cities. Accordingly, ‘this subject has a theoretical base that is open to interpretation, evaluation, and examination, or in it, theoretical debate seems to be rife and a key aspect of the discipline of smart urban planning and development. Having a practical application, the subject of city within this discipline relies on theoretical assumptions and foundations. And it requires environmental, social, cultural, economic, and physical issues to be addressed …, as well as institutional priorities and technological considerations … to be set apart from theoretical matters of urban planning and development as internally consistent models … all in all, this strand of research is concerned with comparing and evaluating concepts and approaches, weighing up arguments, rethinking issues, and challenging discursive assumptions.’ (Bibri and Krogstie 2017a, p. 15) • The analytical work investigates propositions about what makes a new city badges itself, and an existing city regenerates itself, as smart, or what shows that a city is manifestly planning to become smart, as well as the extent to which a modern city uses advanced ICT to fashion advanced urban intelligence functions and simulation models pertaining to different domains and thus directed for various purposes. This strand of research covers descriptions, elaborations, assessments, and/or classifications of smart and smarter cities based on the use and application of emerging and future ICT in relation to operations, functions, services, designs, strategies, and policies by analyzing previous and ongoing projects, initiatives, and programs and their potential effects on the different aspects of urbanity. The recent propositions being investigated tend to put an emphasis on specific technologies (e.g., big data analytics, context-aware computing, cloud and fog computing) and their novel applications and services, along with the challenges involved in achieving various smart and smarter city statuses accordingly. • The impacts advanced ICT can have on how we think about and conceive of cities in the sense that the technology propels us to rethink or alter some of the fundamental or established concepts and approaches through which we understand, analyze, operate, organize, assess, and value urban life toward creating and discovering novel ways of living and working in the city and interacting with the environments in terms of, for example, sustainability. Here, the argument

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is that smart and smarter cities may thrive further or get smarter by leveraging their informational landscape in ways that allow to improve and maintain their contribution to the goals of sustainable development. This is due to the fact that ICT is founded on the application of data science and complexity science, which are well positioned to tackle the complex challenges of urbanization and sustainability. The focus in this context is on understanding the link between the smart and smarter city technologies and their pertinence for providing innovative solutions for sustainability. In this case, the cities standing on a smartness scale spectrum can well embrace and pursue the goals of sustainable development through related initiatives, programs, and projects, and thereby achieve the required level of sustainability as to operations, functions, services, strategies, designs, and policies within urban domains. The deficiencies and misunderstanding concerning the sustainability of smart and smarter cities. The emphasis in this strand of research is on the lack or week connection between smart cities and sustainable cities, and whether or the extent to which the concept of smart and smarter city incorporates the goals of sustainable development. In this regard, it has been argued that the existing definitions of smart and smarter city set up no baseline for sustainability and do not include what sustainable development entails, although defining this concept is of crucial importance for identifying and specifying the purpose for which smart solutions should be used and applied, and also for assessing whether or the degree to which such solutions contribute to sustainability. In fact, the concept of smart and smarter city seems to say little about the manner in which the substance behind the smart solutions is linked to the goals of sustainable development, especially in relation to environmental sustainability. A recent research wave has started to investigate technological propositions about what makes cities particularly smarter in terms of achieving the goals of environmental sustainability; however, these propositions are too often, if not always, mentioned without consideration of the rather established strategies (design concepts and principles and planning practices) through which environmental urban sustainability can be achieved, namely density, diversity, compactness, and mixed land use, as well as ecological design, passive solar design, and sustainable transportation, in addition to environmental management and control, environmental policy, renewable energy, and design coding. The underlying premise is that ICT as an integrative and constitutive technology can make a substantial contribution to enhancing the outcome of these strategies if planned strategically and its implementation is directed for the purpose in the context of smart and smarter cities. The way forward is to adopt the cutting-edge solutions being offered by big data analytics and its novel applications and services associated with environmental sustainability. This strand of research is also part of, and hence, related issues which are examined and discussed in, the next section given their high relevance to the topic of this study. The scientific challenges facing smart cities of the future or smarter cities and pertaining to the use and application of emerging and future technologies such as big data analytics and its novel applications. Such challenges include, but are not limited to, the monitoring of urban infrastructure and its connection with its operational functioning, planning, and development through control, automation, optimization, management, and simulation; the exploration of the idea of smart and smarter cities as innovation labs in terms of developing and applying intelligence functions across different urban domains; the construction and aggregation of many urban simulation models pertaining to various urban systems and domains in terms of their integration and coordination, respectively, and thereby providing portfolios of such models that inform future designs; the development of effective technologies that ensure equity and fairness and improve the quality of city life; the optimization of physical mobility and the improvement of virtual mobility for reducing environmental impacts and enhancing spatial and non-spatial accessibility to opportunities, services, and facilities for citizens; the creation of technologies that enhance citizen participation and engagement as well as create shared knowledge for democratic governance. The potential risks of ICT posed to sustainability. This strand of research looks at the negative implications of the development and implementation of smart and smarter cities in terms of the design, use, application, and disposal of ICT for environmental and social sustainability. Smart and smarter cities pose significant risks to the environment due to the massive use of ICT of pervasive computing. Driving this line of research is a set of questions addressing the way smart and smarter cities should measure and identify risks, uncertainties, and hazards associated with ICT use and set safety standards accordingly, i.e., sustainable design principles and environmental policies. The involved risks of ICT go beyond environmental sustainability to include social sustainability in terms of equity, fairness, participation, inclusion, privacy, security, and so on. In particular, it is important to address the digital divides pertaining to education, age, social status, culture, ethnicity, gender, and disability. Angelidou (2017) found that most smart city strategies are poorly adapted to accommodate the local needs of their area, fail to incorporate bottom-up approaches, and fall short in considering issues of privacy and security. In a recent work, Carrasco-Sáez et al. (2017) propose a new pyramid of needs for the digital citizens as a way of transitioning toward smart human cities or socially sustainable smart cities. Regardless,

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socio-economic factors affect the use of smart technologies, and to fully optimize their potential, such factors need to be addressed so that smart and smarter city technologies can play a part in contributing to sustainability. As stated by Batty et al. (2012, p. 481), ‘New technologies have a tendency to polarise and divide at many levels and we need to explore how new forms of regulation at the level of urban transport and planning, and economic and community development can be improved using future and emerging technologies’. And one way this can be accomplished is by, according to the authors, balancing efficiency and equity. For a detailed account and discussion on the relevant digital gaps associated with ICT of pervasive computing, the interested reader can be directed to Bibri (2015). Visvizi and Lytras (2018a) address the role of policy in making smart cities more socially inclusive. Further, however, the most eminent threat of ICT in the context of smart and smarter cities lies in its multidimensional effects on the environment, as ICT as an enabling, integrative, and constitutive technology is embedded into a much wider socio-technical landscape (economy, institutions, policy, politics, and social values) in which a range of factors and actors other than techno-scientific ones are involved. In addition, the prospect of smarter cities as future visions of smart cities is becoming increasingly the new reality with the massive proliferation of the core enabling technologies of ICT of pervasive computing across urban systems, domains, and environments. Indeed, they typically instantiate the dominating ICT visions in Europe, Asia, and the USA, namely ubiquitous computing, ambient intelligence, and the Internet of things. The evolvement of this smart urban development approach is increasingly driven by the growing application of, and the rising demand for, big data analytics and its novel applications and services as a set of novel technologies. Of importance to underscore in this regard, though, is that these technologies need to be well understood when placing high expectations on and marshaling huge resources for developing and deploying smarter cities or smart cities of the future. There exist intricate trade-offs and relationships between and among the positive impacts, negative effects, and unintended consequences of ICT of pervasive computing in relation to the environment—flowing mostly from the design, development, use, application, and disposal of ICT throughout smart and smarter cities (Bibri 2018a). Nevertheless, there are several potential ways to mitigate the potential risks pertaining to the development of ICT of pervasive computing and thus smart and smarter cities. Especially, most of the related novel applications are still under development, and thus, a lot more can be done in this direction prior to their deployment. It remains to see the extent to which new technological innovation opportunities will be embraced and exploited in this regard, and their effects be realized with regard to environmental sustainability in the context of smarter cities or smart cities of the future, in particular. Bibri (2018a) provides a detailed overview and discussion of the key technical, social, political, institutional, and organizational remedies to deal with the multiple effects triggered by, and associated with, the design, use, application, and disposal of ICT, including direct and indirect effects, rebound effects, systemic effects, and constitutive effects (see Bibri and Krogstie 2016) for a detailed discussion). These remain, however, complex and intricate and thus problematic to tackle. Regardless, it is high time to link ICT research, development, and innovation with the agenda of sustainable development and thus to justify future ICT investments by environmental concerns and socio-economic needs in the context of smarter cities or smart cities of the future. • The general critiques of the notion of smart cities as related to data-driven urbanism. This strand of research is concerned with the issues that tend to make smart cities not universally welcomed, among others. In this line of thinking, smart city initiatives have been criticized for (Kitchin 2015): – treating cities as a set of knowable and manageable systems that act in largely rational, mechanical, linear, and hierarchical ways and can be steered and controlled; – Being largely a historical, aspatial and homogenizing in their orientation and intent, treating cities as if they are all alike in terms of their political economy, culture, and governance; – Placing emphasis on creating technical rather political/social solutions to urban problems, thus overly promoting technocratic forms of governance; – Reinforcing existing power geometries and social and spatial inequalities rather than eroding or reconfiguring them; – Failing to recognize the politics of urban data and the ways in which they are the product of complex socio-technical assemblages; – Promoting an agenda that is being overly driven by corporate interests who are using it to capture government functions as new market opportunities; – Potentially creating buggy, brittle, and hackable urban systems by networking city infrastructure; and – Data-driven, networked urbanism that produces a number of activities that have profound social, political, ethical consequences, including dataveillance and extensive geosurveillance, social and spatial sorting, and anticipatory governance. • The smart and smarter city frameworks, models, and infrastructures are associated with the assessment, development, and implementation of smart and smarter cities and are shaped by socio-cultural factors, technological capabilities, available

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resources, regulatory policies, institutional practices, and so on. The existing frameworks and models are being used to rank or benchmark the existing and emerging smart and smarter cities in relation to smartness and sustainability as well as their synergy and integration. They are based on a variety of dimensions with a set of factors or criteria gauging success, including mobility, environment, energy, transport, life quality, economy, and governance. The existing infrastructures involve the different aspects of ICT in terms of its development and implementation in smart and smarter cities (e.g., sensor technologies, data processing platforms, cloud and fog computing models, wireless communication networks, middleware infrastructures). The purpose is to provide a smart and smarter city basic backbone for enabling ICT-based control, automation, optimization, management, and planning, as well as privacy and security in relation to urban operations, functions, services, designs, and policies. All in all, the state of the scholarly research within the rapidly burgeoning interdisciplinary and transdisciplinary fields of smart and smarter cities shows that the large body of the topical studies carried out thus far tend to focus largely on the advancement and potential of emerging and future technologies and their novel applications and services as new opportunities offering numerous benefits and robust solutions. This relates to diverse urban domains in terms of enhancing the efficiency of urban systems and improving the quality of life of citizens. However, the rapid pace of ICT development and innovation seems to happen ad hoc when new technologies and their applications and services become available—rather than grounded in a focused overall approach or directed to solving the most pressing issues and significant challenges associated with sustainability in an increasingly urbanized world. Indeed, more efforts need to be done for developing and implementing the kind of smart solutions that are oriented toward addressing, or for a realistic tackle of, environmental concerns and socio-economic needs, especially in the context of smarter cities or smart cities of the future. Findings from a recent study carried out by Angelidou et al. (2017) suggest that the smart city and sustainable city landscapes are extremely fragmented both on the technological and policy levels, and that there is a host of unexplored opportunities and horizons toward new approaches to sustainable smart development, many of which are still unknown. Moreover, the research field of smart and smarter cities is currently fragmented due to its ill-defined character and scattered research programs, thereby fostering discontinuity, and consequently, smart perspectives remain too diverse to resolve (Bibri 2018a). At the practical level, to add, there is a great deal of diversity among smart and smarter cities in terms of the previous and ongoing projects and initiatives. And in this sense, it is of relevance to look at the smart and smarter city endeavor as an ambition which can be driven by a wide range of target objectives as well as available resources, technical capabilities, and policy regulations, and also shaped by diverse disruptive technologies and how these are embedded in the socio-cultural context as part of the socio-technical landscape. Obviously, there will be multiple ways to achieve such objectives, manage available resources, design and execute policy regulations. This should have direct implications for the success of smart and smarter cities, including in relation to their sustainability performance and its continuation.

4.1.2 Research Strands of Particular Relevance to the Topic of the Study The topic of this study entails other relevant research strands than the above-mentioned ones in terms of review. These strands are also part of the broad streams of scholarship that constitute the field of smart and smarter cities. With that in mind, the focus of this subsection is on reviewing the field of smart and smarter cities in relation to sustainability and related big data applications. The Inadequate Contribution of Smart Cities of Today to the Goals of Sustainable Development and Thus Their Poor Sustainability Performance Since its adoption in urban planning and development in 1994 until recent years, the concept of smart city has been criticized for not explicitly incorporating the goals of sustainable development in its definition, as well as for lacking the connection with that of sustainable city (e.g., Ahvenniemi et al. 2017; Angelidou et al. 2017; Bibri 2018a; Bibri and Krogstie 2017a; Bifulco et al. 2016; Hollands 2008; Höjer and Wangel 2015). According to a recent study carried out by Ahvenniemi et al. (2017) on the difference between smart cities and sustainable cities, in the former economic and social aspects tend to dominate over environmental aspects. Also, Kramers et al. (2014) point out that the concept of smart city says little about the environmental sustainability performance of cities. Moreover, in examining the concept of smart city through the lens of strategic sustainable development, Colldahl et al. (2013) conclude that this concept is associated with limitations pertaining to sustainability, i.e., ‘does not necessarily allow for cities to develop in a sustainable manner’. Therefore, Ahvenniemi et al. (2017) suggest a redefinition of the smart city concept toward a more integrated direction, a definition that highlights the dimension of environmental sustainability. Furthermore, Bibri (2018a) notes that the contribution of smart cities to

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sustainable development remains vague. In relation to this, while some of the challenges pertaining to urbanization are already being addressed through the development of smart technologies (Castán et al. 2016; Degbelo et al. 2016; Vinod Kumar and Dahiya 2017), many of the proposed smart solutions in this regard are not aligned with sustainability targets, thereby the emergence of sustainable smart cities (Ahvenniemi et al. 2017; Bibri 2018a). Overall, as concluded by Bibri and Krgostie (2017a), the existing smart city approaches raise critical issues, pose special conundrums, and involve significant challenges—when it comes to their development and implementation as to their contribution to the goals of sustainable development. In more detail, Bibri (2018a) provides a detailed review of the field of smart and smarter cities in terms of its state-of-the art research and development and foundations and assumptions, and presents a tabulated version of his discussion on the shortcomings of smart cities in terms of sustainability performance. Among the points addressed and that are of more relevance to the topic of this study are presented below: • There is no general consensus about whether there needs to be any substance behind the claim of smartness for, or how it is linked to, sustainability. • Smart technologies are less focused on providing solutions for the challenges and pressing issues related to sustainability and more focused on optimizing the efficiency of solutions. • There is a discrepancy between smart solutions and sustainability problems. • There particularly is a weak connection between smart solutions and environmental problems. • There is a mismatch between smart targets and sustainability goals. • There are gaps between theory and practice and visions and their realization with regard to the sustainability dimension. • Current ICT investments and technological innovation orientations fall short in considering or embracing the goals of sustainable development. • The field is unable to proceed in anything like a cumulative fashion and to contribute systematically and constructively to the development of innovative technologies for sustainability. • Smart technologies mostly provide preconfigured/preformatted solutions for yet-to-find urban problems, rather than the needed solutions for tackling the challenge of sustainability. • ICT research, development, and application are directed mainly toward economic development. • There are divergences in terms of the current and future use of big data applications, as well as in terms of related innovation. • The existing assessment performance frameworks lack environmental indicators and tend to overemphasize economic aspects. • ICT poses great risks to and negative implications for environmental and social sustainability. Furthermore, concerning the lack of connection, integration, and synergy between smart cities and sustainable cities, Bibri and Krogstie (2017a) provide a list of the key discrepancies in this regard, which include in relevance to the topic of this chapter: • Smart cities focus mostly on ICT advancement and the efficiency of solutions and fall short in considering, if not ignoring, design concepts and principles and planning practices of urban sustainability and their effects and benefits. • Smart cities continue to strive for smart targets rather than integrating them with sustainability goals. • Sustainability goals and smartness targets are misunderstood as to their interconnection. • The two landscapes of the smart city and sustainable city are extremely fragmented on the technical and policy levels. • Smart cities need to leverage their informational landscape together with their physical landscape in line with the vision of sustainability. • Smart technologies are still being developed for building and enabling smart cities without any orientation toward, or any consideration of, improving the contribution to the goals of sustainable development. • The existing smart city performance assessment frameworks need to be redeveloped in ways that incorporate the design concepts and principles and planning practices of sustainability as well as environmental indicators. In relation to the latter point, while a recent wave of research work has started to focus on various technological propositions about what makes cities smart and smarter as to contributing to, or achieving, the goals of sustainable development (Bibri and Krogstie 2017a), such propositions are too often investigated without consideration of the rather established strategies for achieving urban sustainability, specifically design concepts and principles and planning practices,

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such as compactness, mixed land use, density, diversity, passive solar design, sustainable transport, ecological design, and design coding. In line with this, Angelidou et al. (2017) conclude that there is a host of unexplored opportunities toward new approaches to sustainable smart development as a way to address and overcome the existing fragmentation of smart cities and sustainable cities pertaining especially to the technical level. Of importance to underscore here is that for many contemporary urban scholars, theorists, and planners, the adoption of the strategies through which sustainable urban forms can be achieved is necessary for achieving the required level of sustainability (e.g., Dumreicher et al. 2000; Jabareen 2006; Joss 2011; Joss et al. 2013; Kärrholm 2011; Wheeler 2002; Williams et al. 2000). This is irrespective of how intelligently, by using advanced ICT, urban systems (built environment, infrastructure, ecosystem services, human services, and administration) can be managed and integrated and urban domains (transport, energy, mobility, traffic, water and waste, natural environment, health and safety, education, governance, economy, science and innovation, etc.) can be coordinated and coupled, as well as how these systems and domains can be planned and developed (Bibri 2018a; Bibri and Krogstie 2017b). Rather, cities can well become smartly sustainable or sustainably smart if the ubiquity and massive use of ICT could primarily be directed toward improving sustainability (e.g., Batty et al. 2012; Bibri 2018a; Bifulco et al. 2016; Höjer and Wangel 2015; Kramers et al. 2014; Shahrokni et al. 2015). In this regard, smarter cities remain well positioned for providing the kind of computationally augmented urban environments that can provide the favorable conditions and offer the cutting-edge solutions that are conducive to boosting the process of sustainable development (Bibri and Krogstie 2017a). Overall, regardless of the type of the innovative solutions proposed for enhancing sustainability performance in smart and smarter cities, it is of crucial importance to ensure that urban development initiatives and projects resonate with the significant themes in debates on the design concepts and principles and planning practices pertaining to sustainable urban forms. Bibri (2018a) provides a detailed account of these themes and proposes a matrix linking them with big data applications in the context of smart sustainable cities of the future. Moreover, Ahvenniemi et al. (2017) contrast 8 smart city and 8 sustainable city assessment frameworks as performance measurement systems with respect to 12 application domains as a way to examine how they compare with each other regarding both commonalities and differences. They observe a much stronger focus on modern ICT in the former in relation to economic and social aspects and a deficiency in environmental indicators, to reiterate. The 12 application domains included in this study comprise transport; energy; water and waste management; natural environment; built environment; health and safety; education; well-being; and citizen engagement; governance; economy; culture, science and innovation; and ICT, based on three impact categories: environmental, economic, and social sustainability, involving 958 indicators altogether. They conclude that smart cities need to improve their sustainability performance with the support of advanced technologies. They suggest, based on the main identified gap between the two classes of assessment performance frameworks, the improvement of smart city ones in ways that incorporate and use impact indicators that measure the environmental and social targets of sustainable development, in addition to the economic ones, and thus gauge the contribution of smart cities to sustainability. As indeed noted by Marsal-Llacuna (2016), in the academic debate, smart cities are criticised for their focus on the economic dimension of sustainability while disregarding environmental and social dimensions. Taking into consideration the above, smart cities need to direct more efforts into embracing the goals of sustainable development and harnessing their informational assets and physical structures together accordingly so as to mitigate their shortcomings associated with sustainability. This can occur through (re)developing urban environments, areas, and spaces in ways that (re)orientate ICT use and innovation toward contributing to, and enhancing design concepts and principles and planning practices of, sustainability. Especially, several topical studies performed in recent years emphasize on the need for pursuing this alternative developmental path for advancing sustainability (e.g., Angelidou et al. 2017; Bibri 2018a; Bibri and Krogstie 2017a, b). Smart cities can become sustainable and sustainable cities smart when ICT is primarily utilized for and directed toward enhancing sustainability performance with respect to what each of these two urban development strategies lack in terms of any potential inadequacy as to this performance (see, e.g., Al Nuaimi et al. 2015; Batty et al. 2012; Bibri 2018a; Bibri and Krogstie 2017b; Bettencourt 2014; Höjer and Wangel 2015; Kramers et al. 2014; Shahrokni et al. 2015). This pertains mainly to environmental sustainability. Indeed, Ahvenniemi et al. (2017) and Angelidou et al. (2017) report the misalignment between the targets of smart urban growth and sustainable urban development, with the former stating that smart city assessment frameworks downplay the importance of environmental sustainability, and the latter highlighting the unexplored role of smart applications in advancing environmental sustainability. Realizing the Tremendous Potential of Smart Cities of the Future for Advancing Sustainability Since the early 2010s, many scholars have highlighted the crucial role that ICT could play in sustainable urban development by decoupling resource consumption and environmental impact from economic growth while noting that the topic of ICT for

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sustainability has not attracted actionable political interest as of yet. In looking at smart cities through the lens of strategic sustainable development, Colldahl et al. (2013) note that smart cities hold great potential for advancing sustainability, as it is a powerful approach to enabling cities to become more sustainable due to the role of ICT in providing advanced solutions for addressing the complex challenges and pressing issues of sustainability, in addition to planning cities in a more innovative and forward-thinking manner. In reference to smart cities of the future, Batty et al. (2012) point out that cities can only be smart if there are intelligence functions that are able to integrate and synthesize the data to some purpose, ways of improving efficiency, sustainability, equity, and the quality of life. Future ICT in its form of big data analytics and its application is concerned with researching smart cities not simply in terms of their instrumentation: ‘constellations of instruments across many scales that are connected through multiple networks which provide continuous data regarding the movements of people and materials in terms of the flow of decisions about the physical and social form of the city’ (Batty et al. 2012, p. 482), but also in terms of the way this instrumentation is opening up new opportunities for and new forms of advancing sustainability. Taking into account the above, smart cities have recently gained traction among many national governments and international policymakers as a promising response to the challenges of sustainable development in an increasingly technologized and computerized, yet unsustainable, urbanized world (Bibri 2018a). It is of particular relevance here to emphasize that it is not until more recently that the development of smart cities came to the fore as a sort of panacea for solving the kind of wicked and intractable problems that characterize the practice of urbanism—thanks to the advent of big data analytics as a set of advanced technologies, coupled with the recognition of the untapped potential of their novel applications and services for advancing various aspects of sustainability (see, e.g., Al Nuaimi et al. 2015; Batty et al. 2012; Bibri 2018a; Bettencourt 2014; Marsal-Llacuna et al. 2015). Worth noting is that ICT has in fact gained the recognition of offering unsurpassed ways to deal with the environmental, societal, and economic concerns of cities and hence to transform them into urban areas that can adapt to environmental, societal, and economic shocks since the mid-1990s, a few years after the widespread diffusion of the concept of sustainable development and the prevalence of ICT worldwide. Ever since, ICT has been socially and discursively constructed as having an enabling and catalytic role in sustainable development and in envisioning its future form in the context of sustainable smart cities (see Bibri and Krogstie 2016). In smart cities, ICT is proposed as a set of solutions to urban challenges and issues of a complex nature, including sustainability and living standards (Batty et al. 2012; Hashem et al. 2018). In other words, and in more detail, smart cities represent an urban development paradigm that emerged in the late twentieth century as a result of the drive of cities to be more responsive to citizen needs through offering conditions conducive to promoting and enhancing the quality of life in an increasingly globalized world (Angelidou et al. 2017), and then to become more sustainable in an increasingly urbanized world (ITU 2014; UNECE 2015) with support of advanced ICT. The assessment of smart cities builds on ‘the previous experiences of measuring environmentally friendly and livable cities, embracing the concepts of sustainability and quality of life but with the important and significant addition of technological and informational components’ (Marsal-Llacuna et al. 2015, cited in Ahvenniemi et al. 2017, p. 235). This relates particularly to big data technology and its diverse applications and services, which span many urban domains with regard to improving operational functioning, monitoring, and optimizing infrastructures and facilities, reducing resource consumption, providing efficient and faster services to citizens to enhance the quality of their life, and streamlining planning and decision-making processes, all in line with the goals of sustainable development (Bibri 2018a). By means of ICT innovations and thus advanced smart solutions, cities can well evolve in ways that can address environmental concerns and respond to socio-economic needs in a more strategic manner, as they are the incubators, generators, and transmitters of creative and innovative ideas (Bibri and Krogstie 2017a). Indeed, the clear prospects of many major cities to overcome the complex challenges pertaining to sustainability and urbanization through the advanced forms of ICT are the key reason why smart cities of the future have recently gained traction as a holistic urban development strategy among universities, research instituters, policymakers, city governments, and industries. Besides, when discussing ICT solutions for improving the different aspects of sustainability, reference is made to smart cities of the future or smarter cities (see, e.g., Batty et al. 2012; Bibri 2018a) This is predicated on the assumption that ICT of pervasive computing offers great opportunities for monitoring, understanding, and analyzing various aspects of urbanity for operating, managing, and planning urban systems in ways that can be leveraged in the needed transition toward, and the advancement of, sustainability. It is in smart cities of the future that the key to a better world—which is held by emerging and future ICT—will be most evidently demonstrated (Batty et al. 2012). The underlying premise is that the use of ICT of pervasive computing and related big data analytics and its application is increasingly contributing to the further integration of urban systems and the effective assessment of their performance in terms of sustainability; facilitating collaboration and coordination among urban domains for energy and environmental efficiency gains; enhancing and mainstreaming ecosystem and public and social services; and pinpointing

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which kinds of networks need to be coupled or amalgamated. This is due to the emerging wave of urban analytics for which big data constitute the fundamental ingredient thanks to the opportunity of fashioning and utilizing powerful urban intelligence and planning functions and simulation models in relation to urban monitoring, planning, design, and development (Bibri 2018a). In the meantime, the promises of smart cities with respect to sustainability are leading to an exponential growth in data by several orders of magnitude, and thus their datafication. Worth pointing out is that most of the sustainability benefits and opportunities of smart cities tend to be associated with what is labeled ‘smarter cities’. Smarter Cities: Characteristic Features, Social Shaping Aspects, and Current Issues of and Future Potentials for Sustainability Smarter cities typically rely on the fulfillment of the prevalent ICT visions of pervasive computing, namely ubiquitous computing, ambient intelligence, sentient computing, and the Internet of things. See Bibri (2018a) for a descriptive account of these visions. Big data analytics is one of the key prerequisite technologies for realizing these visions in terms of the novel applications and services being in use in a wide variety of urban domains, such as transport, mobility, traffic, energy, environment, power grid, building, planning, design, governance, scientific research, innovation, and so on, to improve sustainability. Recent discoveries in computer science and its advanced ICT applications have given rise to those socially disruptive technologies and thus ubiquitous cities, ambient cities, sentient cities, cities as Internet of everything, and real-time cities. Of importance to note is that the orientation of these cities toward sustainability through embracing and incorporating the goals of sustainable development as part of national urban development initiatives and projects within technologically and ecologically advanced nations is considered as a new research endeavor that aims to leverage the informational landscape of smart cities in the needed transition toward sustainability (Bibri 2018a). In addition, these cities are associated with the core characteristic features of the future vision of technology in the sense that everyday objects communicate with each other and their surroundings in various ways and collaborate across heterogeneous and distributed environments to provide valuable information and limitless services in the form of intelligence to multiple, diverse urban entities in connection with operations, functions, activities, designs, strategies, and policies. For what this vision entails, the prospect of smarter cities is becoming the new reality with the massive proliferation of the core enabling technologies underlying ICT of pervasive computing (Shepard 2011). Enabling diverse computationally augmented urban environments in modern cities and seeking to connect city constituents with each other together with their environments, the underlying technologies will enable different kinds of big data applications to usher in nearly very urban domain, thereby opening up new windows of opportunity for enhancing sustainability performance. Visions of future advances in science and technology (S&T) (and predominately computer science and ICT) inevitably bring with them wide-ranging common visions on how societies, and hence, cities as social fabrics will evolve in the future, as well as the immense opportunities this future will bring (Bibri and Krogstie 2016). This relates to the role of science-based technology in modern society in terms of its development, a subject area which is positioned within the research and academic field of Science, Technology, and Society (STS). This is concerned with the ways in which new technology emerges from different perspectives, why it becomes institutionalized and interwoven with politics and policy—cultural dissemination, as well as the risks it poses to environmental and social sustainability (Bibri and Krogstie 2016). In this context, however, S&T is associated with ICT of pervasive computing and the increasing role it plays in advancing sustainability within contemporary cities. This rapidly evolving form of S&T and related role in sustainable smart cities has recently permeated urban and academic debates as well as politics and policy across the globe, as mentioned and documented above, and is accordingly seen as key for solving the environmental and socio-economic challenges pertaining to sustainability and urbanization facing modern and future cities. ICT of pervasive computing is drastically changing long-standing forms of city structures, systems, and processes, and revolutionizing city transformation models in terms of sustainability and the quality of life (Batty et al. 2012; Bibri 2018a). In particular, major urban transformations are promised as a result of the advent of big data analytics and its application as an instance of ICT of pervasive computing. The existing evidence (e.g., Al Nuaimi et al. 2015; Angelidou et al. 2017; Bibri 2018a; Bettencourt 2014) already lends itself to the argument that the use of big data technology and its novel applications across various urban domains makes this technology a salient factor for improving the goals of sustainable development and thereby advancing sustainability. If its research, development, and innovation continue further to be linked with the agenda of sustainable development and the goals of sustainability, i.e., to be utilized meaningfully and strategically, ICT of pervasive computing will have positive, profound, and long-term impacts on smarter cities or smart cities of the future. It is projected to yield hitherto unrealized environmental gains and socio-economic benefits, owing to its technological superiority in terms of the novel applications and services that provide high performance and concrete value (Bibri 2018a).

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In light of the above, smart cities are ever-changing and morphing into new faces characterized by the profusion of data and massive use of its analytics and related applications. This has been fueled by the modern world becoming rapidly technologized and hence fully computerized. Adding to this is the increasing convergence and advance of ICT as a powerful enabler and driver for ecological modernization and societal transformation, thereby playing a key role in addressing and overcoming the challenges of sustainability and containing the effects of urbanization (Bibri and Krogstie 2016). At the heart of ecological modernization as an analytical approach, policy strategy, environmental discourse, and academic field is an established view of the potential of ICT innovations to bring about advanced solutions to complex environmental problems. Ecological modernization as a theoretical concept is used to analyze those shifts in ‘the central institutions and core practices of modern society deemed necessary to solve, avoid, or mitigate the ecological crisis’ (Bibri 2015, p. 35). One of its key dimensions is technology and the transformation of society (Murphy 2000), meaning particularly that environmental problems are most likely to be tackled through the development and application of advanced sophisticated technologies (Murphy 2000), such as big data analytics and related applications. Several ideas arising from the intended ecological switchover have gained footholds in the context of smart cities of the future. Indeed, the pertinence of such cities with that of environmental sustainability is reflected in the EU’s urban development policy, whereby sustainable technology is seen as an asset toward optimizing energy efficiency and thus reducing GHG emissions as well as fostering urban collective intelligence and innovation (European Commission 2011). From a societal perspective, ICT is socio-culturally constructed to have a determinant role in instigating major social changes on multiple scales due to its transformational power residing or embodied in its disruptive, synergistic, and substantive effects, coupled with being of an enabling, integrative, and constitutive nature (Bibri and Krogstie 2016). In relation to this, the coalescence of computing, data processing, and communication technology is unleashing a wealth of opportunities and proving a powerful driver for innovation and change, as well as blurring the boundaries between domains within different societal spheres (ISTAG 2006). In the metadiscourse of the information society and other derived discourses which metonymically represent it, such as smart cities and sustainable smart cities, advanced ICT is seen as a powerful driver for major transformations. As stated by ISTAG (2006, p. ii), ‘ICT offers a means to respond to many challenges. It is the “constitutive technology” of the first half of this century … ICT does not just enable us to do new things; it shapes how we do them. It transforms, enriches and becomes an integral part of almost everything we do. As ICT becomes more deeply embedded into the fabric of European society it is starting to unleash massive and far-reaching societal … change. ICT is essential for bringing more advanced solutions for societal problems. These constitutive effects amount to a paradigm shift in how our … society function.’ ICT research plays a key role in unlocking the transformational effects of ICT for societal sustainability (ISTAG 2006). It is important not to underplay the radical social transformations that are likely to result from the implementation of ICT visions of pervasive computing (ISTAG 2003). For a detailed analytical account and deep discussion of the diverse dimensions of the social shaping of sustainable smart cities, the interested reader can be directed to Bibri and Krogstie (2016). Smarter cities or smart cities of the future are the product of socio-culturally conditioned frameworks, including the way the related sustainable practices have emerged and become disseminated at the urban level and hence discursively constructed and materially produced through diverse socio-political institutions and organizations (Bibri and Krogstie 2016). Therefore, as noted by Bibri (2018a), smarter cities should not be conceived of as ‘isolated islands’; rather, the interplay between them and other scales and their relation to political and regulatory processes on a macro-level ought to be recognized. Macro-processes of political regulation and policy are deemed of crucial importance for the discursive-material dialectics of smarter cities as urban transformation. In this regard, political action is necessary for the production, insertion, functioning, dissemination, and evolution of smarter cities as an amalgam of innovation systems or a techno-urban discourse. Indeed, political practice is at the core of the theoretical framework of innovation system (Chaminade and Edquist 2010; Kemp 1997; Kemp and Rotmans 2005; Rånge and Sandberg 2015) and the theory of discourse (Foucault 1972). Recommendations for smarter cities as drastic urban transformations are unlikely to proceed without parallel political actions (see Smith 2003). Drastic shifts to technological or sustainable regimes ‘entail concomitantly radical changes to the socio-technical landscape of politics, institutions, the economy, and social values’ (Smith 2003, p. 131). Besides, technology and society and hence cities are shaped at the same time in a mutual process; i.e., the former develops dependently of the latter and then they affect each other and evolve in that process (Bibri 2015). As succinctly put by McLuhan (1964) many decades ago, we shape technology and thereafter it shapes us. This in fact is the kind of challenge that needs to be resolved in the development and implementation of smarter cities with regard to directing ICT toward enhancing their contribution to the goals of sustainable development. To put it differently, the intellectual challenge facing smarter cities lies in that advanced technologies such as big data analytics are not only developed to enable us to shape and alter how we create new and do things in many domains, but also to investigate and assess the processes of their own application and impact on cities as to

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their concrete contribution to sustainability (Bibri 2018a). Regardless, the way modern cities as complex systems and dynamically changing environments can be operated, managed, developed, and planned requires sophisticated approaches and innovation solutions to understanding and analyze them and to avoid and mitigate potential environmental and social impacts resulting from urban operational functioning, planning, and governance in the context of sustainability, respectively. The current state of research in the realm of smarter cities shows that not enough focus has been given to the potential of ICT of pervasive computing for responding to the challenges of sustainability and containing the effects of urbanization (Bibri 2018a). Such cities are mainly striving for smart targets instead of sustainability goals (Bibri and Krogstie 2017a), just like current smart cities (Ahvenniemi et al. 2017; Marsal-Llacuna et al. 2015). In more detail, notwithstanding the relative increase of research on smarter cities—pushed particularly by big data analytics and its application across various urban domains—the bulk of work has tended to deal largely with the advancement of ICT of pervasive computing and its potential only in terms of the use of its novel applications to optimize economic efficiency in terms of productivity, management, cost-effectiveness, and time saving, as well as to improve the quality of life of citizens as regards better, faster, and more efficient services. This leaves more relevant questions largely ignored or barely explored to date involving the rather untapped potential of emerging and future ICT in terms of big data analytics and its application for catalyzing and boosting the process of sustainable development toward achieving the long-term goals of sustainability, including the integration of its dimensions (Bibri 2018a). To put it differently, despite the proven role of the advanced forms of ICT in enhancing urban sustainability performance, the evolving approaches to smarter cities raise several issues, involve special conundrums, significant challenges, and pose potential risks to the environment—when it comes to their development and implementation in the context of sustainability (Bibri 2018a; Bibri and Krogstie 2017a). It is highly important that future studies should go beyond only passing reference to the role of big data competing in addressing and overcoming the challenges of sustainability to emphasize and exploit the numerous opportunities available in this regard. However, for a detailed review of the field of smarter cities in terms of its materialization, characterization, research issues, challenges, and risks, the interested reader can be directed to Chap. 10 of a recent book published by Bibri (2018a). Overall, most of the critical issues discussed earlier concerning smart cities of today as to their inadequate contribution to the goals of sustainable development and thus poor sustainability performance do apply to the emerging smarter cities, so do the tremendous potential for advancing sustainability. The latter has indeed become a topic of major importance in recent years, a mainstream theme in the debate on ICT innovation for sustainability in the context of smart cities of the future, as well as a key research direction and new wave of urban thinking, as adequately discussed above. Smarter cities, which are characterized by the infiltration of computer and information intelligence into the operating and organizing processes of urban life, are extremely well positioned to do a lot more in respect of sustainability. Besides, it is high time for smart cities in their transition to smarter cities to go beyond the technical advancement and industrial competitiveness that have prevailed for more than two decades or so to start focusing their efforts toward solving the urgent problems and pressing issues pertaining to sustainability and urbanization. Especially, future ICT will pervade urban operations, functions, designs, strategies, services, and policies in the context of smarter cities, thereby being in strong position in instigating major transformations. This is anchored in the recognition that it offers fascinating possibilities for monitoring, understanding, analyzing, probing, and planning smarter cities to strategically improve and maintain their contribution to the goal of sustainable development (Bibri 2018a). The underlying premise is that future ICT blends, and its application is founded on, data science, computer science, and complexity sciences in terms of designing, constructing, and planning smarter cities capable of tackling the kind of intractable and wicked problems associated with sustainability and bringing about drastic transformations (e.g., Bibri 2018a; Bettencourt 2014). In reference to smart cities of the future, Batty et al. (2012) note that future ICT is said to unleash the kind of science that can be mobilized to instigate profound changes. Already, emerging ICT is being leveraged in accelerating environmental sustainability in both smart cities and sustainable cities (e.g., Al Nuaimi et al. 2015; Batty et al. 2012; Bibri 2018b; Kramers et al. 2014; Shahrokni et al. 2015), making it possible to approach a range of issues around environmental sustainability in cities from a whole new perspective. Further, it has been suggested that as ICT pervades urban environments, i.e., data sensing, data processing platforms, cloud and fog computing infrastructures, and wireless communication networks become more and more embedded throughout urban systems and domains as well as in citizens’ objects, smart cities can become smarter as to improving sustainability and enhancing the quality of life of citizens (see, e.g., Batty et al. 2012; Bibri 2018a; Piro et al. 2014; Shepard 2011; Townsend 2013). All in all, smarter cities will open new windows of opportunity for drastic sustainable change, especially they are still at the early stage of their development, and thus could, if planned strategically and implemented purposefully, do a lot more to advance sustainability and enhance the quality of life of citizens, including the mitigation of environmental risks and digital divides posed by ICT itself. In particular, the big data computing paradigm that is driving the transition from smart cities to

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smarter cities is noticeably in a penetrative path across various urban systems and domains toward safely fueling unhindered progress on many scales, and hence paving the way for catalyzing and accelerating sustainable development. However, failing to exploit the disruptive and substantive effects of ICT of pervasive computing on sustainability in an increasingly computerized and urbanized world means that the battle for sustainability will be lost in the world’s major cities (Bibri 2018a).

4.2 Big Data Analytics and Its Application in Smart and Smarter Cities 4.2.1 Data Growth Projection The deluge of urban data is, and will continue to be, soaring, amounting to hundreds of Exabytes every year, if not more than that, and covering so many aspects of urbanity in its complexity, breadth, depth, and heterogeneity as manifested in, among others, the nature of urban systems and their continuous integration, that of urban domains and their coordination, and that of urban networks and their coupling. This urban data growth will undoubtedly continue in this direction, and expectedly, the resulting datasets are set to proliferate and be coalesced, integrated, and coordinated. Generally, the digital data are projected to grow from 2.7 to 35 Zbyte by the year 2020 (Malik 2013; Zikopoulos et al. 2012). Manyika et al. (2011) project a growth of about 45% in the global data produced per year. It is estimated that more data are produced every 2 days at present than in all of history prior to 2003 (Kitchin 2014; Smolan and Erwitt 2012). This explosive data growth is due to a number of the core enabling and driving technologies of ICT of various forms of pervasive computing, and their ever-growing embeddedness into the very fabric of modern and future cities, including data sensing devices and sensor networks, data processing platforms, cloud and fog computing infrastructures, and wireless networking technologies as associated with big data analytics. 4.2.2 Research Issues and Future Prospects The past few years have witnessed extensive investments in the ICT infrastructure of smart and smarter cities in terms of large-scale deployments across the globe, especially in big data analytics and its core enabling technologies. This is making it increasingly feasible to collect, store, manage, and analyze large amounts of data throughout urban domains and to deploy the analytical outcome to serve many purposes, despite the limited capacities of the prevailing analytic systems or data processing platforms in use. This new development is opening new windows of opportunity for invigorating the application demand for the urban sustainability solutions that big data analytics can offer. Concurrently, the application of big data analytics has been expanded beyond the ambit of business intelligence (see, e.g., Chen et al. 2012; Provost and Fawcett 2013) in the wake of this development to include the field of smart and smarter cities in terms of their domains (e.g., Al Nuaimi et al. 2015; Batty et al. 2012; Bibri 2018a; Bettencourt 2014; Hashem et al. 2018; Kitchin 2014; Kumar and Prakash 2014; Rathore et al. 2018). However, the research within this field tends to deal largely with economic development (management, optimization, innovation, productivity, etc.) and the quality of life in terms of service efficiency and betterment (e.g., Batty 2013; DeRen et al. 2015; Khan et al. 2015; Khanac et al. 2017; Hashem et al. 2018; Rathore et al. 2018), while overlooking and barely exploring the rather more urgent or pressing issues related to the different dimensions of sustainability. This lack or paucity of research pertains particularly to the untapped potential of big data technologies and their novel applications for enhancing the environmental and social aspects of sustainability (Bibri 2018a). This relates in fact to the deficiencies of smart and smarter cities in this regard. As discussed above, such cities have, irrespective of which ICT visions they tend to instantiate in relation to their operational functioning, management, planning, and development, been subjected to much debate, generating a growing level of criticism that essentially questions their added value to sustainability due to the lack of incorporating the fundamental goals of sustainable development, as well as falling short in considering the environmental and social indicators of sustainability (e.g., Ahvenniemi et al. 2017; Bibri 2018a, b; Bibri and Krogstie 2017a; Höjer and Wangel 2015; Kramers et al. 2014; Marsal-Llacuna 2016). Consequently, a recent research wave has started to focus on enhancing smart and smarter city approaches to achieve the required level of sustainability through aligning urban operations, functions, designs, strategies, services, and policies with the goals of sustainable development using big data applications (e.g., Al Nuaimi et al. 2015; Batty et al. 2012; Bibri 2018a; Bettencourt 2014). Data sensing and processing, cloud and fog computing, and wireless networking technologies associated with big data analytics are being fast embedded into the very fabric of cities badging or regenerating themselves as smart and smarter to pave the way for utilizing and adopting the upcoming innovative solutions to overcome the challenges of sustainability and urbanization in the years ahead. Also, the increasing convergence and advance of ICT are giving rise to new computationally augmented urban spaces that are both drastically changing living and working modes and enabling sophisticated operating

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and organizing processes of urban life, which are quite different from what has been experienced hitherto on many scales. This is in response to the event of cities becoming more and more complex as systems and dynamically changing environments together with their domains getting more and more coordinated, their systems integrated, and their networks coupled. This concerns those domains, systems, and networks that rely heavily on complex technologies to realize their full potential for responding to the challenges of sustainability and urbanization or, possibly, addressing them from the source. All the above points well to new opportunities and alternative ways to develop, operate, probe, plan, and govern smart cities of the future or smarter cities. The expansion and success of big data computing trend are increasingly stimulating smart and smarter city initiatives and projects as well as research opportunities to an increasing extent, especially in technologically and/or ecologically advanced nations. However, there are significant challenges to address and overcome prior to achieving a more effective utilization of big data analytics and related applications in the realm of smart and smarter cities, including technological, computational, organizational, social, cultural, and political. These are the object of the next section.

4.2.3 The Deluge of Urban Data and Its Enabling Capabilities in City Analytics The sustainability of smart and smarter cities (as well as the smartness of sustainable cities) is being digitally fueled by the extensive data collected from such sources for analysis and deployment. The evolving data deluge resulting from the increasing availability of the data being generated in continuous streams on daily basis (e.g., Batty 2013; Kitchin 2014) is pushing research on and the use of big data analytics to expand remarkably and its technologies to proliferate in urban domains on a massive scale. The rationale is that it is increasingly enriching and reshaping our experiences of how smart and smarter cities can evolve and further advance at many levels, thanks to its analytics which is indeed offering new opportunities for generating well-informed decisions and enhanced insights with respect to our knowledge of how fast and best to advance sustainability (Bibri 2018a). This is due to the analytical power of big data as a fundamental ingredient for the next wave of city analytics with regard to the useful knowledge that can be extracted and immediately applied to improve sustainability performance. The increased use of big data analytics as well as the profusion and proliferation of data are being driven by the emerging core enabling technologies: techniques, algorithms, devices, systems, infrastructures, platforms, and networks, as advances in ICT of pervasive computing, and their continuous embeddedness into a wide variety of urban practices, enabling more effective accessibility, production, and sharing of data more than ever (e.g., Kitchin 2014). Important to note, though, big data are about the way they are exploited and their analytics is applied, as well as how new innovations are facilitated and diffused throughout the domains of smart and smarter cities through data themselves, especially in the context of sustainability and in connection with urbanization (Bibri 2018a). City analytics entails the application of various techniques, algorithms, models, and processes based on the fundamental concepts of data science—i.e., data-analytic thinking and the principles of extracting useful knowledge from large masses of data for decision-making (Bibri 2018a). Big data analytics techniques include, but are not limited to, data mining, machine learning, statistical analysis, and database querying, and whose application involves significant challenges due to the interdisciplinary and transdisciplinary character of urban data. Also, their use depends on the nature of the problem to be tackled or solved in relation to a given urban domain. Worth noting is that the process of data mining is the most applied technique in urban analytics within smart and smarter cities (see, e.g., Batty et al. 2012; Bibri 2018a; Khan et al. 2015). The main difference between data mining and other techniques is that it focuses on the automated search for or extraction of useful knowledge from large masses of data (e.g., Provost and Fawcett 2013). However, while this technique has recently become of focus in city analytics in relation to various domains of smart cities of the future (e.g., Batty et al. 2012; Khan et al. 2015; Kitchin 2014) as well as to those of sustainable smart cities of the future (Bibri 2018a; Bibri and Krogstie 2017c), much of the existing knowledge of urban sustainability has long been gleaned from studies characterized by data scarcity (‘small data’ studies) and involving the use of traditional data collection and analysis methods (Bibri 2018a). This form of academic and scientific research in the domain of sustainable urbanism has prevailed for three decades or so. This has consequently impacted the robustness of the obtained research results, and hence, the way sustainability as underpinned by theoretical perspectives and empirical investigations based predominately on such methods has been adopted as a set of practices in urban planning and development (Bibri 2018a; Bibri and Krogstie 2017b). Commonly, in the academic and scientific research within smart sustainable urbanism domain, ‘small data’ studies are associated with high-cost, quick obsolescence, infrequent periodicity, incompleteness, inaccuracy, and inherent biases; moreover, they capture a relatively limited sample of data that are tightly focused, restricted in scope and scale, time and space specific, and relatively expensive to generate and analyze (Batty et al. 2012; Bibri 2018a; Kitchin 2014). Therefore, there is a need for advanced or sophisticated approaches into data collection and analysis in the domain of smart sustainable urbanism that can provide additional depth and insight with respect to complex urban phenomena and dynamics. Accordingly, using big data

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techniques in city analytics holds great potential for transforming the knowledge of sustainable smart and smarter cities through the creation of a data deluge whose analysis can provide, as part of big data studies, much more sophisticated, more inclusive, finer-grained, wider-scope and wider-scale, real-time understanding and control of different aspects of urbanity in terms of its complexity and intricacy (Bibri 2018a).

4.2.4 Core Enabling Technologies Like many domains or areas to which big data analytics can be applied, smart and smarter cities require the big data ecosystem and its components to be put in place as part of their ICT infrastructure prior to designing, developing, deploying, implementing, and maintaining the diverse applications that support sustainability through enhancing and optimizing urban operational functioning, management, planning, and governance accordingly. As a scientific and technological area, the research strand concerned with the core enabling technological components underlying the big data ecosystem involves such sub-areas as low-level data collection and fusion, intermediate-level data processing, and high-level application action and service delivery, adding to cloud and fog computing models for hosting the associated devises, systems, and networks (Bibri 2018a). These are under vigorous investigation in both academic circles and the ICT industry toward the development of computationally augmented urban environments and spaces in smart and smarter cities as part of their informational landscape and as a result of the ICT visions of pervasive computing becoming deployable and achievable computing paradigms. In this respect, big data analytics as a prerequisite technology for realizing such visions entails a number of permutations of the underlying core enabling technologies pertaining indeed to various forms of pervasive computing, and also shaped by the way these forms can be applied and integrated depending on the urban domain concerned and the scale, complexity, and extension of the smart and smarter city projects and initiatives to be developed and implemented. Regardless of the several possible ways in which a set or number of the core enabling technologies can be arranged, it is necessary, as suggested by Chourabi et al. (2012), to take into account flexible design, quick deployment, extensible implementation, comprehensive interconnections, and advanced intelligence. However, while there are various permutations of the core enabling technologies that may well apply to most domains, there are some technical aspects and details that remain specific to the area of smart and smarter cities, more specifically to the requirements, objectives, and resources available of the smart and smarter city projects that are to be developed and implemented, which are usually determined by the nature, scale, and extension of the endeavor within a given context (Bibri 2018a; Bibri and Krogstie 2017c). Most of, if not all, the possible permutations, though, involve sensing technologies and networks, data processing platforms, cloud computing and/or fog computing infrastructures, and wireless communication and networking technologies. These are intended to provide a full analytic system of big data and related functional applications based on advanced decision support systems and strategies and the underlying intelligence functions and simulation models that can be directed toward improving the contribution of smart and smarter cities to the goals of sustainable development and thus achieving the required level of sustainability. On this note, Batty et al. (2012) state that much of the focus on smart cities of the future, ‘will be in evolving new models of the city in its various sectors that pertain to new kinds of data and movements and actions that are largely operated over digital networks while at the same time, relating these to traditional movements and locational activity. Very clear conceptions of how these models might be used to inform planning at different scales and very different time periods are critical to this focus … Quite new forms of integrated and coordinated decision support systems will be forthcoming from research on smart cities of the future’. Many reviews or surveys have, over the last few years, been carried out on big data analytics and its core enabling technologies. They tend to offer different perspectives on, or emphasize various dimensions of, the topic, while overlapping in many computational, analytical, and technological aspects (Bibri 2018b; Bibri and Krogstie 2017c) pertaining to such components as techniques, algorithms, models, software tools, data processing platforms, and application forms, adding to related research issues and opportunities as well as challenges (see, e.g., Chen et al. 2015; Katal et al. 2013; Karun and Chitharanjan 2013; Singh and Singla 2015; Chen et al. 2014; Tsai et al. 2015; Zhang et al. 2016). With regard to the orientation of most of these surveys and other studies conducted thus far, they tend to focus on the business domain (see, e.g., Chen et al. 2012; Hashem et al. 2018; Provost and Fawcett 2013). This implies that the literature and thus research addressing big data analytics and its core enabling technologies in relation to the domain of sustainable urban development remain scant. In response to this paucity of literature and thus research on the core enabling technologies of big data analytics and its application in the context of sustainable smart cities, Bibri and Krogstie (2017c) provide a thorough survey on the topic by identifying and reviewing such technologies, in addition to synthesizing and illustrating the key computational and analytical techniques, processes, and frameworks associated with the functioning and application of big data analytics. In doing so, the authors bring together research directed at a more conceptual, technical, and overarching level, a multiperspectival approach which is intended to stimulate new research opportunities within the city domain, with a particular

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emphasis on the use of big data analytics and its core enabling technologies for advancing sustainability, as well as to add more depth and rigor to the existing studies in the field. The topics of the core enabling technologies of big data analytics addressed in rather more detail by the authors in their topical literature review include, but are not limited to, the following: • Pervasive sensing for urban sustainability in terms of collecting and measuring urban big data; the IoT and related RFID tags; sensor-based urban reality mining; and sensor technologies, types, and areas in big data computing; • Wireless communication network technologies and smart network infrastructures; • Data processing platforms; • Cloud and fog/edge computing in terms of characteristics, benefits, commonalities, and differences; • Advanced techniques and algorithms; • Privacy mechanisms and security measures; • Conceptual and analytical frameworks with a focus on the process of data mining. It might be useful to elaborate on, for instance, data processing platforms as one of the key technological components of the ICT infrastructure of smart and smarter cities. To begin with, while there exist many data processing platforms that can be used to perform big data analytics in terms of storage, manipulation, management, analysis, and evaluation of large masses of data, Hadoop MapReduce platform tends to be the most commonly applied one in the realm of smart cities (see, e.g., Khan et al. 2015; Rathore et al. 2018) and sustainable smart cities (Bibri 2018a, b) due to the suitability of its functionalities as to handling urban data as well as to its benefits related to load balancing, flexibility, processing power, and cost-effectiveness (Bibri 2018b). Additionally, it has become the primary data processing platform given its simplicity, scalability, and fine-grain fault tolerance (Zhang et al. 2016). It has various extensions, including Co-Hadoop, Hadoop++, HadoopDB, Cheetahand, and Dare. And numerous technologies (e.g., Apache PIG, Apache Hive, Apache Tez, Apache Giraph, Apache Cassandra, Apache Spark, Apache Scoop, Apache Zookeepe, Apache HBase, Apache Flume, and Scribe) can, together with HDFS, be built on the top of the Hadoop system to form a Hadoop ecosystem to enhance efficiency and functionality (Bibri 2018a). Several reviews of data processing platforms have been carried out from different perspectives, including conceptual, technological, computational, analytical, and general (see, e.g., Singh and Singla 2015; Karun and Chitharanjan 2013; Zhang et al. 2016). However, Spark is considered one of the more efficient data processing platforms in terms of real-time data handling. Apache S4 platform is designed for processing continuous data streams in real time (Neumeyer et al. 2010). In addition, data processing platforms, standalone or as part of cloud computing or fog computing model, have the function of collecting, storing, coalescing, processing, managing, analyzing, evaluating, and interpreting large masses of data in relation to a given urban system or domain/sub-domain to discover useful knowledge in the form of intelligence intended primarily to enhance decision-making processes by deploying the obtained analytical outcome or feeding it into decision support systems pertaining to urban operations, functions, services, strategies, and policies. Accordingly, the value of the resulting intelligence lies in optimizing the efficiency of infrastructures and facilities, integrating and coupling networks, reducing resource consumption, enhancing service delivery, streamlining planning and governance processes, and smarting up urban forms and physical structures. These occur through such functions as control, automation, optimization, management, modeling, and simulation in the context of sustainability. However, merely keeping up with data flood coming from a single urban domain or sub-domain and storing the more relevant bits are daunting enough, not to mention effectively managing and analyzing colossal datasets to spot hidden patterns and discover meaningful correlations. Nevertheless, massive efforts are being deployed to further advance the existing data processing platforms in the context of smart and smarter cities in response to the emerging wave of city analytics for which big data are the fundamental ingredients, to reiterate, and the underlying role in tackling and responding to the challenges of sustainable development and urban growth (Bibri 2018a). That is, this advancement is necessary for both enhancing the operational functioning and planning of urban systems and facilitating the coordination and coupling of urban domains in line with the vision of sustainability in the context of smart and smarter cities. Further to the point, other topical studies tend to address varied technological components of big data while focusing on their use in relation to specific technologies, especially the IoT. For example, Ahmed et al. (2018) explore the recent advances in big data analytics for the IoT systems as well as the key requirements for managing and analyzing big data in an IoT environment. Bibri (2018b) reviews and synthesizes the existing literature with the main objective of identifying and discussing the state-of-the-art big data applications enabled by the IoT and related sensor technologies, data processing platforms, and cloud and fog computing models in the context of sustainable smart cities of the future. In establishing an

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IoT-based smart city using big data analytics, Rathore et al. (2018) describe their proposed system by its architecture and implementation prototype using Hadoop ecosystem and a wide variety of sensors for different purposes. This system entails data generation and collection, aggregation and integration, filtration, classification, preprocessing, computational analytics, and decision-making. Big data analytics as a set of advanced hardware technologies: devices, systems, platforms, architectures, and networks, constituting a key component of the ICT infrastructure of smart and smarter holds great potential to alter how such cities can be operated, managed, designed, and developed with regard to sustainability. This prospect has become clear as the underlying core enabling technologies will be, in the near future, the dominant mode of monitoring, understanding, analyzing, and planning such cities to improve their contribution to the goals of sustainable development (Batty et al. 2012; Bibri 2018a). Moreover, the broad availability of urban data is pushing research ever more into further advancing software technologies, including methods, techniques, algorithms, models, simulations, and protocols toward enhancing the efficiency of the extraction of useful knowledge pertaining to sustainability for the purpose of enhancing related urban intelligence functions and simulation models associated with energy, transport, mobility, health care, education, planning, and so on (Bibri and Krgostie 2017c). In reference to smart cities of the future and in relation to planning, Batty et al. (2012) point out that sustainability issues will be dealt with using more effective models and simulations in city planning; in the era of big data, this new technology will be a salient factor for planning forms of operation and organization.

4.2.5 Big Data Applications and Their Sustainability Effects and Benefits A Critical Evaluation of Topical Studies The intent here is to point out the differences between the notable topical studies carried out on big data applications that are particularly significant. Critically evaluating this research entails providing opinions as to what extent the findings or statement within this research are true, or to what extent they can be agreed with, as well as providing evidence taken from a range of sources which both agree with and contradict the presented arguments. With that in mind, significant opportunities exist for big data analytics and its application in relation to modernizing and advancing smart and smarter cities as urban development models in terms of sustainability dimensions, among other things, as there is a broad range of urban domains and sub-domains that can utilize big data technology as an advanced form of ICT in connection with sustainable development processes (see, e.g., Angelidou et al. 2017; Batty et al. 2012; Bibri 2018a, b, d). In other words, there exist numerous big data applications whose effects are compatible with the goals of sustainable development, as the knowledge resulting from the analysis of urban data in the form of applied intelligence usher in nearly all the domains of smart and smarter cities. This is due to the ubiquitous nature of ICT of pervasive computing and the associated extensiveness of data and the massive use of its analytics. However, while some topical studies address big data applications, they tend to deal largely with their use in relation to the efficiency of the proposed solutions, and there only are a few recent studies that focus on their use, yet only, in relation to some aspects of sustainability, or pass reference on the role of big data application in improving environmental sustainability. A short review conducted by Al Nuaimi et al. (2015) describes only a few big data applications in smart cities, namely power grid, traffic lights and signals, and eduction, and also explores the opportunities, benefits, and challenges of incorporating big data applications in smart cities. The authors conclude that while many opportunities are available for utilizing big data technology in smart cities, there are still many issues that need to be addressed to achieve better application of this technology. Hashem et al. (2018) describe a few big data applications in terms of efficiency and sustainability, including power grid, transport and traffic, health care, and governance and also discuss the visions of big data analytics as to supporting smart cities by focusing on how big data can change urban populations at different levels. Another detailed survey of big data applications provided by Bibri (2018b) includes more urban domains than the above reviews, including transport, mobility, traffic, energy, power grid, environment, buildings, infrastructure, and large-scale deployment, yet only in relation to the environmental aspects of sustainability and in the context of the IoT as one ICT vision of pervasive computing. In investigating the potential contribution of smart city to environmentally sustainable urban development, Angelidou et al. (2017) analyze comparatively a total of 32 smart city applications that can be found in the Intelligent Cities Open Source (ICOS) community repository. The authors classify the applications according to, among other criteria, the environmental issue they address, namely high traffic density, high amount of waste, increasing air pollution, increasing energy consumption/sinking resources, loss of biodiversity and natural habitat, and sinking water resources. However, they neither specify, nor provide any detail on, which of these applications, and how they, relate to big data analytics. Kumar and Prakash (2014) investigate the real potential of using big data analytics by decision-makers and city planners in smart cities

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using a large number of case studies across the globe and hence including many undergoing pilot project for making cities smarter along with well-being benefits, yet with only a focus on power grids and traffic congestion. Focusing on social sustainability issues in terms of digital divides, Gebresselassie and Sanchez (2018) ask, in their recent study on smart tools for socially sustainable transport, how smartphone applications (apps) can address social sustainability challenges in urban transport, if at all, with a particular focus on transport disadvantages experienced by citizens due to low income, physical disability, and language barriers and based on a review of 60 apps. This study reveals that transport apps have the potential to address or respond to the equity and inclusion challenges of social sustainability by employing universal design in general-use apps, including cost-conscious features and providing language options, as well as by specifically developing smartphone apps for persons with disabilities. However, while this is not to imply that such apps are a panacea for the equity and inclusion issues related to urban transport—but only one of the tools that can be used to address them—there nevertheless are other urban domains where new apps of similar use need to be developed and mainstreamed to address the same issues, including health care, education, and public and social services, and so on. Moreover, while this study brings the social aspects of sustainability to the forefront and helps to gain a better understanding of the application of smart tools for socially sustainable transport, there is no mention of the role of big data analytics in the functioning of such apps, or how they relate to it at all, despite the mention of some articles that in fact address big data analytics and its application in smart cities in terms of the new smart applications proliferating urban transportation systems. Indeed, their operation must be based on big data on travel behavior, mobility models, and multimodal transport. Furthermore, Bibri (2018a) provides a list of the other domains where big data can be applied to reach the required level of the different dimensions of sustainability, including dematerialization and demobilisation, water management, natural ecosystem management, public safety and civic security, ecosystem service provision, urban design and land use, urban planning, and participatory governance. See Chap. 8 for a detailed list of the common big data applications in the context of smart sustainable cities as an integrated and holistic urban planning and development strategy and approach. Such cities can utilize such applications to improve their contribution to the goals of sustainable development through optimizing and enhancing urban operations, functions, services, designs, strategies, and policies, as well as finding answers to challenging analytical questions and advancing knowledge forms. The included data-centric applications pertain to these urban domains: transport and traffic, mobility, energy, power grid, environment, buildings, infrastructures, urban planning, urban design, academic and scientific research, governance, health care, education, and public safety. Furthermore, big data applications can be categorized into two classes in the realm of smart and smarter cities: real-time applications and offline applications. As elucidated by Bibri (2018a, p. 491) regarding the former, ‘the input is instantaneous or near real-time, analysis is fast, and system behavior or application action is based on real-time mining … for decision-making since all real-time applications require immediate responses. This implies that if decisions, … based on analytical results …, cannot be made within a specific time line, they simply become of no value or effect. Hence, it is crucial in this regard to provide the kind of data necessary for mining in a timely manner and to conduct the analysis … in a fast and sound fashion for accurate decision-making purposes. As to the latter, the input tends to be periodic and thus analysis occurs sporadically. System behavior or application action comes in the form of delayed responses. For example, traffic control requires immediate responses to mange traffic in real-time; while environmental monitoring and management is associated with more delayed responses, as decisions are generally made over medium or long-term period.’ Mohamed and Al-Jaroodi (2014) provide an account of real-time and offline applications in the context of smart cities, with a focus on big data analytics. On the whole, in smart and smarter cities, big data analytics and its application are associated with such diverse intelligence functions as control, automation, optimization, management, prediction, and enhancement, which are involved in the operational functioning and planning of urban systems as part of various urban domains. Hence, big data applications are well positioned to enhance the sustainability, efficiency, and resiliency performance of such cities, as well as the life quality, well-being, and equity of their citizens. Yet, the literature and thus research on the uses of big data analytics and its application in relation to sustainable development remain scant in the context of smart and smarter cities. This implies that, to reiterate, the potential of big data computing for advancing sustainability remains untapped and underexplored in the context thereof, and therefore needs to be fully exploited and investigated, respectively. The Key Practical and Analytical Applications of Big Data Technology for Multiple Urban Domains Big data technologies and their applications are increasingly permeating the systems and domains of smart and smarter cities due to their potential for enabling their needed transition to sustainable development in an increasingly urbanized world. The

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range of the emerging big data applications as novel analytical and practical solutions that can be utilized for enhancing their sustainability performance is potentially huge, as many as the case situations where big data analytics may be of relevance to enhance some sort of decision or insight into connection with their domains or sub-domains. Chapter 8 identifies and enumerates the most common big data applications in relation to these domains or sub-domains and also elucidates their sustainability effects associated with the underlying functionalities pertaining to the operations, functions, services, designs, strategies, and policies related to these domains or sub-domains, which specifically include the following: • • • • • • • • • • • • • •

Transport and traffic; Mobility; Energy; Power grid; Environment; Buildings; Infrastructures; Urban planning; Urban design; Academic and scientific research; Governance; Health care; Education; Public safety.

Chapter 8 provides a descriptive account of the big data applications associated with these domains or sub-domains. These applications are by no means, or intended to be, exhaustive. Also, they are synthesized and distilled from many studies conducted in more recent years, the most notable of which in order of priority in terms of their contribution to the synthesis and extracted essential meaning below are: Bibri (2018a, b), Batty et al. (2012), Angelidou et al. (2017), and Al Nuaimi et al. (2015), including the other works that are referenced (credited) in these studies. Of relevance to add, as to the technical processes, tools, and other details underpinning the functioning of big data applications, the interested reader can be directed to Bahga and Madisetti (2016), one of the many books available out there on the topic, for a detailed account from a general perspective, and to Bibri (2018a) for an overview focusing mainly on sustainable smart and smarter cities.

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The Main Scientific and Intellectual Challenges and Common Open Issues

While there is a growing consensus among urban scholars and applied urban science experts that big data analytics and its application will be a determining or salient factor in the operational functioning, management, planning, design, and development of smart cities of the future or smarter cities, there still are significant scientific and intellectual challenges as well as open issues that need to be addressed and overcome for building such cities based on big data computing and the underpinning technologies, and then for accomplishing the desired outcomes related to sustainability and urbanization. Such challenges and issues pose interesting and complex research questions and constitute fertile areas of investigation awaiting interdisciplinary and transdisciplinary teams of scholars, scientists, experts, and researchers working in the field of sustainable smart urbanism. The rising demand for big data analytics and its core enabling technologies coupled with the growing awareness of the associated potential to transform the way urban systems can be operated, managed, planned, and designed in the context of sustainability, comes with major challenges and open issues related to the design, engineering, development, implementation, and maintenance of data-centric applications in sustainable smart cities of the future or smarter cities. The challenges are mostly computational, analytical, and technical in nature, and sometimes logistic in terms of the detailed organization and implementation of the complex technical operations involving the installation and deployment of the big data ecosystem and its components as part of the ICT infrastructure of such cities. They include, but are not limited to, the following, as compiled in Table 2. There are many studies available (e.g., Al Nuaimi et al. 2015; Batty 2013; Batty et al. 2012; Bibri 2018a; Katal et al. 2013; Kaisler et al. 2013; Kharrazi et al. 2016; Kitchin 2014, 2015, 2016; Lacinák and Ristve 2017; Townsend 2013; van Zoonen 2016; Vinod Kumar and Dahiya 2017) that provide a descriptive or detailed account of some of the above-listed

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Table 2 Computational, analytical, technical, and logistic challenges • Design science and engineering constraints • Data processing and analysis • Data management in dynamic and volatile environments • Data sources and characteristics • Database integration across urban domains • Data sharing between city stakeholders • Data uncertainty and incompleteness • Data accuracy and veracity (quality) • Data protection and technical integration • Fault tolerance and scalability • Data governance • Urban growth and data growth • Cost and large-scale deployment • Evolving urban intelligence functions and related simulation models and optimization and prediction methods as part of exploring the notion of smart cities as innovation labs • Building and maintaining data-driven city operations centers or citywide instrumented system • Relating the urban infrastructure to its operational functioning and planning through control, automation, management, optimization, enhancement, and prediction • Creating technologies that ensure fairness, equity, inclusion, and participation • Balancing the efficiency of solutions and the quality of life against environmental and equity considerations • Privacy and security Source Adapted from Bibri (2018a)

challenges (and also some of the open issues addressed below) as related to big data analytics and its applications and uses in smart and smarter cities. For example, Bibri (2018a) provides an overview of some of those challenges and potential ways to address and overcome them in the context of sustainable smart cities of the future, including data management, database integration across urban domains, urban growth and data growth, data sharing, data uncertainty and incompleteness, data accuracy and quality, and data governance. Most of the challenges of big data analytics and its application arise from the nature of the data generated in smart and smarter cities in terms of their attributes (see, e.g., Batty et al. 2012; Bibri 2018a; Kitchin 2014; Laney 2001; Marz and Warren 2012; Mayer-Schonberger and Cukier 2013; Zikopoulos et al. 2012) as: • Consisting of Exabytes or Terabytes of data; • Being structured and unstructured in nature; • Being often tagged with spatial and temporal labels; being commonly streamed from a large number and variety of sources; • Being mostly generated automatically and routinely; being created in, or near, real-time; • Being exhaustive in scope and scale by striving to capture entire populations or systems; • Dramatically exceeding sample sizes commonly in use for small data studies; • Being relational in database systems by containing common fields that enable the conjoining and combination of different datasets; • Being fine-grained in resolution by aiming to be very detailed and uniquely indexical in identification; and holding the traits of extensionality (can add new fields easily), evolvability (can change dynamically), and scalability (can expand in size rapidly). Adding to the above primarily technological challenges are the financial, organizational, institutional, social, political, regulatory, and ethical ones, which are associated with the implementation, retention, and dissemination of big data across the domains of sustainable smart and smarter cities of the future (Bibri 2018a). In this regard, controversies over the benefits of big data analytics and its application involve limited access and related digital divides and other ethical concerns about accessibility (Fan and Bifet 2013). For a detailed discussion of the challenges of urban big data and sustainable development, the reader can be directed to Kharrazi et al. (2016). Kitchin (2014) provides a critical reflection on the implications of big data and smart urbanism, examining five emerging concerns, namely:

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1. 2. 3. 4. 5.

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The politics of big urban data; Technocratic governance and city development; Corporatization of city governance and technological lock-ins; Buggy, brittle, and hackable cities; The panoptic city.

Furthermore, to effectively and successfully use big data analytics for smart and smarter city applications, there are some open issues that need to be addressed and resolved, which mostly stem from the different challenges mentioned above. These issues are currently under investigation by the relevant industry and research communities. Regardless, no full solutions and robust approaches based on big data analytics can be offered in the context of such cities, and therefore, there is always room for improvements and innovations in the field of data-driven sustainable smart urbanism in terms of operational functioning, planning, design, and development. However, regarding the key open issues, there is, and will be, a growing need or increased demand for well-qualified professionals and experts to design, develop, deploy, implement, operate, and maintain smart cities of the future or smarter cities with regard to their infrastructures, platforms, and applications. Specialized education and focused training in the field need to be strategically planned, carefully designed, and widely offered to obtain the needed human resources for fulfilling the purpose and meeting the expectations. In addition, it is necessary to set common assessment methods, measurements, and control policies for big data applications in such cities. Monitoring, controlling, and managing initiatives and implementations using advanced techniques and procedures are of crucial importance for ensuring the effectiveness, viability, quality, and durability of big data applications in the context of sustainability. Furthermore, as discussed previously in relation to smarter cities, political action is determining in the functioning, insertion, and evolution of sustainable smart cities of the future or smarter cities as an academic discourse which epitomises a socio-technical transition that is of significance to society. The political effects on smart and smarter cities play a pivotal role in how they will perform in terms of sustainability (Bibri 2018a). Also, the privilege of access to data by different city constituents with different political positions or powers must be taken into account and addressed carefully (Al Nuaimi et al. 2015). For a detailed account and discussion on the shaping role of political action in sustainable smart cities of the future, the interested reader can directed to Bibri and Krogstie (2016). In addition, it is important to investigate the side or harmful effects of technology use by citizens on their health and living and hence to consider all the possible risks and unintended consequences in this regard. Another open issue requiring special attention and careful consideration in the context of smart cities of the future or smarter cities is security and privacy concerns. When all systems become integrated, networked, and ubiquitous, data will be shared among all urban entities. It is a commonly held view that the more technologies monitor urban environments and collect information, the larger becomes the privacy threats, and the larger the networks, the higher the security risks (see Bibri and Krogstie 2017c for a discussion of privacy mechanisms and security measures). Therefore, the ICT infrastructure and related data processing and cloud/fog computing platforms and infrastructures must be secured, privacy must be preserved, and information must be protected and thus not abused. Privacy—to selectively reveal oneself to the world— remains though the most critical issue in the context of the use of big data analytics and related applications in such cities. In fact, privacy is considered a basic human right in many democratic states, enshrined in national and supranational laws in various ways, and related debates concern acceptable practices as to accessing and disclosing personal and sensitive information about a person (Kitchin 2016). Such sensitive information can relate to a number of a personal facets and domains creating a number of interrelated privacy forms, including (Martínez-Ballesté et al. 2013; Santucci 2013): • • • • • •

Identity privacy (to protect personal and confidential data); Bodily privacy (to protect the integrity of the physical person); Territorial privacy (to protect personal space, objects and property); Locational and movement privacy (to protect against the tracking of spatial behavior); Communication privacy (to protect against the surveillance of conversations and correspondence); and Transaction privacy (to protect against monitoring of queries/searches, purchases, and other exchanges).

These forms of privacy can be threatened and breached through a number of what are normally understood as unacceptable practices, each of which produces a different form of harm, as compiled by Kitchin (2016) (Table 3) and detailed by Solove (2006).

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Table 3 A taxonomy of privacy breaches and harms Domain

Privacy breach

Description

Information collection

Surveillance

Watching, listening to, or recording of an individual’s activities

Information processing

Information dissemination

Invasion

Interrogation

Various forms of questioning or probing for information

Aggregation

The combination of various pieces of data about a person

Identification

Linking information to particular individuals

Insecurity

Carelessness in protecting stored information from leaks and improper access

Secondary use

Use of information collected for one purpose for a different purpose without the data subject’s consent

Exclusion

Failure to allow the data subject to know about the data that others have about her and participate in its handling and use, including being barred from being able to access and correct errors in that data

Breach of confidentiality

Breaking a promise to keep a person’s information confidential

Disclosure

Revelation of information about a person that impacts the way others judge her character

Exposure

Revealing another’s nudity, grief, or bodily functions

Increased accessibility

Amplifying the accessibility of information

Blackmail

Threat to disclose personal information

Appropriation

The use of the data subject’s identity to serve the aims and interests of another

Distortion

Dissemination of false or misleading information about individuals

Intrusion

Invasive acts that disturb one’s tranquillity or solitude

Decisional interference

Incursion into the data subject’s decisions regarding her private affairs

Source Compiled by Kitchin (2016) from Solove (2006)

Data-driven smart sustainable/sustainable smart urbanism, urban science, data science, and big data computing and the underpinning technologies create a number of potential privacy harms for several reasons. Kitchin (2016) addresses five reasons, each of which raises significant challenges to existing approaches to protecting privacy (privacy laws and fair information practice principles), namely: 1. 2. 3. 4. 5.

Datafication, dataveillance, and geosurveillance; Inferencing and predictive privacy harms; Anonymization and re-identification; Obfuscation and reduced control; Notice and consent is an empty exercise or is absent.

There are clearly a number of ethical issues that arise from the development, deployment, and implementation of smart or smarter city technologies and accompanying urban science. The ethical dimensions of big data computing and the underpinning technologies and urban science need to be seriously addressed and much more thoroughly mapped out. As widely acknowledged, many smart urban technologies capture data without the consent of citizens—who ought actually to have full details of what data are being generated, for what purpose these data are being used for, what kind of insights are being extracted from them or additional data inferred from them, how they are being captured, in addition to having shared control and benefit in how all data concerning them are subsequently used. This necessitates full consent together with full transparency with regard to the actions of those who control, process, and analyze data. Urbanism researchers and urban scientists need to consider the ethical implications of their work with respect to privacy harms and citizen permissions, and the purposes their research is intended for—even in the context of sustainability. As suggested by Kitchin (2016, p. 12), ‘Beyond complying with relevant laws and institutional research board requirements, analysts have a duty of care to their fellow citizens not to expose them to harm through their analysis. Admittedly, what constitutes harm is often difficult to define and harms can occur directly or indirectly but nonetheless there is a need to consider how research might be used and to act responsibly. In addition, professional bodies should review their ethical standards in the light of big data and revise accordingly. City managers need to consider the potential pernicious effects of the roll-out of smart [urban] technologies and

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that notice and consent are all but impossible in many cases and take a pro-active role in brokering privacy and security arrangements on behalf of citizens through relevant contracting procedures and parameters. Here, all vendors would be compelled to comply with service level agreements concerning the operation of systems, what data are generated and how these can be used and shared, and be subject to privacy impact assessments.’ From a social perspective, new ethical frameworks based on gifting or sharing, in which citizens swap their data for a tangible return, offer an alternative underpinning for smart cities of the future or smarter cities and urban science. However, the ‘gifting’ remains compulsory with no alternatives and is also done without consent, and the benefits of ‘sharing’ data are most often stacked in favor of those capturing the data (Kitchin 2016). From a technical perspective, while there are several solutions and frameworks (especially modeling and simulations) that have recently been proposed (e.g., Khanac et al. 2017; Lacinák and Ristvej 2017), the problem persists in the quest for unconventional security measures and privacy-enhancing mechanisms (Bibri 2018a). In this respect, several researchers (e.g., van Zoonen 2016) have recently provided clear directions for further empirical research and theory development about privacy concerns, in addition to sensitising techniques to identify the emergence, absence, or presence of privacy concerns among citizens. The same directions apply to security concerns. Regardless, the generation, accumulation, and processing of various data streams across urban domains are projected to continue to raise privacy and security issues, which is in fact, of concern to all the city constituents and stakeholders. Therefore, there is a need for novel measures and mechanisms that can ensure trustable data acquisition, transmission, and processing, not least to legitimate service provisioning associated with transport, traffic, mobility, accessibility, health care, utility, and public and social services, while ensuring citizens’ privacy and guaranteeing services’ integrity in the context of sustainability. Of importance to also consider is to develop smart cities of the future or smarter cities and urban science that have a set of ethical principles and values at their heart, which in fact is at the heart of social sustainability. The challenge is to acknowledge that there are a number of real ethical issues that need to be addressed and overcome, and to search for and find the kind of solutions (i.e., privacy-enhancing mechanisms and security measures) that also enable the sustainability benefits of big data computing and the underpinning technologies to be realized. This is no easy task, but one that needs urgent redress, supported by viable, strategic pathways. Addressing different kinds of challenges and open issues, Kitchin (2015) provides a critical overview of data-driven urbanism and critically examines a number of urban data issues, namely: • • • • •

6

The politics of urban data; Data ownership, data control, data coverage and access; Data security and data integrity; Data protection and privacy, dataveillance, and data uses such as social sorting and anticipatory governance; and Technical data issues such as data quality, veracity of data models and data analytics, and data integration and interoperability.

Discussion and Conclusions

Building smart cities of the future or smarter cities based on big data analytics and its core enabling technologies is deemed necessary to address and overcome many of the complex challenges and pressing issues of sustainability and urbanization. Big data applications are seen as a critical enabler and powerful driver for sustainable smart and smarter cities in the future. Many cities across the globe have already started to exploit the untapped potential of such applications across diverse urban systems and domains. We stand at a threshold in beginning to make sense of big data analytics and data-driven decision-making that are projected to be of massive use in, and interwoven into the very fabric of, smart cities of the future or smarter cities within the next few decades. The ultimate goal is to improve the contribution of such cities to sustainability and to contain the potential effects of urbanization by employing more effective digital ways to monitor, understand, probe, and plan such cities. However, there are currently many challenges and open issues that need to be addressed, and hence, prudent research approaches and long-term strategic plans are required to overcome them. The principal aim of this chapter was to provide a comprehensive, state-of-the-art review and synthesis of the field of smart and smarter cities in regard to sustainability and related big data analytics and its application in terms of the underlying foundations and assumptions, research issues and debates, opportunities and benefits, technological developments, emerging trends, future practices, and challenges and open issues. These issues were addressed through dividing the chapter into pertinent sections and subsections where the relevant conceptual and theoretical subjects as well as thematic and topical

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Discussion and Conclusions

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categories were adequately elaborated on and thoroughly discussed from a variety of perspectives. This interdisciplinary and transdisciplinary review entailed exploring a broad array of the literature at the intersection of various disciplinary and scientific fields and technological areas. It is meant to facilitate collaboration among these fields and areas for the primary purpose of generating the kind of interactional knowledge that is necessary for a more integrated and deeper understanding of the topic of smart and smarter cities in relation to sustainability and related big data applications, as well as new insights and perspectives. The outcome of this extensive review allowed to establish the status of current knowledge about the sustainability of smart and smarter cities in their current state, as well as to highlight the potential of big data analytics and related novel applications for advancing their sustainability in the future. First, the key concepts, theories, and academic discourses are identified, described, and discussed while emphasizing the relevant issues and aspects relating to the cross-disciplinary integration underlying the multidisciplinary topic of this study. Worth noting, however, is that despite the prevalence of the concept and phenomenon of smart city worldwide, there still is obscurity facing its definition; nevertheless, there seems to be an agreement on what the smart or smarter city should achieve as to sustainability and urbanization, and how advanced ICT (particularly big data analytics and its application) should be utilized to mitigate or solve the associated challenges and issues. Also, the concept of smart or smarter city is associated with misunderstanding and deficiencies in regard to incorporating the goals of sustainable development. The review indicates that smart and smarter cities in their current state involve several issues, pose special conundrums, and present significant challenges as to their development and implementation with regard to their contribution to sustainability. Accordingly, there are many critical questions that are worth investigating, which pertain to conceptual, theoretical, analytical, empirical, practical, social, and environmental aspects as related to sustainable development and the role of advanced ICT (especially big data applications) in achieving its goals. These aspects constitute new research avenues and thus opportunities which need to be explored and realized, respectively, to advance the sustainability of smart cities of the future or smarter cities, based on big data applications. This is anchored in the growing recognition that emerging and future ICT is extremely well positioned to make substantial contributions in this regard due to its disruptive, innovative, substantive, and transformational effects on forms of urban operations, functions, services, designs, strategies, and policies. Indeed, the review shows that tremendous opportunities are available for utilizing big data applications in smart cities of the future or smarter cities to improve their contribution to the goals of sustainable development. The most common data-centric applications identified concerning urban domains are: transport and traffic, mobility, energy, power grid, environment, buildings, infrastructures, urban planning, urban design, academic and scientific research, governance, health care, education, and public safety. The potential of big data technology lies in enabling smart cities of the future or smarter cities to harness and leverage their informational landscape in effectively understanding, monitoring, probing, and planning their systems and environments in ways that enable them to achieve the required level of sustainability. To put it differently, the use of big data analytics is projected to play a significant role in realizing the key characteristic features of such cities in terms of sustainability, namely the efficiency of operations and functions, the efficient utilization of natural resources, the intelligent management of infrastructures and facilities, the improvement of the quality of life and well-being of citizens, and the enhancement of mobility and accessibility. With the above in mind, the untapped potential of big data applications is evident and needs to be unlocked and exploited within such cities. Worth noting in this regard is that, as smarter cities are still emerging and in the early stage of their development, could, if planned strategically and linked to the agenda of sustainable development, do a lot more in this direction before they become widely adopted. Just as there are many new opportunities and benefits ahead to embrace and exploit, there are significant challenges and open issues ahead to address and overcome in relation to big data analytics to achieve a successful implementation of related novel applications in the context of smart cities of the future or smarter cities. These challenges are mostly of computational, analytical, technical, and logistic kinds. While most of these challenges and open issues are currently under investigation and scrutiny by the relevant research and industry communities, supported by technology and innovation policies, deploying big data applications in smart cities of the future or smarter cities requires overcoming other organizational, institutional, political, social, ethical, and regulatory challenges. These are likely to hinder the development and implementation of big data applications in such cities. Nevertheless, with all the success factors in place, coupled with a deep understanding of the emerging phenomenon of smart cities and an acknowledgment of the potential of big data computing, making such cities smarter in achieving sustainability becomes an attainable goal in an increasingly urbanized world. Important to add, while smart city and big data computing research is still in its infancy, the solutions to the involved challenges and issues can make it a very practical field.

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Concerning the value of this review and synthesis, the findings enable researchers and scholars to focus their work on the identified real-world challenges and open issues and the existing knowledge gaps pertaining to smart and smarter cities as urban development strategies in the context of technology and sustainability, respectively. Practitioners and experts can make use of these findings to identify common weaknesses and potential ways to solve them as part of the ongoing and future endeavors of sustainable smart urban development. In view of that, this interdisciplinary and transdisciplinary review provides a valuable reference for researchers and practitioners in related research communities and the necessary material to inform these communities of the latest developments in the field. It moreover serves to inform various city stakeholders about the yet unexploited benefits of big data applications with regard to sustainability. Lastly, this chapter provides a form of foundation for further discussion to debate over the disruptive, substantive, synergetic, and transformational effects of big data analytics and its application on forms of the operational functioning, management, planning, and development of smart and smarter cities in terms of sustainability practices in the future. Also, it presents a sort of basis for stimulating in-depth research on smart and smarter cities and big data computing in the form of both qualitative analyses and quantitative investigations focused on establishing, uncovering, substantiating, and/or challenging the assumptions and claims underlying the relevance and meaningfulness of big data applications as technological advancements with regard to advancing sustainability. For example, owing to the disciplinary origins of ICT-oriented literature which resorts to what is labeled ‘normative bias’ of smart city research (Visvizi and Lytras 2018b), and thus respective authors’ literacy in advanced sophisticated technologies, there is a fertile area of research that may challenge the promises and claims that new discoveries in big data computing as futuristic advances in ICT hold for urban spaces at the expense of the basic consideration of factors that hamper or facilitate the implementation of big data applications. Indeed, attempts at dwelling at this intersection regarding technological advancements exist in the body of research on smart cities (see, e.g., Ishida and Isbister 2002; Lytras et al. 2018; Van de Voorde et al. 2011; Visvizi et al. 2017). Nevertheless, much more needs to be done to fully exploit it and thus promote sustainable interdisciplinary and transdisciplinary smart and smarter city research (see, e.g., Bibri 2018a; Visvizi and Lytras 2018b).

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Advancing Sustainable Urbanism Processes: The Key Practical and Analytical Applications of Big Data for Urban Systems and Domains

Abstract

Sustainable cities have been the leading global paradigm of urbanism. Undoubtedly, sustainable development has significantly positively influenced city planning and development since the early 1990s. This pertains to the immense opportunities that have been explored and, thus, the enormous benefits that have been realized from the planning and development of sustainable urban forms as an instance of sustainable cities. However, the existing models of such forms, especially compact cities and eco-cities, are associated with a number of problems, issues, and challenges. This mainly involves the question of how such forms should be monitored, understood, and analyzed to improve, advance, and maintain their contribution to sustainability and hence to overcome the kind of wicked problems, intractable issues, and complex challenges they embody. This in turn brings us to the current question related to the weak connection between and the extreme fragmentation of sustainable cities and smart cities as approaches and landscapes, respectively, despite the great potential of advanced ICT for, and also its proven role in, supporting sustainable cities in improving their performance under what is labeled ‘smart sustainable cities.’ This integrated approach to urbanism takes multiple forms of combining the strengths of sustainable cities and smart cities based on how the concept of smart sustainable cities can be conceptualized and operationalized. In this respect, there has recently been a conscious push for cities across the globe to be smarter and thus more sustainable by particularly utilizing big data technology and its applications in the hopes of reaching the optimal level of sustainability. Having a twofold aim, this chapter firstly provides a comprehensive, state-of-the-art review of the domain of sustainable urbanism, with a focus on compact cities and eco-cities as models of sustainable urban forms and thus instances of sustainable cities, in terms of research issues and debates, knowledge gaps, challenges, opportunities, benefits, and emerging practices. It secondly highlights and substantiates the real, yet untapped, potential of big data technology and its novel applications for advancing sustainable cities. In so doing, it identifies, synthesizes, distills, and enumerates the key practical and analytical applications of big data technology for multiple urban domains. This study shows that sustainable urban forms involve limitations, inadequacies, difficulties, fallacies, and uncertainties in the context of sustainability, in spite of what has been realized over the past three decades or so within sustainable urbanism. Nevertheless, as also revealed by this study, tremendous opportunities are available for exploiting big data technology and its novel applications to smarten up sustainable urban forms in ways that can improve, advance, and sustain their contribution to the goals of sustainable development by optimizing and enhancing their operations, functions, services, designs, strategies, and policies across multiple urban domains, as well as by finding answers to challenging analytical questions and transforming the way knowledge can be developed and applied. Keywords








Sustainable cities Compact cities Eco-cities Big data computing Sustainable development Typologies Urban domains
















Smart sustainable cities Big data applications Sustainable urbanism Design concepts

© Springer Nature Switzerland AG 2019 S. E. Bibri, Big Data Science and Analytics for Smart Sustainable Urbanism, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-17312-8_8

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1

Introduction

Generally, cities are seen to occupy a central position and to have a defining role in sustainable development in terms of its operationalization and application. In this respect, sustainable cities have been the leading global paradigm of urban planning and development (Bibri 2019b; Van Bueren et al. 2011; Wheeler and Beatley 2010; Whitehead 2003; Williams 2009) for more than three decades. The subject of ‘sustainable cities’ remains endlessly fascinating and enticing, as there are numerous actors involved in the academic and practical aspects of the endeavor, including engineers and architects, green technologists, built and natural environment specialists, and environmental and social scientists. All these actors are undertaking research and developing strategies and programs to tackle the challenging elements of sustainable urbanism. This adds to the work of policymakers and political decision-makers in terms of formulating and implementing regulatory policies and devising and applying political mechanisms and governance arrangements to promote and spur innovation and monitor and maintain progress in such urbanism. In light of this, the usefulness and relevance of the findings produced by the research in the field of sustainable urban development and planning (or sustainable urbanism) have led sustainable cities as a drastic urban transformation to figure in many documents and agenda of policymakers of influential weight, such as the United Nations (UN), the European Union (EU), and the Organization for Economic Co-operation and Development (OECD). Also, such transformation has been provided as political statements and argumentations by many governments and organizations. The point is that urban politics and policy around the world are infused with the language of sustainability (Williams 2009). Since its emergence, sustainable development has had significant positive impacts on the design, planning, and development of cities in terms of the different dimensions of sustainability (Bibri 2018a, 2019b; Bibri and Krogstie 2017a). It has also revived the discussion about the form of cities (Jabareen 2006). Unquestionably, it has inspired a whole generation of urban scholars and practitioners into a quest for the immense opportunities and fascinating possibilities that could be enabled and created by, and the enormous benefits that could be realized from, the planning and development of sustainable urban forms (especially compact cities and eco-cities)—i.e., forms for human settlements that will meet the required level of sustainability and enable the built environment to function in a constructive way by continuously improving their contribution to the goals of sustainable development in terms of reducing material use, lowering energy consumption, mitigating pollution, and minimizing waste, as well as in terms of improving equity, inclusion, and well-being (Bibri 2018a, 2019b). However, new circumstances require new responses. This concerns the widespread of urbanization and the rise of ICT and how they are drastically changing sustainable urbanism. By all indicators, the urban world will become largely technologized, computerized, and urbanized within just a few decades, and ICT as an enabling, integrative, and constitutive technology of the twenty-first century will accordingly be instrumental, if not determining, in addressing many of the conundrums posed, the issues raised, and the challenges presented by urbanization (Bibri 2018a, 2019b). It is therefore of strategic value to start directing the use of emerging ICT into understanding and proactively mitigating the potential effects of urbanization, with the primary aim of tackling the many intractable and wicked problems involved in urban operational functioning, management, planning, and development, especially in the context of sustainability. Indeed, the rapid and anticipated urbanization of the world poses significant and unprecedented challenges associated with sustainability (e.g., David 2017; Han et al. 2016; Estevez et al. 2016) due to the issues engendered by urban growth in terms of resource depletion, environmental degradation, intensive energy usage, air and water pollution, toxic waste disposal, endemic traffic congestion, ineffective decision-making processes, inefficient planning systems, mismanagement of urban infrastructures and facilities, poor housing and working conditions, public health and safety decrease, social vulnerability and inequality, and so on (Bibri 2018a, 2019b). In short, the multidimensional effects of unsustainability in modern and future cities are most likely to exacerbate with urbanization (Bibri 2018a). Urban growth will jeopardize the sustainability of cities (Neirotti et al. 2014). Therefore, ICT has come to the fore and become of crucial importance for containing the effects of urbanization and facing the challenges of sustainability, including in the context of sustainable cities which are striving to improve, advance, and maintain their contribution to the goals of sustainable development. As s pointed out by Bibri (2018a, 2019a), the use of advanced ICT in sustainable cities constitutes an effective approach to decoupling the health of the city and the quality of life of citizens from the energy and material consumption and concomitant environmental risks associated with urban operations, functions, services, designs, strategies, and policies. As an advanced form of ICT, big data technology and its applications are increasingly becoming of crucial importance to new approaches to smart sustainable urban planning and development, gaining traction and foothold among urban scholars, scientists, practitioners, and policymakers over the past few years (Bibri 2019a, b). Urban big data technologies have become essential to the operational functioning of cities, and consequently, urban planning, governance, and services are becoming highly responsive to a form of data-driven urbanism (Kitchin 2016). Indeed, big data computing as a new paradigm is

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fundamentally changing the way modern cities can smartly and sustainably be operated, managed, planned, developed, and governed, shaping and driving decision-making processes within many urban domains (Bibri 2018a, 2019b), especially with regard to optimizing resource utilization, mitigating environmental risks, responding to socio-economic needs, and enhancing the quality of life and well-being of citizens in an increasingly urbanized world. This paradigm is clearly on a penetrative path across various urban systems and domains that rely on advanced ICT. This is due to its multifaceted potential in the context of smart cities, which has indeed been under investigation by the United Nations (2015) through their study on ‘Big Data and the 2030 Agenda for Sustainable Development.’ In particular, there is an urgent need for developing and applying data-driven innovative solutions and sophisticated methods to overcome the challenges of urbanization and sustainability (Batty et al. 2012; Bibri 2018a, b, c, d, 2019a, b). Undoubtedly, the main strength of the big data technology is the high influence it will have on many facets of smart sustainable cities and their citizens’ lives (Bibri 2018a, 2019a, b; Pantelis and Aija 2013). In light of the above, recent research endeavors have started to focus on smartening up sustainable cities in ways that can improve, advance, and maintain their contribution to the goals of sustainable development through enhancing and optimizing their operational functioning, management, planning, development, and governance in line with the vision of sustainability under what is labeled ‘smart sustainable cities of the future’ (Bibri 2018a, b, c, d, 2019a, b). This integrated approach tends to take several forms in terms of combining the strengths of sustainable cities and smart cities based on how the concept of smart sustainable cities can be conceptualized and operationalized, just as it has been the case for sustainable cities: There are conceptualization and operationalization of multiple processes of, and pathways to achieving, sustainable urbanism and also an understanding of the interplay between ecological, social, and technical solutions. Indeed, several topical studies (e.g., Angelidou et al. 2017; Bibri 2018a; Bibri and Krogstie 2017b; Kramers et al. 2014, 2016; Rivera et al. 2015; Shahrokni et al. 2015; Yigitcanlar and Lee 2013) have addressed the merger of the sustainable city and smart city approaches from a variety of perspectives on how advanced ICT can improve sustainability, namely using ubiquitous computing, big data computing, and/or context-aware computing to advance urban metabolism, urban form (design and planning), urban public and ecosystem services, urban operations and functions, urban strategies and policies, urban governance and citizen participation, or using simply ICT to optimize energy efficiency and provide solutions for everyday life practices. This point to the fact that there is a host of opportunities yet to explore toward new approaches to smart sustainable urbanism. Furthermore, sustainable cities are associated with a number of problems, issues, and challenges (i.e., limitations, inadequacies, difficulties, fallacies, and uncertainties) when it comes to their management, planning, design, development, and governance in the context of sustainability (Bibri 2018a, 2019b; Bibri and Krogstie 2017a, b). This mainly involves the question of how sustainable urban forms should be monitored, understood, and analyzed in order to be effectively managed, planned, designed, developed, and governed in terms of enhancing and maintaining their sustainability performance (Bibri 2019b). The underlying argument is that more innovative solutions and sophisticated approaches are needed to overcome the kind of wicked problems, intractable issues, and complex challenges pertaining to sustainable urban forms. This brings us to the current question related to the weak connection between and the extreme fragmentation of sustainable cities and smart cities as approaches and landscapes, respectively (e.g., Angelidou et al. 2017; Bibri 2018a, 2019a, b; Bibri and Krogstie 2017a; Bifulco et al. 2016; Kramers et al. 2014), despite the great potential of advanced ICT for, and also its proven role in, supporting sustainable cities in improving their performance under what is labeled ‘smart sustainable cities’ (e.g., see, Bibri 2018a, b, 2019b; Bibri and Krogstie 2017b; Kramers et al. 2014; Shahrokni et al. 2015). In particular, tremendous opportunities are available for utilizing big data computing and the underpinning technologies and their novel applications in sustainable cities to improve, advance, and maintain their contribution to the goals of sustainable development. Against the preceding background, having a twofold aim, this chapter, first, provides a comprehensive, state-of-the-art review of the domain of sustainable urbanism, with a focus on compact cities and eco-cities as models of sustainable urban forms and thus instances of sustainable cities, in terms of research issues and debates, knowledge gaps, challenges, opportunities, benefits, and emerging practices. It, second, highlights and substantiates the real, yet untapped, potential of big data technology and its novel applications for advancing sustainable cities in terms of enhancing and optimizing their operational functioning, management, design, planning, development, and governance. In so doing, it identifies, synthesizes, distills, and enumerates the key practical and analytical applications of big data for multiple urban domains. The remainder of this chapter is structured as follows. In Sect. 2, the conceptual and theoretical background is provided, comprising sustainable cities and sustainable urban forms: urban form and its sustainable dimension, the built environment, compact city, and eco-city. Section 3 provides a comprehensive, state-of-the-art review of sustainable urbanism, with a focus on compact cities and eco-cities as the most prevailing and advocated models of sustainable urban form, in terms of current issues, benefits, opportunities, limitations, inadequacies, fallacies, uncertainties, challenges, and prospects. Section 4 provides an account of the technological and global factors driving the endeavor of smartening up sustainable cities toward

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smart sustainable cities of the future, as well as presents some relevant work done recently in this direction. Section 5 identifies, synthesizes, distills, and enumerates the key practical and analytical applications of big data, as well as elucidates their sustainability effects and benefits in terms of enhancing and optimizing urban operations, functions, services, strategies, and policies pertaining to multiple urban domains in the context of smart sustainable cities of the future. Section 6 discusses some technology and policy issues related to big data analytics and its applications in the context thereof. The chapter ends, in Sect. 7, with concluding remarks together with some reflections and final thoughts.

2

Conceptual and Theoretical Background

2.1 Urban Planning and Design As an academic discipline, urban planning is concerned with research and analysis, strategic thinking, sustainable development, transportation planning, environmental planning, land use planning, policy recommendations, public administration, urban design, landscape architecture, and civil engineering (e.g., Bibri 2018a; Nigel 2007). As a governmental function in most countries, urban planning is practiced on neighborhood, district, municipality, city, metropolitan, regional, and national scales, with land use, environmental, transport, and local planning representing more specialized foci. It has been approached from a variety of perspectives, often combined, including physical, spatial, geographical, ecological, technical, economic, social, cultural, and political. As a concept, it refers to the process of guiding and directing the use and development of land, urban environment, urban infrastructure, and ecosystem and human services—in ways that ensure the responsible management and efficient utilization of natural resources, the efficiency of urban operations and functions, optimal economic development, and high quality of life (Bibri 2018a). In more detail, urban planning entails drawing up, designing, developing, organizing, coordinating, standardizing, evaluating, and forecasting physical arrangements, spatial patterns, and infrastructural systems of a city and related processes, functions, and services. The ultimate aim of urban planning is to make cities more sustainable, efficient, safe, resilient, and attractive places (Bibri 2018a). Urban planning is associated with urban systems: the operating and organizing processes of urban life. Urban systems include the following: • Built form (buildings, streets and boulevards, neighborhoods, districts, residential and commercial areas, schools, parks, public spaces, etc.); • Urban infrastructure (transport systems, water and gas provision systems, sewage systems, power distribution systems, etc.); • Ecosystem services (provisioning energy, water, air, and food; regulating climate; supporting nutrient cycles and oxygen production, etc.); • Human services (public services, social services, cultural and recreational facilities, etc.); • Administration (organizational structures, governance arrangements, creating and implementing mechanisms for adherence to regulatory frameworks, practice enhancements, policy design and recommendation, technical and assessment studies, etc.). In terms of the operational processes, the above urban systems involve the following: • • • • •

Design and evaluation regarding built form; Monitoring, operation, and control concerning urban infrastructure; Provision and distribution as regards ecosystem services; Delivery and optimization as to human services; Development, use, and evaluation with respect to administration.

Urban design constitutes part of urban planning, or the latter overlaps with the former. As an academic field, it is concerned with planning, landscape architecture, and civil engineering (Van Assche et al. 2013), in addition to sustainable urbanism, ecological urbanism, sustainable design, ecological design, and strategic urban design (Bibri 2018a; Bibri and Krogstie 2017a). Dealing with the design and management of the public domain and the way this domain is experienced and used by urbanites, urban design refers to the process of designing, shaping, arranging, and reorganizing urban physical

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structures and spatial patterns depending on diverse contexts (Bibri 2018a). Accordingly, it involves buildings, streets, neighborhoods, districts, public infrastructures and facilities, public spaces, parks, and so on. In relation to its sustainable dimension, it is aimed at making urban living more environmentally sustainable and urban areas more attractive, functional (e.g., Aseem 2013; Boeing et al. 2014; Larice and MacDonald 2007), and equitable. In this respect, urban design is about making connections between forms for human settlements and environmental and social sustainability, built environment and ecosystems, people and the natural environment, economic viability and well-being, and movement and urban form (Bibri 2018a). In the context of this chapter, the emphasis is on the planning and design of sustainable urban forms as a set of integrated typologies and design concepts and principles, i.e., a form of organized, coordinated, standardized physical arrangements and spatial organizations designed for the purpose of achieving the required level of sustainability. The way cities are planned and designed is of paramount importance to sustainable development. McHarg (1995) describes and illustrates an ecologically sound approach to urban planning and design, and Wheeler and Beatley (2010) provide a range of perspectives on sustainable urban planning and development.

2.2 Sustainable Cities The concept of sustainable cities, and thus sustainable urbanism, has become more established as a result of the widespread diffusion of sustainability as an important global shift, which is still at play across the world today. In more detail, while this concept has been around for more than three decades or so, it did gain strong foothold and become powerful a few years after the inception and dissemination of the notion of sustainable development by the World Commission on Environment and Development (WCED 1987). Indeed, this notion has been applied to, or adopted within, urban planning and development since the very early 1990s (e.g., Bibri 2018a, 2019b; Bibri and Krogstie 2017a; Wheeler and Beatley 2010). This adoption was marked by the emergence of the notions of sustainable urban development and urban sustainability. There are multiple views on what a sustainable city should look like or be, hence multiple ways of defining it or conceptualizing it. Generally, it can be understood as a set of approaches into operationalizing sustainable development in cities or practically applying the knowledge about sustainability and related technologies to the operational functioning and thus planning and design of existing and new cities or districts (Bibri 2018a, 2019b; Bibri and Krogstie 2017a). A sustainable city represents an instance of sustainable urban development, which in turn is a strategic approach to achieving the long-term goals of urban sustainability. Thus, it is designed with the primary aim to simultaneously retain a balance between environmental integration and protection, social equity and justice, and economic development and regeneration over the long run. This can be attained through a process of change aiming at fostering innovation and advancement in built environment, urban infrastructure, operational functioning, management, planning, governance, as well as human and ecosystem service provisioning, while continuously optimizing efficiency gains, all in line with the vision of sustainability (Bibri 2018a, 2019b). Overall, sustainable cities, as put succinctly by Bibri and Krogstie (2017a, p. 11), ‘strive to maximise the efficiency of energy and material use, create a zero-waste system, support renewable energy production and consumption, promote carbon-neutrality and reduce pollution, decrease transport needs and encourage walking and cycling, provide efficient and sustainable transport, preserve ecosystems, emphasise design scalability and spatial proximity, and promote livability and community-oriented human environments.’

2.3 Sustainable Urban Forms: Compact City and Eco-city Models 2.3.1 Urban Form and Its Sustainable Dimension It is deemed useful to operationalize the term ‘urban form’ for its frequent use in this chapter. Lynch (1981, p. 47) defines it as ‘the spatial pattern of the large, inert, permanent physical objects in a city.’ Specifically, urban form represents aggregations of repetitive elements as amalgamated characteristics pertaining to land use patterns, spatial organizations, and other urban design features, as well as transportation systems and environmental and urban management systems (Handy 1996; Williams et al. 2000). In other words, urban form results from bringing together many urban patterns, which ‘are made up largely of a limited number of relatively undifferentiated types of elements that repeat and combine’ (Jabareen 2006, p. 39). In sum, the spatial pattern entails similarities and grouped conceptual categories (Lozano 1990) that comprise such components as building densities, block sizes and shapes, street designs, area configurations, spatial scales, public space arrangements, and park layouts (Jabareen 2006; Van Assche et al. 2013).

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There are various instances of sustainable city as a meta-model, but compact cities and eco-cities are advocated as more sustainable urban forms and, indeed, the most prevalent and environmentally sound models of such forms. There are multiple definitions of compact cities and eco-cities in the literature (e.g., Hofstad 2012; Jabareen 2006; Jenks et al. 1996a, b; Joss 2010, 2011; Joss et al. 2013; Rapoport and Vernay 2011; Register 2002; Roseland 1997). Further, the formulation of these definitions, as exemplified below, tends to be based on the socio-cultural context in which these two models of sustainable urban form are embedded in terms of projects and initiatives. In relation to eco-cities, for example, Rapoport and Vernay (2011) uncover the diversity underneath the various uses of the concept of eco-city and determine the extent of convergence or divergence on the way projects conceive of what an eco-city should be. The authors suggest that there is a great deal of diversity among eco-city projects, going beyond just their size, location, and ambition to expand to their vision of what a sustainable urban future looks like, the techniques that planners and designers should use to achieve it, as well as the kind of actors that should be involved. In this sense, they argue that it is better to think of the eco-city as an objective which there will be multiple ways to achieve. In relation to compact city, there are great differences between cities in terms of their urban form whose key elements can be distinguished: density, surface, land use, public transport infrastructure, and the economic relationship with the surrounding environment (Van Bueren et al. 2011). Further, however, a sustainable urban form can be defined as a form for human settlements that seeks to meet the required level of sustainability and enable the built environment to function in a constructive and efficient way in terms of operations, functions, services, designs, strategies, and policies associated with urban systems and domains (Bibri 2018a, 2019b). According to Jabareen (2006), the compact city and the eco-city as the most prevalent models of sustainable urban form entail overlaps among them in their concepts, ideas, and visions: The compact city emphasizes density, compactness, diversity, and mixed-land use, whereas the eco-city focuses on renewable resources, passive solar design, ecological and cultural diversity, urban greening, environmentally sound policies, and environmental management. In addition to land use patterns and design features, the compact city emphasizes sustainable transportation (e.g., transit-rich interconnected nodes), environmental and urban management systems (Handy 1996; Williams et al. 2000), energy-efficient buildings, closeness to local squares, more space for bikes and pedestrians, and green areas (Phdungsilp 2011). In view of that, Jabareen (2006) ranks compact city as more sustainable than eco-city from a conceptual perspective using a thematic analysis. However, the effects of these models are compatible with the goals of sustainable development in terms of transport provision, mobility and accessibility, travel behavior, energy conservation, pollution and waste reduction, economic viability, life quality, and social equity (Bibri 2018a).

2.3.2 The Built Environment The built environment is a term that is used to describe the human-made surroundings that provide the setting for human activity and what this entails in terms of land use, transport systems, and the spatial patterns of physical objects and their design features. It encompasses urban places and spaces created, restructured, and redesigned by people, including buildings, parks, and public infrastructure. The built environment is at the core of sustainable urban forms in the sense that the latter is intended to enable the former to function in a sustainably constructive way, e.g., to environmentally contribute beneficially to the planet for the present and future generations in terms of reducing material use, lowering energy consumption, mitigating pollution, and minimizing waste. However, the concept of the built environment has been defined in multiple ways. Handy et al. (2002) describe it as an amalgam of land use, urban design, and the transportation system, including patterns of human activity and mobility within the physical environment. Roof and Oleru (2008) define it as the human-made space in which people live, work, and recreate on a day-to-day basis. Past studies within urbanism have typically focused on different spatial levels of the built environment, including the neighborhood, district, city, and regional scales. For example, Handy et al. (2002) discuss measures of the built environment by categorizing them into neighborhood and regional features, with at least five interrelated and often correlated dimensions of the built environment at the neighborhood scale, as suggested by several studies (see Table 1). 2.3.3 Compact City As an idea that is aligned with the goals of sustainable development, the compact city was indeed envisioned as, as first proposed by Dantzing and Saaty (1973) 15 years before the diffusion of sustainable development, a city that enhances the quality of life but not at the expense of the next generation. So, its notion and development were just revived by the popularization of sustainable development. Subsequently, it became a preferred response to the challenges of sustainability since the early 1990s (e.g., Hofstad 2012; Jenks and Dempsey 2005; Van Bueren et al. 2011) Sustainable development provides the basis for the argument underlying the compact city (Welbank 1996). As a concept, it entails ‘many strategies that aim to create compactness and density that can avoid all the problems of modernist design and cities. The popularization

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Table 1 Dimensions of the built environment Dimension

Definition

Examples

Density and intensity

Amount of activity in a given area

Persons per acre or jobs per square mile Ratio of commercial floor space to land area

Land use mix

Proximity of different land uses

Distance from house to nearest store Share of total land area for different uses dissimilarity index

Street connectivity

Directness and availability of alternative routes through the network

Intersections per square mile of area Ratio of straight line distance of network distance average block length

Street scale

Three-dimensional space along a street as bounded by buildings

Ratio of building heights to street width Average distance from street to buildings

Aesthetic quality

Attractiveness and appeal of a place

Percent of ground in shade at noon Number of locations with graffiti per square mile

Regional structures

Distribution of activities and transportation facilities across the region

Rate of decline in density with distance from downtown Classification based on concentrations of activity and transportation network

of sustainable development has contributed to the promotion of the urban compactness idea by enhancing the ecological and environmental justifications behind it’ (Jabareen 2006, p. 46). It was around the mid-1990s when the research led to the advocacy of combining compactness and mixed-land use (Jabareen 2006). Mixed-land use should be encouraged in cities (Breheny 1992). Fundamentally, the compact city is characterized by high-density and mixed-land use with no sprawl (Jenks et al. 1996a; Williams et al. 2000). Accordingly, it is more energy-efficient and less polluting because its dwellers can live in close proximity to work and leisure facilities and can walk, bike, or take transit (Bibri 2018a). Therefore, it offers great opportunities for reducing fuel consumption for traveling, as well as for reusing urban land, supporting local facilities, and protecting rural land from further development (Jabareen 2006). Travel distances between activities are shortened due to heterogeneous zoning that enables compatible land uses to locate in close proximity to one another (Parker 1994). Such zoning in turn reduces the use of automobiles for commuting and leisure and shopping trips due to nearby location (Alberti 2000; Van and Senior 2000). The argument is that people are encouraged to cycle and walk due to many services and facilities being within a reasonable distance. Indeed, as concluded by Newman (2000), the compact city is the most fuel-efficient of existing sustainable urban forms. Integrating land use, transport, and environmental planning is key to minimizing the need for travel and to promoting efficient modes of transport (Sev 2009), and population densities are sufficient for supporting local services and businesses (Williams et al. 2000). In this respect, the compact city ideally secures environmentally sound, socially beneficial, and economically viable development through dense, diverse, and mixed use patterns that rely on sustainable transportation (Burton 2000, 2002; Dempsey 2010; Dempsey and Jenks 2010; Jenks and Dempsey 2005). It can be implemented at various scales, e.g., neighborhood, district, city, and so on, including entirely new settlements. To sum up, the compact city model has been advocated as more sustainable urban form due to several reasons: ‘First, compact cities are argued to be efficient for more sustainable modes of transport. Second, compact cities are seen as a sustainable use of land. By reducing sprawl, land in the countryside is preserved and land in towns can be recycled for development. Third, in social terms, compactness and mixed uses are associated with diversity, social cohesion, and cultural development. Some also argue that it is an equitable form because it offers good accessibility. Fourth, compact cities are argued to be economically viable because infrastructure, such as roads and street lighting, can be provided cost-effectively per capita’ (Jabareen 2006, p. 46). Neuman (2005) enumerates, and adds some of, the characteristics of the compact city, as shown in Table 2.

2.3.4 Eco-city The idea of the eco-city is widely varied in conceptualization and operationalization and also difficult to delineate. According to the most comprehensive survey of eco-cities to date performed by Joss (2010), the diversity and plurality of the projects and initiatives reflected in the use of the term ‘eco-city’ across the globe make it difficult to develop a meaningful definition. Therefore, the concept of the eco-city has taken on many definitions in the literature. Register (2002), an architect widely credited as the first to have coined the term, describes an eco-city as ‘an urban environmental system in which input (of resources) and output (of waste) are minimized.’ Joss (2011) states that an eco-city must be, using three analytical categories, developed on substantial scale, occurring across multiple domains, and supported by policy processes. As an umbrella

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228 Table 2 Compact city characteristics Compact city characteristics

1. High residential and employment densities 2. Mixture of land uses 3. Fine grain of land uses (proximity of varied uses and small relative size of land parcels) 4. Increased social and economic interactions 5. Contiguous development (some parcels/structures may be vacant or abandoned or surface parking) 6. Contained urban development, demarcated by legible limits 7. Urban infrastructure, especially sewerage and water mains 8. Multimodal transportation 9. High degrees of accessibility: local/regional 10. High degrees of street connectivity (internal/external), including sidewalks and bicycle lanes 11. High degree of impervious surface coverage 12. Low open-space ratio 13. Unitary control of planning of land development or closely coordinated control 14. Sufficient government fiscal capacity to finance urban facilities and infrastructure Source Neuman (2005)

metaphor, the eco-city ‘encompasses a wide range of urban-ecological proposals that aim to achieve urban sustainability. These approaches propose a wide range of environmental, social, and institutional policies that are directed to managing urban spaces to achieve sustainability. This type promotes the ecological agenda and emphasizes environmental management through a set of institutional and policy tools’ (Jabareen 2006, p. 47). This implies that realizing an eco-city requires making countless decisions about urban design, urban planning, urban governance, sustainable technologies, and so on (Rapoport and Vernay 2011). This in turn signifies that the relationship between sustainable development objectives and urban design and planning interventions is a subject of much debate (Bulkeley and Betsill 2005; Williams 2009). Irrespective of the way the idea of the eco-city has been conceptualized and operationalized, there are still some criteria that have been proposed to identify what a desirable or ideal ‘eco-city’ is or looks like, comprising the environmental, social, and economic goals of sustainable development. Roseland (1997) and Harvey (2011) describe an ideal ‘eco-city’ as a city that fulfills the following set of requirements: • • • • • • • •

operates on a self-contained, local economy; maximizes efficiency of energy resources; is based on renewable energy production and carbon neutrality; has a well-designed urban city layout and sustainable transport system (prioritizing walking, cycling, and public transportation); creates a zero-waste system; support urban and local farming; and ensures affordable housing for diverse socio-economic and ethnic classes; and raises awareness of environmental and sustainability issues and decreases material consumption.

As added by Graedel (2011), the eco-city is scalable and evolvable in design in response to urban growth and need changes. Based on these characteristic features, the eco-city and green urbanism overlap or share several concepts, ideas, and visions in terms of the role of the city and positive urbanism in shaping more sustainable places, communities, and lifestyles. Beatley (2000, pp. 6–8, cited in Jabareen 2006) views, while arguing for the need for new approaches to urbanism to incorporate more ecologically responsible forms of living and settlement, a city exemplifying green urbanism as one that: • • • • • •

strives to live within its ecological limits; is designed to function in ways analogous to nature; strives to achieve a circular rather than a linear metabolism; strives toward local and regional self-sufficiency; facilitates more sustainable lifestyles; and emphasizes a high quality of neighborhood and community life.

The eco-city approaches tend to emphasize different aspects of sustainability, namely passive solar design, greening, sustainable housing, sustainable urban living, and living machines (Jabareen 2006). Worth noting, as a general consensus,

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the eco-city is formless or eco-amorphous in terms of typologies, although it emphasizes passive solar and ecological design (Jabareen 2006). Indeed, it is evident that urban form specificities are on less focus on eco-city development. That is to say, the built environment of the city in terms of urban design features and spatial organizations is inconsequential or insignificant, unlike the compact city which focuses on the spatial patterns of physical objects and typologies. Rather, what counts most is how the city as a social fabric is organized, managed, and governed. In this line of thinking, Talen and Ellis (2002, p. 37), state, ‘social, economic, and cultural variables are far more important in determining the good city than any choice of spatial arrangements.’ In view of that, the focus is on the role of different environmental, social, economic, institutional, and land use policies in managing and governing the city to achieve the required level of sustainability (e.g., Council of Europe 1993; European Commission 1990; Jabareen 2006; Robinson and Tinker 1998).

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Sustainable Cities—Compact City and Eco-city Models of Sustainable Urban Form

3.1 The Key Benefits of Sustainable Cities Sustainable development has significantly positively impacted urban design, planning, and development in terms of sustainability dimensions (Bibri 2018a, 2019b; Bibri and Krogstie 2017a). It has also revived the discussion about the form of cities (Jabareen 2006). Unquestionably, it has inspired a whole generation of urban scholars and practitioners into a quest for the immense opportunities and fascinating possibilities that could be enabled and created by, and the enormous benefits that could be realized from, the development of sustainable urban forms—i.e., forms for human settlements that will meet the required level of sustainability and enable the built environment to function in a constructive way by continuously improving their contribution to the goals of sustainable development in terms of reducing material use, lowering energy consumption, mitigating pollution, and minimizing waste, as well as in terms of improving equity, inclusion, and well-being. Indeed, the concept of sustainable development has given a major stimulus to the question of the contribution that certain urban forms might make in the context of sustainable urbanism. In view of that, the idea of applying the concept of sustainable development to various urban forms has intensively been investigated and adequately discussed by researchers and planners during the last three decades or so (see, e.g., Bibri and Krogstie 2017b; Jabareen 2006; Kärrholm 2011; Williams 2009). Sustainable urban forms provide numerous benefits in terms of sustainability. Bibri (2018a) lists in a series of bullet points the main advantages of sustainable cities (see Table 3). The research field of urban sustainability as inherently interdisciplinary and transdisciplinary in nature has mainly focused on how to translate sustainability into urban forms and related planning and policymaking practices through an array of design and planning principles and approaches (Bibri and Krogstie 2017a). Yet, the urban analytics and thus design and planning approaches are drastically changing with the emergence of the advanced forms of ICT, especially big data computing and its application (e.g., Batty et al. 2012; Bibri 2018a). In light of this, urban scholars and planners are

Table 3 Key benefits of sustainable cities (compact cities and eco-cities combined) Key benefits of sustainable cities (compact cities and eco-cities combined) • Theoretically informed and practically grounded urban strategies for achieving the required level of sustainability • Established approaches into applying the knowledge of urban sustainability to the design, planning, and development of cities and districts • Strategies for fostering innovation in urban and green infrastructures and their operational functioning, management, and planning, as well as in natural resources management • Best practices of the implementation of sustainably sound typologies and design concepts • Advanced knowledge of models of sustainable urban form at different spatial levels: regional level, metropolitan level, city level, community level, neighborhood level, and building level • Different combinations of spatial patterns and urban policies, with different levels of sustainability performance in terms of environmental, social, and economic dimensions • Successful practices of ecological diversity, cultural diversity, green technology, integrated renewable solutions, zero-waste buildings, and carbon-neutral neighborhoods or districts • Advanced frameworks for urban metabolism and urban technical systems • Environmental, social, institutional, and land use policy instruments for managing urban spaces in terms of the different aspects of sustainability Source Bibri (2018a)

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increasingly realizing that the principles and approaches originally developed for supporting and improving urban sustainability through various forms have become inadequate for dealing with the complexity of emerging problems in an increasingly urbanized world (Bibri 2018a). Hence, there is a need for more effective solutions that can support the dynamic conception of sustainable cities in response to emerging changes.

3.2 Design Concepts and Typologies of Compact Cities and Eco-cities: Characteristic Features and Sustainability Effects To achieve sustainable urban forms requires such typologies as compactness, density, diversity, and mixed-land use, and such design concepts and principles as sustainable transport, ecological design, and passive solar design, supported by high standards of environmental and urban management (e.g., Dumreicher et al. 2000; Jabareen 2006; Van Bueren et al. 2011; Williams et al. 2000). Next, the most prevalent typologies and design concepts and principles of sustainable urban forms are described, and their sustainability effects are enumerated and distilled based on numerous studies. See Bibri (2018a) for further details.

3.2.1 Compactness As a widely acceptable strategy for achieving sustainable urban forms, the notion of compactness of the built environment or urban space means that urban development should be driven by contiguity and connectivity in the sense of taking place adjacent to existing urban structures (Jabareen 2006; Wheeler 2002). Compactness emphasizes ‘density of the built environment and intensification of its activities, efficient land planning, diverse and mixed-land uses, and efficient transportation systems’ (Jabareen 2006, p. 46). This intensification includes mainly the development of not fully or less developed urban land and redevelopment of previously developed sites, as well as additions and extensions and conversions and subdivisions (Jenks 2000). Indeed, the concept of compactness is taken to mean the containment of further sprawl when the concept is applied to existing rather than new urban fabric (Hagan 2000). With respect to the significant themes evident in the current debates on compactness, the positive implications of the latter for sustainability include the following: • Supporting reductions in per capita resource use and benefiting public transit developments; • Providing building densities to conserve energy, mitigate pollution, and minimize waste; • Reducing the number and length of trips by the modes of transport that are detrimental to the environment in terms of CO2 emissions; • Encouraging cycling and walking due to many services and facilities being within a reasonable distance and close proximity; • Protecting rural land from further development; • Promoting the quality of life in terms of social interaction and accessibility to facilities and services.

3.2.2 Density As a critical typology of sustainable urban forms, density denotes the ratio of dwelling units or buildings and their inhabitants to land area. To make urban functions or activities viable depends on the sufficiency of generating the necessary interactions, which is based on the number of people within a given area (Jabareen 2006). Sustainable cities are about density (Carl 2000) and dwelling types, which affect sustainability through differences in the consumption of resources as well as land for housing and urban infrastructure (Walker and Rees 1997). Regarding the major themes evident in the current debates on density, the claimed sustainability benefits of the latter include the following: • • • • • • •

Saving and conserving energy; Achieving urban efficiency; Supporting low-carbon mobility; Optimizing resource efficiency of buildings; Minimizing automobile travel needs and thus CO2 emissions; Providing and enhancing accessibly to facilities and services; Eliminating urban sprawl and hence high dependence on private automobiles, inefficient infrastructure, loss of farmlands and natural habitats, pollution, and so on.

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3.2.3 Mixed-Land Use Land use denotes the distribution of activities across space, grouped into relatively coarse categories (Bibri 2018a). Widely recognized among scholars and planners for its important role in achieving sustainable urban form, mixed-land use (heterogeneous zoning) refers to the diversity and proximity of land uses in terms of institutional, cultural, residential, commercial, and industrial aspects. This is to decrease the travel needs and distances between activities and functions due to the availability and proximity of many services and facilities. Concerning the significant themes evident in the current debates on mixed-land uses, the positive sustainability effects of the latter encompass the following: • • • • • • • •

Enhancing accessibility to services and facilities; Reducing automobile use for multiple purposes; Decreasing the travel distances between activities; Encouraging cycling or walking; Decreasing vehicle trip generation rates and travelled time; Improving security in public spaces for disadvantaged groups; Reducing air pollution and traffic congestion; Stimulating the interaction of residents by increasing pedestrian traffic.

3.2.4 Diversity Diversity is a preferred typology in several urban planning and development approaches, such as new urbanism, green urbanism, sustainable urbanism, and even smart growth. The diversity of functions and spatial patterns is essential to modern cities and their sustainability. Without diversity, the urban system declines as a living place (e.g., Jacobs 1961). Diversity has been a pervasive and persistent feature of sustainability debates and a powerful idea of redefining them (Taylor 1986; Neuman 2005). Overlapping with mixed-land uses in urban planning, diversity entails, in addition to a mixture and multiplicity of land uses, building densities, housing for all income groups through inclusionary zoning, a variety of housing types, job-housing balances, household sizes and structures, cultural diversity, and age groups (e.g., Jabareen 2006; Wheeler 2002), thereby epitomizing the socio-cultural context of the urban form. Topographic and functional diversity is a prerequisite element for social and cultural mixture and integration (Peterek 2012). As major themes evident in the current debates on diversity, the positive implications of the latter for sustainability include the following: • Enhancing the quality of life in terms of social interaction; • Reducing traffic congestion and air pollution; • Encouraging walking and cycling.

3.2.5 Sustainable Transport As widely argued in academic and urban circles, transportation is a major issue in the environmental debates on urban form (e.g., Jenks et al. 1996a). As a system, transport involves both the physical infrastructure, including roads, railroad tracks, sidewalks, and bike paths, as well as the level and quality of service provided to city inhabitants as determined by the traffic levels and train and bus frequencies. Jordan and Horan (1997, p. 72) define sustainable transportation as ‘transportation services that reflect the full social and environmental costs of their provision; that respect carrying capacity; and that balance the needs for mobility and safety with the needs for access, environmental quality, and neighborhood livability.’ As to the major themes evident in the current debates on sustainable transportation, the latter provides the following benefits: • • • • • • • • • •

Operating transport at maximum efficiency; Providing favorable conditions for energy-efficient forms of transport; Conserving energy in several ways; Limiting CO2 emissions and minimizing waste; Reducing the need for mobility; Providing equitable accessibility to services and facilities; Promoting renewable energy sources; Maximizing the use of renewable resources from the site; Decreasing travel needs and costs; Achieving a healthy and desirable quality of life;

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• Minimizing land use; • Supporting a vibrant economy.

3.2.6 Greening—Ecological Design Green or ecological design is an important concept in sustainable urbanism (urban planning and development). Green space contributes positively to sustainable development agenda (Swanwick et al. 2003). The key themes evident in the current debates on greening urban spaces, the sustainability benefits of the latter include the following: • • • • • • • • •

Bringing nature into the life of inhabitants through diverse open landscapes; Making urban places attractive and pleasant, as well as more sustainable; Improving the urban image and the quality of life; Enhancing health benefits; Moderating urban climate extremes; Ameliorating the physical urban environment by reducing pollution; Preserving and enhancing the ecological diversity of the environment of urban places; Maintaining biodiversity through the conservation of a range of urban habitats; Increasing economic attractiveness in urban areas.

3.2.7 Passive Solar Design Passive solar design is one of the key design concepts for achieving a sustainable urban form in the context of the eco-city. It is about reducing the demand for energy and the sustainable use of solar passive energy through particular design measures (Jabareen 2006). The orientation of buildings and urban densities as a design feature affects the form of the built environment (Thomas 2003). The environmental impacts and contextual implications of the building in relation to the site are key two criteria for the urban designer to look at, adding to searching for different alternatives to orient the building according to the sun path for passive solar gain and daylighting (Gordon 2005; Yeang 1997). By means of design, orientation, layout, and landscaping, solar gain and microclimatic conditions can be used in an optimal way to minimize the need for buildings’ space heating or cooling by conventional energy sources (Owens 1992). The built form, coupled with the street widths and orientation, largely determines urban surfaces’ exposure to the sun (Jabareen 2006). Orientation and clustering of buildings determined by the settlement formation of a city affect the microclimatic conditions (Jabareen 2006). Yannas (1998, cited in Jabareen 2006, p. 42) summarizes some design parameters for achieving environmentally sustainable urban forms and improving urban microclimate: ‘(1) built form—density and type, to influence airflow, view of sun and sky, and exposed surface area; (2) street canyon—width-to-height ratio and orientation, to influence warming and cooling processes, thermal and visual comfort conditions, and pollution dispersal; (3) building design—to influence building heat gains and losses, albedo and thermal capacity of external surfaces, and use of transitional spaces; (4) urban materials and surfaces finish—to influence absorption, heat storage, and emissivity; (5) vegetation and bodies of water—to influence evaporative cooling processes on building surfaces and/or in open spaces; and (6) traffic—reduction, diversion, and rerouting to reduce air and noise pollution and heat discharge.’ Furthermore, the interaction between energy systems and urban structures occurs at all spatial scales, ranging from the regional, city, community, and neighborhood to the building (Owens 1992). Also, passive solar design techniques can be applied to both new buildings and existing buildings through retrofitting. In a nutshell, sustainable urban design has a tremendous potential for reducing the environmental impacts of the built environment. Concerning the major themes evident in the current debates on passive solar design, the sustainability effects the latter entails include the following: • • • • • •

Influencing building heat gains and losses; Influencing warming and cooling processes; Influencing evaporative cooling processes on building surfaces and/or in open spaces; Influencing absorption, heat storage, and emissivity; Influencing airflow, view of sun and sky, and exposed surface area; Reducing and rerouting traffic to reduce air and noise pollution and heat discharge.

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3.2.8 High Standards of Environmental and Urban Management To achieve sustainable urban forms requires, in addition to the above typologies and design concepts, a range of urban management and development approaches, including, but not limited to, the following (Bibri 2018a): • • • • • • • •

Evaluation methods and tools; Simulation and operational models; Implementation procedures of strategies and plans; Environmental, social, institutional, and land use policy frameworks; Mechanisms for adherence to the regulatory frameworks; Platforms for providing public, social, and cultural services and facilitating access to related facilities; Methods for practice improvements and policy recommendations; Environmental management systems.

3.3 The Built Environment and Sustainable Urbanism: Issues and Prospects The built environment at various spatial scales has a broad spectrum of impacts on our lives. It involves physical, economic, social, and environmental impacts on both individuals and communities. It affects land use and soil contamination, energy and material depletion, water depletion, air and water pollution, waste generation, human health, and climate change. Responsible urbanization practices on micro- and macro-levels, coupled with advanced technologies, can mitigate or overcome the negative effects of the built environment. Therefore, there is a need for research and innovation endeavors aimed at creating a smart sustainable built environment that reduces resources consumption, minimizes waste, combats environmental degradation, and provides a better environment for living through the reconciliation of the sustainability pillars. With the discussion about it being revived by the emergence and widespread of the concept of sustainable development, coupled with the major stimulus given by this concept to the question of the contribution that certain urban forms might make to sustainability, the form of cities has been at the forefront of the minds of many scholars and practitioners in different disciplines as manifested in their motivation and inducement for seeking forms for human settlements that will meet the required level of sustainability and enable the built environments to function in a sustainably beneficial and constructive way. This entails continuously proposing new frameworks for redesigning and restructuring urban places to achieve sustainability toward developing more convincing and robust models. This pursuit is explicitly primarily justified by the form of the contemporary city being long perceived as particularly a source of environmental problems (e.g., Alberti et al. 2003; Beatley and Manning 1997; Jabareen 2006). In Our Built and Natural Environment, the EPA (2001) concludes that the urban form directly affects ecosystems, habitat, endangered species, and water and air quality through habitat fragmentation, land consumption, and replacement of natural cover with impervious surfaces. In addition, urban form affects soil pollution and contamination; global climate and noise; premature loss of farmland, wetlands, and open space; and travel behavior, which, in turn, affects air quality (Cervero 1998). Also, GHG concentrations are accumulating at an alarming rate, owing to the excessive use of fossil fuels around the world. Overall, the existing built environment is associated with numerous environmental, social, and economic impacts, including unsustainable energy use and concomitant GHG emissions, materials depletion, increased air and water pollution, land use haphazard, environmental degradation, inappropriate urban design and related social deprivation and community disruption, increased transport needs and traffic congestion, ineffective mobility and accessibility, and public safety and health decrease (Bibri 2018a; Bibri and Krogstie 2017a). The effects of the built form are expected to worsen with the increasing urbanization (Bibri 2018a). Urban growth gives rise to numerous challenges associated with intensive energy consumption, endemic congestion, saturated transport networks, air and water pollution, toxic waste disposal, resources depletion, social inequality and vulnerability, public health decrease, and so on (Bibri and Krogstie 2017a). In short, it raises a variety of problems that tend to jeopardize the environmental, economic, and social sustainability of cities. Therefore, urbanization as a dynamic clustering of people, buildings, infrastructures, and resources puts an enormous strain on the built environment and the underlying systems and processes, i.e., urban infrastructures and their operational functioning and planning. All in all, contemporary cities are increasingly under pressure due to the unsustainability of their form (e.g., Bibri 2018a; Bibri and Krogstie 2018). Sustainable urbanism has emerged as powerful approach to avoiding the dire prospects for the future and to acting collectively to alter energy-dependent practices and lifestyles in cities, among others. Urgent changes are needed in the

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design and planning of the built form. Sustainable urban planning and development represent a process of change in the built environment, which promotes the health of citizens, communities, and natural ecosystems and fosters economic development while conserving resources in the face of urbanization. The way forward for cities to better cope with the restructuring and changing conditions is to adopt the long-term approach that emphasizes sustainability (Bulkeley and Betsill 2005). While the concept of sustainable development has enhanced urban forms with the planning principles and ecological design of sustainability (Jabareen 2006), smart development as being predominately driven by big data computing has recently revived the debate over urban form and is attempting to enhance existing sustainable urban forms by smartening up the performance of the underlying design concepts and typologies in various ways (Bibri 2019b). It has become of high pertinence and importance to augment sustainable urban forms with big data technology and its novel applications—so as to boost this performance in terms of sustainability (Bibri and Krogstie 2017b).

3.4 Limitations, Inadequacies, Fallacies, Uncertainties, Challenges, and Prospects To begin with, scholars and practitioners from different disciplines and professional fields have, over the past three decades or so, sought a variety of sustainable urban forms that could contribute to sustainability over the long run in response to the rising concerns about the environment and the changing socio-economic demands and needs (Bibri and Krogstie 2017a, b). The compact city (e.g., Jenks et al. 1996a, b; Hofstad 2012; Neuman 2005) and the eco-city (e.g., Joss 2010, 2011; Joss et al. 2013; Register 2002) are the most prevalent models of sustainable urban form, as well as often advocated as more sustainable (e.g., Bibri 2018a; Jabareen 2006; Kärrholm 2011; Van Bueren et al. 2011; Rapoport and Vernay 2011). These models are compatible and not mutually exclusive, but there are some distinctive concepts and key differences for each one of them (Jabareen 2006). However, the challenge of meeting the goals of sustainable development has induced scholars, planners, policymakers, international organizations, civil societies, and governments to propose these models, among others, as a way of redesigning and restructuring urban areas to achieve sustainability. In this respect, these models have been addressed on different spatial levels, including the regional level, the metropolitan level, the city level, the community level, the neighborhood level, and, in relation to the environmental dimension of sustainability, the building level. However, the underlying challenge continues to induce researchers, practitioners, and decision-makers to work collaboratively to enhance existing models across several spatial scales to achieve the requirements of sustainability and, ideally, to integrate its physical, environmental, economic, social, and cultural dimensions (Bibri 2018a; Bibri and Krogstie 2017a, b). The ultimate goal of the endeavor is to develop more robust models of sustainable urban form. This has indeed been one of the most significant intellectual and practical challenges for more than three decades (e.g., Bibri 2018a; Bibri and Krogstie 2017a, b; Jabareen 2006; Kärrholm 2011; Neuman 2005; Rapoport and Vernay 2011; Williams 2009). As concluded by Jabareen (2006, p. 48) after analyzing a distinctive set of the design concepts and principles and planning practices characterizing compact cities and eco-cities as models of sustainable urban form, among others, and how these can be compared and classified in terms of their contribution to sustainability, ‘neither academics nor real-world cities have yet developed convincing models of sustainable urban form and have not yet gotten specific enough in terms of the components of such form.’ This implies that it has been a challenging task to translate sustainability into the built form and, thus, evaluate the extent to which existing models of sustainable urban form contribute to the goals of sustainable development. Indeed, it is not evident which of these models are more sustainable and environmentally sound, although there seems to be in research on sustainable urban forms and anthologies a consensus on topics of relevance to sustainability (e.g., Bibri and Krogstie 2017b). In line with this argument, a critical review of such forms demonstrates a lack of agreement about the most desirable form in the context of sustainability (e.g., Jabareen 2006; Tomita et al. 2003; Williams et al. 2000). Besides, it is not an easy task to ‘judge whether or not a certain urban form is sustainable’ (Kärrholm 2011, p. 98). Even in practice, many governments, planning experts, landscape architects, and so on are grappling with the dimensions of models of sustainable urban forms by means of a variety of design, planning, and policy approaches (Jabareen 2006; Kärrholm 2011). In addition, there is a lack of theory that can serve to compare different forms according to their contribution to the goals of sustainable development, as well as to evaluate whether a given urban form contributes to sustainability (Jabareen 2006). The main argument is that not only in practice, but also in theory and discourse, has the issue of sustainable urban form been problematic and difficult to deal with as manifested in the kind of the non-conclusive, limited, conflicting, contradictory, uncertain, and weak results of research obtained (Jabareen 2006; Kärrholm 2011; Neuman 2005; Williams 2009), particularly when it comes to the actual effects of the benefits of sustainability as assumed or claimed to be achieved by design concepts and principles and planning practices. Conclusively, ‘yet knowing if we are actually making any progress towards sustainable cities is problematic. In one sense, so much has been achieved in raising the profile of sustainability and

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sustainable cities over the last 30 years that the rate of change is inspiring … We seem to be going backwards to the extent that it is hard to see where there is any room for optimism. Urban problems … are becoming more acute as populations rise and resources become scarcer’ (Williams 2009, p. 2). In addition, the conventional sustainable urban planning approach alone is no longer of pertinence as to ensuring or maintaining the effectiveness of sustainable urban forms with regard to the operation, function, and management of urban systems, as well as the integration and coordination of urban domains, in the context of sustainability due to the issues being engendered by the rapid urbanization. In relation to this argument, Neuman (2005) contends, in reference to the fallacy of compact cities, that conceiving cities in terms of forms remains inadequate to achieve the goals of sustainable development; or rather, accounting only for urban form strategies to make cities more sustainable is counterproductive. Instead, conceiving cities in terms of ‘processual outcomes of urbanization’ holds great potential for attaining these goals, as this involves asking the right question of ‘whether the processes of building cities and the processes of living, consuming, and producing in cities are sustainable,’ which raises the level of, and may even change, the game (Neuman 2005). The underlying argument is that while the layout or urban form can influence the environmental impact, it is rather the people and their behavior that ultimately determine the negative or positive environmental impact of urban areas. Monitoring, understanding, and analyzing the latter set of processes, in particular, can well be enabled by big data analytics as an advanced form of ICT to further improve sustainability. Indeed, Townsend (2013) portrays urban growth and ICT development as a form of symbiosis, to reiterate. In other words, an increasing urgency to find and apply smart solutions is driven by the rapid urban growth in terms of seeking out ways to address and overcome the associated challenges and ensuing effects pertaining also to sustainability (Bibri and Krogstie 2017a; Nam and Pardo 2011). However, the process-driven perspective as to be enabled by big data technology paves the way for a more dynamic conception of urban design and planning that reverses the focus on urban forms governed by static design and planning tools (Bibri and Krogstie 2017a). This holds more promise in attaining the elusive goals of sustainable development (Neuman 2005). Existing models of sustainable urban form as to the underlying typologies and design concepts tend to be static and fail to account for changes over time (Bibri and Krogstie 2017a, b). All these arguments provide valid reasons to strongly believe that the conclusion they support is true. This conclusion entails that it is timely and necessary to apply the innovative solutions and sophisticated approaches being offered by big data technology to deal with the challenges of sustainability and urbanization, including the way they affect one another in the context of compact city and eco-city models. Besides, a well-established fact is that cities evolve and change dynamically as urban environments, so too is the underlying design and planning knowledge that perennially changes in response to new emergent factors and changes. Explicitly, cities need to be dynamic in their conception, scalable in their design, efficient in their operational functioning, and flexible in their planning in order to be able to deal with population growth, environmental pressures, changes in socio-economic needs, global shifts/trends, discontinuities, and societal transitions (Bibri 2018a). For example, Durack (2001) argues for open, indeterminate planning due to its advantages, namely the tolerance and value of topographic, social, and economic discontinuities; continuous adaptation; and citizen participation, which is common to human settlements. This can best be attainable through incorporating the advanced forms of ICT in the approaches to urban design and planning due to their innovative, disruptive, substantive, and synergistic effects. Furthermore, in urban planning and policymaking, ‘the concept of sustainable city has tended to focus mainly on infrastructures for urban metabolism—sewage, water, energy, and waste management within the city’ (Höjer and Wangel 2015, p. 3), and thereby fall short in considering smart solutions and sophisticated methods in relation to the operation, function, and planning of technical urban systems (Bibri 2018a; Bibri and Krogstie 2017b). The concept of urban sustainability has long been promoted by systems scientists using the pragmatic framework for urban metabolism; smart urban metabolism as an ICT-enabled evolution of such framework is being implemented to overcome some of its limitations in the context of eco-city (Shahrokni et al. 2015). The purpose is to assess and sustain the levels of sustainability of this urban form. In this regard, urban metabolism involves collecting, processing, and analyzing a large amount of data on the use of material and energy resources as well as waste generation. This is to compute the ecological footprint and then identify and suggest alternative routes of development that would reduce such footprint while ushering in new relations with the immediate surrounding lands and water. This relates to the concepts of ecosystem services, urban technical systems, the various urban sustainability principles, as well as the distribution of functions and the population in the city. Overall, there are several critical issues that remain unresolved as well as underexplored for applied purposes with regard to the extent to which the challenges of urban sustainability can be addressed and also the contribution to it can be continuously improved and thus maintained, despite the promotion of sustainable cities as a desirable goal within the context of policy and planning. In relation to this, Williams (2009) identifies two fundamental, critical, and interesting challenges pertaining to policies and monitoring strategies. The first is, the challenge of ‘the vision’: Do we know what ‘the sustainable city’ is? And the second is, the challenge of change: Do we know how to bring about ‘sustainable urban development’? The

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latter entails developing a deeper understanding of the multifaceted processes of change required to achieve more sustainable cities. This relates to the view that there are multiple processes of sustainable urbanism, and hence multiple visions of, and pathways to achieving, the sustainable city. On this note, Williams (2009, p. 3) adds that if we understand and respect this view, ‘then we need to accept that making our cities more sustainable (through sustainable urban development processes) will be dependent on a similarly wide-ranging selection of actions. Some actions will be ‘top–down’ and require strong leadership and, perhaps, large-scale investment programmes, other changes may be bottom–up, and rely on … shifts in behavior. These changes shift in prominence in any given place, and will happen at different paces …, and at difference spatial scales.’ In the above line of thinking, it seems that the eco-city and the compact city as instances of sustainable cities are relatively well understood as a way of practically applying existing knowledge about what makes a city sustainable to the design and planning of new and existing cities or districts. Notwithstanding this dominant view in the prescriptive literature, what seems to prevail in research about the relationship between urban design and planning interventions and sustainability objectives is a subject of much debate (Bibri and Krogstie 2017a; Bulkeley and Betsill 2005; Williams 2009). This means that realizing an eco-city requires making countless decisions about sustainable (green) technologies, urban layouts, building design, and governance (Rapoport and Vernay 2011), just like the case for compact city (see Kärrholm 2011; Van Bueren et al. 2011). Furthermore, several studies (e.g., Guy and Marvin 1999; Jabareen 2006; Rapoport and Vernay 2011; Van Bueren et al. 2011; Williams 2009) point to the issue of diversity underneath the various uses of the terms eco-city and compact city and shed light on the extent of divergence on the way projects and initiatives conceive of what eco-city and compact city models should be or look like. Indeed, in relation to the compact city, there are great differences between cities in terms of their urban form whose key elements can be distinguished: density, surface, land use, public transport infrastructure, and the economic relationship with the surrounding environment (Van Bueren et al. 2011), to reiterate. Similarly, Rapoport and Vernay (2011) determine the differences in the way projects and initiatives conceive of what an eco-city should be. Guy and Marvin (1999) address the issue of the different models and pathways in terms of the diversity of sustainable urban futures. Williams (2009) offers a conceptualization of multiple pathways and processes of sustainable urbanism and argues that a move to a deeper understanding of the interplay between social and technical solutions for sustainable cities is required. The point at issue is that there is a great deal of heterogeneity among city initiatives and projects that are considered to be sustainable cities. This goes beyond their ambition to include their vision of what the future of sustainable urban development should be about (Bibri and Krogstie 2017a). Yet, there is a need for recognizing that these multiple pathways and processes of sustainable urbanism need some coherence of purpose. Or else, there will be no conceptual ‘anchor’ due to the continuing conflicts and contradictions within sustainable urbanism thinking and practice, where sustainability principles, environmental and natural resources use, and equity remain highly useful and relevant. Consequently, such pathways and processes might be perceived as an alphabet soup of a set of conceptualizations and operationalizations and consequently result in a cacophony that may lead to an exasperating confusion within the domain of sustainable urbanism. Notwithstanding this, the understanding of the multiplicity and diversity of socially constructed visions of sustainable urbanism is at the heart of advancing research and practice, as long as it is driven by some coherence of purpose. In this respect, it has been interesting to witness how many socio-culturally specific ideas have been replicated in different locations across the world, with little consideration or investigation of their appropriateness (see, e.g., Williams 2004, 2009). As asserted by Guy and Marvin (1999, p. 273), ‘the role of research is to keep alive a multiplicity of pathways by opening a wider discourse and dialogue about the types of future we might be able to create.’ Regardless, conceptualizing and operationalizing sustainable urbanism over different temporal and spatial contexts are complex and infused with politics whose issues have indeed become the focus of much debate and will continue undoubtedly as such over the next decade. In addition, it has been argued that, in compact city planning practices, the economic bias tends to be not balanced by goals in support of environmental and social sustainability. Focusing on the social, environmental, and economic goals linked to densification and mixed-land use development in his investigation of urban plans in four Scandinavian cities Hofstad (2012, p. 2012) concludes that it is only ‘on a discursive level that the social, environmental and economic goals are represented in compact city strategies. Institutionalized practices … show that economic goals remain at the core of planning. Environmental and social aims still play second fiddle, but new measures are in development that may gradually strengthen their influence over urban development practices,’ thereby leaning toward supporting the balancing of the goals of sustainable development. In relation to the ongoing efforts for smartening up sustainable urban forms using big data analytics and its application, Bibri (2018a) points out that one of the key scientific and intellectual challenges pertaining to smart sustainable urban forms is to relate the underlying design concepts and typologies and thus urban infrastructures to their operational functioning and planning through control, automation, management, and optimization. This relates to new urban intelligence functions as

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new conceptions of the way such forms can function and utilize the complexity sciences in fashioning new powerful forms of simulation models and optimization and prediction methods (on the basis of big data analytics) that generate urban forms and structures that advance existing design concepts and principles and planning practices and hence sustainability, efficiency, equity, and the quality of life (e.g., Bibri 2018a; Bibri and Krogstie 2017b). All in all, there will always be challenges to overcome and hence improvements to realize in the field of sustainable cities, and this has much to do with the perception underlying the conceptualization of progress concerning sustainable cities. As argued by Williams (2009, p. 3), it is the conceptualization of sustainable cities in terms of progress that lies at the heart of most of the underlying challenges, which centers around what we think we are aspiring to, what we assess ‘progress’ to be, and what changes we want to make. Bibri (2018a) identifies many gaps and issues within the flourishing field of smart sustainable urban forms, the most relevant among which to this chapter are the following: • There is a need for solidifying existing applied theoretical foundations in ways that provide an explanation for how the contribution of sustainable urban forms to sustainability can be improved and maintained on the basis of big data analytics and its application. • There is no strategic framework for merging the informational and physical landscapes of existing models of sustainable urban form or their integration in an increasingly technologized, computerized, and urbanized world. • Sustainable urban forms remain static in conception, unscalable in design, inefficient in operational functioning, ineffective in planning without advanced ICT in response to urban growth, environmental pressures, changes in socio-economic needs, global trends, discontinuities, and societal transitions. • Sustainable urban forms fall short in considering smart solutions within many urban systems and domains where such solutions could have substantial contributions to the different aspects of sustainability. The main argument in the ongoing debate over sustainable urban forms as instances of sustainable cities is that urban systems are in themselves very complex in terms of operational functioning, management, planning, and development, so too are urban domains in terms of coordination and integration as well as urban networks in terms of coupling and interconnection. Therefore, it is timely and necessary again to develop and employ innovative solutions for solving, and sophisticated approaches into dealing with, the challenges of sustainability and, to add, the potential effects of urbanization in the context of sustainable urban forms. This requires a blend of sciences for creating powerful design and engineering solutions, which ICT is extremely well placed to initiate, as its application to urban systems, domains, networks, as well as the associated processes is founded on computer science, data science, and complexity science (e.g., Batty et al. 2012; Bibri 2018a, c; Bettencourt 2014). Indeed, the role of ICT-enabled solutions in improving the different aspects of sustainability is becoming evident in light of the ongoing endeavors to advance both sustainable cities and smart cities (see, e.g., Al Nuaimi et al. 2015; Batty et al. 2012; Bibri and Krogstie 2017b; Bettencourt 2014; Kramers et al. 2014; Shahrokni et al. 2015).

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Toward Smartening up Sustainable Cities: Driving Factors and Conceptual and Analytical Frameworks

The research on smart sustainable cities of the future is garnering increased attention and rapidly burgeoning, and its status is consolidating as one of the most enticing areas of research today, especially in ecologically advanced nations, making the relevance and rationale behind the smart sustainable city debate of high significance and value with respect to the future form of urbanism (Bibri 2019b). Smart sustainable cities as an integrated and holistic approach aim primarily at substantiating and strengthening the growing potential and role of advanced ICT in enabling sustainable cities to improve, advance, and maintain their contribution to the goals of sustainable development and, thus, to rise to the challenges of urbanization (Bibri 2018a, 2019b). With that in mind, the way forward for developing and realizing smart sustainable cities is through amalgamating the sustainable city and smart city approaches and strategies, a process which typically takes various forms depending on several factors, including objectives, requirements, resources, and interpretations, as well as the social, cultural, national, and local contexts in which these factors arise and are embedded. This is to achieve the optimal level of sustainability with respect to such urban aspects as operations, functions, services, designs, strategies, and policies as manifestations of processual outcomes of urbanization, irrespective of the ambition of the projects and initiatives considered to be smart sustainable cities, which there will indeed be multiple ways to achieve (Bibri 2019b).

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As an advanced form of emerging and future ICT of pervasive computing, big data computing and the underpinning technologies are clearly on a penetrative path across all the systems and domains of smart sustainable cities that rely on sophisticated technologies in relation to their operational functioning, management, planning, development, and governance (Bibri and Krogstie 2018, 2017b). This is manifested in the proliferation and increasing utilization of the core enabling and driving technologies of big data analytics within those cities badging or regenerating themselves as smart sustainable or smart/sustainable for storing, managing, processing, analyzing, and sharing colossal amounts of urban data for the primary purpose of extracting useful knowledge in the form of applied urban intelligence functions and urban simulation models (Bibri 2019b). Big data are regarded as the most scalable and synergic asset and resource for modern cities to enhance their performance on many scales. Indeed, they have become the fundamental ingredient for the next wave of urban analytics and planning. We live in a world where ICT has become deeply embedded and interwoven into the very fabric of contemporary cities, i.e., the operating and organizing processes of urban life and thus urban systems and domains are dominated by data and pervaded with information intelligence and high levels of automation and computation (Bibri 2018a, 2019b). It follows that it is high time for sustainable cities to smarten up as an effective way to meet the optimal level of sustainability. For such cities to improve, advance, and maintain their contribution to the goals of sustainable development, they need to leverage their informational landscape by embracing what emerging and future ICT has to offer to make urban living more intelligently sustainable and attractive over the long run in an increasingly computerized and urbanized world (Bibri and Krogstie 2017b). This is predicated on the assumption that emerging and future ICT offers tremendous potential for, and unsurpassed ways of, monitoring, understanding, analyzing, and planning such cities (Bibri 2018a, b, 2019a, b), which can be beneficially directed toward addressing the rather complex challenges of sustainability and the effects of urbanization within. The quest for, and the challenge of, finding more effective ways to merge the physical and informational landscapes of the emerging smart sustainable cities in the form of a more robust model in ways that can continuously improve, advance, and maintain their contribution to the goals of sustainable development and advance their sustainability is currently motivating, inducing, and inspiring many researchers, scholars, academics, and practitioners within smart sustainable urbanism, as well as real-world cities (Bibri 2019b). Building such model will play a pivotal role in laying the foundation for and spurring the development, implementation, and deployment of such cities. This will in turn stimulate its replication in different places around the world, thereby mainstreaming this drastic urban transformation. Toward this end, Bibri and Krogstie (2017b) attempt to integrate the typologies and design concepts and principles underlying sustainable cities with the big data applications being offered by smart cities of the future from a conceptual perspective. Their work is in response to the need as expressed in academic and urban circles for smartening up such cities as an effective way to enhance, advance, and maintain their contribution to the goals of sustainable development toward achieving the optimal level of sustainability. Especially, new ICT is increasingly becoming spatially all-pervading, located anywhere and everywhere across urban environments, to reiterate, thereby providing the necessary basic infrastructure backbone for sustainable cities to realize their full potential through innovative solutions in terms of enhancing and optimizing their operations, functions, designs, services, strategies, and policies in line with the vision of sustainability. In addition, their study has been developed further as part of a comprehensive study by adding an analytical framework (see Fig. 1) in support of the conceptual framework: Chap. 7 of the recently published book authored by Bibri (2018a). The intent of conducting this study is to respond to the need for conceptual and analytical frameworks for merging the physical and informational landscapes of smart sustainable urban forms as instances of smart sustainable cities of the future given that the latter is an emerging techno-urban phenomenon. It is moreover to spur their development and deployment based on big data computing. The aim of Bibri’s (2018a, pp. 371–372) study is twofold. ‘First, it intends to examine and substantiate the potential of big data … computing to improve urban sustainability. This entails integrating the big data … applications of smart sustainable cities with the typologies and design concepts of sustainable urban forms to achieve multiple hitherto unrealized goals, or in ways that intelligently improve the contribution of sustainable urban forms to the goals of sustainable development. In doing so, a conceptual framework in the form of a matrix of smart sustainable urban form [is offered] to help planners and scholars in understanding and analyzing how the contribution of such form to sustainability can be improved with support of advanced … ICT. Second, it explores the opportunity of merging the physical and informational landscapes of smart sustainable cities to achieve the goals of sustainable development. Accordingly, an analytical framework is proposed, in which the components of the physical landscape of sustainable urban forms and those of the informational landscape of smart sustainable

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Fig. 1 An analytical framework for merging the physical and informational landscapes of smart sustainable urban forms based on big data technology and its novel applications. Source Bibri (2018a)

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cities are identified on the basis of a thematic analysis, and then merged together to enable and support data-centric … applications across urban … domains in the context of sustainability.’ The study identifies the most influential technology and its novel applications pertaining to smart sustainable cities, as well as three design concepts and four typologies related to sustainable urban forms. In addition, Bibri (2018b) addresses the role of the IoT as a form of ubiquitous computing in smart sustainable cities of the future by developing an analytical framework for sensor-based big data applications for environmental sustainability. The IoT is one of the key components of the ICT infrastructure of smart sustainable cities due to its unique potential for advancing environmental sustainability. Being associated with big data analytics and its application, the IoT plays a key role in optimizing resources utilization and efficiency and thus mitigating environmental impacts in various ways and at different levels. With that in regard, the key aims of Bibri’s (2018b, p. 230) study are: ‘to review and synthesize the relevant literature with the objective of identifying and discussing the state-of-the-art sensor-based big data applications enabled by the IoT for environmental sustainability and related data processing platforms and computing models in the context of smart sustainable cities of the future,’ and to explore ‘the opportunity of augmenting the informational landscape of smart sustainable cities with big data applications to achieve the required level of environmental sustainability.’ Important to note, most of these applications are based on real-world examples of projects and initiatives as case studies. The proposed analytical framework, which can be tested, evaluated, and improved in empirical research, will add an additional depth to the studies being conducted in this direction in the field of smart sustainable cities. Figure 1 shows a combination of technological components (namely big data sources, repositories and storage facilities, cloud computing infrastructure and data processing platform, and big data applications) and urban components (namely typologies and design concepts, urban systems and domains, and urban entities and their activities). For a descriptive account of these components, the interested reader can be directed to Bibri (2018a).

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The Key Practical and Analytical Applications of Big Data Technology for the Multiple Systems and Domains of Smart Sustainable Cities

Sustainable cities are increasingly being permeated with big data technologies and their novel applications in terms of their domains (Bibri 2018a, b; Bibri and Krogstie 2017b). This can be seen as a new ethos added to the era of sustainable urbanism in response to the rise of ICT and the spread of urbanization as major global shifts at play today. The characteristic spirit of the smart sustainable urbanism era is manifested in the behavior and aspiration of sustainable cities toward embracing what big data computing has to offer in order to bring about sustainable development and achieve sustainability under what is labeled ‘smart sustainable cities of the future.’ This is due to the tremendous potential of this advanced form of ICT for adding a whole dimension to sustainable urbanism in an increasingly technologized and urbanized world. The range of the emerging big data applications as novel analytical and practical solutions that can be utilized in this regard is potentially huge, as many as the case situations where big data analytics may be of relevance to enhance some sort of decision or insight in connection with the domains or sub-domains of sustainable cities. In the sequel, the most common big data applications are identified and enumerated in relation to the key domains or sub-domains of sustainable cities, and their sustainability effects are elucidated, which are associated with the underlying functionalities pertaining to urban operations, functions, services, designs, strategies, and policies in the context of such cities (see Table 4). However, they are by no means, or intended to be, exhaustive. Moreover, they are synthesized and distilled from many studies conducted in more recent years, the most notable of which in order of priority in terms of their contribution to the synthesis and extracted essential meaning below are: Bibri (2018a, b, 2019a), Batty et al. (2012), Angelidou et al. (2017), and Al Nuaimi et al. (2015), including the other works that are referenced (credited) within such studies. Of relevance to add, as to the technical processes, tools, and other details underpinning the functioning of big data applications, the interested reader can be directed to Bahga and Madisetti (2016), one of the many books available out there on the topic, for a detailed account from a general perspective, and to Bibri (2018a) and Bibri and Krogstie (2017c) for an overview focusing mainly on smart sustainable cities.

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Table 4 Key analytical and practical applications of big data technology for multiple systems and domains of smart sustainable cities Smart sustainable city domains

Big data applications: operations, functions, services, designs, strategies, and policies, in addition to analytical questions and advanced forms of knowledge

Transport

Monitoring and analyzing road conditions and traffic jams to detect accidents early on and then quickly responding to them by providing alerts and road assistance, thereby reducing or avoiding them and ensuring safety to drivers Controlling traffic flows and predicting traffic conditions with the aim to reduce roads’ congestion by opening new roads and directing vehicles to alternative ones, thereby improving traffic patterns as well as enhancing or re-engineering transport infrastructure on the basis of historical congestion data Using open-source frameworks to implement large-scale agent-based simulation models where different scenarios can be supported by these models, such as air pollution from traffic Explaining why traffic significantly varies from one hour or day to another even if demand profiles are similar and how and the extent to which this may affect energy consumption patterns and concomitant GHG emissions levels accordingly Predicting spatiotemporally the development and propagation of traffic congestion with small errors and explaining how the severity of these effects can be stronger in case of non-recurrent events (e.g., accidents), as well as how this can affect the productivity and resilience of transportation systems Helping to understand whether or the extent to which the real urban traffic can be considered an equilibrium system, equilibrium conditions with small variations, with respect to cost functions as well as how people really make choices in transportation networks for long periods and how these choices affect the development and propagation of traffic congestion in such networks Providing effective ways to identify the macroscopic observables and control parameters that are of influence on individual decisions and integrating them in agent-based simulation models, based on the large number and variety of trajectories and disaggregated traffic data in different locations and of different sizes Modeling the traffic evolution under strong or significant changes of network topologies Calculating and analyzing the costs and environmental impacts of the transportation choices or decisions of people, combining all modes of transit Interconnecting various components of transportation systems (vehicles, infrastructure, drivers, roads, networks, parking spaces, etc.) for enhancing the control, management, and optimization of different processes (in relation to, e.g., energy efficiency, GHG emissions, land use) Providing location-based services related to onboard navigation systems, which allows effective use of existing transport infrastructure and network and thus cost- and time-efficient routes. This in turn minimizes traffic congestion Addressing equity and inclusion issues in urban transport using smartphone apps and thus playing a key role in creating and mainstreaming socially sustainable urban transport Providing proximity-based services showing information when passengers really need it and thereby enabling them to choose different modes of transport in real time Enhancing transportation system efficiency by influencing personal travel behavior decisions using advanced platforms and smartphone apps Providing visibility into transit system performance based on cloud-based solution and helping cities make better decisions about transportation by combining big data and spatial analytics Advanced parking allows efficient management of multiple parking spaces using and integrating sensors, as well as access to real-time and historical data and making optimal use of parking resources Gathering, integrating, and delivering the data on parking spaces by combining Wi-Fi infrastructure with IP cameras, sensors, and smartphone apps and then providing visibility into parking analytics, including usage and vacancy periods, which can help with long-term city planning Enabling an integrated solution to the parking search problems, a location-based smart application which monitors and controls sensors deployed on the curb-side, and communicates the information in real time to the drivers

Mobility

Finding answers to many challenging analytical questions about travel or mobility behavior, such as: What is the spatiotemporal distribution of individual travel following the most popular itineraries? How do individual behave when approaching a key attractor, such as a central station and airport? How can we predict areas of dense traffic in the near future? How can we predict travel behavior in mixed-land use areas across spatial and over different temporal scales? How can we classify mobility behavior in high-density areas? How can we classify travel behavior according to some contextual variables (e.g., spatiotemporal setting)? How can we predict areas of frequent cycling and walking mode in the near future? How can we find useful travel behavior categories or collective mobility patterns? How can we find correlation between mobility modes and environmental and life quality indicators? Explaining how travel behavior or mobility mode is related to the network topology and how small or large perturbations in demand profiles and network characteristics affect the choices of individuals concerning routes, modes, and departure times. Explaining how urban design features and related planning tools affect the choices of people in terms of travel mode, behavior, and route and how this in turn affects social structures and economic networks (continued)

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Table 4 (continued) Smart sustainable city domains

Big data applications: operations, functions, services, designs, strategies, and policies, in addition to analytical questions and advanced forms of knowledge Gathering, integrating, and analyzing real-time mobility data and data from large-scale datasets that can simultaneously record and calibrate dynamical traces of individual and collective mobile movements across various spatial scales and over different temporal scales to understand the dynamic interplay between individual and collective mobility and social interactions Using big mobility data to scrutinize different spatiotemporal patterns together with the intensity and frequency of social interactions as well as social structures, thereby coupling mobility patterns and social networks, which can edge toward understanding and studying the evolutionary dynamics of cities as social spheres and their evolving borders Analyzing travel behavior and mobility modes together with transport systems and networks for discovering patterns, making correlations, and then acting upon the results by deploying them across different decision support systems Enabling new business models such as mobility-as-a-service, such as car sharing, bike sharing, and driver service (as well as premium parking and city parking) Enabling local authorities to monitor and respond to mobility in real-time manner Improving the different aspects of physical and virtual mobility for effective spatial and non-spatial accessibility to opportunities, services, and facilities Enabling complex knowledge discovery processes from the raw data of individual trajectories up to high-level collective mobility knowledge, capable of supporting the decisions of mobility and transportation administrators in relation to different aspects of sustainability Advancing the content of social media to extract useful information that might be linked to new schemes for mobility management. Social media data will come on stream that is likely to be more focused as social media technology becomes widespread Allowing seamless, efficient, and flexible travel across various modes, i.e., multimodal transport system. For example, a multimodal trip planner allows users to schedule transit, travel, and map information, and gives detailed step-by-step directions alongside interactive route maps and also details of public transport services required and transfer information Providing hassle-free usage of multiple modes of shared and public transport Enabling citizens to spend less time in traffic and more time for important things in life, to have flexibility to use the best-fitting transport mode, to enjoy safer and sustainable transport system, and to benefit from lower costs, as well as allowing cities to reduce or optimize the use of land resources

Energy

Finding answers to several analytical questions about energy usage levels and consumption patterns, such as: How can we predict energy consumption increase and decrease in the near future? How can we predict or characterize urban energy usage in dense and/or mixed-land use areas? How can we predict or characterize household energy consumption? How can we predict GHG emissions and their environmental impacts in the near future? How can we predict urban energy usage over different temporal scales? Allowing citizens to have access to live energy prices and to adjust their use accordingly Enabling the use of pricing plans in accordance with energy demand and supply models Reorganizing energy demand and supply using advanced pricing and billing mechanisms, based on the energy market and production Providing incentives to the users and consumers that save energy and creating other incentives to use renewable or carbon-neutral energy at a certain time by offering a better price for electricity on a windy or sunny day Self-optimizing and self-controlling energy consumption through integrating sensing and actuation systems in relation to different kinds of appliances and devices for balancing power generation and usage Enabling distributed energy systems to become self-managing and self-sustaining, as well as services in the energy market to become dynamically reorganized and coordinated Enabling new mechanisms for trade on the basis of supply and demand in the energy market Allowing consumers to manage their usage based on what they actually need and afford Enabling users to remotely control their home appliances and devices based on the IoT and providing them with advanced functions like scheduling, programming, and reacting to different contextual situations Controlling millions of connected distributed energy resources across the Internet using demand response optimization and management systems Allowing users and consumers to precisely estimate rooftop solar electric potential (PV panels) for almost every building by a simple click or by inputting an address using an interactive online rooftop solar mapping tool Enabling energy systems to gather and act on near real-time data on power demand, generation, and consumption from end-user connections (information about producers and consumers’ behavior) (continued)

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Table 4 (continued) Smart sustainable city domains

Big data applications: operations, functions, services, designs, strategies, and policies, in addition to analytical questions and advanced forms of knowledge

Power grid

Supporting decision-making pertaining to the generation and supply of power in line with the actual demand of citizens and other city constituents to optimize energy efficiency and thus achieve energy savings Optimizing power distribution networks associated with energy demand and supply Monitoring and analyzing energy consumption and GHG emissions levels in real time across several spatial scales and over different temporal scales, with the purpose to curb energy usage and thus mitigate environmental impacts, as well as enhancing the performance and effectiveness of the power system Managing distribution automation devices to improve the efficiency, reliability, and sustainability of power production and distribution Avoiding potential power outages resulting from high demand on energy using dynamic pricing models for power usage by increasing charges during peak times to smooth out peaks and applying lower charges during normal times Avoiding the expensive and carbon-intensive peaks in power grid using new ways of coordination with regard to the overall ensemble of users and consumers and provide dynamic pricing schemes Enabling power distribution based on a community or neighborhood model instead of a broadcasting model Improving coordination and planning around power generation from renewable energy plants depending on wind or sun, as good estimations of power generation from wind, solar panels, and photovoltaic plants can be made in advance

Environment

Improving the environment through increasing air quality and reducing noise pollution and GHG emissions by deploying and setting up stations across the city as well as mounting sensors on bike wheels and cars for measuring and analyzing air data and acting upon the obtained results Providing information about air quality extracted from cities’ preexisting environmental monitoring networks using Web applications, a rapid and effective technological answer to the needs of people with special sensitivity to environmental allergies Connecting data, citizens, and knowledge to serve as a node for building open indicators and distributed tools, and thereafter the collective construction of the city for its own inhabitants, using an open-source platform for crowd-sourced environmental monitoring Predicting future environmental changes based on spatial and temporal geographical maps and detecting natural disasters to save lives and resources Removing many types of pollutants detrimental to the pubic health through pervasive sensors deployed for detecting pollution in the air and water systems Monitoring the urban climate and analyzing related data to discover the origins of GHG emissions, as well as measuring and monetizing cities’ CO2 emissions by combining satellites and ground sensors’ data

Buildings

Monitoring and optimizing the operational energy use within residential, industrial, public, and commercial buildings by means of an integrated system of sensors and actuators associated with the mechanical, electrical, and electronic systems of heating, ventilation, and air-conditioning (HVAC). This can even be more effective if implemented across several spatial scales and over different time spans Monitoring and managing the environmental conditions in buildings as well as demand control ventilation and control temperature, in addition to the energy system performance Minimizing heat/cooling losses and monitoring CO2 emission levels Managing window and door operations and providing lighting based on occupancy schedules Allowing the digital and physical objects in buildings to, based on a sensor and actuator system, process data, self-configure, and make independent decisions pertaining to their operations and functions by reacting to the physical environment Building energy benchmarking through visualization tools that make it possible to view energy usage for individual buildings using maps, charts, and statistics to hone in on a region of interest and view energy usage

Infrastructures

Monitoring and controlling the operations and structural conditions of urban infrastructures, including roads, railway tracks, bridges, tunnels, power grids, and water systems to minimize risk, decrease cost, and ensure safety and service quality, thereby improving incident management, emergency response coordination, service efficiency, and operational costs reduction Allowing for scheduling repair and maintenance activities in an efficient manner by coordinating tasks between different service providers and operators of urban infrastructures and facilities Monitoring, managing, and enhancing waste and water systems and related distribution networks Relating urban infrastructures effectively to their operational functioning through control, automation, optimization, and management enabled by data analytics Smart waste systems designed for public spaces, which comprise modular components that enable cities to deploy waste and even compost stations that respond to the needs of each station’s locations Increasing efficiency and transparency in waste management based on sensor solutions, through tracking container fill levels and optimizing pickup routes, thereby reducing the environmental footprint of the waste (continued)

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Table 4 (continued) Smart sustainable city domains

Big data applications: operations, functions, services, designs, strategies, and policies, in addition to analytical questions and advanced forms of knowledge Enabling a dynamic routing system for waste management using software tools and sensors to lower costs of services by building, delivering, and analyzing the most efficient routes for a fleet Using simulation models to estimate water supply and demand. Users can explore how water sustainability is influenced by different scenarios of regional growth, climate change impacts, drought, and water management policies A cloud-based platform for data-driven water demand management intended, which maximizes water-use efficiency and improve financial forecasting accuracy through engaging citizens Smartening up urban metabolism by collecting, processing, and analyzing a large amount of data pertaining to the use of material and energy resources as well as waste generation and then identifying and suggesting alternative routes of development that would reduce the ecological footprint of the city while ushering in new relations with the immediate surrounding lands and water

Urban planning

Relating the urban infrastructure to its planning through monitoring, analysis, modeling, simulation, prediction, and intelligent decision support associated with engineering, strategy development, and policy design Fully integrating urban systems, coordinating urban domains, and coupling urban networks to enhance land use and development, optimize resource utilization, reduce city costs, and streamline processes Integrating urban systems in terms of operations, functions, services, strategies, and policies for more effective and efficient functioning, management, and planning Helping cities quickly identify underperforming domains, evaluating improvement and cost-saving potential, and prioritizing domains and actions for energy and performance efficiency interventions using decision support tools Developing intelligence functions for the efficiency of energy systems, the improvement of transport and communication systems, the effectiveness of distribution networks, the optimal use and accessibility of facilities, and the optimization of ecosystem and human service provision Using urban simulation models to aid urban planners and strategists in understanding under what conditions urban systems and domains may fail to deliver or underperform at the level of sustainability and what to do about it Using advanced modeling and simulation systems to predict changes and forecast potential problems and accordingly to enhance current designs, mitigate environmental impacts, and avoid public health risks Predicting population growth and socio-economic changes and needs and thus devising more effective strategies in terms of seamlessly integrating advanced technologies and sustainable urban design and planning principles Grouping, characterizing, and profiling citizens in relation to sustainable lifestyles for inducing behavioral changes and improving the quality of life and well-being Enabling joined-up and integrated planning which allows system-wide effects to be tracked, understood, analyzed, and built or integrated into the very designs and responses that characterize urban operations, functions, and services Analyzing policies and their impact and effectiveness with the aim to improve or change them according to new social and urban trends and major global shifts Enabling space–time convergence in planning (and design) methods based on sophisticated simulation models using computer models of various kinds that operate at various spatial scales and over different time spans as to predicting changes and understanding how cities function in connection with land use, densification, public transport, location of physical activities, and so on Enabling short termism in city planning—what takes place in cities measured, evaluated, modeled, and simulated over days or months instead of years or decades

Urban design

Monitoring, analyzing, and evaluating the environmental and social performance of urban sustainability strategies (typologies and design concepts) in terms of the extent to which they contribute to sustainable development goals Analyzing and evaluating the relationship between individual and collective mobility and environmental and socio-economic performance assumed to be achieved through urban sustainability strategies, i.e., spatial and urban proximity, contiguity, agglomeration, and/or connectivity Enhancing the performance and practicality of urban sustainability strategies through augmenting them with smart applications and services or improving their integration based on different spatial scales using simulation models Optimizing sustainable urban design in terms of the principled set of organized and coordinated spatial patterns and structures and physical arrangements with regard to the contribution to sustainable development goals Informing future designs on the basis of predictive insights and forecasting capabilities enabled by the aggregated urban simulation models of different situations of urban life thanks to the recent advances in, and pervasiveness of, sensor technologies and their ability to provide information about medium- and long-term changes Facilitating the application of systems thinking and complexity sciences to solve the existing wicked problems associated with sustainable urban design, such as the distribution of sustainable typologies across several spatial scales Allowing citizens to view the location and size of their city’s trees, submit information to help tag them, and advocate for more trees in their area, based on an interactive Web application that measures cities’ green spaces. This relates specifically to greening which is a key concept of sustainable urban design (continued)

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Table 4 (continued) Smart sustainable city domains

Big data applications: operations, functions, services, designs, strategies, and policies, in addition to analytical questions and advanced forms of knowledge

Academic research

• Overcoming the limitations of ‘small data’ studies associated with such data collection and analysis methods as surveys, focus groups, case studies, participatory observations, interviews, content analyzes, and ethnographies, including high cost, infrequent periodicity, quick obsolescence, inaccuracy, incompleteness, as well as subjectivity and biases • Overcoming the inherent deficiencies of limited samples of data that are tightly focused, time- and space-specific, restricted in scope and scale, and relatively expensive to generate and analyze, which affects the robustness of research results • Drastically changing the way the research data can be collected, processed, analyzed, modeled, and simulated within various academic and scientific research domains so as to make decisions easier to judge and more fact-based in relation to urban operations, functions, strategies, plans, policies, and other practices • Completely redefining urban problems and understanding them in new ways, as well as enabling entirely novel ways to tackle them, thereby doing more than just enhancing existing practices, especially in relation to sustainability • Transforming and advancing knowledge based on the deluge of urban data that seeks to provide more sophisticated, wider-scale, finer-grained, real-time understanding, and control of various aspects and complexities of urbanity • Enabling well-informed, knowledge-driven practices based on advanced forms of intelligence with regard to the operational functioning, management, design, planning, and development of urban systems in the context of sustainability • Promoting and facilitating openness and access to public data and their integration with the private information assets for use in city analytics and big data studies to advance the knowledge about sustainability • Advancing the development of environmental indicators and objective targets for the purpose of monitoring progress, implementing strategies, allocating resources, and increasing the accountability of stakeholders • Enabling novel and harmonizing urban-level metrics for monitoring the goals of sustainable development through more objective and robust indicators and targets developed and continuously enhanced based on big data analytics • Exploring and discovering laws and principles of sustainability pertaining to environmental and socio-economic aspects and allowing an inference of stakeholders’ responses to operations, functions, services, strategies, designs, and policies in relevance to sustainability

Governance

Enabling governments to establish, formulate, and implement more effective policies based on the enhanced insights (trends, shifts, lifestyles, environmental concerns, etc.) resulting from the useful knowledge that is extracted from large masses of data on citizens and their behavior and tendencies in terms of sustainability, education, and health care Facilitating platforms for shared knowledge for ensuring democratic governance and informed participation by allowing citizens to get more involved and engaged and to blend their knowledge with that of urban experts Enabling widespread participation of citizens in relation to several functions of city governance and planning Building up e-governance tools and connecting the cooperative participation with the personal knowledge of citizens with respect to promoting environmentally friendly activities, such low-carbon mobility, sustainable travel behavior, emission-free transport, demand-based utility, incentive-based energy usage, etc. Organizing and coordinating various governmental agencies with common interests toward collaboration, integration, optimization, and further development Enabling responsive e-government to rich, dynamic, and real-time data for efficient service delivery, enhanced interaction, and empowered citizenry, or more effective government management. This can be enabled through wireless communication networks, data processing platforms, cloud/fog computing, distributed computing, and mobile computing that have the ability to transform relations with citizens and other relevant arms of government Reducing corruption, enhancing transparency, providing convenience, decreasing costs, achieving equity and inclusion, and promoting citizen empowerment through advanced e-government

Health care

Predicting epidemics, disease outbreaks, and cures, as well as preventing or avoiding preventable death Flagging potential health issues frequently or on a demand basis by monitoring and analyzing complex occurrences and events Enabling efficient healthcare systems that provide permanent monitoring, traceability of patients and their medical devices, and full accessibility of their data Using monitoring devices or specialized sensors to quickly detect anomalies, recognize patients’ behaviors, and identify and predict changes in their normal parameters Enabling remote health monitoring systems by observing patients outside of conventional medical or clinical settings, thereby reducing healthcare delivery costs Integrating clinical devices into living spaces to enable patients to communicate health data to hospitals or medical centers using smartphone apps (continued)

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246 Table 4 (continued) Smart sustainable city domains

Big data applications: operations, functions, services, designs, strategies, and policies, in addition to analytical questions and advanced forms of knowledge Enabling efficient emergency notification systems by facilitating the dissemination of messages to many groups of people alerting or notifying them of an extant or pending emergency situation Connecting medical centers, patients, and doctors with data repositories and health monitoring software tools Enabling doctors to detect the warning signs of serious illness during the early stage of treatment Facilitating rapid changes in the models of treatment delivery and many decisions behind these changes Using consumer devices to encourage healthy living, especially for senior or elderly citizens Mining DNA of citizens to discover, model, simulate, and improve health aspects Enabling responsive and proactive environments that allow for easy participation of citizens in their own healthcare management, as well as a remote monitoring of physical activity and well-being and e-inclusion for citizens with physical disabilities Mainstreaming and tailoring care services, enhancing diagnosis processes, and providing precautionary and proactive care services as well as accurate, appropriate, and history-aware responses to health issues

Public safety

Monitoring urban environments to alert citizens and inform public services of potential risks and vulnerabilities Contributing to risk assessment and hazard identification and providing immediate response to perceived threats Allowing or denying access to certain individuals to public places as well as preventing potential unrest and thereby protecting public places and citizens Predicting natural disasters to save lives and resources Enabling a data-driven approach to understanding and addressing transportation-related health issues using an online database and analytical tool to inform public and private efforts to improve transportation system safety and public health Tracking and predicting pollution or spread of chemicals in certain urban areas to prevent or mitigate adverse health effects by notifying citizens to evacuate or avoid those areas

Education

Improving education and learning methods in terms of efficiency, effectiveness, and richness through adaptable, personalized, flexible, and pertinent processes and services Optimizing evaluation methods as to finding out whether the allocated resources are producing the right results or the allocation is being done efficiently, as well as whether there is a need for the integration and coordination of these resources for further effectiveness, efficiency, and cost reduction Enhancing learning attitudes and behaviors by analyzing interactions with the different sorts of academic material and reactions to academic curriculums and the acting upon the obtained results Enhancing the existing, or creating new, education and learning practices based on deep insights into emerging social trends and global shifts, extracted as a result of big data analytics Allowing citizens to actively engage in, and benefit from, the kind of leaning environments that are conducive to the adaptation to societal development and change in terms of new scientific paradigms, emerging intellectual transitions, discontinuities, disruptive innovations, technological advancements, and so on Continuously advancing knowledge production, teaching, and learning methods to deliver and disseminate the most relevant and useful forms of education with regard to current societal needs and market demands Reducing private education cost, providing life-long learning and education opportunities, and enabling self-learning and creative education

6

Discussion of Relevant Policy and Technology Issues

Big data analytics and related applications provide a very rich nexus of possibilities for enhancing urban operations, functions, services, strategies, and policies in terms of sustainability, efficiency, and resilience, which comes with benefits for the quality of life and well-being of citizens. These benefits are associated with smart sustainable cities of the future. One of the core ideas underlying the development and implementation of big data applications in such cities is to harness solutions, improve services, integrate approaches, and enhance outcomes with respect to urban practices and city life (Bibri 2018b). One way of achieving this is through integrating urban systems, coordinating urban domains, and coupling socio-economic networks using more effective ways of monitoring, understanding, analyzing, planning, and governing modern cities. Overall, exposing big data via a socially synergistic and environmentally substantive as well as evolvable, extensible, dynamic, scalable, and reliable big data ecosystem in smart sustainable cities of the future offers a wide range of opportunities with regard to sustainability dimensions and their integration (Bibri 2018a). The advanced forms of ICT and the underlying computational and data analytics are as primordially needed as the interdisciplinary and transdisciplinary knowledge in sustainable smart urban development as a complex area of study. There is huge potential for using big data analytics to address many of the pressing issues and wicked problems involved in sustainable cities through innovative solutions for and sophisticated approaches to, and new practices of, decision-making

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Discussion of Relevant Policy and Technology Issues

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informed by high levels of intelligence enabled by the analytical outcome of the urban data deluge. Thus, this advanced form of ICT offers such cities more capabilities and resources that can allow them to realize their full potential for meaningful progress as urban development models in the face of urbanization and in the era of big data. In this regard, understanding the characteristics of such cities, identifying the complex sustainability and urbanization issues, and acknowledging the potential of big data analytics and its application facilitate the process of putting in place and maintaining what is technologically and sociopolitically required to develop, apply, and mainstream the needed smart applications for advancing sustainability. The three main components that policymakers can explore as to how to plan and construct smart sustainable cities of the future are: the construction of public infrastructure, the construction of public platform for such cities, the construction of application systems based on big data analytics, the construction of innovation labs, and the construction of participatory governance models. These all involve issues and challenges that constitute future fields of study. Those components are to be addressed and relevant solutions to be devised, as the existing plans evolve and new ones are developed in response to new urgencies requiring swift actions, as well as more R&D activities and efforts are made in relation to city development in terms of the implementation of cutting-edge technologies together with sustainable design concepts and typologies. This requires clear, reliable, strategic, and astute plans for city development and realization, rather than piecemeal initiatives, scattered projects, or standalone programs. In this regard, the requirements and objectives of smart sustainable cities of the future for technological, physical, and social infrastructures must be taken into account in such plans, instead of treating each part as its own silo. This holistic approach into city development provides a clearer and more focused perspective on what is needed and of priority to address and will result in more rounded solutions (well developed in all aspects or complete and balanced for the city), rather than isolated islands of components and applications that could hardly connect with each other. Hence, the efforts to be poured into the development of smart sustainable cities of the future should concentrate on creating a roadmap for success that covers several phases, including, but not limited to, the following: • Create a mission statement that can guide the development of smart sustainable city of the future and help fulfill its long-term goal. • Set up the direction of such city by crafting its vision and identifying its strategic and operational objectives, in particular in relation to technological innovation and sustainable development. • Establish policies, regulations, and rules, as well as determine resources and expertise required to govern big data usage and the use of other advanced forms of ICT. • Build public infrastructures and platforms based on big data analytics and its application to support innovative smart applications. This entails analyzing and assessing the current situation and determining the necessary transformations or changes to reach the desired outcomes in terms of technology and design in line with the vision of sustainability. • Identify priorities with regard to different technology and sustainability dimensions and use them to determine the most important and relevant city components and applications that would offer the greatest effects with the smallest investment possible. • Integrate city infrastructures and activities in terms of operations, functions, services, strategies, and policies and big data applications to develop more efficient urban life and more effective urban environment. • Optimize continuously the operating and organizing processes of urban life and environment based on new advances in big data analytics and its application to identify the needed improvements or changes. • Stimulate and realize new opportunities for R&D by monitoring current progress and its effect and the potentially arising issues and challenges, thereby creating new requirements and objectives. Evidenced by the urban world evolving increasingly into becoming fully technologized and computerized based on big data, the prospect has become clear that smart sustainable cities of the future will be enabled and developed using the core enabling technologies of big data analytics and hence related novel applications to effectively and efficiently cater for the needs of diverse urban constituents as well as meet their aspirations in an unsustainable and rapidly urbanized world. This might well call for funnelling huge investments into the kinds of resources, infrastructures, platforms, and expertise that are required to support the construction and deployment of the core enabling technologies of big data analytics throughout the various design and development stages of smart sustainable cities of the future. This strategic move is deemed essential to reap the sustainability benefits in terms of environmental and socio-economic gains in such cities. To help optimize the city design and development as an endeavor and minimize its costs, it is recommended to include important activities in the process, some of which are presented below:

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• Developing advanced modeling and simulation systems to help predict potential problems and forecast possible changes, with the primary purpose of mitigating or avoiding any risks that might arise, as well as reducing the implementation and testing costs following city design and development. Simulation models and prediction methods have great potential to modernize sustainable smart city design and development in the future (Bibri 2018a, c). Indeed, using simulations is generally cheaper, safer, and faster than studying real-time processes or conducting real-world experiments. Also, simulations allow a flexible configuration of the parameters within the different sub-processes found in the operational application field of smart sustainable cities as complex systems and dynamically changing environments. • Learning and benefitting from previous experiences in sustainable smart urban planning and development to adopt best practices and follow successful models and avoid problematic approaches. • Benefitting from the eminent experts, scholars, and researchers in the field to investigate new possibilities for more advanced technological systems of suitability to the objectives of smart sustainable cities of the future with regard to sustainability. • Investigating the relevance of big data applications to such cities in this direction, an understanding which will help incorporate the right data into the right applications to make accurate, knowledge-driven decisions and implement them to enhance and optimize urban operations, functions, services, designs, strategies, and policies in line with the goals of sustainable development.

7

Discussion and Conclusion

Despite the huge advances in different areas of knowledge and a number of impressive practical initiatives and programmes in the domain of sustainable urbanism and thus sustainable cities, there is still much more that needs to be done according to what arises ‘on the ground’ in terms of improvement and change. Hence and again, it has become of high significance and importance to amalgamate the design concepts and principles and planning practices of sustainability with the kind of sophisticated approaches and innovative solutions being offered by big data technology in the context of smart sustainable urbanism. It is equally of significance and importance to create new typologies and design concepts using new powerful forms of simulation models. The ultimate aim is to find more effective ways and more robust methods to improve and maintain the contribution of sustainable cities to the goals of sustainable development by assessing, optimizing, and enhancing the underlying strategies and approaches using cutting-edge technologies under what is labeled ‘smart sustainable cities of the future’ (Bibri 2019b). This is highly of relevance to embrace and pursue in an increasingly computerized and urbanized world. Especially, big data computing is offering great opportunities for, and unsurpassed ways of, effectively monitoring, understanding, analyzing, and planning such cities to achieve the required level of sustainability (Bibri 2018a). This chapter provided a comprehensive, state-of-the-art review of the domain of sustainable urbanism, with a focus on compact cities and eco-cities as models of sustainable urban forms and instances of sustainable cities, in terms of research issues and debates, knowledge gaps, challenges, opportunities, benefits, and emerging practices. This study shows that sustainable urban forms still raise critical issues involving limitations, inadequacies, difficulties, fallacies, and uncertainties in the context of sustainability, in spite of what has been realized over the past three decades or so within sustainable urbanism. What has been realized in this context is the immense opportunities that have been created and explored and, thus, the enormous benefits that have been realized from the planning and development of sustainable urban forms, especially compact cities and eco-cities, in terms of reducing material use, lowering energy consumption, mitigating pollution, and minimizing waste, as well as in terms of improving equity and well-being. Specifically, sustainable urban forms seek to achieve different objectives. The most prominent among them are decreased energy and material use, reduced CO2 emissions, minimized waste, reduced automobile use, increased walking and cycling, enhanced quality of life and well-being, improved equity and inclusion, effective mobility and accessibility, preserved open-space and sensitive ecosystems, and community-oriented and livable human environments (Bibri 2019b). The issue of sustainable urban forms has always been problematic and daunting to deal with. Accordingly, the significant intellectual challenge to produce convincing models of sustainable urban form with clear and specific components continues to induce scholars, academics, and even real-world cities to create more successful and robust models of such form (Bibri 2019b). The debate over the ideal or desirable urban form dates back to the end of the nineteenth century, and obviously, the concept of sustainable development revives it and develops existing models of sustainable urban form further by enhancing them with the planning principles and ecological design of sustainability (Jabareen 2006). Again, smart development as predominately driven by big data computing has recently revived this debate and is attempting to enhance existing models of

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Discussion and Conclusion

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sustainable urban form by smartening up the performance of the underlying design concepts and typologies. It has become of high pertinence and importance to augment sustainable urban forms with big data technology and its novel applications—so as to boost this performance in terms of sustainability (Bibri 2019b; Bibri and Krogstie 2017b). Additionally, this chapter highlighted and substantiated the real, yet untapped, potential of big data technology and its applications for advancing sustainable cities in terms of enhancing and optimizing their operational functioning, management, design, planning, development, and governance. In so doing, it identified, synthesized, distilled, and enumerated the key practical and analytical applications of big data technology for diverse urban domains and sub-domains. This study reveals that tremendous opportunities are available for exploiting such applications to smarten up sustainable urban forms and thus sustainable cities in ways that can improve, advance, and sustain their contribution to the goals of sustainable development by optimizing and enhancing their operations, functions, services, designs, strategies, and policies across various urban domains, as well as by finding answers to challenging analytical questions and transforming the way knowledge can be developed. The most common data-centric applications identified concerning urban domains are transport and traffic, mobility, energy, power grid, environment, buildings, infrastructures, urban planning, urban design, academic and scientific research, governance, health care, education, and public safety. The potential of big data technology lies in enabling sustainable cities of the future to harness and leverage their informational landscape in effectively understanding, monitoring, probing, and planning their systems and environments in ways that enable them to reach the optimal level of sustainability. To put it differently, the use of big data analytics is projected to play a significant role in realizing the key characteristic features of such cities, namely the efficiency of operations and functions, the prudent utilization of natural/environmental resources, the intelligent management of infrastructures and facilities, the improvement of the quality of life and well-being of citizens, and the enhancement of mobility and accessibility. In a nutshell, the untapped potential of big data applications is evident and needs to be unlocked and exploited to achieve the optimal level of sustainability within sustainable cities. While there is a host of unexplored opportunities toward new approaches to smart sustainable urbanism as an effective way to mitigate or overcome the weak connection between sustainable cities and smart cities, the approach focusing on big data technology as an advanced form of ICT tends to dominate in light of the ongoing and planned smart sustainable city projects and initiatives worldwide. In relation to this, many major cities have, whether within ecologically or technologically advanced nations, already started to implement big data applications to reap their sustainability benefits. It is an urban world where the physical landscape of sustainable cities and the informational landscape of smart cities are increasingly being merged. This implies that it is high time for existing sustainable urban forms to embrace and leverage what smart cities have to offer as innovative solutions and sophisticated approaches related to big data computing to overcome the more complex problems and challenges of sustainability. The need for big data analytics and its application to permeate such forms is anchored in the recognition that urban sustainability applications are deemed of high relevance and salience to the research agenda of big data computing (Bibri 2018a, 2019b). To unlock and exploit the underlying potential, the field of sustainable urbanism is required to extend its boundaries and to broaden its horizons beyond the ambit of the built form of cities to include technological innovation opportunities (Bibri and Krogstie 2017b).

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The Unfolding and Soaring Data Deluge for Transforming Smart Sustainable Urbanism: Data-Driven Urban Studies and Analytics

Abstract

There has recently been much enthusiasm about the immense opportunities and fascinating possibilities created by the unfolding and soaring deluge of exhaustive, fast, indexical data and its new and extensive sources as to understanding, analyzing, and planning smart sustainable/sustainable smart cities in ways that improve, advance, and maintain their contribution to the goals of sustainable development. This is owing to the underlying power of thinking data-analytically about sustainability in terms of finding answers to challenging questions for addressing the wicked problems and disentangling the intractable issues related to the practice of urbanism: operational functioning, planning, design, and development. In the meantime, as widely acknowledged within the field of smart and sustainable urbanism as regards academic and scientific research, ‘small data’ studies are associated with high cost, infrequent periodicity, quick obsolescence, incompleteness, inaccuracy, as well as inherent subjectivity and biases. In addition, such studies capture a relatively limited sample of data that is tightly focused, less representative, restricted in scope and scale, time and space specific, and relatively expensive to generate and analyze. Indeed, much of our knowledge of urbanism has been gleaned from scholarly studies characterized by data scarcity and involving the use of traditional data collection and analysis methods with inherent limitations and constraints. Therefore, this chapter endeavors to develop, illustrate, and discuss a systematic framework for city analytics and ‘big data’ studies in relation to the domain of smart sustainable/sustainable smart urbanism based on cross-industry standard process for data mining. This endeavor is in response to the emerging paradigm of big data computing and the increasing role of underpinning technologies in operating, organizing, planning, and designing smart sustainable cities as a leading paradigm of urbanism. The intention is to utilize and apply well-informed, knowledge-driven decision-making and enhanced insights to improve and optimize urban operations, functions, services, designs, strategies, and policies in line with the long-term goals of sustainability. I argue that there is tremendous potential for advancing smart sustainable urbanism or transforming the knowledge of smart sustainable cities through creating a data deluge that can, through analytics, provide much more sophisticated, finer-grained, wider-scale, real-time understanding and control of various aspects of urbanity in the undoubtedly upcoming Exabyte/Zettabyte Age. Keywords












Smart sustainable/sustainable smart cities Big data analytics Predictive and descriptive data mining Urban analytics Urban sustainability Smart sustainable urbanism Big data studies

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Introduction

The data-intensive scientific development as a new paradigm, which has materialized as a result of the recent advances in data science systems, processes, and methods and thus big data computing and the underpinning technologies, is instigating a drastic shift in city-related academic and scientific disciplines or fields (Bibri 2018a). These include, but are not limited to, urban planning, urban design, urban development, urban sustainability science, environmental science, urban computing, urban science, and urban informatics. Adding to this is the opportunity for such disciplines or fields to be potentially integrated into new interdisciplinary or transdisciplinary disciplines or fields. Turing award winner Jim Gray envisions data © Springer Nature Switzerland AG 2019 S. E. Bibri, Big Data Science and Analytics for Smart Sustainable Urbanism, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-17312-8_9

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science as the new paradigm of science and asserts that everything about science is changing because of the impact of advanced ICT and the evolving data deluge (Bell et al. 2009). The Exabyte Age is upon us, and the data deluge makes the scientific method—hypothesize, model, test—obsolete. This is the way science has worked for hundreds of years: that hypothesized models as systems visualized in the minds of scientists are tested, and subsequently, experiments confirm or falsify the models of how the world works. Data-intensive scientific discovery is the fourth paradigm of scientific development where science involves the exploration and mining of scientific data and using advanced data mining techniques to unify theory, simulation, and experimental verification—with the first paradigm being where science used empirical methods thousands of years ago; the second paradigm where science became a theoretical field a few hundred years ago, involving the process of generating and testing hypotheses; and the third paradigm where science used calculation, conducting simulation and verification by computation in recent decades (Bell et al. 2009). Using the process of data mining is increasingly gaining traction and foothold in many academic and scientific research fields, taking over the method of formulating and testing hypotheses, which has prevailed for centuries. Here, the use of this process is seen as an important and effective way to, in addition to conducting scientific exploration and discovery based on big data, solve complex problems within a wide number and variety of domains, including smart sustainable/sustainable smart urbanism. By mining urban data, it is possible to discover laws and principles of sustainable development pertaining to environmental and socio-economic aspects of the city (Bibri 2018a). This development will allow an inference of the varied city stakeholders’ responses to operations, functions, services, designs, strategies, and policies in relation to multiple urban domains in the context of sustainability. Indeed, data-analytic and sustainable thinking and practice as an integrated approach into urbanism connects the best elements of data science and urban sustainability. The amount of data in the world is unfolding and soaring, amounting to hundreds of exabytes every year. Urban data deluge results from the increasing availability of the data being generated in continuous streams and on a daily basis in the urban environment. Within smart sustainable/sustainable smart cities, urbanites, processes, systems, structures, activities, networks, facilities, services, spaces, and objects all contribute to generating the huge amounts of data as involving heterogeneous and distributed sources (Bibri 2018a). There is a phenomenal growth in data production across the world. The digital data are projected to grow from 2.7 to 35 Zbyte by the year 2020 (Malik 2013; Zikopoulos et al. 2012). Manyika et al. (2011) projected a growth of 40% in data generated globally per year. It is estimated that more data are being produced every 2 days at present than in all of history prior to 2003 (Smolan and Erwitt 2012, cited in Kitchin 2014). Such explosive growth in data is due to a number of different enabling and driving technologies, infrastructures, systems, techniques, and processes as new developments in ICT of pervasive computing, and their rapid infiltration into everyday practices and environment, enabling the accessing and sharing of data, as well as due to the digital means by which much big data can be generated and collected (Bibri 2018a, b; Kitchin 2014). Accordingly, the urban data deluge being generated will in principle remain a continuing stream, and thus the datasets across all urban domains will proliferate. The role of big data processing technologies lies in collecting, storing, processing, analyzing, and interpreting large masses of data on every urban system and domain to discover useful knowledge and employ it to enhance decision-making and insights. Specifically, the value of this knowledge lies in improving physical forms, infrastructures, facilities, resources, networks, and services by developing and applying urban intelligence functions for automating and supporting decisions pertaining to control, automation, optimization, management, and short-term planning for the purpose of improving the contribution of smart sustainable/sustainable smart cities to the goals of sustainable development, or advancing smart sustainable/sustainable smart urbanism. Therefore, big data constitute the fundamental ingredient for the next wave of city analytics in the domain of smart sustainable urbanism (Bibri 2018a, b). However, merely keeping up with data flood, and storing the bits that might be useful, is challenging enough, not to mention analyzing datasets to discover patterns, make correlations, and eventually extract useful knowledge. Nevertheless, the deluge of big data has already started to transform the knowledge of smart sustainable/sustainable smart cities (Bibri 2018a, 2019a). It has great potential for good—as long as city governments make the right choices about when, how, and where to restrict the flow of data and to spur and encourage it. Using the data deluge, city analytics and ‘big data’ studies involve the application of various techniques based on data science fundamental concepts—i.e., data-analytic thinking and the principles of extracting useful knowledge from large masses of data. Data mining is one of the most applied techniques in the domain of smart sustainable/sustainable smart urbanism provides some of the clearest illustrations of the principles of data science. While this technique is gaining a strong footing in this domain, its application is associated with significant challenges due to the interdisciplinary and transdisciplinary nature of urban data, and what this entails in terms of all kinds and levels of complexities (Bibri 2018a). However, over the last 20 years or so, research within the area of data mining has been mainly active in such areas as banking, customer relationship management, targeted marketing, fraud detection, finance, retail, manufacturing, telecommunication, medicine and healthcare, media, and so on. Accordingly, there is a paucity of research on this topic in relation to the domain

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Introduction

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of smart sustainable/sustainable smart urbanism. In particular, while big data analytics have recently become of focus in research in the context of smart cities and sustainable smart cities (e.g., Batty 2013a; Batty et al. 2012; Bettencourt 2014; Bibri 2019a; Khan et al. 2015; Kitchin 2014, 2016), as well as in sustainable cities and smart sustainable cities of the future (Bibri 2018a, b; Bibri and Krogstie 2017b, c), the research on the next wave of city analytics based on big data computing using a data mining technique in relation to academic and scientific research in the domain of smart sustainable urbanism remains scant and thus needs to be expanded. In the meantime, as widely acknowledged in the domain of (smart) sustainable urbanism in relation to academic and scientific research, ‘small data’ studies are associated with high cost, infrequent periodicity, quick obsolescence, incompleteness, inaccuracy, as well as inherent subjectivity and biases. In addition, such studies capture a relatively limited sample of data that are tightly focused, less representative, restricted in scope and scale, time and space specific, and relatively expensive to generate and analyze. Indeed, much of our knowledge of sustainable urbanism has been gleaned from studies characterized by data scarcity and involving the use of traditional data collection and analysis methods with inherent limitations and constraints. New approaches to city analytics in the domain of smart sustainable/sustainable smart urbanism are needed to provide an additional depth and insight with respect to urban phenomena and dynamics and the underlying complexities and intricacies, as well as to bring robustness to the research results within this domain. Against the preceding background, this chapter is about thinking data-analytically about, and infusing data-driven practices into, smart sustainable urbanism to transform and advance the knowledge of smart sustainable/sustainable smart cities. This can be aided by a conceptual framework with well-defined stages to help structure and systemize urban data-analytic thinking and practice. Specifically, this chapter endeavors to develop, illustrate, and discuss a systematic framework for city analytics and ‘big data’ studies in relation to the domain of smart sustainable/sustainable smart urbanism based on cross-industry standard process for data mining. This endeavor is in response to the emerging paradigm of big data computing and the increasing role of underpinning technologies in operating, organizing, planning, and designing smart sustainable cities as a leading paradigm of urbanism. The intention is to utilize and apply well-informed, knowledge-driven decision-making and enhanced insights to improve and optimize urban operations, functions, services, designs, strategies, and policies in line with the long-term goals of sustainability. The main motivation for this work is to put forward new approaches into conducting city analytics and ‘big data’ studies for advancing smart sustainable/sustainable smart urbanism. The remainder of this chapter is structured as follows. Section 2 introduces, describes, and discusses data mining as a concept and process. Section 3 provides a state-of-the-art review of ‘big data’ studies. Section 4 elucidates and discusses supervised and unsupervised methods in relation to predictive and descriptive data mining along with some illustrative examples. Section 5 provides an explanatory account of how urban sustainability problems can be transformed into data mining tasks. Section 6 presents, describes, illustrates, and discusses the proposed data mining framework for city analytics, with a particular focus on data-analytic solutions to urban sustainability (or sustainable urbanism) problems. A set of recent empirical applications of data mining for city analytics in the context of smart sustainable/sustainable smart urbanism is presented in Sect. 7. Section 8 highlights the kind of urban knowledge being offered by data science as practiced within the field of urban science and provides an account of the application of big data analytics in academic and scientific research within the context of big data studies. This chapter ends, in Sect. 9, with concluding remarks and some reflections and final thoughts.

2

Data Mining as a Concept and Process

Data mining (also known as knowledge discovery) is the computational process of probing colossal datasets in order to find frequent, hidden, and previously unsuspected and unknown patterns and subtle relationships; to make useful, meaningful, and valid correlations from these discoveries; and to summarize the results in novel ways and then visualize them in understandable formats prior to their deployment for decision-making purposes. Among the data mining models used to perform data processing and analysis, functions include distributed data mining, multilayer mining, data mining from multitechnology integration, and grid-based mining (Bin et al. 2010). There are a variety of data mining algorithms or tasks that can be used to solve problems pertaining to urban sustainability, including classification, clustering, regression, profiling, similarity matching, causal modeling, predictive link, and co-occurrence grouping (see Bibri 2018a for a descriptive account of these algorithms with illustrative examples in relation to smart sustainable cities). These algorithms use supervised learning methods, unsupervised learning methods, or either. Covering data mining algorithms, which would normally fill multiple books, is beyond the scope of this chapter. But there are many books out there addressing them from practical guides to mathematical and statistical treatments. While many data mining algorithms are exactly the embodiment of the

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fundamental concepts of data science, this chapter focuses rather on such concepts and how they help urban analysts (ideally data scientists specialized in urban analytics) to think about urban sustainability problems where data mining is brought to bear. Hence, the intent is not to provide the deep technical details of how the data mining algorithms actually work, but enough detail so that the reader can understand what these algorithms do as a form of tasks, as well as how they are based on the fundamental principles of data science. Data mining is associated with open-source initiatives, which include the following (Fan and Bifet 2013): • Apache Mahout: a scalable machine learning and data mining algorithm (classification, clustering, frequent pattern mining, etc.) based mainly on Hadoop architecture. • R: a software environment and programming language intended for statistical analysis and visualization and for cluster-based scalable machine learning. • MOA: a stream data software for real-time mining (classification, clustering, regression, frequent item-set mining, frequent graph mining, etc.) and also for cluster-based scalable machine learning. • SAMOA: a software designed for distributed stream mining integrating S4 and Storm with MOA. • and Pegasus: a big graph mining system used for finding patterns in massive real-world graphs and built on top of MapReduce. • R in combination with Hadoop MapReduce can be utilized for data mining in smart cities. In the context of smart sustainable/sustainable smart cities, these software tools and applications can be used separately or combined based on the urban application domain and related sub-domains and the way these interrelate. According to several codifications of the process of data mining (e.g., Chapman et al. 2000; Ponce and Karahoca 2009; Provost and Fawcett 2013; Shearer 2000), this process consists of well-defined stages, namely problem understanding, data understanding, data preparation, model building, result evaluation, and result deployment. As to the latter, the resulting knowledge can be used for supporting or automating decisions pertaining to operations, functions, and services, as well as for enhancing existing practices, strategies, and policies. Indeed, in the context of smart sustainable/sustainable smart cities, the process of data mining targets optimization and intelligent decision support pertaining to the control, efficiency, management, and planning of urban systems as operating and organizing processes of urban life, as well as to the enhancement of the associated ecosystem and human services related to energy, water, healthcare, education, safety, and so on (Bibri 2018a). Additionally, the process targets the improvement of practices, strategies, and policies by changing them based on new trends and shifts. In sum, the analytical outcomes of data mining can serve to improve urban operational functioning, optimize resource utilization, reduce environmental risks, enhance the quality of life and well-being of citizens, and streamline planning and governance processes. There is a large body of work that addresses cross-industry standard process for data mining (e.g., Kurgan and Musilek 2006; Marbán et al. 2009; Ponce and Karahoca 2009; Chapman et al. 2000; Shearer 2000). This process emphasizes the idea of iteration. This implies that solving a particular problem may require going through the process more than once. The outer circle symbolizes the cyclic nature of data mining itself. Furthermore, the data mining process model exists in many variations, e.g., a simplified process like (1) preprocessing, (2) data mining, and (3) result validation. The literature shows that the CRISP-DM methodology is the leading methodology used by data miners.

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A State-of-the-Art Review: ‘Small Data’ and ‘Big Data’ Studies and City Analytics

Big data are referred to particularly with respect to their humongous size and wide variety, with a particular focus on the deluge of urban data (i.e., datasets collected and coalesced through data warehousing for wide-city uses) that are directed toward advancing smart sustainable/sustainable smart urbanism or transforming the knowledge of smart sustainable/sustainable smart cities, in particular in relation to sustainability and the integration of its dimensions. Such data are, in addition to being enormous and varied, real-time, exhaustive in scope, fine-grained in resolution, indexical, dynamic, flexible, evolvable, and relational. With these characteristic features, such data enable real-time analysis of city systems in terms of the operating and organizing processes of city life, new modes of urban planning and governance, and advanced approaches to interconnecting and fusing data across urban domains to provide detailed views of the relationships between urban data, as well as provides the raw material and favorable conditions for enacting and envisioning more sustainable, efficient, resilient, equitable, open, and transparent cities.

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A State-of-the-Art Review: ‘Small Data’ and ‘Big Data’ Studies and City Analytics

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The above epitomizes a sea change in the kind of data that we generate about city systems across multiple urban domains as to what happened and where, when, why, and how, as well as what will happen and what should be done, in the context of smart sustainable/sustainable smart cities. In this respect, Bibri (2018a) argues that data mining (or knowledge discovery) has innovative potential to revolutionize city analytics in the form of ‘big data’ studies by providing novel way of thinking data-analytically about sustainable urbanism/urban sustainability problems. In addition, data mining enables well-informed, knowledge-driven decision-making and enhanced insights with regard to operations, functions, strategies, designs, strategics, and policies associated with multiple urban domains for increasing the contribution of smart sustainable/sustainable smart cities to the goals of sustainable development. Especially, ‘small data’ studies using such data collection and analysis methods as questionnaire surveys, focus groups, case studies, participatory observations, audits, interviews, content analyzes, and ethnographies—‘capture a relatively limited sample of data that is tightly focused, time and space specific, restricted in scope and scale, and relatively expensive to generate and analyze.’ (Kitchin 2014, p. 3), to reiterate. For example, as pointed out by Batty et al. (2012), the mainstream analytical tools of transportation engineering, such as origin/destination matrices, are based on semantically rich data collected by means of field surveys and interviews. In a nutshell, much of what we know about cities to date has been gleaned from studies that are characterized by data scarcity (Miller 2010) and based on traditional data and analysis methods. This form of academic and scientific research in the domain of sustainable urbanism has prevailed for three decades or so. This has consequently impacted the way sustainability as underpinned by empirical investigations based predominately on such methods has been adopted as a set of practices in urban planning and development (Bibri 2018a; Bibri and Krogstie 2017b). Big data can be used to overcome the constraints and limits of traditional data collection and analysis methods, namely their high cost, infrequent periodicity, quick obsolescence, incompleteness, inaccuracy, inflexibility, and inherent subjectivity and biases in the domain of smart sustainable/sustainable smart urbanism (Batty et al. 2012; Bettencourt 2014; Bibri 2018a; Bibri and Krogstie 2017a, b; Kitchin 2014). These issues have indeed long affected the robustness and reliability of research results (theories, generalizations, models, and other valid forms of knowledge, etc.) within the field of sustainable urbanism. This has in turn influenced urban practices in terms of the application of the concepts and principles of sustainability in urban planning and development, to reiterate but in other words. In the context of sustainable urbanism, many studies investigating, or referring to other research works carried out on, the correlation between travel behavior, mobility, equity, life quality, energy consumption, and other indicators of environmental and social sustainability performance, on the one hand, and density, compactness, diversity, mixed-land use, and other typologies through which sustainable urban forms can be achieved, on the other hand, point implicitly or explicitly to the disadvantages of the traditional data collection and analysis methods and how these compromise the value of the obtained research results (Bibri and Krogstie 2017b; Jabareen 2006; Neuman 2005). These studies usually generate non-conclusive, weak, limited, unreliable, conflicting, or uncertain results. This relates the issue of sustainable urban forms being problematic and of a challenging nature to deal with (Bibri 2018a). The interested reader might want to gain further insights into this by turning to Bibri and Krogstie (2017b), where a detailed discussion is provided on several topics related to sustainable urbanism, more specifically sustainable urban forms, including, in addition to big data analytics as an alternative to traditional data collection and analysis methods for investigating sustainable urban forms, the role of big mobility data in evaluating the environmental and socio-economic performance of sustainable urban forms as well as urban simulation models as an innovative approach into strategically assessing and optimizing the contribution of sustainable urban forms to sustainability based on big data analytics. On the whole, big data are seen as the most scalable and synergic asset or resource for smart sustainable/sustainable smart cities of the future and constitute the fundamental ingredient for the next wave of city analytics. The potential and hope of big data lie in transforming the knowledge of smart sustainable/sustainable smart cities through the creation of a data deluge that can, through analytics, provide much more sophisticated, finer-grained, wider-scale, real-time understanding and control of the kind of complex aspects and intractable problems of urbanity (Bibri 2018a), just like smart cities (Kitchin 2014). With that in mind, there are several topical studies that provide or suggest new ways of mobility enhancement, transport management, energy management, environmental management, water management, waste management, health care, education, citizen services, and so forth across multiple urban domains, both in relation to sustainability and efficiency (see Bibri 2018a). In more detail, some of these studies offer a set of applications of data mining related to the different aspects of the systematic study of smart sustainable/sustainable smart cities and related problems pertaining to sustainability. In this regard, Zhao et al. (2016) compared traditional methods and data mining process for energy consumption analysis, as well as discuss the energy consumption data with these parameters: public building, structure, construction, and behavior pattern. The authors attempt to fill the existing gap by utilizing data mining in the energy efficiency evaluation of buildings. In their study, Zhou et al. (2016) address big data-driven smart energy management in terms of how to turn big data to big insights into

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advanced analytics. They propose a process model for smart energy management and also provide insights into data collection, integration, processing, sharing, and security. Khan et al. (2012) compared the effectiveness and performances of several data mining techniques as to predicting irrigation water demand. Addressing various issues related to healthcare, Milovic and Milovic (2012) state that data mining in health care aids in organizing a large amount of data, using advanced technologies for automation, conducting early diagnosis, maintaining the security, predicting new trend, and so on. The authors further suggest the classification and regression, association rule, cluster analysis, and text mining techniques for analysis of healthcare data. Benevolo et al. (2016) discussed six objectives of mobility in the context of smart cities, namely pollution, noise pollution, traffic blocking, transfer speed, transfer cost, and people safety. The authors suggest four key factors: public companies, private companies, local government agencies, and the integration of these three to form integrated transport system (ITS), with a focus on ICT-based ITS which includes video surveillance for security, traffic control, and traffic data collection system, as well as other innovative solutions such as the use of ICT for smartphone-based integrated ticketing system, car sharing, and car reservation. Sin and Muthu (2015) identified the challenges and issues facing education, including performance prediction in learning; designing courseware, assessment, and research; and predicting future failures and finding solutions. The authors suggest the use of different open-source data processing platforms, such as MongoDB, Hadoop, and Orange, in relation to data mining. In a nutshell, the coupling of big data analysis and computational modeling and simulation can open new horizons for city analytics and planning in the context of smart sustainable/sustainable smart urbanism. All in all, data science as practiced within the field of urban science offers the potential for the kind of urban knowledge that is inherently longitudinal and has greater breadth, depth, scale, and timeliness (Batty et al. 2012; Kitchin 2016; Lazer et al. 2009). In this respect, an array of new big data analytics techniques is being developed and applied in the urban domain that utilizes advanced mathematical models and sophisticated algorithms designed to process and analyze enormous datasets. However, the role of data mining technique in transforming the knowledge of smart sustainable/sustainable smart cities as a holistic approach to urbanism (urban planning and development) is largely ignored or barely explored to date (Bibri 2018a, 2019a), notwithstanding the relevance and usefulness of this advanced data analytics technique in relation to the future form of smart sustainable/sustainable smart urbanism. Indeed, in the near future, the core enabling technologies of big data analytics, namely digital sensing technologies and networks, data processing platforms, cloud and fog/edge computing models, and wireless communication networks, will be the dominant mode of monitoring, understanding, and analyzing smart sustainable/sustainable smart cities so as to improve, advance, and maintain their contribution to the goal of sustainable development through enhancing and optimizing their operations, functions, services, strategies, and policies in line with the vision of sustainability (Bibri 2018a, b, c).

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Supervised Versus Unsupervised Methods and Their Application: Predictive and Descriptive Data Mining

The concepts of supervised and unsupervised learning were inherited from the area of machine learning, a subfield of artificial intelligence that deals with artificial systems that are able to improve their performance overtime in response to their experience in the world. Supervised and unsupervised methods are applied in different types of data mining tasks (classification, regression, clustering, profiling, causal modeling, etc.) and hence underlie data mining techniques. They are also associated with other big data analytics techniques. Further, the field of data mining is seen as an offshoot of the field of machine learning, and both fields remain closely linked, as they are associated with the analysis of data to discover informative patterns. In view of that, existing techniques and algorithms for knowledge discovery are used in both fields. Indeed, researchers from data mining and machine learning communities’ transition between them seamlessly. Research focused on urban sustainability applications and urban issues of data analysis tend to gravitate toward the data mining/knowledge discovery community rather than to machine learning. In terms of the process of data mining, a vital part at the very early of it is to determine whether the line of attack will involve supervised or unsupervised methods prior to identifying which data mining techniques and algorithms are to be adopted. In supervised learning (predictive data mining), experience means that objects have been assigned class labels, and performance typically concerns the ability to classify new (previously unseen) objects. Accordingly, in the model generated by supervised learning, a relationship between a set of selected variables and a predefined target variable is described based on particular data. The model predicts the value of the target variable for unseen instances as a probabilistic function of other descriptive attributes. Regarding unsupervised learning (descriptive data mining), experience entails objects for which no

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class labels have been given, and performance typically concerns the ability to output useful characterizations (or groupings) of objects. While descriptive data mining focuses on finding patterns for human interpretation by producing non-trivial characteristic information on the basis of the available dataset, predictive data mining uses some variables in the dataset to predict future or unknown values of variables of interest (target) on the basis of models generated by training sets and described by particular data (attributes or features). Concerning descriptive data mining, there is no specific purpose specified for the grouping, i.e., there is no guarantee that the similarities generated from the grouping are useful for any particular reason, and hence, the data mining problem is referred to as unsupervised. For example, consider a question we might ask about a mobility mode of citizens: Do citizens naturally fall into different categories? Concerning predictive data mining, there is a specific target defined, and thus, the data mining problem is referred to as supervised. Now we contrast our example with a slightly different question: Can we find groups of citizens who have particularly high likelihoods of walking and cycling when dwelling in an urban area characterized by mixed-land use or diversity? In this case, segmentation can be done for a specific reason: to take action (promote and adopt mixed-land use as a typology for sustainable mobility in newly developed urban environments) based on the likelihood of walking and cycling. Overall, a supervised learning task is when the learner is provided with specific target information along with a set of examples (training data where the value for the target attribute is known), and an unsupervised learning task is when the learner might be provided with the same set of examples but without the target information, i.e., the learner would be left to draw its own conclusions (of descriptive nature) about what the provided set of examples has in common, as it would be given no information about the purpose of learning. Supervised and supervised tasks entail different techniques, and the outcomes often are much more useful in relation to urban sustainability problems. Supervised methods are generally associated with classification, regression, and causal modeling, and unsupervised methods concern clustering, co-occurrence grouping, and profiling. As to link prediction, similarity matching, and data reduction, they could be solved either with unsupervised or supervised methods. In particular, the methods for extracting predictive models from data using supervised methods have been investigated and developed in several scientific fields contemporaneously, most notably applied statistics, machine learning, and pattern recognition. These fields have therefore become so closely tied that the separation between them has blurred.

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From Urban Sustainability Problems to Data Mining Tasks

Each data-driven decision-making problem as part of urban analytics or ‘big data’ studies is unique, consisting of its own combination of objectives, requirements, and constraints. However, there are sets of common data mining tasks that underlie the urban sustainability problems pertaining to various urban domains and how they may be interrelated or coordinated. In collaboration with urban stakeholders (authorities, agencies, departments, administrators, institutions, enterprises, etc.), city analysts, researchers, scholars, developers, and planners, data scientists decompose an urban sustainability problem into subtasks, and the solution can subsequently be composed to solve such problem. This relates to energy, transport, mobility, traffic, built environment, health care, education, safety, or other urban (application) domains. The know-how of data scientists resides in their ability to decompose a data analytics problem of a particular aspect of urban sustainability into subtasks for which tools and techniques are available and can be used separately or combined. Some of these subtasks remain common data mining tasks, and others are unique to the particular context of an urban sustainability problem. In recent years, there has been a major shift in scientific and practical knowledge used to solve the common data mining tasks. Overall, what matters in data analytics problems is to possess the ability of recognizing urgent and common problems and their solutions, and doing this in ways that avoid wasting resources and time by reinventing the wheel when considering new projects or endeavors associated with data analytics for advancing sustainability through enhancing and optimizing operations, functions, services, designs, strategies, and policies pertaining to various urban domains. This implies that the data mining process in this context is not only about the automated extraction of useful knowledge from data for enhanced decision-making and insights, but also about creativity, common sense, acumen, expert knowledge, and so on. Data mining focuses on the automated search for extracting useful knowledge through finding patterns, regularities, and correlations in data. And it is important for the urban analysts (affiliated with, for example, city authorities, urban departments, service agencies, enterprises, and institutions) to be able to recognize what sort of the analytic techniques among the available ones is appropriate for addressing a particular urban sustainability problem within a given urban domain. The fundamental principles of data mining underlie a number of types of techniques, including classification, regression, causal modeling, similarity matching, link prediction, data reduction, clustering, and co-occurrence grouping (Bibri 2018a). The use

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and combination of these techniques often depend on the nature of the urban sustainability problem to solve or tackle. Although there are a large number and variety of specific data mining algorithms developed hitherto (from such fields as machine learning, statistics, artificial intelligence, database systems, and pattern recognition) to perform different data analysis tasks, there are only a small amount of fundamentally different kinds of tasks these algorithms perform. The tasks apply to, in the context of smart sustainable/sustainable smart cities, different kinds of human or inanimate entities (e.g., citizen, mobility system, utility system, traffic system, transport system, energy system, travel behavior, and spatial organization) about which we have data. In many urban sustainability analytics projects, the desire is to find correlations between one or more particular variables describing an entity and other variables. For example, in historical or real-time urban data, we may know which typologies (density, diversity, mixed-land use, etc.) are environmentally sound across a particular spatial scale, which citizens are environmentally sustainable in terms of travel behavior, or which traffic conditions cause endemic congestions. We may want to find out which other variables correlate with a typology being environmental sound across a particular spatial scale, and similarly, which other variables correlate with a citizen being environmentally sustainable in terms of travel behavior and with traffic conditions causing endemic congestions. Finding such correlations illustrates the most basic examples of classification tasks. Among the most prominent data mining tasks in the domain of sustainable urbanism are classification, clustering, regression, and data reduction, which are described and exemplified below:

Classification Classification is a common predictive task of data mining attempts to predict, for each entity, which of a small set of classes this entity belongs to. It is about discovering a predictive learning function that classifies a new example or instance into one of the several predefined classes. Classification is the most commonly applied data mining technique in many urban domains. An example classification question related to sustainable urban forms as instances of sustainable cities would be: Which of the existing typologies is likely to conserve energy, minimize automobile use, decrease travel needs, enhance accessibly to facilities and services, encourage cycling and walking, reduce air pollution and traffic congestion, and/or improve the quality of life in terms of social interaction? For a classification task in this context, a data mining procedure generates a model that, given a new entity focused on a particular dimension of sustainability, determines which class that entity belongs to in relation to environmental, social, and/or economic sustainability based on the multivalue of the target variable.

Regression As related to, yet different from, classification, regression is about value estimation, as it involves a numeric target, in contrast to classification which involves a categorical target. It attempts to estimate the numerical value of some variable for each entity. As a data mining task, it can be used to model the relationship between one or more independent variables (attributes already known) and response variables (attributes to be estimated). An example regression question would be: How much will a given citizen benefit from a healthcare or transport service in a given district or city? The property (variable) to be estimated here is the degree of the quality of life in terms of using these two types of service. In this context, a model could be generated by looking at other citizens in the context of smart sustainable/sustainable smart cities and their usage of such services compared to, for example, sustainable cities or smart cities. A regression generates a model that estimates the value of the particular variable specific to a given citizen in this context. The target variable in the case of regression is the amount of actual or estimated usage of a given service per citizen in the context of smart sustainable/sustainable smart cities. On the whole, in contrast to classification which predicts whether something will happen, regression estimates how much something will happen. As with classification, a model can be adjusted to do regression and classification using, for example, neural networks and decision tree algorithms.

Clustering This data mining task attempts to group entities together by their similarity by identifying clusters (based on similar classes of objects) to describe or characterize the data. This grouping is not driven by any specific purpose, contrary to classification. Clustering, as a by-product of its normal function, allows for identifying dense and sparse regions in object space, as well as for discovering distribution patterns and correlations among data attributes. An example clustering question in the context of smart sustainable/sustainable smart cities would be: Which class of citizens does mostly use sustainable transport? Do citizens form natural segments in connection with mixed-land use as a typology of sustainable urban forms? Do mobility patterns vary depending on which typologies prevail across different spatial scales? Clustering can be very useful in preliminary exploration pertaining to various urban domains to see which kind of grouping exists, as this grouping

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in turn may suggest other data mining approaches whose analytical outcomes may contribute to improving, integrating, or rethinking certain typologies as part of spatial organizations. Indeed, clustering can also be used as input to decision-making processes related to urban design and planning focusing on questions such as: What kinds of typologies should we promote or mainstream as part of spatial organizations to improve environmental sustainability performance?

Data Reduction As applied to colossal datasets, data reduction attempts to reduce the amount of the data intended for processing by taking a large dataset and replace it with a small one that contains the most relevant or much of the important information in the large one. The main purpose of data reduction is to reduce the complexity associated with the computational and analytical techniques involved in solving a particular urban sustainability problem. Besides, the use of the small datasets may still achieve the intended goal, as long as it better reveals the information needed for further processing. For example, a massive dataset on mobile movements of citizens may be reduced to a much smaller dataset revealing the activities associated with environmental indicators in terms of CO2 emissions. As a detailed example taken from Batty (2013a, pp. 277–278), ‘We have 1 billion or so records of all those who have tapped ‘in’ and ‘out’ of the public transport systems deploying the smart ‘Oyster’ card for paying for travel …. The time period for the data is over 6 months … The dataset is remarkable in that we know where people enter the system and leave it, apart from about 10% of users who do not tap out due to open barriers. The dataset is thus further reduced in its comprehensiveness. Because tap-ins and tap-outs cannot be associated with origins and destinations of trips, we cannot easily use this data in our standard traffic models without some very clever detective work on associating this travel trip data to locations of home and work and other land use activities. This is possible by good estimation but requires us to augment and synthesize the data with other independent datasets, and thus, there is always error.’ This is a good example of how data-driven urban analytics shapes urban planning in terms of using traffic models of effective patterns and correlations between travel behavior and locations of home and work and other land use activities. However, while data reduction involves loss of information under normal conditions, the trade-off for enhanced insights is of importance and significance. The data mining tasks relate to different analytical methods, including descriptive (what happened?), diagnostic (why did it happen?), predictive (what will happen?), and prescriptive (what should be done?) used to solve different decision-making problems related to urban sustainability in terms of urban operations, functions, services, designs, strategies, and policies (Bibri 2018a). The first three types of analytics are concerned with decision-making and its support, which entails human intervention, the level of which would vary depending on the nature and complexity of the application in connection with various urban domains and sub-domains. The last one is associated with decision automation and some kind of decision support. The targets of decision-making and action-taking are associated with the operating and organizing processes of urban life in line with the goals of sustainable development.

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A Data Mining Framework for Urban Analytics: Data-Analytic Solutions to Urban Sustainability Problems

This section presents some of the common fundamental principles of data science underlying the common types of data mining tasks. Provost and Fawcett (2013) provide a detailed overview of the fundamentals of data science and data mining, in particular how they allow to think about problems where data mining may be brought to bear. As mentioned above, a fundamental concept of data science is the process of data mining, which involves relatively well-understood stages entailing the application of ICT tools and methods pertaining to the automated discovery of patterns and correlations from large masses of data across distributed environments, as well as creativity, expert knowledge, and common sense (as to, for example, defining and crafting variables and structuring problems as part of data preparation as a key component of the proposed data mining framework). In short, data mining involves the application of a considerable amount of science and technology, as well as skills and know-how. Next, a systematic framework for urban analytics or ‘big data’ studies is presented (see Fig. 1), which places a structure on the problems pertaining to urban sustainability, allowing reasonable consistency, repeatability, and objectiveness. The derivation of this data mining framework is based on cross-industry standard process for data mining (e.g., Kurgan and Musilek 2006; Marbán et al. 2009; Ponce and Karahoca 2009; Chapman et al. 2000; Shearer 2000). The deep technical details of the components of this data mining framework and thus how they are linked to urban domains and sub-domains in relation to sustainability dimensions is beyond the scope of this chapter.

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Fig. 1 A data mining framework for urban analytics

Applicable to the domains of smart sustainable/sustainable smart cities (transport, land use, traffic, mobility, energy, environment, healthcare, education, public safety, governance, etc.), this systematic framework emphasizes the idea of iteration as the case for all existing codifications of the process of data mining. This implies that solving a particular urban sustainability problem may require going through the process more than once. The entire process is an exploration of the urban data and how it can be integrated and harnessed. The rationale for the iteration is for the group of data scientists (or city analysts) in collaboration with urban researchers, scholars, and planners across various urban entities to increase their understanding and thus gain more knowledge as they explore the problem and devise the right solution to it. Indeed, after the first iteration, the process becomes well-informed and revealing. Also, the lessons learned during the process can trigger new, often more focused and fine-grained sustainability questions, and subsequent data mining procedures will benefit from the experiences of previous ones. In addition, the sequence of the components is not strict, and moving back and forth between different components is always required. The arrows indicate the most important and frequent dependencies between the components. A data mining process continues after a solution has been deployed. The components are discussed next in more detail in relation to urban sustainability.

6.1 Understanding and Specifying Urban Sustainability Problems Initially, it is crucial to understand the urban sustainability problem that is to be tackled. This entails asking the right or relevant question to go about exploring or investigating. This is not an evident or clear-cut thing per see. Sustainability endeavors within diverse urban domains and how they relate to physical, environmental, social, and economic dimensions of smart sustainable/sustainable smart cities hardly ever come prepackaged as unambiguous or straightforward data mining problems. Usually, recasting the problem (organizing it in a different approach) associated with one or more dimensions of urban sustainability and devising an acceptable solution is an iterative (nonlinear) process of discovery taking the form of cycles within a cycle. The initial formulation of any urban sustainability problem is unlikely to be complete, thereby the need and relevance for multiple iterations to achieve an adequate, or ideally optimal, solution formulation. The understanding stage of the urban sustainability problem represents part of competence and skillfulness where the urban analysts or specialized data scientists’ creative and innovative ideas become determining in the process of the problem formulation with regard to how to cast the urban sustainability problem as a set of data science problems. In the domain of urban sustainability, both specialized and interdisciplinary knowledge help urban analysts come up with novel problem and solution formulations.

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Typically, the early stages of any problem in this regard entail designing a solution that takes advantage of data mining techniques. This can signify engineering the urban problem in ways that consist of one or more subproblems that involve building clusters or constructing models for classification, clustering, probability estimation, regression, or causal modeling. To understand the urban sustainability problem as the first stage, the urban analysts who are in charge of structuring the problem should think carefully about the use scenario. Provost and Fawcett (2013) devote two entire chapters (Chaps. 7 and 11) to this concept, which is one of the most important concepts of data science. The understanding stage involves understanding the project objectives and requirements from an urban sustainability perspective, converting this knowledge into a data mining problem definition, and creating a preliminary plan to achieve the objectives. That is to say, based on the concept of use scenario, what exactly we want to carry out, how exactly we would carry it out, what aspects of use scenario constitute possible models of data mining, and which kind of models are most relevant. As to the latter case, the way forward is to begin with a simplified view of the use scenario (e.g., traffic light control, energy demand management, and car sharing implementation). As the process will evolve, we will loop back and adjust the use scenario to better reflect the actual urban need. This entails framing the urban sustainability problem in ways that can allow us to systematically and effectively decompose it into data mining tasks. In fact, ‘a critical skill in data science is the ability to decompose a data analytics problem into pieces such that each piece matches a known task for which tools are available. Recognizing familiar problems and their solutions avoids wasting time and resources reinventing the wheel. It also allows people to focus attention on more interesting parts of the process that require human involvement—parts that have not been automated, so human creativity and intelligence must come into play’ (Provost and Fawcett 2013, p. 20). In relation to urban sustainability use scenarios, several environmental, social, and economic indicators could be extracted from historical urban data, assembled into predictive models (associated with mobility, travel, traffic, energy, healthcare, education, utility, and so on), and then deployed in the operating and organizing urban processes and activities as part of data-centric applications (or strategics and policies) spanning a variety of urban domains in relation to the operational functioning, management, and planning of smart sustainable/sustainable smart cities. In this regard, the predictive model constituting an aspect of a particular use scenario abstracts away most of the complexity of the urban world, focusing on a specific set of environmental, social, or economic indicators that correlate in some way with the value of the target variable to be predicted (see Bibri 2018a for further clarification). For example, an environmental urban sustainability problem would be to find out if a group of dwellers will be environmentally sustainable. A subtask of data mining that will likely be part of the solution to such problem is to estimate from historical data the probability of dwellers being environmentally sustainable in a given district characterized by certain typologies (e.g., density, diversity, and mixed land-use) on the basis of a set of descriptive attributes entailing different environmental and spatial indicators. The resulting predictive model (patterns and correlations) could then be used as insights into sustainable urban strategies or planning practices.

6.2 Understanding Urban Data In this stage, the focus is on matching the problem with the data, a process that involves understanding the strengths and weaknesses of the available data. In this respect, the solution to the urban sustainability problem as a goal is to be built from the available raw material. Historical urban data often are collected and stored for purposes unrelated to the current urban problem, or sometimes for no explicit purpose at all. Different databases are available across diverse urban domains and owned by different urban entities, covering different information on citizens, transactions, movements, observations, and so on, and may have varying degrees of reliability, different formats, and varied costs. In the urban domain, some data are open and thus accessible to the public for use while other data are confidential and hence pose privacy issues. As to open data, there are many municipal governments around the world that have started to release various kinds of administrative and operational data using various kinds of open data models (see Ferro and Osella 2013 for an overview of eight different models). Also, some data are available virtually for free, while other data require effort to obtain or even need to be acquired. Still not all the data needed for building solutions to a given urban sustainability problem exist. Hence, some data are likely to necessitate entire ancillary projects (providing necessary support to the primary activities or operation of the involved urban stakeholders) as organizations, institutions, and enterprises to arrange their collection and storage. In the context of smart sustainable/sustainable smart cities, it is necessary to put in place a cross-service domain system to ensure that access to the data from different urban domains is available at all times in terms of data input and result visualization by the different urban entities involved in the domain of sustainability planning (Bibri 2018a). Prior to this, it is important to ensure that the diverse kinds of datasets pertaining to the areas associated with urban sustainability (traffic,

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transport, energy, environment, mobility, health care, education, etc.) are open for use by the city constituents with respect to data-driven applications in relation to operations, functions, services, designs, strategies, and policies. Taking into account the above, data warehousing is of critical importance in terms of urban data understanding. It serves to collect and coalesce data from across urban systems involving multiple urban entities (e.g., departments, authorities, agencies, institutions, and enterprises), each with a set of its own databases. The purpose is to enable various analytical systems across urban domains to have access to the central data warehouse. Residing usually at a single site, data warehouse is a massive repository of information collected from multiple sources but stored under a unified schema. In this sense, data warehousing can be regarded as a facilitating technology of data mining in the context of smart sustainable/sustainable smart cities. This relates to such data mining models used to perform data processing and analysis functions as distributed data mining, grid-based mining, or data mining from multitechnology integration. Data warehousing is of crucial importance in the domain of urban sustainability. Indeed, smart sustainable/sustainable smart cities rely on data warehouses or storage facilities so that they can apply data mining more broadly and deeply. For example, if a data warehouse integrates records from travel behavior, energy consumption as well as traffic system, and mobility patterns, it can be used to find characteristic patterns of effective typologies and design concepts in terms of urban planning. A critical part of the data understanding stage in the context of smart sustainable/sustainable smart cities is estimating the costs and benefits of data sources and data repositories, and deciding whether further effort is merited, in particular in relation to the investment associated with the collection, storage, and processing of urban data. In relation to data warehousing and processing, urban data do require additional effort to be collated after all datasets are acquired and accessible and to be subsequently mined. As data understanding progresses in relation to attempting to solve various urban sustainability problems, solution paths may change direction and new insights come into play in response, and the efforts of urban analysts may even fork, as sometimes one problem may have different solutions, or two problems of the same concern may be categorized significantly different in terms of which of such techniques as classification, regression, and probability estimation or profiling, clustering, and co-occurrence grouping are more suitable. It is important, when attempting to understand data, to dig beneath the surface to uncover the structure of the urban sustainability problem in terms of what analytical tasks are of more relevance to solving that problem, as well as in terms of the data that are available. Afterward, we come to match these data to relevant data mining tasks for which there are substantial scientific and technological methods and systems to apply. It is not unusual for an urban sustainability problem, whether be it physical, environmental, economic, or social, to contain several data mining tasks, often of different types, whose solutions should be integrated for effective outcomes (see Chap. 11 from Provost and Fawcett (2013) for more detail). In sum, urban data understanding encompasses the following steps: • • • • • •

Data collection and warehousing; Familiarity with the data; Identification of data quality problems; Discovery of first insights; Detection of interesting subsets; Formation of hypotheses for hidden information.

6.3 Preparing and Combining Urban Data from Diverse Sources The analytic techniques to bring to bear as to solving urban sustainability problems impose certain requirements on the data they employ. They require the data be in a certain form, which often is different from how the data are provided originally or naturally and collected automatically. Therefore, some conversion of the data into suitable forms is necessary in order to be able to achieve better outcomes. Typical examples of data preparation in the urban domain entail coordinating data from different sectors by bringing the different elements of urban entities or complex activities into a relationship that will ensure efficiency or harmony (e.g., data from transport, traffic, and environment), converting data to tabular format, combining tables with similar or shared attributes, inferring or completing missing values, removing repetitive values, and converting data to various types for consistency. Concerning the latter, some data mining techniques are, as discussed above, designed for categorical data (e.g., for classification) while others deal only with numerical values (e.g., for regression). These must often be normalized so that they become computationally comparable and yield the desired results. The interested reader can be directed to Chap. 3 from Provost and Fawcett (2013) for a detailed discussion of the most typical format for mining data

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and rules of thumb for doing such conversions. In fact, data preparation techniques could be the topic of a whole book by themselves. A related aspect to conversion, which pertains to the early stages of the automatic processing of the data, is to provide guidelines on how all the different cross-thematic data categories can be integrated. More generally, urban analysts team may spend considerable time early in the process defining and crafting the variables used later in the process as part of structuring the urban sustainability problem, an aspect that has a lot to do with expert knowledge (urban sustainability) as well as creativity and common sense. Overall, data preparation entails constructing the final dataset to be fed into the data mining algorithms, where tasks encompass tables, records as well as attribute selection, data transformation, and data cleaning.

6.4 Building Models and Generating Patterns as True Regularities Generally speaking, a model is an (over)simplified view of the real world or a representation of reality created to serve a purpose. In the context of smart sustainable/sustainable smart cities, this oversimplification is based on assumptions about what is and is not important for the purpose of advancing their contribution to the goals of sustainable development in relevance to diverse urban domains, or sometimes based on constraints on information or tractability. However, the output of modeling in the process of data mining is some sort of patterns capturing true regularities in the urban data. These data pertain to different urban domains and hence may typify travel behavior, mobility form, energy consumption, land use, network performance, service delivery, facility accessibility, and so on. It is during modeling when data mining techniques are applied to the data through building models from both historical and real-time data depending on the urban sustainability problem that is to be solved. In this respect, it is crucial to have some understanding of the sorts of available techniques and algorithms as part of the fundamental ideas of data mining. In this stage, the urban analysts ensure that various data mining techniques and algorithms are selected and applied in relevance to the urban sustainability problem, and parameters are learned. Also, some methods may have specific requirements on the form of input data, and therefore going back to the data preparation stage may be needed. There are mainly two types of modeling in data mining based on supervised and unsupervised learning methods: predictive data mining and descriptive data mining, respectively. Predictive data mining generates models described by particular urban data (from mobility, travel, traffic, utility, education, health care, well-being, and so on) and uses some variables, with important, informative gains, in the dataset to predict future or unknown values of target variables. Speaking of informative gains and in relation to supervised segmentation, finding important, informative variables in the dataset pertaining to the entities described by the data as one of the fundamental ideas of data mining means the variables that are most predictive of the target, or alternatively, have the best correlation with the target. While for variables to be informative usually varies among applications, underlying informativeness is the idea that information generally represents a quantity that reduces uncertainty about something in the sense that the better the information provided, the more the uncertainty is reduced by that information. Having a target variable crystalizes the idea of finding informative attributes with regard to identifying whether there are one or more other variables (or finding knowable attributes that correlate with the target of interest) that reduces the uncertainty about the value of the target or in it. Determining these correlated variables is intended to provide key insights into the urban sustainability problem on focus. Commonly, prediction signifies forecasting a future event. A predictive model is a formula (mathematical or logical statement but usually hybrid of the two) for estimating the future or unknown value of the target. This implies that this value could be something in the future, in the present, or in the past. Indeed, in the urban domain, data mining may deal with historical or real-time data, and hence models are built and tested using events from the past and present. Now we exemplify predictive modeling for illustrative purposes. We can think of many urban questions involving predictive modeling, such as how can we segment the citizens with respect to mobility as something that we would like to predict or estimate in relation to a particular spatial scale (e.g., district, city, and region). The target of this prediction can be something we would like to relate to environmental sustainability or social sustainability, such as which individual movements are likely to increase emissions and which ones are likely to be influenced by existing typologies and design concepts across a particular spacial scale, or which individual movements are likely to be associated with enhanced spatial accessibility to services and facilities as an aspect of the quality of life. In this regard, the target might be cast in a negative or positive light. Furthermore, a

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predictive model (based on such data mining tasks as classification, regression, and/or probability estimation) focuses on estimating the value of some particular target variable of interest. To extract patterns from data (useful knowledge pertaining to mobility) in a supervised manner entails segmenting the citizens dwelling in a particular district into subgroups with instances of similar values for the target variable as part of subgroups that have different values for the target variable. In this case, the segmentation is performed using values of variables that are known when the target variable is not in order to predict its value accordingly. In addition, the segmentation may concurrently provide a human-understandable set of segmentation patterns. An example of a segment expressed in English would be: ‘people who live in density and mixed land-use oriented areas and prefer walking and cycling on average have an emission rate of 5%’. Specifically, 5% describes the predicted value of the target variable for the segment whose definition (which references some particular attributes) is ‘people who live in density and mixed land-use areas and prefer walking and cycling.’ As regards to descriptive data mining, it produces new, non-trivial characteristic or grouping information based on the available urban dataset (e.g., life quality, travel behavior, mobility, accessibility, network performance, land use, and transport) while focusing on finding patterns for human interpretation. In descriptive modeling, the primary purpose of the model is to gain meaningful insights into the underlying phenomenon of urban sustainability in relation to its various dimensions. A descriptive model of citizen travel behavior or mobility mode would tell us what citizens who use sustainable transport or cycling and walking typically look like. A descriptive model must be judged in part on its intelligibility and easiness of understanding for an effective deployment in relation to urban services, designs, strategies, or policies, in contrast to a predictive model which may be assessed solely on the basis of its predictive performance determined by previous experience (training data). As some of the same techniques and algorithms can be used for both descriptive and predictive modeling, the difference between supervised and unsupervised data mining models is not as strict as this may imply. Below are some examples of predictive and descriptive data mining in relation to smart sustainable cities taken from Bibri (2018a): Predictive Questions • • • • • • • •

Classify citizen travel behavior; Classify mobility modes in high-density areas; Classify household energy consumption patterns; Predict how GHG emissions will go in the next month (increase or decrease); Predict how energy consumption will go in the next year (increase or decrease); Predict areas of dense traffic in the near future; Predict travel behavior in connection with particular sustainable typologies at a particular spatial scale; Predict spatiotemporally the development and propagation of traffic congestion with small errors.

Descriptive questions • • • • • • • •

Find useful travel behavior or mobility mode categories Find interesting collective or individual mobility patterns Find characteristic information about traffic jams and road congestion Describe normal accessibility to facilities in urban areas characterized by mixed-land use or density Find groups of sustainable typologies that share similar aspects in terms of environmental performance Find groups of citizens that share similar travel behavior patterns across various spatial scales Find association rules between mobility or commuting behavior and environmental indicators Discover the subgroups of travel characterized by common behavior, time length, and purpose.

6.5 Evaluating and Interpreting the Obtained Results After building the desired models (or patterns capturing regularities in the data), it is important to assess the data mining results rigorously and to gain confidence that they are valid and reliable before moving on in the process. This is what the evaluation stage is about. It is desirable to have confidence that the generated models represent true regularities and not just sample anomalies, odds, or idiosyncrasies. The underlying assumption is that an urban analyst can always find patterns by

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looking hard enough at any dataset, but these patterns may not survive careful scrutiny. This relates to one of the fundamental concepts of data science: overfitting. Moreover, it is inadvisable to deploy the results of data mining immediately as to their use for decision-making purposes. Prior to their deployment, results have to be evaluated in ways that ensure the generated models satisfy the original urban goals in terms of supporting decision-making or making decisions with regard to improving various aspects of sustainability in the context of smart sustainable/sustainable smart cities. This involves finding a data-analytic solution to the urban sustainability problem being explored or investigated. A data mining solution (model) often is only a piece of the larger solution to the urban sustainability problem in question. And it needs to be evaluated as such. There are different external factors that should be taken into account when evaluating models, which might make them impractical, although they pass strict evaluation tests in a controlled laboratory setting. The complexity and scale of urban sustainability projects imply that they involve various urban stakeholders that have interests in the urban sustainability decision-making that will be supported by the resultant models. This signifies that the results of data mining are to be evaluated using both qualitative and quantitative metrics. However, the stakeholders need to be satisfied by the outcome of the evaluation with regard to the quality of the models’ decisions in order to be ‘sign off’ on the deployment of the resultant models. Irrespective of the domain application of urban sustainability in this context, the basic idea guiding this quality is to ensure the model is unlikely to make mistakes or to be useless in directing urban operations, functions, services, designs, strategies, and policies in line with the goals of sustainable development. To facilitate this kind of qualitative evaluation, the urban analyst must think about the comprehensibility of the model to urban stakeholders, and accordingly attempts to find ways to render the behavior of the model comprehensive, if the latter happens to be not so due to some complex mathematical formula, for example. The rationale for performing the evaluation early on to provide a comprehensive assessment framework before its use (production) is that it may be difficult to obtain detailed information on the performance of a deployed model due to the limited access to the production environment where such model is applied. Adding to this is that the model may be deployed as part of decision-making systems related to diverse urban domains. In addition, deployed systems (e.g., related to urban operations, functions, services, strategies, designs, strategies, and policies) typically contain many parts that they tend to move around in the underlying system, and assessing the contribution of a single part of the system remains difficult. To obtain the most realistic evaluations before taking the risk of deploying the resultant models across urban systems, it is recommended to build test bed environments that can reflect production data as closely as possible. The technical details of how to do so, as well as to design advanced experiments for instrumenting deployed systems for evaluations to ensure that some external factors or urban dynamics are not changing to the detriment of the model’s decision-making, are beyond the scope of this chapter. The interested reader could though turn to Kohavi et al. (2012) for pertinent insights. Additionally, for a detailed system design to help deal with other evaluation-in-deployment issues (e.g., change to input data as to format and substance), the reader can be directed to Raeder et al. (2012). In sum, the evaluation stage requires current model have a high quality from a data mining perspective, and before final deployment, it is important to test whether the model achieves all urban sustainability project objectives.

6.6 Deploying the Results for Urban Operations, Functions, Services, Strategies, and Policies By mining the big data deluge generated within smart sustainable/sustainable smart cities, it is of high potential to discover new principles of urban sustainability planning as well as complex urban dynamics in the form of true regularities: unknown patterns and subtle relationships from which useful, meaningful, and valid correlations can be made. The discovery of this knowledge will further enhance the urban systems and domains’ performances in terms of operations, functions, designs, strategies, and policies in the context of sustainability. This is what the intent of the deployment of the results of data mining is about in the realm of smart sustainable/sustainable smart cities. In the deployment stage, the results of data mining are put into real use in terms of making, supporting, or automating different kinds of decisions associated with urban operational functioning, planning, and service delivery. The clearest cases of deployment in this regard entail implementing predictive or descriptive models in the processes operating and organizing urban life (traffic, transport, energy, etc.) or information systems associated with public services (health care, education, safety, utility, etc.) as part of decision support systems across various urban entities. In this context, a model for predicting or describing travel behavior could, for example, be used in public transport system engineering or management. In relation to this, travel ‘data are potentially extremely useful for figuring out disruptions on the [transport] system. We do need, however, to generate some clever cognitive analyses of how people make their way through the various transport systems, just as we need to assign travelers to different lines to ensure that we can measure the correct number of travelers on each line … The state of the art in what we know about navigation in complex environments is still fairly primitive. Many assumptions have to be made and we have no data on what different

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users of the system have actually learned about their routes. New users of the system will behave differently from seasoned users and this introduces further error. We can see disruption in the data by determining the times at which travelers enter and exit the system, but to really predict disruption on individual lines and in stations, we need to match this demand data to the supply of vehicles and trains that comprise the system’ (Batty 2013a, p. 278). Many other kinds of models can be built into environmental systems, energy systems, water distribution systems, communication systems, building systems, traffic systems, and a wide variety of service-oriented information systems (utility, health care, education, safety, etc.) to increase the contribution of smart sustainable/sustainable smart cities to the goals of sustainable development in terms of environmental regeneration, economic efficiency, and social equity and well-being. The data mining techniques themselves can be deployed in the urban domain, rather than the models produced by a data mining system, due to the fact that smart sustainable/sustainable smart cities are highly complex and dynamic systems that can evolve faster than the data scientists specialized in urban analytics can adapt. Deployment can also be much less technical when a set of rules discovered by data mining techniques could help to quickly diagnose and fix a common error in some systems (e.g., typologies, design concepts, bicycle, or car-sharing approaches). In this case, the deployment can be in the form of disseminating new practices containing the rules or principles in question. Deployment can moreover be much more subtle, such as a change to operations, functions, services, and strategies resulting from insights gained from mining the urban data. Results deployment often returns to the urban sustainability problem understanding phase, irrespective of whether it is successful. The process of data mining generates a great deal of insights into the urban sustainability problem as well as the difficulties surrounding its solution, which can be mitigated by a second iteration. This is to enhance the solution through new ideas for improved performance, an endeavor that usually emanates from the experience of thinking about the urban data and the performance goals of urban systems in terms of their contribution to the goals of sustainable development. Important to note is that starting the cycle again of data mining is not necessarily related to the failure of results deployment. The stage of evaluation may reveal that the resultant models are suitable for deployment, thereby the need for adjusting the problem formulation or obtaining new or different data. This is illustrated in Fig. 1: the link from results evaluation back to urban sustainability problem understanding. To sum up, it is worth pointing out that in urban analytics, it is critically important for urban analysts to be able to formulate urban sustainability problems well in relevance to each urban domain (e.g., traffic system management) and sub-domain (e.g., traffic light control), to prototype (analytical) solutions quickly, to make realistic assumptions in the face of ill-defined and ill-structured problems, to design scientific procedures for making meaningful discoveries, and to analyze results. These qualities necessitate seeking and building a strong data science team with specialized expertise and interdisciplinary knowledge rather than traditional software engineering professionals. In view of this, new partnerships and alliances among different urban entities are necessary for the use of big data analytics in the context of smart sustainable/sustainable smart cities, especially city authorities are likely to lack data scientists and hence must borrow them from academic institutions and industrial organizations. Also, to facilitate data science, more data scientists are to be acquired by diverse urban departments. Otherwise, novel tools are needed for translating big data into easily understandable analytical approaches so that data analysts within urban sustainability can handle data by running predefined forms of analytics.

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On the Emerging Applications of Data Mining for Urban (Sustainability) Analytics

As part of urban reality mining (e.g., Batty et al. 2012), urban sustainability mining (e.g., Bibri 2018a, b) which pertains to sensing complex environmental and socio-economic dynamics and changes by means of ubiquitous sensors embedded, wirelessly networked throughout urban environments, is a key determinant of how smart sustainable/sustainable smart cities developing and responding to the challenge of sustainability are becoming smarter (Bibri 2018b). Mining of urban sustainability depends on dedicated, powerful software applications to log urban infrastructures, spatial and physical organizations, and mobility patterns, as well as natural ecosystems and related services based on sensor data (Bibri 2018b). The analysis of derived large datasets will help extract computationally complex activity, behavior, process, and environment models to identify and gain predictive and descriptive insights into new structures, processes, systems, and forms as to how smart sustainable/sustainable smart cities can increase their contribution to the goals of environmentally sustainable development through enhancing urban intelligence functions for decision-making in this regard. There are many applications emerging in this direction (e.g., Benevolo et al. 2016; Bibri 2018a; Batty et al. 2012; Khan et al. 2012; Milovic and Milovic 2012; Sin and Muthu 2015; Zhao et al. 2016; Zhou et al. 2016), providing or suggesting new ways of mobility enhancement,

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transport management, energy management, environmental management, water management, waste management, health care, education, citizen services, and so forth across diverse, multiple urban domains.

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The Unfolding and Soaring Deluge of Urban Data for Big Data Studies

In contrast with urban knowledge derived from longer standing, more traditional urban studies, data science as practiced within the field of urban science offers the potential for the kind of urban knowledge that is inherently longitudinal, and has greater breadth, depth, scale, and timeliness (Batty et al. 2012; Kitchin 2016; Lazer et al. 2009) in the context of smart sustainable/sustainable smart cities (Bibri 2018a). This is being enabled and afforded by the unfolding and soaring deluge of urban big data. With respect to the data-driven urban knowledge, the emphasis has been on the development of new data analytics that utilizes sophisticated techniques and advanced mathematical models designed to process and analyzes enormous datasets (e.g., Batty 2013b; Kitchin 2014, 2016). This pertains to the process of knowledge discovery, which involves carefully choosing variable selection mechanisms, encoding schemes, preprocessing, reductions, and projections of the data prior to discovering the intended patterns and building the relevant models, as well as their evaluation, interpretation, and visualization (Bibri 2018a). Conducting scientific and academic research using advanced big data analytics techniques has positive implications for sustainability that are wide-ranging, spanning such urban processes as operations, functions, services, designs, strategies, and policies pertaining to multiple urban domains in terms of advancing various forms of knowledge and related practices within the field of smart sustainable/sustainable smart urbanism (Bibri 2018a). Such implications include, but are not limited to, the following: • Overcoming the limitations of ‘small data’ studies associated with such data collection and analysis methods as surveys, focus groups, case studies, participatory observations, interviews, content analyzes, and ethnographies, including high cost, infrequent periodicity, quick obsolescence, inaccuracy, incompleteness, as well as subjectivity and biases. • Overcoming the inherent deficiencies of limited samples of data that are tightly focused, time- and space-specific, restricted in scope and scale, and relatively expensive to generate and analyze, which affects the robustness of research results. • Drastically changing the way, the research data can be collected, processed, analyzed, modeled, and simulated within various academic and scientific research domains so as to make decisions easier to judge and more fact-based in relation to urban operations, functions, strategies, plans, policies, and other practices. • Completely redefining urban problems and understanding them in new ways, as well as enabling entirely novel ways to tackle them, thereby doing more than just enhancing existing practices, especially in relation to sustainability. • Transforming and advancing knowledge based on the deluge of urban data that seeks to provide more sophisticated, wider-scale, finer-grained, real-time understanding, and control of various aspects and complexities of urbanity. • Enabling well-informed, knowledge-driven practices based on advanced forms of intelligence with regard to the operational functioning, management, design, planning, and development of urban systems in the context of sustainability. • Promoting and facilitating openness and access to public data and their integration with the private information assets for use in city analytics and big data studies to advance the knowledge about sustainability. • Advancing environmental indicators and objective targets for the purpose of monitoring progress, implementing strategies, allocating resources, and increasing the accountability of stakeholders. • Enabling novel and harmonizing urban-level metrics for monitoring the goals of sustainable development through more objective and robust indicators and targets developed and continuously enhanced based on big data analytics. • Exploring and discovering laws and principles of sustainability pertaining to environmental and socio-economic aspects, and allowing an inference of stakeholders’ responses to operations, functions, services, strategies, designs, and policies in relevance to sustainability. Smart sustainable/sustainable smart cities are increasingly being permeated with big data technologies and their novel applications in terms of their systems and domains (Bibri 2018a, 2019a). This can be seen as a new ethos added to the smart sustainable/sustainable smart urbanism culture in response to the rise of ICT and the spread of urbanization as major global shifts at play today. The characteristic spirit of such urbanism era is manifested in the behavior and aspiration of such cities toward embracing what big data computing has to offer in order to bring about sustainable development. This is due to the tremendous potential of this advanced form of ICT for adding a whole new dimension to urban sustainability in an increasingly technologized, computerized, and urbanized world. The range of the emerging big data applications as novel

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analytical and practical solutions that can be utilized in this regard is potentially huge (see Bibri 2018a, b for a detailed list pertaining to various urban domains or sub-domains), as many as the case situations where big data analytics may be of relevance to support and enhance some sort of decision or insight into connection with the various aspects of sustainability.

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Discussion and Conclusion

We stand at a threshold in beginning to make sense of big data analytics and data-driven decision-making and related processes, systems, and methods that will be of massive use in, and interwoven into the very fabric of, smart sustainable/sustainable smart cities of the future as complex and dynamic systems within the next decades. We are certainly entering a new urban era, an increasingly computerized and data-driven urban world where new and more extensive data sources, coupled with advanced data processing technologies, will be instrumental in discovering useful knowledge in large masses of data that no one has been able to discover hitherto for making a wide range of well-informed, strategic, and fact-based decisions across the diverse domains of smart sustainable/sustainable smart cities of the future in terms of the underlying operating and organizing processes of urban life. The ultimate aim is to advance the different aspects of sustainability by employing more innovative and effective ways to investigate, assess, and enhance the contribution of such cities to the goals of sustainable development. With big data analytics, and particularly the use and combination of different tasks as part of the data mining and knowledge discovery processes to handle and solve various decision-making problems pertaining to urban sustainability, we will be able to better monitor, understand, and analyze smart sustainable/sustainable smart cities so as to be more intelligently operated, managed, planned, developed, and governed in terms of improving and maintaining their contribution to sustainability. Data mining for urban analytics has become a key factor for transforming and advancing the knowledge of smart sustainable/sustainable smart cities in terms of sustainability and the integration of its dimensions. Especially, such cities generate a huge amount of data which should be harnessed to sustainably enhance their operations, functions, services, designs, and policies. Data mining tasks will help to extract useful knowledge from the available data deluge, which can drive sustainably intelligent decisions with regard to energy transport, traffic, mobility, environment, water and waste management, buildings, land use, design, health care, education, safety, governance, economic development, and the quality of life. This chapter aimed to develop, illustrate, and discuss a systematic framework for city analytics in relation to the domain of smart sustainable urbanism based on cross-industry standard process for data mining. This endeavor was in response to the emerging paradigm of big data computing and hence the increasing influence of big data analytics and its application in enabling, operating, organizing, and planning the processes of smart sustainable/sustainable smart cities as a holistic urban development approach. The proposed data mining framework for urban analytics consists of six components, namely: 1. 2. 3. 4. 5. 6.

Understanding and specifying urban sustainability problems; Understanding urban data; Preparing and combining urban data from diverse sources; Building models and generating patterns as true regularities; Evaluating and interpreting the obtained results; Deploying the results for urban operations, functions, services, strategies, and policies.

The prominence of this framework as a set of conceptual tools lies in the value of the related well-defined stages in enhancing the likelihood of successful and relevant results. This framework as a systematic organization is very useful for analyzing a wide variety of urban sustainability problems as part of academic and scientific research endeavors. It provides a set of questions that can be asked about the proposed project to help understand whether it is well conceived or is fundamentally flawed with respect to the intended goals in terms of the decision-making pertaining to the fundamental goals of sustainable development. It can be tested and used in empirical applications in the city domain. It has an innovative potential to advance urban analytics by providing a novel way of thinking data-analytically about urban sustainability problems. It will provide fertile insights into how to conduct ‘big data’ studies in the field of urban sustainability. This work can serve to bring together city analysts, data scientists, urban planners and scholars, and ICT experts on common ground in their endeavor to transform and advance the knowledge of smart sustainable/sustainable smart cities. The unique features or advantages of the proposed framework lie in its novelty in terms of extending its applicability to sustainability problems in the context and domain of smart sustainable/sustainable smart cities. The current cross-industry

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standard process for data mining is confined to business domain and thus business intelligence applications. In this chapter, it has been expanded and enhanced in response to the need for transforming the knowledge of smart sustainable/sustainable smart cities with respect to academic and scientific research on sustainability by applying novel data collection and analysis methods. Generally, big data analytics is becoming increasingly a salient factor for academic and scientific research and innovation with regard to addressing complex challenges and pressing issues, i.e., responding to major environmental concerns and socio-economic needs. Indeed, the best opportunity for using big data is to harness and analyze data not as an end in itself—but rather to develop big theories about how smart sustainable/sustainable smart cities function and can be managed and planned as to their quest for addressing the challenge of sustainability. I argue that there is tremendous potential for advancing smart sustainable/sustainable smart urbanism or transforming the knowledge of smart sustainable/sustainable smart cities through creating a data deluge that can, through analytics, provide much more sophisticated, finer-grained, wider-scale, real-time understanding and control of various aspects of urbanity in the undoubtedly upcoming Exabyte/Zettabyte Age. Using and applying novel ways of understanding, managing, analyzing, and planning such cities as complex and dynamic systems stems from the key characteristics of big data. This is in term of consisting of Exabytes or terabytes of data; being structured and unstructured in nature; being often spatially and temporally referenced; being created and analyzed in, or near, real-time; being exhaustive in scope and scale by striving to capture entire populations or systems; dramatically exceeding sample sizes commonly in use for small data studies; being relational in database systems by containing common fields that enable the conjoining and combination of different datasets; being fine-grained in resolution by aiming to be very detailed and uniquely indexical in identification; and holding the traits of extensionality (can add new fields easily), evolvability (can change dynamically), and scalability (can expand in size rapidly) (see, e.g., Batty et al. 2012; Bibri 2018a, 2019a; Boyd and Crawford 2012; Kitchin 2014; Laney 2001; Marz and Warren 2012; Mayer-Schonberger and Cukier 2013; Zikopoulos et al. 2012). Further, big data studies enable a shift from coarse aggregation to high resolution; static snapshots to dynamic unfoldings; and relatively simple hypotheses and models to more complex, sophisticated simulations and theories (Kitchin 2013). All in all, scientific knowledge can advance even without coherent models, unified theories, or any mechanistic explanation at all (Anderson 2008). The proposed framework is a general-purpose solution, having a range of potential uses in multiple urban domains. What might be more useful is to derive and devise a set of data mining frameworks specific to each urban domain or even sub-domain. This should still draw on the cross-industry standard process for data mining given its robustness. The need for the domain perspective lies in effectively designing and evaluating such frameworks and related data mining technologies according to the requirements and objectives of each urban domain or sub-domain. One advantage of domain-specific frameworks is the performance enhancement related to the outcome of the well-defined stages, especially the obtained results and their deployment for diverse operations, functions, services, designs, strategies, and policies associated with each domain. The above constitutes the next step to investigate as part of future research work. In this regard, the focus will be on examining the key distinctive features and specificities of various urban domains from technical, computational, and analytical perspectives so as to design a set of frameworks tailored to each urban domain and, optimistically, to each sub-domain. Of equal importance is to develop effective evaluation methods for these frameworks. The standard process for data mining could well inspire further research in this direction, as it provides useful insights into how to design such evaluation techniques, yet specific to each urban domain based on related requirements and objectives, among other things. I hope that this study will stimulate the research community to deepen the discussion about how to provide better solutions for developing, implementing, evaluating, and improving the proposed framework, as well as to find effective ways of how to tailor it to each urban domain, instead of opting for one-solution-fits-all approach that seems to prevail in use in most cases.

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Novel Intelligence Functions for Data–driven Smart Sustainable Urbanism: Utilizing Complexity Sciences in Fashioning Powerful Forms of Simulations Models

Abstract

We are moving into an era where instrumentation, datafication, and computation are routinely pervading the very fabric of the city as a complex system and dynamically changing environment, and vast troves of contextual and actionable data are being generated and used to control, manage, regulate, and organize the urban life. At the heart of this emerging era of data-driven urbanism is a computational understanding of urban systems and processes that reduces urban life to a set of logic, calculative, and algorithmic rules and procedures. Such understanding entails drawing together, interlinking, and analyzing urban big data to provide a more holistic and integrated view and synoptic intelligence of the city. This is being increasingly directed for improving, advancing, and maintaining the contribution of both sustainable cities and smart cities to the goals of sustainable development. Indeed, a new era is presently unfolding wherein smart sustainable urbanism is increasingly becoming data-driven. In light of this, smart sustainable urbanism has become even more complex with the very technologies being used to make sense of and deal with it as involving special conundrums, wicked problems, intractable issues, and complex challenges associated mainly with sustainability and urbanization. Consequently, to tackle smart sustainable cities requires, I contend, innovative solutions and sophisticated approaches as to the way they can be monitored, understood, and analyzed so as to be effectively operated, managed, planned, designed, developed, and governed in line with the long-term goals of sustainability. Therefore, this chapter examines and discusses the approach to data-driven smart sustainable urbanism in terms of computerized decision support and making, intelligence functions, simulation models, and optimization and prediction methods. It also documents and highlights the potential of the integration of these advanced technologies for facilitating the synergy between the operational functioning, planning, design, and development of smart sustainable cities. I argue that data-driven urbanism is the mode of production for smart sustainable cities, which are accordingly becoming knowable, tractable, and controllable in new dynamic ways thanks to urban science and complexity science. I conclude that the upcoming developments and innovations in big data computing and the underpinning technologies, coupled with the evolving deluge of urban data, hold great potential for enhancing and advancing the different practices of smart sustainable urbanism. This work contributes to bringing data-analytic thinking and practice to smart sustainable urbanism, in addition to drawing special attention to the crucial role and enormous benefits of the emerging paradigm of big data computing as to transforming the future form of such urbanism. Keywords





Data-driven smart sustainable urbanism Decision support Intelligence functions Complexity science Complex systems

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Smart sustainable cities Big data analytics Decision making Simulation models Optimization and prediction methods




Introduction

By 2050, demographers predict that 70% of the world’s population will live in cities. If this model is accurate, the urbanization of the planet will present serious challenges to sustainability. Also, as widely estimated, the urban world will become largely technologized and computerized within just a few decades, and ICT as an enabling, integrative, and © Springer Nature Switzerland AG 2019 S. E. Bibri, Big Data Science and Analytics for Smart Sustainable Urbanism, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-17312-8_10

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constitutive technology will accordingly be instrumental, if not determining, in addressing and solving many of the issues raised and the challenges presented by urbanization (Bibri 2019a). It is therefore of strategic value to start directing the use of emerging ICT into understanding and proactively mitigating the potential effects of urbanization, with the primary aim of tackling many of the wicked problems involved in urban operational functioning, management, planning, and development, especially in the context of sustainability. Indeed, the anticipated urbanization of the world poses significant challenges associated with sustainability (e.g., David 2017; Han et al. 2016; Estevez et al. 2016) due to the issues engendered by urban growth in terms of resource depletion, environmental degradation, intensive energy usage, air and water pollution, toxic waste disposal, endemic traffic congestion, ineffective decision-making processes, inefficient planning systems, mismanagement of urban infrastructures and facilities, poor housing and working conditions, public health and safety decrease, social vulnerability and inequality, and so on (Bibri 2018a, 2019a), in spite of urbanization epitomizing an emblem of social evolution. In short, the multidimensional effects of unsustainability in modern and future cities are most likely to exacerbate with urbanization (Bibri 2018a). Indeed, urbanization as a dynamic clustering of people, buildings, infrastructures, and resources strains and puts pressure on the limited urban and natural resources and affects the resilience to the growing demands on them, and urban functioning, management, planning, and governance face ever-mounting challenges. The underlying argument is that urban growth will jeopardize the sustainability of cities as to its environmental, economic, and social dimensions. To disentangle this kind of intractable problems requires evidently major shifts in urban thinking and planning—i.e., newfangled ways founded on more innovative solutions and sophisticated approaches with respect to how cities can be understood, operated, managed, planned, designed, developed, and governed (Bibri 2018a; Bibri and Krogstie 2017a). In this regard, advanced ICT can provide integrated information intelligence for enhancing urban functioning and planning, socio-economic forecasting, and policy design on the basis of participatory, polycentric, and digital models and processes of governance. Against the backdrop of the unprecedented rate of urbanization and the complex problems of sustainability, an array of alternative ways of understanding, operating, managing, planning, designing, developing, and governing cities based on advanced ICT is materializing and evolving in terms of how smart cities can transition toward the needed sustainable development and sustainable cities can enhance their sustainability performance. This can be attained through adopting a set of integrated frameworks, strategies, and policies to foster advancement and innovation in urban systems and domains in line with the goals of sustainability. An increasing urgency to find and apply advanced and innovative solutions is driven by the increasing urban growth and the diffusion of sustainable development in terms of seeking out ways to circumvent the associated effects and challenges, respectively. In particular, Townsend (2013) portrays urban growth and ICT development as a form of symbiosis. This entails an interaction that is of advantage to, or a mutually beneficial relationship between, ICT and urbanization (Bibri 2018a, e; Bibri and Krogstie 2017a). One way of looking at this form of tie-in is that urbanization can open entirely new windows of opportunity, or simply provide a fertile environment, for cities to act as vibrant hubs of technological innovations in a bid to solve a wide variety of environmental, social, and economic problems and challenges, thereby containing the potential effects of urbanization. Indeed, a large number and variety of sophisticated technologies and their novel applications pertaining to big data computing are being developed and applied in response to the need for finding more effective ways to deal with the complexity of the knowledge necessary for understanding, operating, managing, planning, designing, developing, and governing modern cities as complex systems and dynamically changing environments. In consideration of the above, there has recently been a conscious push for cities across the globe to be smarter and thus more sustainable by developing and implementing big data applications across various urban domains to enhance and optimize their operations, functions, services, designs, strategies, and policies in the hopes of reaching the required level of sustainability and improving the living standard of citizens (Bibri 2018a). This is justified by the kind of well-informed, knowledge-driven decision-making enabled and supported by the process of big data analytics through the automated extraction of useful knowledge and valuable insights in the form of applied intelligence, to reiterate. Underneath advanced ICT solutions, there indeed is a vast deluge of big data that is being harnessed, analyzed, and put to work for the benefit and health of cities in terms of sustainability, efficiency, resilience, and the quality of life. Thus, big data are the new natural resource that when mined well provide the foundation for tomorrow’s smarter, more sustainable societies. As a research wave and direction, big data analytics and its application have recently attracted urban scholars and scientists from diverse disciplines as well as urban practitioners from different professional fields due to their importance and influence within urbanism, in addition to being a major intellectual, scientific, and practical challenge (e.g., Batty 2013; Batty et al. 2012; Bibri 2018a, 2019a, b; Bibri and Krogstie 2017a, b; Bettencourt 2014; Kitchin 2014a, 2016). Big data analytics is increasingly seen to provide unsurpassed and innovative ways to address a range of complex environmental challenges and rising socio-economic concerns facing the contemporary city through enhancing and optimizing its operational functioning, planning, design, and development in line with the vision of sustainability. Therefore, urban planners, strategists, and

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policymakers are faced with unique opportunities in this direction. Big data analytics is enriching and reshaping our experiences of how the city can be operated, planned, designed, developed, and governed due to the kind of the underlying well-informed, knowledge-driven decision-making associated with the knowledge of how effectively, fast, and best to advance sustainability (Bibri 2018a, 2019a). Unsurprisingly, a number of advanced infrastructures, platforms, systems, methods, techniques, and algorithms pertaining to big data computing are being developed and implemented in response to the urgent need for handling the availability of the vast troves of urban data being generated and using them to manage, control, regulate, and plan the city by extracting the kind of knowledge needed for integrating urban systems, coordinating urban domains, and coupling urban networks in ways that enhance sustainability performance in the realm of smart sustainable/sustainable smart cities. Besides, there is an urgent need for developing and applying data-driven innovative solutions as novel applications and sophisticated methods to overcome the challenges of sustainability and urbanization (Bibri 2018a, b, c, d, 2019a, b). In view of that, data-driven smart sustainable/sustainable smart urbanism has become a new direction of research representing a fertile area of investigation that merits further attention. Furthermore, our world continues to change rapidly and become more and more complex and uncertain, and thus, systemic thinking and complex thoughts are necessary for managing, adapting, and seeing the wide range of choices we have before us. This provides the opportunity to identify the root causes of sustainability problems and see new opportunities in an increasingly urbanized world. ‘Especially, some of our solutions have created further problems, and many complex problems have been solved by focusing on external factors because they are embedded in larger systems. As real messes, the problems rooted in the internal structure of complex systems as well as their interaction with their environment (e.g., pollution, environmental degradation, toxic waste, economic instability, social inequality, unemployment, and chronic disease) have been difficult to deal with and refused to go away. They persist despite the analytical ability, technical intelligence, and human brilliance that have been directed toward circumventing or eradicating them. They persist because they constitute intrinsically systems problems—undesirable patterns of behavior characteristic of the system structures and reciprocal relationships resulting from the profound interactions that produce those patterns. They will yield only as we reclaim our holistic thinking as well as intuition and thereby see the whole system as the source of its own problems, and find the astuteness and wisdom to restructure it and reshape its interaction in ways that control or predict the cycling of reciprocal relationships to yield positive patterns of behavior’ (Bibri 2018a, p. 300). Smart sustainable/sustainable smart urbanism has become even more complex with the very technologies being used to make sense of and deal with it as being associated with special conundrums, wicked problems, intractable issues, and complex challenges related to sustainability and urbanization. This is well reflected in the planning, design, and development of smart sustainable/sustainable smart cities as a leading paradigm of urbanism. Consequently, to tackle such cities as complex systems and dynamically changing environments requires innovative solutions and sophisticated approaches as to how they can be monitored, understood, and analyzed so as to be effectively operated, managed, planned, designed, developed, and governed in line with the long-term goals of sustainability, thereby strategically improving, advancing, and maintaining their contribution to sustainable development (Bibri 2018a, 2019a, b). Importantly, such cities necessitate advanced thinking approaches to be well understood, which is of crucial importance for enabling more effective, or making visible possible places for the kind of, actions that are necessary for enhancing their operational functioning, planning, design, and governance, as well as their adaptation to changes in ways that guide their overall development toward sustainability. This can be accomplished by developing and applying new urban intelligence functions as new conceptions of the way such cities function and utilize complexity science, data science, and urban science in fashioning new powerful forms of simulation models and optimization and prediction methods on the basis of big data analytics that generate urban forms and structures that improve sustainability, efficiency, and the quality of life (Bibri 2018a, 2019a, b). Especially, building models of such cities functioning in real time from routinely and automatically sensed data is becoming the new reality, coupled with urban ubiquitous sensing getting closer to providing quite useful information about longer-term changes. Set against the preceding background, this chapter examines and discusses the approach to data-driven smart sustainable/sustainable smart urbanism in terms of computerized decision support and making, intelligence functions, simulation models, and optimization and prediction methods. It also documents and highlights the potential of the integration of these advanced technologies for facilitating the synergy between the operational functioning, planning, design, and development of smart sustainable/sustainable smart cities for the primary purpose of improving, advancing, and maintaining their contribution to the goals of sustainable development. This work serves to contribute to bringing data-analytic thinking and practice to smart sustainable/sustainable smart urbanism, and seeks to promote and mainstream its adoption across the urban world, in addition to drawing special attention to the crucial role and enormous benefits of the emerging paradigm of big data computing as to transforming the future form of such urbanism.

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The remainder of this chapter is organized as follows. Section 2 introduces, describes, and discusses the relevant theoretical constructs, namely smart sustainable urbanism in terms of planning, design, and development; complexity science and complex systems; and modeling and simulation in relation to planning and design. Section 3 provides a survey of related work. The data-driven components of such urbanism and related issues are addressed in Sect. 4, including decision support system, decision-making process, big data analytics for enhancing decision-making, the process of knowledge discovery/data mining as an advanced form of urban intelligence, as well as some key expected advancements and opportunities. Section 5 sheds light on and discusses some new approaches and perspectives with respect to urban planning and design. In doing so, it covers new simulation models and prediction methods for urban planning and design, the wicked problems and the potential of big data analytics for tackling such problems, and a novel typology of smart sustainable/sustainable smart city dimensions and functions. The evolving new urban intelligence functions and related simulation models and optimization and prediction methods constitute the object of Sect. 6. Section 7 explains and discusses the various aspects of advanced simulation models and related methods. Section 8 provides an account of smart sustainable/sustainable smart cities as complex systems, encompassing complexity aspects, complexity science relevance and usefulness, and some essential tensions. Complex systems simulation models are addressed in Sect. 9 in terms of challenges and driving forces, new opportunities and future prospects, and novel urban simulation models as informed by the dynamical properties of complex systems. This chapter ends, in Sect. 10, with concluding remarks along with discussions and final thoughts. The data-driven components of such urbanism and related issues are addressed in Sect. 3, including decision support system, decision-making process, big data analytics for enhancing decision-making, the process of knowledge discovery/data mining as an advanced form of urban intelligence, as well as some key expected advancements and opportunities. Section 4 sheds light on and discusses some new approaches and perspectives with respect to urban planning and design. In doing so, it covers new simulation models and prediction methods for urban planning and design and the wicked problems and the potential of big data analytics for tackling such problems. The evolving new urban intelligence functions and related simulation models and optimization and prediction methods constitute the object of Sect. 5. Section 6 explains and discusses the various aspects of advanced simulation models and related methods. Section 7 provides an account of smart sustainable/sustainable smart cities as complex systems, encompassing complexity aspects, complexity science relevance and usefulness, and some essential tensions. Complex system simulation models are addressed in Sect. 8 in terms of challenges and driving forces, new opportunities and future prospects, and novel urban simulation models as informed by the dynamical properties of complex systems. This chapter ends, in Sect. 9, with concluding remarks along with discussions and final thoughts.

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Theoretical Background

2.1 Smart Sustainable Urbanism: Planning, Design, and Development The evolving research and practice in the field of smart sustainable/sustainable smart urban planning and development tends to focus on exploiting, harnessing, and leveraging the unfolding and soaring deluge of urban data flooding from those urban systems (namely built form, urban infrastructure, ecosystem services, human services, and administration and governance) and urban domains (namely, transport, traffic, mobility, energy, built and natural environment, land use, health care, education, science and innovation, and public and social safety) that are associated with the environmental, physical, social, and economic dimensions of sustainability—in the needed transition toward sustainable development. This entails developing and applying new urban intelligence functions and simulation models on the basis of the useful knowledge to be extracted from such deluge by means of big data analytics, which is primarily directed for enhancing decision-making associated in this context with sustainability advancement. The outcome of the analysis of this deluge involves valuable insights into how and the extent to which urban systems, urban domains, and urban networks can interrelate of interlink, as well as into how they can be integrated, coordinated, and coupled, respectively, for the purpose of enhancing and optimizing urban operations, functions, services, designs, strategies, and policies in line with the goals of sustainable development. Sustainable urban planning is the process of guiding and directing the use and development of land, urban environment, urban infrastructure, and related processes, activities, and services in ways that seek to achieve the required level of sustainability. As such, it involves defining the long-term goals of sustainability; formulating sustainable development objectives to achieve such goals; arranging the means and resources required for attaining such objectives; and implementing, monitoring, steering, evaluating, and improving all the necessary steps in their proper sequence toward reaching the

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overall aim (Bibri 2018a, d). Its technical features entail the application of advanced ICT as a set of computational and scientific approaches and technical processes to direct and guide the use and development of land use, natural ecosystems, physical structures, urban forms, spatial organizations, natural resources, urban infrastructures, socio-economic networks, and ecosystem and human services in line with the long-term goals of sustainability. Recent evidence (e.g., Al Nuaimi et al. 2015; Angelidou et al. 2017; Batty et al. 2012; Bettencourt 2014; Bibri 2018a, e; Bibri and Krogstie 2017b) lends itself to the argument that an amalgamation of these defining elements of urban planning with cutting-edge big data technologies as an advanced form of ICT in terms of such functions as control, automation, management, optimization, enhancement, and prediction can create more sustainable, resilient, safe, and livable cities. All in all, the data–driven approach is of paramount importance to strategic sustainable urban planning. Besides, the operation and organization of urban systems, domains, and networks and related processes in the field of sustainable urban planning require not only complex interdisciplinary and transdisciplinary knowledge, but also sophisticated technologies and powerful data analytics capabilities. Sustainable development goals and smart targets should be well understood with respect to their synergy and integration (see, e.g., Ahvenniemi et al. 2017; Angelidou et al. 2017; Batty et al. 2012; Bibri 2018a, b, 2019b; Bibri and Krogstie 2017b; Bifulco et al. 2016; Kramers et al. 2014). This is a valuable force for defining or setting the kind of integrated objectives needed for achieving sustainability in the context of smart sustainable/sustainable smart urbanism. As a management function, sustainable urban planning involves formulating a detailed plan to achieve an optimum balance of demands for growth with the available resources and the need to protect the environment, or to provide and maintain a livable and healthy human environment in conjunction with minimal demand on resources and minimal impacts on the environment. This can be accomplished by integrating sustainable urban strategies with advanced technologies and their novel applications for sustainability, as well as by formulating and implementing effective policy instruments and institutional frameworks. On the whole, smart sustainable urban planning uses ICT in ways that ensure a continuous access, improvement, and advancement of the contribution of the city to the goals of sustainable development. What is known about the relationship between urban planning interventions, advanced technologies, and sustainability objectives is a subject of much debate (Bibri 2018a). This means that realizing smart sustainable/sustainable smart cities requires making countless decisions about urban forms, urban designs, sustainable technologies, and governance. Regardless, the emerging urban planning initiatives should consist in adopting a holistic approach to decision-making, a pathway which can be pursued by employing advanced technological systems and sophisticated analytical approaches, thereby the relevance of big data analytics and related data-driven decision-making processes (Bibri 2018a, d). As noted by Angelidou et al. (2017), the incorporation of the systematic use of big data in the urban policy development and monitoring processes is a key success factor for formulating and implementing effective policies with significant positive impacts on multiple levels. Big data analytics and data-driven decision-making processes are of wide-ranging use in different areas of urban planning, including strategic thinking, sustainable development, transportation planning, environmental planning, land use planning, policy recommendations, public administration, urban design, landscape architecture, and civil engineering. Indeed, the uses of big data analytics are associated with an array of multitudinous decisions involving control, management, optimization, recommendation, and improvement associated with urban operations, functions, services, designs, strategies, and policies. This should be taken into account in, or constitute an integral part of, any comprehensive plan to be formulated for developing and implementing smart sustainable/sustainable smart cities, where the focus is typically on a wide range of sustainability issues, including energy consumption, pollution, waste, traffic congestion, land use inefficiency, and social and environmental policy (Bibri 2018a, d). Urban design constitutes part of urban planning, or the latter overlaps with the former. Specifically, urban design is concerned with urban planning, landscape architecture, and civil engineering, as well as sustainable design, ecological design, sustainable urbanism, ecological urbanism, and strategic urban design, whereas urban planning involves transportation planning, environmental planning, land use planning, policy recommendations, and public administration, as well as strategic thinking, sustainable development, landscape architecture, civil engineering, and urban design (Bibri 2018a; Bibri and Krogstie 2017a). However, the way cities are intelligently designed and planned is of paramount importance to strategic urban sustainability. In this regard, the link between the emerging urban intelligence functions being developed using advanced ICT and urban design concepts and principles lies in that the city structures, forms, and spatial organizations are generated by new powerful forms of simulation models and optimization and prediction methods fashioned on the basis of complexity science and urban science, which are in turn to be utilized by such functions as new conceptions of the way smart sustainable/sustainable smart cities function. In short, the way such cities are designed as informed by such models and methods determines or shapes their operational functioning. Such functions represent a form of advanced decision support pertaining to diverse urban systems and domains in terms of their operational functioning.

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In light of the above, smart urban design entails a blend of sciences and artistic architectures, which the big data analytics system and related simulation models and optimization and prediction methods are extremely well placed to initiate and contribute to. Specifically, such models and methods generate urban structures and forms in terms of design concepts and principles and planning practices that can improve sustainability, efficiency, and resilience (Bibri 2018a; Bibri and Krogstie 2017b). In particular, such models hold great potential to inform future urban designs. Furthermore, in urban science, a field in which data science is practiced, the emerging disaggregate urban models entail exploring many different kinds of models based on complexity science, as well as building many different models of the same situation in the context of sustainability, efficiency, and resilience, a pluralistic approach which is a key to enhancing the understanding of this complexity. In addition, the new immediacy of constructing urban simulation models is being enabled and motivated by the emerging ‘real-time city’ (Kitchin 2014a) and related sensing infrastructures and networks advancing toward providing information about medium- and long-term changes (e.g., Batty et al. 2012; Bibri 2018a; Bibri and Krogstie 2017b) and their prediction. The emerging new models of scientific (knowledge) discovery are germane to how to figure out good designs for efficient, equitable (Nielsen 2011), sustainable, and resilient cities (Bibri 2018a). The idea of advanced ICT penetrating wherever it can to improve sustainability performance (or make urban living more sustainable) is central to the quest for making smart sustainable/sustainable smart cities function as a social organism by design (Bibri 2018a, 2019b). Considering the above, there are new methods emerging for urban design (and planning) driven by the increasing space– time convergence in modern cities. In this respect, advanced urban simulation models operating at different spatial scales and over different temporal spans are characterized by the ability to simulate complex aspects of urbanity using various lenses which enable different systems within urban domains to be predicted using computer models of various sorts (Bibri 2018a). Strategic sustainable urban development can be viewed as an alternative approach to urban thinking and practice focused primarily on addressing and overcoming the escalating environmental problems and the mounting socio-economic issues associated with the current path or predominant paradigm of city development by mitigating or eliminating its negative impacts on the environment and human well-being. In short, sustainable urban development is a strategic approach to achieving urban sustainability. As such, it seeks to guide and direct scholars, practitioners, organizations, institutions, and governments to agree upon concrete ways to determine the most strategic actions in a concerted effort to reach a sustainable future. In this respect, it is guided by a shared understanding of the agreed-upon sustainability principles that embody the end goal for sustainability. The sustainability principles for achieving socio-ecological sustainability as developed through scientific consensus and thus peer-reviewed by the international scientific community are derivative from basic laws of science, including laws of thermodynamics, cycles of nature, conservation of matter, and so on (e.g., Holmberg and Robèrt 2000) and stem from social, cultural, and ethical foundations. In the sustainable society, according to Holmberg and Robèrt (2000), nature is not subject to systematically increasing … 1. 2. 3. 4.

… concentrations of substances extracted from the Earth’s crust; … concentrations of substances produced by society; … degradation by physical means, and in that society … people are not subject to conditions that systematically undermine their ability to meet their needs.

The purpose of articulating sustainability with scientific rigor is to make it more intelligible, comprehensible, and useful for measuring, analyzing, and managing human activities within society. A significant contribution in this line was the development of the above four guiding sustainability principles. The sustainability principles should be, according to Holmberg and Robèrt (2000, p. 298): • • • • • •

based on a scientifically agreed-upon view of the world; necessary to achieve sustainability; sufficient to achieve sustainability; general to structure all societal activities relevant to sustainability; concrete to guide action and serve as directional aides in problem analysis; non-overlapping or mutually exclusive in order to enable comprehension and structured analysis of the issues.

In relation to the city as a clear example of society, to be strategic in moving toward urban sustainability from an environmental perspective requires a clear understanding of environmental sustainability principles, which are employed to set the minimum requirements of a sustainable city in terms of being sensitive to, and in support of, the environment (Bibri

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2018a). Further, sustainability principles define an end goal for urban sustainability, which serves to plan strategically, knowledgeably, and holistically in terms of development to attain socio-ecological sustainability in the city. This relates to strategic sustainable urban development as a planned development that strives to address and overcome the environmental, social, and economic issues facing the city in the context of sustainability in a rigorous, meaningful, and scientific way to achieve a sustainable city as an instance of urban sustainability. This can occur through, or rather requires, tackling the root causes resulting in the systematic decline in the potential of the city by developing upstream solutions necessary to sustain the functioning of its systems and making it more resilient. In this context, the smart dimension of sustainable urban development is of equal importance. Strategic smart sustainable urban development denotes a process of change in the built environment driven by big data technology and its novel applications that seek to promote sustainable built form, environmental integration, economic regeneration, and social equity as a set of interrelated goals in the context of smart sustainable/sustainable smart cities. In other words, to foster economic development while conserving resources and promoting the health of the ecosystem and its users requires innovative solutions and sophisticated approaches resulting from unlocking the untapped potential and transformational power of advanced ICT in terms of its disruptive, substantive, and synergetic effects, coupled with its integrative and constitutive nature (Bibri 2018a, d). This process of change must be based on combining data science, urban science, and urban informatics in terms of ideas and tools with the objectives of sustainable urbanism as an applied domain.

2.2 Complexity Science and Complex Systems As an emerging approach to research and multidisciplinary subject, complexity science is the scientific study of complex systems, systems composed of many parts connected and joined together by a web of relationships that interact to generate collective behaviors that cannot easily be explained on the basis of the interaction between the individual constituent elements. In this respect, complexity entails the way a vast number of complicated and dynamic sets of relationships, interactions, or dependencies can produce some behavioral patterns. Complexity science is a set of conceptual tools and theories from an array of disciplines (Benham-Hutchins and Clancy 2010; Paley and Gail 2011). It deals with complex systems as a collection of interconnected parts and relationships that are dynamical, unpredictable, and multidimensional in nature. It has been discussed in both natural and social sciences. In a wide range of related complex systems, computational modeling, as based on mathematical developments and modeling approaches from physics, is undertaken to study the behavior of such systems to better understand them. Software engineering expertise can be used to apply new results as well as to inspire new approaches in this regard (Batty et al. 2012). Complex systems are characterized by nonlinearity and indeed require more than simplistic linear thinking, as they feature a large number of interacting elements (patterns, agents, processes, etc.) whose aggregate activity (behaviors, relationships, interactions, etc.) does not emanate from the summations of the activity pertaining to the individual elements (Bibri 2019b). As such, they typically exhibit hierarchical self-organization under some kind of selective pressures. Examples of complex systems include cities, ecosystems, organisms, global climate, neural network, human brain, ICT network, and the entire universe. Moreover, advanced ICT is founded on the application of complexity theory to urban problems and issues in terms of tracking the changing dynamics, disentangling the intractable issues, and tackling the challenges pertaining to urban systems, which are in and of themselves becoming even more complex. Besides, complex systems cannot be understood and studied without the use of sophisticated computational and data analytics’ (Bibri 2018a, pp. 297–298). In light of this, complexity science is linked to many different disciplines and professional fields that have the city as their concern. In particular, urban planning and design is a distinct area that is central to urban scholarly and scientific research, while data science and urban science are keys to the development of big data analytics and underpinning technologies. Cities can only be studied in an interdisciplinary context, and the perspective here involves developing a social physics and data-driven science of cities that are consistent with treating their structure and evolution as complex systems. However, as an approach to science, complex systems investigate how the dependencies, relationships, or interactions between the system’s parts give rise to its collective behaviors, and how the system interacts and forms relationships with its environment (Yaneer 2002). Thus, it is principally concerned with the behaviors and properties of systems. As a research approach, it deals with problems in many different disciplines, including information theory, computer science, mathematics, statistical physics, biology, ecology, nonlinear dynamics, sociology, and economy. As an interdisciplinary field, it draws on theoretical contributions and perspectives from those disciplines, e.g., spontaneous order from the social sciences, chaos from mathematics, cybernetics from technology, self-organization from physics, adaptation from biology, and many others (Bibri 2019b).

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The concerns that complexity science addresses have grown out of investigations from a varied intellectual ancestry, including cybernetics, general systems theory, chaos theory in dynamical systems, complex systems, mathematical systems, and complex adaptive systems (social systems, technological systems, urban systems, etc.) where the parts actively change the way they interact. The increased use of computer simulation created research in the simulation of adaptive behavior in the 1990s (Bibri 2018a). From 2000s and onward, complexity science takes stock of what has been accumulated as substantive knowledge of all this rich background of work. A key part of the current emphasis of complexity science is its application to practical technological and engineering systems in that control systems need to be designed, managed, and constructed as they proliferate and increase in size and connectivity in a variety of contexts, e.g., control systems associated with big data technologies and engineering analytical solutions in relation to smart sustainable/sustainable smart cities. It is desirable to have the ability to build systems that are scalable, robust, and adaptive by using such properties as self-organization, self-adaptation, self-regulation, self-repair, and evolution as a way of mimicking biological systems. Complexity science is a subject of study that is well positioned to bringing together deep scientific questions pertaining to sustainability and urbanization with big data application-driven goals within the field of smart sustainable/sustainable smart urbanism. Its contemporary applications are complemented by a rich background of theoretic work. Complexity science touches on all facets of science and technology, creating an array of multitudinous new opportunities within numerous research domains. Important to underscore in this context is that complexity is not just determined by the large number of parts of a system with very intricate design, but rather by such dynamical properties as self-organization, spontaneous order, adaptation, emergence, feedback loops, and nonlinearity (Bibri 2019b). In the context of smart sustainable/sustainable smart cities, technological and engineering systems based on big data analytics are primarily designed to minimize these tricky dynamical properties. These can otherwise make such cities as complex systems difficult to design, predict, and control. However, if desirable emergent behaviors and processes can be managed, harnessed, and exploited, they can allow to move beyond the limits of conventional technological and engineering systems that are merely complicated. Apart from that, we are dealing with the traditional approach to tackling complexity, which aims to reduce or constrain it and thereby typically involves compartmentalization: dividing a large system into separate parts. Technological and engineering systems are susceptible to failure for they are often designed using modular components, and where failure usually results from the potential issues arising to bridge the divisions. Dynamical properties such as feedback loops, adaptation, nonlinearity, emergence, networks, and spontaneous order as important concepts specific to complex systems originate in systems theory. Complex system is indeed a subset of systems theory. Accordingly, both complex systems theory and general systems theory focus on the collective or system-wide properties and behaviors of interacting entities. But the latter is concerned with a much broader class of systems, including linear systems where the effect is directly proportional to cause, or non-complex systems where reductionism may hold viable. Indeed, systems theory entails the ordered arrangement of knowledge accumulated from the study of all classes of systems in the observable world. As such, it seeks to describe, explain, and explore all categories of systems, and one of its objectives is the invention of classes that are of value to researchers across a wide variety of fields. Generally, given the link between systems theory and complex systems, the former provides two key contributions to the latter: (1) an interdisciplinary perspective in that the shared properties linking systems across disciplines justify the quest for modeling approaches applicable to complex systems across disciplines, and (2) an emphasis on the way in which system’s components interact and depend on each other can determine system-wide properties that produce collective behaviors.

2.3 Modeling and Simulation Modeling and simulation is an emerging discipline that is based on developments in diverse areas of computer science, as well as influenced by developments in complexity science, data science, urban science, systems engineering, and systems theory. This foundation brings together elements of art, science, engineering, and design in a complex and unique way that requires domain experts to enable appropriate decisions when it comes to the application and use of modeling and simulation methods within the domain of smart sustainable/sustainable smart urbanism (Bibri 2018a, c). Padilla et al. (2011) distinguish between modeling and simulation science, engineering, and applications.

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• Modeling and simulation science contributes to the underlying theory, defining the academic foundations of the discipline. • Modeling and simulation engineering is rooted in theory but looks for applicable solution patterns. The focus here is on the general methods that can be applied in various problem domains (e.g., urban design, urban planning, urban sustainability, etc.). • Modeling and simulation applications solve real-world problems by focusing on solutions using modeling and simulation methods. Often, the solution results from applying a method, but many solutions are very problem domain specific and thus derived from problem domain expertise and not from any general modeling and simulation method or theory. The concepts of modeling and simulation are often used interchangeably, or as synonyms within disciplines. Within the discipline of modeling and simulation, however, these two concepts are treated as distinctive and of equal importance. Modeling is understood as the purposeful abstraction of reality, resulting in the formal specification of a conceptualization and the underlying assumptions and constraints. Simulation entails the production of a computer model of something. Modeling and simulation involves models that are used to support the implementation of an executable version on a computer over time. In short, modeling focuses mainly on the conceptualization aspects, and simulation mainly on the implementation aspects. In more detail, modeling and simulation is the use of models—e.g., physical, mathematical, or logical representation of a system or process—as a basis for simulations—i.e., computational methods for implementing a model to develop data as a basis for decision-making pertaining to planning, design, operational functioning, and so forth. In this regard, management and engineering knowledge and guidelines are needed to ensure that the conceptualization and implementation parts—modeling and simulation—are well connected for related activities are mutually dependent, although they can be carried out by separate professionals (modelers and simulysts). Simulation as a process has great potential to revolutionize urbanism in terms of planning, design, and development. Among the reasons for the increasing interest in simulation applications include the following: • Using simulations is generally cheaper, safer, and faster than conducting real-world experiments or studying real-time processes. • Simulations allow a flexible configuration of the parameters within different processes and sub-processes found in the operational application and use of complex systems for optimization purposes. • Simulations enable efficient if-then-else analyses of different alternatives, in particular when the necessary data to initialize the simulation can easily be obtained from operational data (energy, environment, transport, mobility, traffic, etc.). This use of simulation relies on decision support simulation systems. Modeling and simulation is of high importance and relevance within research in the domain of smart sustainable/sustainable smart urbanism. Representing the real systems either via physical reproductions at smaller scale or via computational models that allow representing the dynamics and changes of the city via simulation (see, e.g., Batty et al. 2012; Bibri 2018a) allows exploring the system behavior in an articulated way, which is often not possible, too expensive to deploy, or too risky in the real world. Modeling and simulation is a key enabler for engineering activities and operations associated with complex systems, such as smart sustainable/sustainable smart cities in terms of planning, design, and development, as the computational representation of the system as a model enables planners and engineers to reproduce the system behavior and act upon the outcome in ways that enhance and optimize the system design and thus operational functioning. A collection of applicative modeling and simulation methods to support systems engineering activities is provided in (Gianni et al. 2014). One of the existing taxonomies of modeling and simulation that is of relevance to smart sustainable/sustainable smart city planning, design, and operation entails the following: • Analyses support is conducted in support of urban planning (see, e.g., Batty et al. 2012; Bibri 2018a; Bibri and Krogstie 2017b). Very often, the search for an optimal solution (e.g., integration of design concepts and principles and planning practices with big data technology and its novel applications to advance sustainability) that shall be implemented is driving these efforts. What-if analyses of alternatives fall into this category as well. This sort of work is often accomplished by simulysts. A special use of analyses support is applied to urban operations. Simulation methods improve the functionality of decision support systems by adding the dynamic element, as well as allow to compute estimates and predictions, including optimization and what-if analyses.

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• Systems engineering support is applied for the design, development, and testing of systems (energy, transport, traffic, etc.). It can start in early phases and include topics like executable system architectures. And it can support testing by providing a virtual environment in which tests can be carried out. This sort of work is often accomplished by engineers and architects.

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A Survey of Related Work

In one of the earlier works on data-driven urbanism, Batty (2013) describes how the growth of big data is shifting the emphasis from longer-term strategic planning to short-term thinking about how cities function and can be managed. His argument revolves around the sea change in the kinds of data that are emerging about what happens where and when in cities, and how it is drastically altering the way we conceive of, understand, and plan smart cities. Bettencourt (2014) explores how big data can be useful in urban planning by formalizing the planning process as a general computational problem. The focus in his paper is on scientific (complexity science) and engineering principles (big data technologies) pertaining to data-driven urbanism, and how they particularly relate to urban policy, management, and planning as to achieving new solutions to wicked and intractable urban problems. In his article ‘The Real-time City? Big Data and Smart Urbanism,’ Kitchin (2014a) focuses on smart cities as increasingly composed of and monitored by pervasive and ubiquitous computing, and drawing on a number of examples, details how cities as being instrumented with digital devices and infrastructure produce big data which enable real-time analysis of city life, new modes of urban governance, and provide the raw material for envisioning and enacting more efficient, competitive, productive, open, and transparent cities. He moreover provides a critical reflection on the implications of big data and smart urbanism, examining five emerging concerns: the politics of big urban data; technocratic governance and city development; corporatization of city governance and technological lock-ins; buggy, brittle, and hackable cities; and the panoptic city. A large part of this examination is also the aim of Kitchin’s (2015) paper, which indeed provides a critical overview of data-driven, networked urbanism and smart cities focusing in particular on the relationship between data and the city (rather than network infrastructure or computational or urban issues), and critically examines a number of urban data issues, including corporatization, ownership, control, privacy and security, anticipatory governance, and technical challenges. Kitchin (2016) examines the forms, practices, and ethics of smart cities and urban science, paying particular attention to: instrumental rationality and realist epistemology; privacy, dataveillance, and geosurveillance; and data uses, such as social sorting and anticipatory governance. Overall, the above works lack an important strand to the topic of smart or data-driven urbanism: sustainability, and also tend to focus on either technical or political issues related to urban big data. In this light, Bibri (2019a) provides a comprehensive, state-of-the-art review and synthesis addressing the sustainability and unsustainability of smart urbanism and related big data applications in terms of research issues and debates, knowledge gaps, technological advancements, as well as challenges and common open issues. With respect to the latter, the author identifies significant scientific and intellectual challenges and common open issues that need to be addressed and overcome prior to achieving a more effective utilization of big data analytics and its applications in the realm of sustainable smart and smarter cities. Such challenges and issues pertain, by extension, to smart sustainable cities, as addressed in (Bibri 2018a, 2019b). The challenges are mostly of computational, analytical, technical, and logistic kinds. While most of the challenges and open issues are currently under investigation and scrutiny by the relevant research and industry communities, supported by technology and innovation policies, deploying big data technologies and their novel applications in smart sustainable/sustainable smart cities of the future requires overcoming other organizational, institutional, political, social, ethical, and regulatory challenges (see Bibri 2018a for an overview).

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The Data-Driven Components of Smart Sustainable Urbanism and Related Issues

4.1 Decision Support System A decision support system (DSS) can be either fully computerized, human-powered, or a combination of both. With respect to the former given the focus of this chapter, DSS refers to a class of computerized information system that supports decision-making activities, or a set of computer programs and the data required to assist with analysis and decision-making within smart sustainable/sustainable smart cities as organizations. It is an informational application that analyzes urban data and presents the analytical outcome so that urban actors can make decisions more easily in relation to the different practices

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of smart sustainable/sustainable smart urbanism. Therefore, it represents interactive computer-based systems and subsystems intended to help decision-makers use communications technologies, data, documents, models, and/or knowledge to complete the tasks of decision-making. A properly designed DSS serves to compile useful information to identify and solve problems and make decisions. In light of the use of big data analytics as a set of tools and techniques to enhance decision-making, a DSS is most likely to include an expert system or artificial intelligence, a form of knowledge-based systems. An example of typical information that a DSS might gather and present would be the consequences of the different decision alternatives in terms of the comparison of their accuracy and efficiency that are to be converted to a sequence of activities or course of actions in relation to the operational functioning, planning, design, and/or development of smart sustainable/sustainable smart cities as part of urban processes related to multiple urban domains. This signifies that a DSS well serves these levels of urbanism, as well as help planners make decisions about problems that may be rapidly changing and not easily specified in advance—i.e., unstructured and semi-structured decision problems. From what it implies, a DSS is a tool that supports and facilitates decision-making process. However, some decision support systems include a decision-making software component. Sprague (1980) defines a properly termed DSS as one that tends to be aimed at the less well-structured, underspecified problem; attempts to combine the use of models or analytic techniques with traditional data access and retrieval functions; specifically focuses on features which make them easy to use in an interactive mode; and emphasizes flexibility and adaptability to accommodate changes in the environment and the decision-making approach. These characteristic features are at the core of the process of data mining as a big data analytics technique (e.g., Bibri 2018a; Bibri and Krogstie 2018). With respect to the taxonomies of DSS, the kinds of DSS smart sustainable/sustainable smart urbanism are concerned with are the active and cooperative systems. The former both aids the decision-making process as and brings out explicit decision suggestions or solutions (Haettenschwiler 1999). The latter allows for an iterative process between human and system toward the achievement of a consolidated solution: The decision-maker can modify, complete, or refine the decision suggestions provided by the system, before sending them back to the system for validation, and likewise the system again improves, completes, and refines the decision suggestions and sends them back to the decision-maker for validation (Haettenschwiler 1999). This relates to the kind of DSS that is both fully computerized and human-powered. Another taxonomy for DSS of relevance in this context is the one developed by Power (2000), who differentiates data-driven DSS, document-driven DSS, knowledge-driven DSS, and model-driven DSS. These pertain to the various functionalities of the big data analytics system (e.g., knowledge discovery, data mining, statistical analysis, optimization and simulation modeling, etc.) in the context of smart sustainable/sustainable smart cities with regard to the decision-making processes associated with their operational functioning, planning, design, and development as urbanism practices. • A data-driven (or data-oriented) DSS emphasizes access to and manipulation of the various parameters of different kinds of data in terms of the measurable factor forming one of a set that defines a system or sets the conditions of its operation. • A document-driven DSS manages, retrieves, and manipulates unstructured information in a variety of electronic formats. • A knowledge-driven DSS provides specialized problem-solving expertise stored as facts, rules, procedures, or in similar structures. • A model-driven DSS emphasizes access to and manipulation of a statistical, optimization, or simulation model. Model-driven DSS data use and parameters are intended to aid decision-makers in analyzing a situation; they are not necessarily data-intensive. Concerning the development frameworks for DSS, related systems require a structured approach. According to Sprague and Carlson (1982), such a framework includes people, technology, and the development approach. As the latter, an iterative development approach allows for the DSS to be changed and redesigned at various intervals. Once a DSS is designed, it will need to be tested and revised where necessary for the desired outcome. However, the early framework for DSS (notably Simon 1965) consists of four stages, namely: • • • •

Intelligence—Searching for conditions that call for decision; Design—Developing and analyzing possible alternative actions; Choice—Selecting a course of action among those; Implementation—Adopting the selected course of action in the decision situation.

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The process starts with the need for the decision. This need can be triggered automatically by the system. Once triggered, the decision task needs to be modeled before any further action. After the model exists, the DSS is able to generate a decision proposal. In the decision-making process, the decision-maker is able to change which data are used, and possibly the parameters in the model to support the evaluation of the alternatives and the decision on the best alternative prior to adopting the sequence of activities related to the decision.

4.2 Decision-Making Process Decision-making is a daily activity for any human being. It is the process of making choices by identifying a decision, gathering relevant information, and assessing alternatives based on the values, preferences, needs, and beliefs of the decision-maker. This relates to the psychological perspective from which human performance has been a subject of active research. A cognitive perspective, from which human performance has also extensively been researched, regards the decision-making process as a continuous process integrated in the interaction with the environment. Both perspectives are of high relevance to the performance of smart sustainable/sustainable smart cities as complex entities in terms of their operational functioning, planning, design, and development in the context of sustainability. In relation to scientific or science-based domains, decision-making is the most critical process due to its role in shaping or determining the successfulness of various kinds of performance. In the decision-making process, we choose one course of action from a set of available or a few possible alternatives, and may use many tools and techniques, e.g., data mining or knowledge discovery as big data analytics techniques and related intelligent decision support systems. Indeed, in the context of data-driven smart sustainable/sustainable smart urbanism, decision-making is regarded as a computational process supported by human input that results in the selection of a course of action among a finite set of alternative possibilities pertaining to a large number and variety of urban operations, functions, designs, strategies, and policies across various urban domains and thus in relation to different urban systems. A major part of the decision-making process involves the analysis of a number of alternatives described in terms of evaluative criteria, with the aim of the task being to rank these alternatives in terms of how useful and relevant they are or to find the best alternative when all the criteria are considered simultaneously in both cases. In regard to the nature of smart sustainable/sustainable smart cities as complex systems, the surrounding environment can play a part in the decision-making process in the sense that the complexity of such environment is a factor that influences decision-making activity and outcome. Such complexity involves a large number of different possible states which come and go over time (Godfrey-Smith 2001). In short, a decision can be influenced by the varied contexts in which it takes place. Furthermore, decision-making can be regarded as a problem-solving activity related in this context to the different dimensions of sustainability (i.e., physical, environmental, social, and economic) relying on different big data analytics techniques, among others, terminated by a solution deemed to be optimal, or at least satisfactory. It is therefore a process which can be more or less rational and can be based on explicit knowledge of an applied nature generated by means of one or a combination of such techniques. This relates to the normative perspective from which human performance has also been the subject of active research: the analysis of the decisions concerned with the logic of decision-making and the invariant choice it leads to (Kahneman and Tversky 2000). Logical decision-making is an important part of all scientific disciplines or fields where the knowledge in a given area is applied to make informed decisions. For example, decision-making within smart sustainable/sustainable smart urbanism often involves the analysis of physical, environmental, social, and economic issues and the selection of appropriate practical interventions related to, for example, urban operation, organization, planning, design, and governance on the basis of the analytical outcome in terms of the useful knowledge extracted from the deluge of urban data. Here, advanced ICT, such as big data computing and the underpinning technologies, is instrumental in both the analysis and selection processes. However, in some situations with higher time pressure, increased ambiguities, or higher stakes, experts may result to intuitive decision-making based on their experience, creativity, or common sense rather than structured approaches to arrive at a course of action without weighing alternatives. There are different characteristics of decision-making. They entail that every decision-making process generates a final choice out of a set of alternatives, which is intended or supposed to prompt some action. They include the following: • • • • •

establishing the objectives; classifying the objectives and placing them in order of priority or importance; developing the alternative actions; evaluating the alternatives against all the objectives; selecting the tentative decision based on the alternative that is able to achieve all the objectives;

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• evaluating the tentative decision for more possible consequences; • tacking the decisive action with additional actions to be taken to prevent any adverse consequences from becoming problems. In view of the above, the steps of the decision-making process tend to differ as to their number and details. The following are two examples of the steps this process consists of, with such difference in mind: Simon (1976) divides the task of decision-making into three steps, combining the descriptive: what decisions could and should be made, and normative: how decisions should be made, views on decision-making, namely: • The identification and listing of all the alternatives • The determination of all the consequences resulting from each of the alternatives • The comparison of the accuracy and efficiency of each of these sets of consequences. Another set of the steps of the decision-making process is presented as follows. • The identification of the purpose of the decision in terms of what the problem is exactly, the reason for solving the problem, the affected parties of the problem, and whether the problem has a deadline or a specific timeline • Information gathering for weighing the alternatives in terms of collecting data on the number of actors and factors involved and affected by the problem as part of the process of solving it. • The principles for evaluating the alternatives in terms of determining or setting up the baseline criteria for evaluation depending on the context. Likewise, baseline principles should be identified relative to the problem at hand. • Brainstorming and analyzing the different choices in terms of idea generation as being vital to understand the causes of the problem and their prioritization, and generating all possible solutions for the problem at hand. • The evaluation of the alternatives in terms of using the evaluation principles and decision-making criteria to assess each alternative and comparing each alternative for its pros and cons or weighing its positive and negative consequences. • Selecting the best alternative based on the outcome of the above stated steps. The selection of the best alternative is an informed decision since it is based on a methodology to derive and choose the alternative. • Executing the decision in terms of converting it into an action plan or a sequence of activities. • Evaluating the results: the outcome of the decision, in terms of whether there is anything that needs to be learned and then corrected in future decision-making endeavors.

4.3 Big Data Analytics for Enhancing Decision-Making Data-driven decision-making is the action or process of making important decisions based on big data analytics as entailing a set of DSS in relevance to the nature of the problem to be solved. Big data analytics uses innovative forms of data processing for enhanced decision-making, i.e., advanced algorithms, techniques, and platforms that work beyond the limits of the conventional analytic systems normally applied to automatically extract useful knowledge and valuable insights from large masses of data for accurate and timely decision-making directed for different purposes depending on the application domain (Bibri 2018a, 2019a). As far as the complexity of big data analytics is concerned, it is commonly characterized by four (e.g., Bibri 2019a), namely: (1) in situ analytics which directly operates on the data where it sits without requiring an expensive process of Extract, Transform, Load (ETL); (2) interactive analysis where the analysts work interactively with data and the subsequent questions are formulated depending on the results of the previous ones; (3) incremental analysis which requires maintaining models under high data arrival rates and datasets be interactively analyzed based on the previous results; and (4) iterative analysis which iterates over the data several times in order to build and train a model of the data (e.g., predictive data mining) rather than just extract data summaries or make grouping (e.g., descriptive data mining). These Is imply that the process of big data analytics involves different kinds or a combination of DSS to facilitate decision-making process. These DSSs are to be used at the operational functioning, planning, design, and development levels

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within smart sustainable/sustainable smart cities, to reiterate. Furthermore, the analytical outcome (the results obtained from the analysis of urban data deluge) is intended to be deployed for enhancing decision-making (e.g., improving, adjusting, or changing an operation, function, service, strategy, or policy) (Bibri 2018a) in support of sustainability. In other words, the process of big data analytics can turn a large amount of urban data into useful knowledge and valuable insights for well-informed decision-making pertaining to various urban domains, including transport, mobility, traffic, environment, energy, land use, education, health care, planning and design, public safety, science and innovation, built environment, and governance (Bibri 2018b). Therefore, big data analytics has become a key component of the ICT infrastructure of smart sustainable/sustainable smart cities due to its role in improving sustainability, resilience, efficiency, and the quality of life through effective decision-making processes and thus desired outcomes. In this context, it targets intelligent decision support and optimization and simulation associated with the operational functioning, planning, design, and development of urban systems as operating and organizing processes of urban life in terms of control, automation, management, efficiency, enhancement, and prediction as urban intelligence functions. One example of such functions concerns the provision of ecosystem services and the delivery of human services, as well as the effectiveness of strategies and policies based on emerging trends and shifts, in line with the long-term goals of sustainability (Bibri 2018a). As put by Kitchin (2016), various kinds of data-informed urbanism have been occurring for as long as data have been generated about cities; that is, data have been used as the evidence base for formulating urban policies, programmes, and plans to track their effectiveness and to model and simulate future development. Further, however, the targets big data analytics pursues entails the implementation of decision-taking processes, optimization strategies, and simulation models. In a nutshell, the analytical outcome serves to optimize resources utilization, reduce environmental risks, enhance the quality of life and well-being of citizens, and so on. There is a growing consensus that big data analytics and its application will create and enable, considering the projected advancements and innovations within related platforms, techniques, processes, and methods, immense possibilities and fascinating opportunities in the near future. Especially, for smart sustainable/sustainable smart cities to be able to achieve the desired outcomes from applying and using big data analytics in relation to sustainability, the associated applications pertaining to diverse urban domains are required to be supported by cutting-edge and sophisticated technologies, ideally standing out as unconventional renditions in the ambit of computing and ICT. To put it differently, the potential of big data analytics and related data-driven decision support and decision-making as innovations lies in transforming smart sustainable/sustainable smart urbanism in ways that formalize and systemize its approaches into advancing the contribution of both smart cities and sustainable cities to the goals of sustainable development and eventually evolve into sustainable smart cities and smart sustainable cities, respectively.

4.4 The Process of Knowledge Discovery/Data Mining: Advanced Decision Support as Urban Intelligence In the context of smart sustainable/sustainable smart urbanism, decision support is a form of urban intelligence. Urban intelligence functions utilize much wider participation in decision-making as well as the real-time construction and use of a variety of simulations, optimizations, and predictions of relevance to decision support. Building models of smart sustainable/sustainable smart cities functioning in real time from routinely and automatically sensed data is becoming the new reality, coupled with urban ubiquitous sensing getting closer to providing quite useful information about longer-term changes. The use of the clear conceptions of how the evolving new models of such cities in their various domains that pertain to new kinds of data that are largely operated over digital networks is intended to inform the planning of such cities at different scales and over different time spans. And the use of such models is meant to demonstrate how new decision support systems can be fashioned for planning such cities by developing a wide portfolio of modeling tools. The structures for such systems that involve such tools associated with the planning of such cities are still in their infancy (Bibri 2018a). Quite new forms of integrated and coordinated forms of decision support systems as urban intelligence are emerging in response to the need for developing, deploying, and implementing smart sustainable/sustainable smart cities across the globe. This integration and coordination is also justified by the complexity of smart sustainable/sustainable smart cities as organizations in terms of planning. The integration and coordination of databases and models from across urban domains supporting the development of new urban intelligence (decision support) entails novel ways of both presenting data and urban sustainability problems based on advanced visualization methods as well as using tools for informing and predicting the impacts of future scenarios related to planning that need to be refashioned into integrated systems that operate in a continuous fashion and that are built to be robust. This is to be developed for several different, yet related, types of planning

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Fig. 1 Process of data mining. Source Shearer (2000)

problem in the context of sustainability, at different scales and over different timescales, resolving issues of multiscale and multitemporal modeling following basic ideas of integrated modeling (see Batty et al. 2012). Several projects of knowledge discovery across the globe as precursors in mining data related to different urban domains have developed various analytical and mining methods for spatiotemporal and spatial data (Batty et al. 2012; Bibri 2018a). They have shown to support the complex knowledge discovery process from the raw urban data, capable of supporting the decisions of different urban administrators and managers, thereby revealing the striking analytical power of big data. This is at the core of new urban intelligence functions which are bringing about new changes in the way we conceive of the operational functioning of smart sustainable/sustainable smart cities as well as the construction of models that respond to the kind of changing and evolving systems. This involves how the emerging changes in the process of planning and making decision as to such cities can embrace the ability to sense them in real time. The new prospect is that the space–time convergence due to the creation of the real-time data showing immediately the functioning of such cities in real time as well as implying how long-term changes in such cities can be detected will change both the models that we are able to build as well as the way in which they can inform the planning and decision process with simulations and decision support being telescoped across space and time (Batty et al. 2012). As an advanced form of decision support and urban intelligence, the complex process of knowledge discovery/data mining is by far the most applied big data analytics technique or widely used framework for automatically extracting useful knowledge and valuable insights from large masses of data for enhanced decision-making in the domain of smart sustainable/sustainable smart urbanism (see, e.g., Batty et al. 2012; Bibri 2018a). Data mining (also known as knowledge discovery) is the computational process of probing colossal datasets in order to find frequent, hidden, and previously unsuspected and unknown patterns and subtle relationships; to make useful, meaningful, and valid correlations from these discoveries; and to summarize the results in novel ways and then visualize them in understandable formats prior to their deployment for decision-making purposes (Bibri 2018a; Bibri and Krogstie 2018). According to several codifications of the process of data mining, this process consists of well-defined stages, namely problem understanding, data understanding, data preparation, model building, result evaluation, and result deployment, as illustrated in Fig. 1. A data mining framework for urban analytics and big data studies is developed, illustrated, and discussed in Chap. 9. It consists of six steps, namely: 1. understanding and specifying urban sustainability problems; 2. understanding urban data; 3. preparing and combining urban data from diverse sources;

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Fig. 2 Process of knowledge discovery in databases (KDD). Source Fayyad et al. (1996)

4. building models and generating patterns as true regularities; 5. evaluating and interpreting the obtained results; 6. deploying the results for urban operations, functions, services, strategies, and policies. Chapter 9 provides a detailed description and discussion of these steps in the context of smart sustainable/sustainable smart urbanism. Some views argue that knowledge discovery and data mining are slightly two different processes in that the former emphasizes the high-level application of particular techniques and algorithms of data mining. This implies that data mining involves the application of these techniques and algorithms for extracting patterns and building models from data without the additional steps of the process of knowledge discovery. As illustrated in Fig. 2, data mining is simply an essential step in the process of knowledge discovery in databases. Prior to selection, urban data are collected, stored, and integrated from multiple sources. These techniques are performed on the data contained in the databases, data warehouses, or other data repositories. 1. Data selection involves retrieving the relevant data from the databases for analysis. Creating a target dataset entails focusing on a subset of variables or data samples on which knowledge discovery is to be performed. The server of the databases fetches the relevant data on the basis of the data mining request. 2. Preprocessing involves collecting necessary information to account for noise, as well as handling missing data fields through denoising, filtering, fusing, and standardizing, thereby removing redundant, unnecessary, and irrelevant data. This is intended to make the dataset ready for processing. 3. Data transformation is about data reduction and projection, that is, finding useful features/attributes to represent the data depending on the objective of the data mining task to be performed, and using dimensionality reduction to decrease the effective number of variables under consideration. In other words, the data in this stage are consolidated into forms relevant for mining by performing summary operations. 4. Data mining is where the data mining task is selected in order to build models (decision tree, neural network, linear or nonlinear equation, etc.), or to search for patterns of interest in the data in a particular representational form based on the algorithms being applied, in addition to deciding which models and parameters may be appropriate and matching the applied data mining algorithms with the overall criteria of the whole process. 5. Patterns evaluation entails assessing the data mining results and gaining confidence that the resultant models are valid and reliable, e.g., identifying the truly interesting patterns capturing regularities in the data and not just sample anomalies, odds, or idiosyncrasies. In other words, the identified patterns should represent knowledge based on some interestingness measures, according to the domain knowledge used to assess the interestingness of the resulting patterns. In employing interestingness thresholds or constraints, the step interacts with the data mining module (or may access such thresholds or constraints stored in the knowledge base) in order to focus the search toward interesting patterns. As to the knowledge presentation, visualization techniques are used to present the mined knowledge in an understandable format for human interpretation. This entails graphical user interfaces between the data mining system and the data analyst to allow them to interact with each other for different kinds of simple and complex tasks, including carrying out exploratory data mining, browsing database and data warehouse schemas, and visualizing the mined patterns in different forms.

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In view of the above, to make the decision on what qualifies as useful knowledge in the form of applied intelligence with regard to decision-making, knowledge discovery involves carefully choosing variable selection mechanisms, encoding schemes, preprocessing, reductions, and projections of the data prior to discovering the intended patterns and building the relevant models, as well as their evaluation, interpretation, and visualization (Bibri 2018a). Further, other views argue that knowledge discovery and data mining as processes can be used interchangeably. This argument emanates from the illustration of how the two processes converge on most of the stages for extracting useful knowledge from data. For example, the selection step in knowledge discovery includes developing an understanding of the application domain, the relevant prior knowledge, and the objectives of the project. These represent the problem understanding phase of the process of data mining. See Table 1 in Chap. 6, which illustrates the link between the two processes.

4.5 Expected Advancements and Opportunities The data-driven decision support systems and decision-making processes associated with big data analytics underlie the new urban intelligence functions that fashion new powerful forms of simulations models and optimization and prediction methods, a combination of sophisticated tools that can be used within smart sustainable/sustainable smart cities in connection with their operational functioning, planning, design, and development with respect to improving, advancing, and maintaining their contribution to sustainability. In the context of smart sustainable/sustainable smart urbanism, the ultimate goal of big data analytics is to extract useful knowledge and valuable insights in the form of applied intelligence from large masses of urban data. This represent an advanced form of DSS that facilitates and enhances the decision-making process pertaining to urban operations, functions, services, designs, strategies, and policies across various urban domains with respect to sustainability. The data-driven approach to urbanism is heralding major changes in understanding sustainability and tackling related issues, problems, and challenges under what is termed smart sustainable/sustainable smart cities. In this respect, it is increasingly pushing the planning of such cities into short-termism, which is adding a new dimension to the strategic approach to sustainable development. In more detail, one of the key uses of the data-driven approach is enabling and supporting what has been termed short-term planning of how such cities function, thereby shifting the emphasis away from long-term planning—what takes place in cities measured, evaluated, modeled, and simulated over years, decades, or generations. This has prevailed in urban studies and planning theories for more than half a century (Bibri 2018a). As stated by Batty (2013, p. 276), urban big data can be employed ‘to derive rather new theories of how cities function in ways that focus on much shorter term issues than hitherto, and much more on movement and mobility than on … the long-term functioning of the city system. This is city planning in a new guise—that is, thinking of cities as being plannable in some sense [more routinely] over minutes, hours and days, rather than years, decades or generations.’ Indeed, in relation to data-driven urban analytics, the urban analysts are often expected to produce answers in days rather than months (where the analytical solutions often are only a piece of the larger solution to the urban problem being tackled), work by exploratory analysis and rapid iteration based on processes with well-defined stages, and to produce and present results with dashboards by displaying them in an understandable format for human interpretation rather than reports (Bibri 2018a). This entails using data and analytical talent to find and interpret rich data sources, managing large volumes of data while dealing with several technical constraints, integrating data sources, ensuring consistency of datasets prior to processing, building diverse models based on patterns from data, creating visualizations to aid in understanding data, and presenting and communicating the results. The deployment of the results obtained from the data mining process can be technical (e.g., procedures related to energy or traffic management systems), less technical (e.g., a set of rules discovered by data mining to help to quickly diagnose and fix a common error in service provisioning), or much more subtle (e.g., a change in an urban strategy). Furthermore, the goal of data science and thus urban science is to enhance strategic decision-making associated with the operational functioning, planning, design, and development of smart sustainable/sustainable smart cities through the practice of basing urban decisions on the analysis of data-driven decision-making. In this light, the way the systems of such cities as operating and organizing processes of urban life can be monitored, understood, and analyzed is drastically changing thanks to big data computing and its potential for enabling novel urban intelligence and planning functions for dealing with sustainability and related planning wicked problems (Bibri 2018a). This unprecedented data-driven change in smart sustainable/sustainable smart urbanism has been made possible by data science and what it entails in terms of the scientific systems, processes, and methods being applied within the field of urban science to extract useful knowledge and valuable

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insights from the deluge of urban data for decision-making purposes. The sort of decisions we are concerned with are the ones for which meaningful discoveries need to be made within urban data, which could be found through formulating urban sustainability problems and devising relevant solutions to them in a data-analytic fashion. These decisions are associated with control, automation, management, optimization, enhancement, and prediction relating to urban operations, functions, services, strategies, and policies in the context of smart sustainable/sustainable smart cities with regard to their contribution to the goals of sustainable development, to reiterate. In relation to this, several environmental, social, and economic indicators could be extracted from urban data; assembled into predictive or descriptive models related to such urban domains as transport, mobility, traffic, energy, natural environment, built environment, health care, education, public safety, and so on; and then deployed in the processes operating and organizing urban life as part of big data applications. On the whole, as the clearest illustration of data science principles, the process of data mining/knowledge discovery is radically changing smart sustainable/sustainable smart urbanism in terms of operational functioning, planning, design, and development. The transformation associated with the deluge of urban data in terms of the characteristics of such data has been enabled by the emerging networked, digital technologies embedded into the fabric of urban systems in terms of the operating and organizing processes of urban life that underpin the drive to develop and implement smart sustainable/sustainable smart cities. Such technologies particularly involve sensing devices and wireless communication networks, such as digital cameras, sensors, transponders, meters, actuators, transduction loops, GPS, satellite remote-sensing, and scanning technologies (Bibri 2018b). Within the next decade or so, most of the data that can be used to monitor, understand, analyze, and plan such cities will come from digital sensors recording observations, movements, interactions, and transactions, and will be available in various forms, with temporal tags as well as geotags in many instances. This will pave the way for big data analytics to become the dominant mode of urban analytics, which requires exploiting and extending a variety of data mining techniques through which the visualization of patterns and correlations in the form of a variety of advanced models will be of utmost importance for advancing the contribution of such cities to the goals of sustainable development. These tools and methods will be utilized based on the needs of smart sustainable/sustainable smart urbanism in the form of formulating and solving different urban sustainability problems using data mining solutions (see Bibri 2019b and Bibri and Krogsgie 2018 for more details). The deluge of urban data is unfolding and soaring, big data movement is gaining momentum and traction, and the use of big data technologies is sharply focused on how we might integrate data using novel forms of data warehousing as well as powerful forms of database design and integration adapted to and distributed at the citywide scale. As part of urban big data, open and crowdsourcing data are key to many new datasets that will be useful within such urbanism with respect to what we think about key sustainability problems. Another fundamental element of the emerging wave of urban analytics is big datasets pertaining to human mobility, fostered by the widespread diffusion of wireless and mobile technologies for capturing GPS tracks from navigation devices and for recording call details on mobile networks and daily movements of citizens. Thus, these network infrastructures allow for sensing and collecting massive repositories of spatiotemporal data, which provide a powerful social microscope and represent society-wide proxies for human mobile activities (Batty et al. 2012). These big mobility data may help us understand human mobility in relation to different principles of urban sustainable, in addition to discovering the hidden patterns and correlations based on descriptive modeling, which provides characteristic information about the trajectories people follow during their daily movements and activities (Bibri 2018a). Large-scale experiments will enable to find solutions to many urban sustainability problems entailing challenging analytical questions about mobility behavior and a wide variety of data mining techniques (e.g., classification, clustering, regression, link prediction, etc.). There are numerous urban (environmental, economic, and social) sustainability questions that the urban analysts need to answer. Finding answers to such questions is still beyond the computational and processing capabilities of the core enabling technologies of big data analytics. The massive volume or colossal amount of both structured and unstructured urban data makes it very difficult to explore and analyze such data using traditional database management systems and software applications. This implies that the existing computing models and practices remain unfit for handling the evolving deluge of urban data. Nevertheless, these limits will be pushed back with the projected advancements and innovations in the scientific discipline of data science and thus the field of urban science, coupled with the prospect and potential of ICT of pervasive computing as to providing more technically matured and sophisticated distributed computing environments along with novel forms of database integration and models coordination that will enhance decision support and thus facilitate decision-making as enabled by the process of big data analytics in the context of smart sustainable/sustainable smart cities.

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Urban Planning and Design and Related Issues: New Approaches and Perspectives

5.1 New Simulation Models and Prediction Methods for Urban Planning and Design The emergence of smart sustainable/sustainable smart cities poses enormous challenges for traditional forms of simulation and optimization modeling. Hence, advanced urban simulation models operating at different spatial scales and over different temporal spans are being developed for understanding how such cities function and operate, as well as for, as a prelude to their use, informing the process of planning and design. They are characterized by the ability to simulate complex aspects of urbanity using various lenses, such as environmental, economic, social, and geographic, which enable, for example, transport, land use, mobility mode, travel behavior, population growth, and so on to be predicted using computer models of various sorts. Advanced extensions of such models are being made as in urban function and operation research used in the context of intelligent planning and design support systems. New circumstances require new responses with respect to smart sustainable/sustainable smart urbanism and what it poses as complex challenges for traditional simulation, prediction, and optimization modeling. Moreover, the emergence of smart sustainable/sustainable smart cities is pushing for more sophisticated approaches to simulation and optimization modeling (e.g., Bibri 2018a). This is due to several reasons, namely: • Physical actions are increasingly complemented by extensive use of big data analytics and its novel applications, and accordingly, physical places are increasingly merged with computationally augmented and informational environments. • Computer control is ushering in nearly all, or replacing many, routine urban functions and operations, and information intelligence and automation are increasingly being blended with human action. • The provision of data from these new functions and operations offers the prospect of a city wherein the implication of the way it is functioning and operating is continuously available, and planning is, thanks to such immediacy, facing the prospect of becoming continuous as data deluge floods from different urban systems and domains and is updated in real time. • Developing intelligence and planning functions based on how the city is changing or evolving in its nature due to the same functions being used to operate it is becoming a clear prospect. This kind of space–time convergence entails a level of complexity that can only be addressed by the combination of data science, urban science, and complexity science. The evolving new simulation models of smart sustainable/sustainable smart cities pertaining to various domains are associated with new kinds of data and movements and activities that are largely operated over digital networks while relating these to traditional movements and activities (Bibri 2018a). Of critical focus here is the clear conceptions of how these models might be used to inform the planning and design of such cities at different spatial scales and over different time spans. This entails quite new forms of integrated and coordinated decision support systems. Planning is much wider in its import than several institutional frames as manifested at national, regional, metropolitan, local, district, and neighborhood levels with different kinds of planning foci for it is exercised as a function of many stakeholder groups. This calls for joined-up planning as a key component of urbanism which entails a kind of integration across the board, selectively and sensitively, that enables the system-wide effects to be monitored, tracked, understood, and built into the very designs, strategies, and responses that characterize the operations and functions of such cities (Bibri 2018a). These processes and practices are associated with such urban intelligence functions as control, automation, optimization, management, and improvement, which indeed span operations and functions as well as designs, strategies, and responses. The sort of intelligence functions envisaged for smart sustainable/sustainable smart cities would be woven into the fabric of related institutions whose mandate is to promote and advance sustainability and generate a better quality of life for citizenry. Envisaging the smart sustainable city focuses on the components that make it function as a smart sustainable entity as well as a social organism. Central to this quest is the idea of advanced ICT, especially big data technology, penetrating wherever it can to improve the performance of whatever can be enacted by various stakeholders, as well as to enhance the quality of life (Bibri 2019b). In sustainable urban planning, integration, coordination, and coupling are treated as different perspectives. Designing and developing smart sustainable/sustainable smart cities can be viewed as a program of interlinking their systems, domains, and networks in terms of functions and operations for achieving better outcomes in terms of sustainability performance. This requires advanced forms of database integration and management systems, new software platforms for linking hitherto unconnected systems, domains, and networks; an entirely new holistic system of big data analytics and its novel

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applications; and new forms of institutional setups and governance arrangements (Bibri 2018a). The latter can enable the interconnection of such systems, platforms, and applications to become effective and diffused, among other things. The primary purpose of such connectivity is to enhance and maintain the contribution of smart sustainable cities to the goals of sustainable development as part of planning processes. In particular, integrating databases from across diverse urban systems and domains with respect to operations and functions based on networking coordination typify powerful intelligence functionalities for advancing sustainability. As part of planning, smart sustainable/sustainable smart cities seek to strike a balance between sustainability, efficiency, equity, and the quality of life through employing advanced ICT, especially big data computing and the underpinning technologies, with an emphasis on enhancing the ability of their citizenry to contribute to this sought goal through active participation, cooperation, and engagement (Bibri 2018a). For example, when developing technologies associated with fairness and a better quality of life, equity must be balanced with efficiency. Indeed, emerging technologies have a propensity to polarize at many levels and thus open up various divides (Bibri 2019a). There is a need to explore how new forms of legislation and regulation at the level of urban planning can be continuously improved using emerging big data technologies. However, the goals and criteria of sustainable development in the context of smart sustainable/sustainable smart cities lie behind the mission statements produced by city governments.

5.2 The Dilemma of Wicked Problems and the Potential of Big Data Analytics for Tackling It The essential opportunities and challenges of the use of big data computing and the underpinning technologies in smart sustainable/sustainable smart cities have, despite their appeal, not been sufficiently systemized and formally structured. In particular, the necessary conditions for the strategic application of big data in such cities need to be spelled out, and their limitations must also be anticipated and elucidated (Bibri 2018a). There are different ways of addressing these and other important questions considering the available interdisciplinary and transdisciplinary knowledge of smart sustainable/sustainable smart urbanism (see Chap. 3 for a detailed overview) in the age of big data. In this line of thinking, Bettencourt (2014) attempts to answer some of these questions by formalizing the use of big data in urban policy and planning in light of the conceptual frameworks of engineering, and shows that this formalization enables to identify the necessary conditions for the effective use of big data in urban policies that address a large array of urban issues. This is intended to demonstrate that big data computing and the underpinning technologies as an instance of ICT of pervasive computing are providing new opportunities for the application of advanced engineering solutions (based on data science and urban science) to smart sustainable/sustainable smart cities. From a different perspective, Kitchin (2015) provides a critical overview of data-driven urbanism focusing in particular on the relationship between data and the city, and critically examines a number of urban data issues, namely: • • • • •

the politics of urban data; data ownership, data control, data coverage and access; data security and data integrity; data protection and privacy, dataveillance, and data uses such as social sorting and anticipatory governance; and technical data issues such as data quality, veracity of data models and data analytics, and data integration and interoperability.

However, the scope of this study does not include any potential ways of how to address these issues in relation to the use of big data in urbanism. Regardless, the problems of smart sustainable/sustainable smart cities are primarily about citizens. Physical, environmental, economic, and social issues in contemporary cities define what planners call ‘wicked problems’ (Rittel and Webber 1973), a term that has gained currency in urban planning and policy analysis, especially after the application of sustainable development to urban planning and development in the early 1990s. This kind of problems is not expected to yield to engineering solutions for the specific reasons (Rittel and Webber 1973) that break the assumptions of feedback control theory (Astrom and Murray 2008). Bettencourt (2014) addresses the issue of wicked problems in light of computational complexity theory, drawing on More and Mertens (2011), to formally argue that comprehensive or detailed urban planning is computationally intractable. This implies that the solutions involving the knowledge and prediction of the chains of detailed behaviors in smart sustainable/sustainable smart cities as complex systems have the basic property that they become practically impossible, irrespective of the scale and diversity of data available, and this ‘clarifies the central dilemma of urban planning and policy: planning is clearly necessary to address the long-term issues that span the city … and

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yet the effects of such plans are impossible to evaluate a priori in detail’ (Bettencourt 2014, p. 13), although urban planning is informed and underpinned by established knowledge and solid theoretical foundations (e.g., sustainability science, systems science, complexity science, etc.). The urban world is constantly changing, intrinsically unpredictable, and infinitely rich (Bibri 2018a). For example, a wicked problem in the context of sustainable urban forms relates to what Kärrholm (2011) describes as dynamic and multiscalar approach in urban planning and design. In this case, attempting to solve a problem at a certain scale may improve the situation (e.g., positive sustainability effects) at that scale yet can have adverse effects on another scale or a larger scale. The underlying argument is that when tackling wicked problems, they become worse due to the unanticipated effects and unforeseen consequence which were overlooked because the urban form was not approached from a holistic perspective, or were treated in too simplistic terms. The contradictions pertaining to sustainability go deeper still, as the same effort might improve environmental sustainability on one scale (e.g., metropolitan) while decreasing economic sustainability on another (e.g., district). To address this wicked problem, Kärrholm (2011) sheds light on tendencies toward scale stabilization, i.e., the tendencies of planning from the perspective of a few prefixed scales, and views scales as effects of processes and activities of the lived environment. His view implies that the effects contributing to the goals of sustainable development are always enacted at different spatial levels in terms of dimension and size, thereby their multiscalar nature, a proposition that is supported by the premise that urban forms participate in the production of effects on different scales. In other words, the outcome of discussing the effects of a certain scale (e.g., district) is certainly different from the perspective of another scale (e.g., metropolitan, city, motor way system, cycle way system, or local street). In order to yield the most effective sustainability effects, taking scales into account is of critical importance for the implementation and integration of design concepts (e.g., sustainable transportation, passive solar design, and ecological design) and typologies (e.g., compactness, density, diversity, and mixed-land use) of sustainable urban forms. The same goes for building new neighborhoods or blocks in terms of how their spatial structure affect everyday life on the street, social interaction, walking and cycling, and access to green space, as well as the role they play on different scales. Given the complexity of city systems, most of the environmental, economic, and social problems of sustainability are of an ill-structured nature, as they do not yield a right answer or one solution for they mirror real-world situations in which issues and views tend to be conflicting, plural, and contentious. Indeed, the integration of the environmental, economic, and social goals of sustainable development seems to be contradictory because the different dimensions of sustainability rely on different criteria for success, which tend to be usually conflicting and involve uncertainty. Sustainable urban development is characterized by achieving a balance between the development of and equity in the urban areas and the protection of the environment. However, the conflicts among sustainable development goals are very challenging to tackle and daunting to overcome. This has indeed been, and continues to be, one of the toughest challenges facing urban planners and scholars as to planning and decision-making in the realm of sustainable cities (Bibri 2018a; Bibri and Krogstie 2017a). This also applies to smart sustainable/sustainable smart cities in terms of the multidimensional risks they pose to environmental sustainability due to the ubiquity and massive use of ICT (Bibri 2018a, 2019a; Bibri and Krogstie 2016). Despite sustainable urban development seeking to provide an enticing, holistic approach into evading the conflicts among its goals, these conflicts ‘cannot be shaken off so easily,’ as they ‘go to the historic core of planning and are a leitmotif in the contemporary battles in our cities,’ rather than being ‘merely conceptual, among the abstract notions of ecological, economic, and political logic’ (Campbell 1996, p. 296). As a consequence, planners will in the upcoming years ‘confront deep-seated conflicts among economic, social, and environmental interests that cannot be wished away through admittedly appealing images of a community in harmony with nature. Nevertheless, one can diffuse the conflict, and find ways to avert its more destructive fall-out’ (Campbell 1996, p. 9). Using big data analytics as part of the engineering solutions directed for advancing sustainable urbanism in terms of planning, design, and development is an advanced approach to averting the destructive fallout of the conflict in question (see Bibri 2018a for a detailed account of how to put this idea into practice or to apply this approach). Based on the above reasoning, there is no perfect solution to sustainable urbanism problems in terms of planning and design, and each set of identified solutions would contain strengths and weaknesses. The difficulty in formulating the right solutions to wicked/ill-structured problems encountered by urban planners and designers lies in the chaotic nature of multiple cause-and-effect relationships and the way they shape the behavior of patterns of cities. In view of that, sustainable urban planning and design is required to examine the associated problems from a holistic perspective and to find the best possible solutions accordingly. The great innovation of ICT of pervasive computing as underpinned by big data technology and its application is that the complex issues of urbanism should be approached in full knowledge of the dilemmas pertaining to wicked problems (Bibri 2018a). One of the relevant topics that help connect the biological inspiration with the challenges pertaining to technological and engineered systems is feedback control. This relates to the concept of feedback loop, a term commonly used to describe a

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situation or system where part of the output of a situation or system is used for new input in the sense that the latter can, for example, be increased or decreased depending on the outcome of the former. In biology, feedback is a response within a system that influences its continued activity or productivity in some way. In essence, it is the control of a biological reaction by the end products of that reaction. Feedback loops are either positive or negative (also referred to as reinforcing or balancing) in nature. With respect to the former, they tend to enhance or amplify changes, which tends to move a system away from its equilibrium state and make it more unstable as systems. This occurs when a disturbance is accentuated. As to the latter, they tend to dampen or buffer changes, which tends to hold a system to some equilibrium state making it more stable. In other words, changes are detected and reversed. In short, a positive feedback mechanism is the exact opposite of a negative feedback mechanism. In a positive feedback system, the output enhances the original stimulus, and in a negative feedback, the output dampens the original stimulus. In other words, feedback loops take the system output into consideration, which enables the system to adjust its performance to meet a desired output response. A feedback loop is a powerful tool when designing a control system related to technological and engineered systems. In this context, feedback is valuable information that can be used to make important decisions in the context of smart sustainable/sustainable smart cities with respect to the performance of their systems as driven by computational and analytical engineering solutions in ways that contribute to the goals of sustainable development and thus to advancing sustainability through enhancing and optimizing urban operations, functions, services, strategies, and policies across multiple urban domains. Such information can be automatically extracted from the deluge or large masses of urban data through data mining or knowledge discovery as big data analytics processes. It is indeed intended for enhanced decision-making pertaining to the operational functioning of urban systems as a set of interrelated procedures and mechanisms, which can be planned, implemented, assessed, and modified in a continuous way (Fig. 3 illustrates continuous feedback loop) depending on their output with respect to sustainability performance. One aspect of planning here is the way urban structures and forms can be designed since they directly affect the outcome of urban operational functioning. Accordingly, feedback loop is at the core of urban intelligence functions and related simulations models and optimization methods. This relates to what has been termed data-driven feedback loop. Remaining on the same topic, ‘relatively simple-minded solutions, enabled by precise measurements and prompt responses, can sometimes operate wonders even in seemingly very complex systems where traditional policies or technologies have failed in the past’ (Bettencourt 2014, p. 14). To put it differently, solutions with no great intelligence involved can, under specific circumstances, solve very challenging problems. One manifestation of this has to do with using fast and precise enough measurement and adequate simple reactions instead of applying the so-called smart methods. This relates to the logic of feedback control theory as part of modern engineering (Astrom and Murray 2008). Accordingly, knowing the desired operating point for a system and having the means to operate on the system, while observing its state change via feedback loops, can enable to turn it into a simple problem under general, crucial conditions that can measure and recognize potential problems, just as they start to arise and act to make the necessary corrections (Bettencourt 2014). In this context, the important issue is the temporal scales which are at the core of urban big data and their use in urban planning in terms of short-term thinking about the way smart sustainable/sustainable smart cities can function; every urban system has intrinsic

Fig. 3 Continuous feedback loop

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timescales at which problems develop—minutes, hours, days, years, and decades (Bibri 2018a). Cycles of measurement and reaction must act well within this window of opportunity to avoid such complex problems by simple means (Bettencourt 2014). It is conspicuous now that big data science and analytics may offer radically novel solutions to the wicked problems of urban sustainability related to planning, design, and development. Big data computing is so fast in comparison to most physical, environmental, social, and economic phenomena that myriads of key urban planning and policy problems are falling within this window of opportunity (Bibri 2018a). In such circumstances, models of system response enabled by big data analytics can be very simple and crude and typically be linearized (see Astrom and Murray 2008). Thus, the analytical engineering approach conveniently bypasses the complexity that can arise in the nested systems of smart sustainable/sustainable smart cities at longer temporal or larger spatial scales. Table 1 presents a summary of some key urban sustainability problems with their typical temporal and spatial scales and the nature of their operating outcomes. The potential miracle of big data science and analytics in this regard lies in essentially advancing urban sustainability and solving related complex issues without coherent models, unified theories, or any mechanistic explanation at all thanks to the data deluge that makes the scientific method obsolete, and within big data studies correlation supersedes causation. Many examples of planning, design, management, and policy practices in those cities that use data successfully can have this flavor, irrespective of whether their development and implementation involve organizations or computer algorithms. For example, considering urban transportation systems, e.g., a bus network, ‘buses should carry passengers who wait a few minutes to be transported over a few kilometers. Measuring the time in between buses at each stop, possibly together with the number of passengers waiting, gives the planner the basis for a feedback control solution: Communicate with buses to enforce desired standards of service, quickly place more or fewer units in service where these parameters start to deviate from the ideal metrics, and the quality of service as measured by per person waiting times will improve. This type of strategy can be operated intuitively by human dispatchers but possibly can also be implemented automatically by an ICT algorithm with access to the necessary measurements and actions. Feedback control theory provides the framework for the development and optimization of any of these solutions’ (Bettencourt 2014, p. 15). Other similar strategies can be applied to reduce GHG emissions, minimize traffic congestion, enhance mobility and accessibility, and optimize energy efficiency. That is to say, similar mechanisms and procedures could possibly be devised for grid management, traffic management, water and waste management, land use management, and so on. These could also be integrated together in a constructive way. While progress in some urban sustainability problems is fundamentally an ICT problem (big data analytical solution), enabled by simple actions, strategies, or policies that nudge the states of urban processes toward optimal performance, others, especially those that are primarily environmental and social, acquire a different character due to their ill-(or not well-)defined operating points and the diffused nature of their dynamics, in addition to playing out over large temporal or spatial scales. Thus, it has remained challenging to develop engineering solutions (analytical procedures) to problems of sustainability aspects involving health care, energy and environment, land use, and economic development on the city or regional scale. Therefore, there has been a growing recognition that health care, for example, should involve advanced analytics to interpret vast amounts of data to improve healthcare outcomes. Some health issues (diseases) ‘are often characterized by simple metrics and by local processes of social contact between individuals,’ but health conditions that play out over longer times and possibly have more complex and diffuse social causation … have proven far more difficult’ (Bettencourt 2014, p. 15). Hence, the simplicity of performance metrics expressed as objective

Table 1 Urban sustainability issues, their temporal and spatial scales, and the character of their associated metrics

Problem

Timescale

Spatial scale

Outcome metrics

Transportation (buses)

Minutes

Meters

Simple

Infrastructure (roads, bridges, cables)

Days

Meters

Simple

Traffic

Minutes

Meters to kilometers

Simple

Mobility

Days

Kilometers

Simple

Energy and environment

years

Citywide

Complex

Education

Decades

Citywide

Complex

Health care

Years

Citywide

Complex

Economic development

Decades

Citywide

Complex

Land use

Decades

Citywide

Complex

Source Bibri (2018a)

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quantitative quantities relative to the properties of the controlling system—the policymaker or the algorithm—such as their response times, and the knowledge of their proximate causes in space and time are the crucial conditions for successful analytical solutions inspired by engineering (Bettencourt 2014). In this context, big data analytics as a set of technological and scientific processes becomes of crucial importance for achieving progress through the increasing automation of solutions to the intractable kind of environmental, social, and economic sustainability issues in the context of smart sustainable/sustainable smart cities. While the physical aspects of such cities seem at first sight manageable through engineering practices, their social, environmental, and infrastructural aspects may become entangled over the long run. Nevertheless, big data analytics utilizes sophisticated computational and modeling approaches to analyzing complex urban phenomena in relation to planning. The underlying core enabling technologies allow for generating and processing an overwhelming deluge of urban data flooding from urban systems and domains across different spatial scales and over different time spans, which is of high relevance to urban planning with respect to tackling related wicked problems. Spatial scales and timescales are being collapsed by the emergence of the real-time data from the bottom up, manifested in the creation of the kind of datasets that show immediately the operational functioning of the real-time city and also imply how related long-term changes can be detected and predicted. In short, the prospect of the real-time data to be collected at any instant will provide the opportunity for aggregating such data to deal with urban changes at any scale and over any time period. It remains a long way off and is near on impossible to reach due to the fact that more and different data will have to be collected as we find them. Nevertheless, it has promising potential for enabling a real-time view of changes at different spatial scales and over different timescales, especially in relation to sustainability aspects. The essential character of wicked problems is that, according to Rittel and Webber (1973), they cannot be solved in practice by urban planning. The authors argue that the planning problem has two distinct aspects: (1) the knowledge problem and (2) the calculation problem. The first problem refers to the data needed to map and understand the current state of the smart sustainable/sustainable smart city. It is conceivable that urban life and physical infrastructure could be adequately sensed in several million places at fine temporal rates, generating huge but manageable rates of information flow by advanced ICT. It is not impossible, albeit still implausible, to conceive and develop advanced technologies that would enable access to detailed information about every aspect of the infrastructure, services, social lives, and environmental states in a smart sustainable/sustainable smart city. The second problem refers to the computational complexity to carry out the actual task of planning in terms of the number of steps necessary to identify and assess all possible scenarios and choose the best possible course of action. Unsurprisingly, the exhaustive approach of assessing all possible scenarios in such city is impractical due to the fact that it entails the consideration of impossibly or unreasonably large spaces of possibilities. For the formalization of this statement in the form of a theorem and related mathematical details and the sketch of a proof, the interested reader can be directed to Bettencourt (2014). Given the proviso of some stipulations and limitations, it can be demonstrated that the planning of smart sustainable cities in detail is computationally intractable. This shows that the use of complex models (Portugali 2011) in the detailed planning of such cities has its limits and cannot be exhaustively mapped and solved in general, irrespective of how much urban data may be available for urban planning purposes, to reiterate. Here comes the role of big data analytics in such cities and thus the conception of urban planning under the stipulations and limitations in question. The key here is the nature of the self-organization of environmental, social, and economic life in such cities and the development of a general quantitative understanding of how the operating and organizing processes function in such cities as vast networks across diverse domains and sub-domains, spanning large spatial and temporal scales, to draw on Bettencourt (2014). The development of the urban theory recognizing that individual details are of irrelevance to characterizing complex systems as a whole while identifying general dynamics follows from the increasing urban data availability from around the world in terms of observations and from experiments (Bettencourt 2014). All in all, the ‘dilemma between the need for planning and coordination and its impossibility in detail is resolved by the recognition that cities are first and foremost self-organizing social networks embedded in space and enabled by urban infrastructure and services. As such, the primary role of big data in cities is to facilitate information flows and mechanisms of learning and coordination by heterogeneous individuals. However, processes of self-organization in cities, as well as of service improvement and expansion, must rely on general principles that enforce necessary conditions for cities to operate and evolve. Such ideas are the core of a developing scientific theory of cities, which is itself enabled by the growing availability of quantitative data on thousands of cities worldwide, across different geographies and levels of development’ (Bettencourt 2014, p. 12), including sustainable development. These three uses of big data and ICT in smart sustainable/sustainable smart cities constitute then the necessary pillars for more successful urban planning, management, and policy that promote and strengthen the fundamental role of such cities as enabling arenas for sustainable development goals and engines of sustainability innovation in human societies.

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New Urban Intelligence Functions and Related Simulation Models and Optimization and Prediction Methods

In the context of this chapter, the concept of urban intelligence refers to the planning, development, integration, and deployment of big data computing and the underpinning technologies as an ecosystem (both physical and virtual assets) to support the interoperability between resources and technologies and hence the integration of urban systems and the coordination of urban domains to serve the city and its stakeholders and citizens with respect to sustainability dimensions. In short, urban intelligence entails the use of big data analytics and the underlying core enabling technologies to address and overcome the problems and challenges facing smart sustainable/sustainable smart cities. These are depicted as composed of and monitored by ICT of pervasive computing and underpinned by big data technology and its novel applications for improving sustainability, efficiency, resilience, and the quality of life. This implies that they are instrumented with digital devices, systems, platforms, and infrastructures which generate the big data deluge that enable real-time monitoring, understanding, and analysis of the operating and organizing processes of urban life as well as new modes of urban planning and governance, in addition to providing the raw material for enacting more sustainable, efficient, resilient, open, and equitable cities (Bibri 2019a, b). Building models of cities functioning in real time from routinely and automatically sensed data is becoming the new reality, and the sensing of such cities is getting closer to providing quite useful information about longer-term changes necessary for informing future urban design and thus optimizing and enhancing urban operational functioning in line with the vision of sustainability (Batty et al. 2012; Bibri 2018a, 2019b). Smart sustainable/sustainable smart cities are a class of cities in which different advanced forms of ICT are merged with urban systems, domains, and networks, integrated, coordinated, and coupled, respectively, using new digital technologies, and wherein new intelligence functions are able to integrate and synthesize urban data to the purpose of sustainability, ways of improving environmental integration, economic efficiency, equity, and the quality of life. Emerging and future ICT in its form of big data analytics and its application is concerned with researching such cities not simply in terms of their instrumentation: ‘constellations of instruments across many scales that are connected through multiple networks which provide continuous data regarding the movements of people and materials in terms of the flow of decisions about the physical and social form of the city’ (Batty et al. 2012, p. 482), but also in terms of the way this instrumentation is opening up new opportunities for, and providing new perspectives on, advancing sustainability on the basis of the useful knowledge and valuable insights resulting from the process of big data analytics in the form of applied intelligence (Bibri 2019a). The value of such knowledge and insights lies in enhancing urban forms, infrastructures, facilities, resources, networks, and services by developing and applying urban intelligence functions for automating and supporting decisions pertaining to control, automation, management, and optimization for the purpose of improving and maintaining the contribution of such cities to the goals of sustainable development, or advancing smart sustainable/sustainable smart urbanism (Bibri 2018a, 2019a). Urbanism is increasingly relying on data-driven decision-making that results from monitoring, understanding, and analyzing urban systems, domains, and networks across several spatial scales and over multiple time spans to improve and advance sustainability. At the core of smart sustainable/sustainable smart cities is an advanced form of urban intelligence for decision support: New urban intelligence functions as new conceptions of urban functioning and operations that utilize complexity science and urban science in developing new powerful forms of urban simulation models and related optimization and prediction methods built on top of the minded patterns and models enabled by the process of big data analytics. This implies that the outcome of such analytics as associated with various urban systems, domains, and networks and how these can interrelate or interlink is of central importance to the development, functioning, and performance of urban intelligence functions for enhanced decision-making and insights in relation to sustainability. Big data applications based on by such functions involve advanced computational understanding of the urban environment and react from such understanding in a knowledgeable manner through intelligent decision support pertaining to control, automation, optimization, management, and prediction in relation to urban operational functioning as shaped by well-informed urban design determined by the kind of urban planning that is underpinned by the principles of sustainable development. As an advanced form of decision support, urban intelligence functions integrate, synthesize, and analyze data flows for the purpose of improving the sustainability, efficiency, resilience, equity, and quality of life in smart sustainable/sustainable smart cities. This relates to exploring the notion of smart sustainable/sustainable smart cities as innovation labs. Accordingly, the kind of urban intelligence functions that such cities should evolve in the form of laboratories that enable their monitoring, planning, design, and development include, but are not limited to, the following.

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the efficiency of energy systems; the improvement of transportation and communication systems; the improvement of water, power, and sewage systems; the enhancement of urban metabolism; the effectiveness of distribution systems; the robustness and resilience of urban infrastructures in terms of their ability to withstand adverse conditions and to quickly recover from difficulties; the efficiency and scalability of urban design in terms of forms, structures, and spatial organizations; the optimal use and accessibility of facilities; the efficiency of human services delivery; the optimization of ecosystem services provision; the dynamic, continuous, and short-term forms of planning.

Such functions represent new conceptions of how smart sustainable/sustainable smart cities function and utilize and combine complexity science and urban science in fashioning new powerful forms of urban simulations models and optimization and prediction methods that can generate urban structures and forms as well as spatial organizations and scale stabilizations that improve sustainability, efficiency, resilience, and the quality of life. Especially, building models of such cities operating and functioning in real time from routinely and automatically sensed data has become a clear prospect (see, e.g., Batty et al. 2012; Kitchin 2014a). Prior to exploring the idea of smart sustainable/sustainable smart cities as innovation labs and hence enabling them to evolve urban intelligence functions in the form of laboratories that enable their monitoring, operation, design, and development, it is of crucial importance for such cities to overcome the following scientific challenges: • Monitoring smart sustainable/sustainable smart cities and relating their strategies, design concepts and principles, spatial organizations, scale stabilizations, infrastructures, and ecosystem and human services to their operational functioning and planning through control, automation, optimization, management, enhancement, and prediction in the form of intelligence functions. This entails using advanced analysis, modeling, and simulation methods based on big data analytics and the underpinning technologies to enhance decision-making processes pertaining to such functions. In this respect, major efforts should be directed toward showing how developments in big data technology and its novel applications can be integrated so that cities can become truly smart sustainable/sustainable smart in the way urban planners and citizenry can use such technology and its capabilities to improve the different aspects of sustainability and the quality of life. • Mastering the complexity of the knowledge discovery process for smart sustainable/sustainable smart cities, which requires building an entirely new holistic system for big data analytics involving design and operation functions. The analytical process associated with extracting useful knowledge and valuable insights in the form of intelligence functions pertaining to decision-making processes and thus decision support systems should be expressible within systems that support the stages of the knowledge discovery process, namely data acquisition, data management, data integration, data selection, data preprocessing, data transformation, data mining and pattern recognition, data evaluation/interpretation, data visualization, and data deployment. • Developing advanced modeling and simulation systems to help predict potential problems and forecast possible changes, with the primary purpose of mitigating or avoiding any risks that might arise through planning, as well as reducing the implementation and testing costs following city design and development. Simulation models have great potential to modernize smart sustainable/sustainable smart city design and development in the future (Bibri 2019a, c). Indeed, to reiterate, using simulations is generally cheaper, safer, and faster than studying real-time processes or conducting real-world experiments. Also, simulations allow a flexible configuration of the parameters within the different sub-processes found in the operational application field of such cities as complex systems and dynamically changing environments. Urban intelligence functions are best to take the form of centers for scientific and social research and innovation directed primarily for improving, advancing, and maintaining the contribution of smart sustainable/sustainable smart cities to physical, environmental, social, and economic sustainability. The kind of intelligence functions envisioned in this regard will be woven into the institutional direction with respect to promoting sustainability and enhancing the quality of life for citizens. However, the decision support systems associated with new urban intelligence functions and related simulation

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models and optimization and prediction methods are still in their infancy (Bibri 2018a), and also much needs to be done to provide the raw material for the development and implementation of such functions across multiple urban domains. From a conceptually different angle, with the projected advancements and innovations in big data computing and the underpinning technologies, the process of building intelligence functions for smart sustainable/sustainable smart cities will shift from top-down (expert and professional organizations) to engaging citizens with experts due to the complexity underlying the planning, design, and development of such cities (Bibri 2018a). This entails integrating databases and models from across various urban domains for supporting the development of this sort of integrated intelligence functions, with new or refashioned ways at different levels, including visualization of data and urban sustainability problems, using tools for informing and predicting the impacts of future sustainability scenarios, and engaging citizens and their useful, relevant recommendations, all into a form of a holistic system that operates in accordance with sustainability requirements at various spatial and temporal scales (Bibri 2018a). Hence, the issues of multiscale and multitemporal modeling associated with urban intelligence functions could be resolved on the basis of the ideas involving basic principles as to integrated modeling. In addition, urban innovation labs are intended to work directly with various urban entities (e.g., government agencies, public authorities, organizations, institutions, companies, communities, citizens, etc.) to acquire, process, and analyze data and then derive knowledge and insight from data in the form of applied intelligence. Their core aim is to solve tangible and significant problems of city operational functioning, management, planning, design, development, and governance through data-driven decision-making. This involves delivering problem-oriented research that serves the dual purpose of advancing the scientific understanding of cities in terms of sustainability and urbanization and how they intertwine with and affect one another, as well as in terms of having a direct impact on decision-making and action taking in the sense of enhancing and advancing urbanism practices. In this context, urban innovation labs take a multidisciplinary approach to smart sustainable/sustainable smart urbanism, bringing the perspectives of design thinking and planning to the visions and practices of such urbanism.

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Advanced Urban Simulation Models and Related Methods

Sustainability issues will be dealt with using more effective models and simulations in urban planning and design in the era of big data, and this new technology will be determinant in the planning forms of operation and organization informed by data-centric simulation-driven design. It is the deluge of urban data through analytics as demonstrated in this deluge being wrangled through an array of multitudinous algorithms to discover the most salient factor concerning complex urban phenomena that can be used to fashion and enhance urban models and simulations to guide urban planning, design, and development in line with the vision of sustainability. One of the reasons for the increasing interest in prediction and simulation modeling application is enabling efficient if-then-else analyses of different alternatives, in particular when the necessary data to initialize the simulation can easily be obtained or generated from the different aspects of urban operational functioning as to, for example, energy, environment, transport, mobility, traffic, and land use. This use of simulation modeling relies on decision support simulation systems. As the role and significance of big data analytics and its application continues to grow, modeling and simulation as a big data analytics technique or part of the process of knowledge discovery is increasingly being used in the domain of smart sustainable/sustainable smart urbanism in relation to planning, design, and operational functioning. In this context, it relates to new emerging big data systems and integration. The pursuit of mastering the complexity of the process of knowledge discovery for smart sustainable/sustainable smart cities requires building an entirely new holistic system for big data analytics involving their operational functioning, planning, design, and development in terms of applied intelligence functions directed primarily for improving, advancing, and maintaining their contribution to the goals of sustainable development through continuously optimizing and enhancing their operations, functions, services, designs, strategies, and policies in line with the vision of sustainability. The entire analytical process able to create the needed knowledge services or associated with extracting useful knowledge and valuable insights in the form of such functions pertaining to decision-making processes should be expressible within a system that supports the following: • • • • • • •

the acquisition of data from multiple distributed sources; the management of data streams; the integration of heterogeneous data into coherent databases; the definition of observables to extract relevant information from available datasets; data transformation and preparation; methods for distributed data mining and network analytics; the organization and composition of the extracted models and patterns as well as the evaluation of their quality;

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tools for visual analytics to study the behavioral patterns and models; the availability of visualizations to planners, strategists, and decision-makers; methods for the simulation and prediction of the mined patterns and models; mining strategies for overcoming the scalability issues associated with big data in distributed environments.

Partial examples of this line of research are available in such domains as mobility, transport, and traffic. The ultimate goal is to create a system that encompasses all the domains of data, patterns, models, and simulations for smart sustainable/sustainable smart cities, a continuing research challenge for future big data computing and the underpinning technologies as an advanced form of ICT and key enabling technology for pervasive computing (Bibri 2018a). In the context of smart sustainable/sustainable smart cities, the process of modeling and simulation entails creating and analyzing a set of separate or combined digital prototypes of physical, infrastructural, environmental, socio-economic, spatiotemporal, operational, and/or functional models in terms of how they perform, interrelate, and affect one another for the purpose of identifying problems and predicting changes in the various aspects of the system behavior of such cities due to the reciprocal relationships that cycle to produce the behavioral patterns that such cities might exhibit as a result of their operational functioning, planning, design, and development in the context of sustainability (Bibri 2018a). This allows to determine or forecast potential issues in the real world, and to look at some effective ways to overcome or eradicate them before or after they happen. Important to note, though, as simulations entail translating the structural diagram of the system into a set of equations characterizing the nature of the relationships (and their direction and strength) laid out in the structural diagram on a computer, the key theoretical question to answer is whether the set of reciprocal relationships pieced together do produce the patterns of behavior generated by smart sustainable/sustainable smart cities as complex systems (Bibri 2018a). The modeling and simulation of smart sustainable/sustainable smart cities denote their conceptualization as complex systems into a set of models, which serve as a basis for a set of simulations in terms of implementing these models to develop data for decision-making pertaining to planning, design, operational functioning, and development in the context of sustainability. This involves evaluating the performance of the system behavior of such cities in respect of the different aspects of sustainability, and allows to adjust the relevant parameters within the system under investigation and then to optimize it to increase success in terms of enhancing these aspects across various urban domains (Bibri 2018a; Bibri and Krogstie 2017b). This invoves alterations in urban systems and domains in terms of operations, functions, designs, services, strategies, and policies, as well as in how these systems and domains interrelate in terms of integration and coordination, respectively. Aiming at developing a deep level of understanding of complex systems, complexity science involves the study of the whole system and its collective behavior as well as the mutual interactions between its parts, simulation models are the vehicle most often employed to describe and aid in explaining complex systems. This is of high relevance and applicability to smart sustainable/sustainable smart cities as complex systems, in particular in relation to the investigation and evaluation of their contribution to the goals of sustainable development, as well as to what should be done to optimize and sustain this contribution as part of their collective behavior. In addition, modeling and simulation can facilitate the understanding the behavior of such cities or that of their sub-systems without actually testing them in a real-world setting or instantiating them in an operating environment. However, to ensure that the results of simulation are applicable to the real world, it is necessary to understand the assumptions, conceptualizations, and implementation constraints of modeling and simulation. Only then could useful knowledge and valuable insights into the decisions pertaining to the operational functioning, planning, design, and development of such cities be gleaned without actually yet developing them or some of their components. This is of crucial importance to save financial, physical, social, and human resources that would otherwise be huge and expensive in relation to the deployment and implementation of the actual city. In addition, modeling and simulation can support experimentation (e.g., the coordination of urban domains, the integration of urban systems, the coupling of urban networks, etc.) that occurs totally in software environments where simulation represents systems or generates data needed to meet experiment objectives. The use of modeling and simulation within urbanism is well recognized (e.g., Batty et al. 2012; Bibri 2018a). Modeling and simulation technology belongs to the tool set of planners, developers, engineers, and architects within most of the urban application domains. In a nutshell, modeling and simulation helps to reduce costs, save resources, increase the quality and performance of systems, and document and archive lessons learned (Bibri 2018a). One of the key scientific challenges pertaining to smart sustainable/sustainable smart cities is constructing and aggregating many different urban simulation models related to various urban systems, domains, and networks in terms of their integration, coordination, and coupling, respectively, as well as to human mobility in terms of its link to spatial organizations, scale stabilizations, typological arrangements, transport systems, socio-economic performance, environmental performance, and land use (Bibri 2018a). The aim of providing portfolios of such models is to inform the future design of smart

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sustainable/sustainable smart cities on the basis of predictive insights and forecasting capabilities. This is becoming increasingly attainable due to the recent advances in, and the pervasiveness of, sensor technologies and their ability to provide information about medium- and long-term changes in the realm of real-time cities (Bibri 2018a, b), to reiterate. Therefore, there clearly is an immediacy in the construction of urban simulation models. Adding to this is to explore and diversify the approach to the construction and evolution of such models based on the science of complexity. Indeed, it is important to build many different models of the same situation in the belief that a pluralistic approach is central to enhancing the understanding of this complexity (Batty et al. 2012). The importance of urban simulation models in this context lies in aiding urban planners, engineers, architects, and experts in understanding by means of evaluation procedures under what conditions and in what ways urban systems and domains might fail to deliver at the level of some dimensions of sustainability, and what to do about potentially predicted changes, emergent properties, or forecasted problems, e.g., whether there is a need to further enhance the integration of urban systems or some of their components, to further organize and coordinate urban domains, to create better or merge hitherto unconnected typologies and design concepts across different spatial scales, and/or to consider new approaches into scale stabilizations. In short, the aim is to inform the future design and planning of smart sustainable/sustainable smart cities on the basis of predictive insights and forecasting capabilities in ways that continuously assess, enhance, optimize, and maintain their contribution to the goals of sustainable development. One approach to advancing urban simulation models is to coordinate diverse, hitherto unconnected urban domains (e.g., transport, mobility, traffic, land use, water management, waste management, energy, natural environment, built environment, public health, public safety, education, governance, economy, and science and innovation) on the basis of environmental or socio-economic criteria (Bibri 2018a). This also applies to urban systems in terms of integration, which consist of the following: • built form (buildings, streets and boulevards, neighborhoods, districts, residential and commercial areas, schools, parks, public spaces, etc.); • urban infrastructure (transport systems, water and gas provision systems, sewage systems, power distribution systems, etc.); • ecosystem services (provisioning energy, water, air, and food; regulating climate; supporting nutrient cycles and oxygen production; etc.); • human services (public services, social services, cultural and recreational facilities, etc.); • administration (organizational structures, governance arrangements, creating and implementing mechanisms for adherence to regulatory frameworks, practice enhancements, policy design and recommendation, technical and assessment studies, etc.). Irrespective of the approach pursued to advance urban simulation models, it is necessary to support the process with novel methods for coordinating and integrating decision support strategies and systems. Of importance to highlight in this regard is that such models should be grounded in clear conceptions in terms of the way in which they can be employed and extended to inform the planning and design of smart sustainable/sustainable smart cities on different spatial scales and over multiple time spans. In addition, regarding the use of urban data across spatiotemporal scales by such models, real-time data need to be merged with traditional data from across different urban entities as sectional sources of data based on simulations that link real-time urban sustainability issues to long-term strategic sustainable urban planning and design (Bibri 2018a). Underlying the idea of smart sustainable/sustainable smart cities of the future in light of big data computing is that the development and implementation of a new class of simulation models constructed based on big data analytics and supported by database integration and network coupling associated with various urban domains and sub-domains entails that such models themselves will grow sophisticated and expend as urban structures, forms, and spatial organizations will evolve over time and hence urban operational functioning will require new complex procedures and advanced processes, thereby producing new patterns of behavior that such cities might exhibit due to the kind of the underlying reciprocal relationships that normally generate these patterns. This is anchored in the underlying assumption that simulation models and prediction methods are built on top of the mined behavioral patterns and dynamically changing models pertaining to such cities as functioning in real time (Bibri 2018a). Agent-based models (e.g., Batty et al. 2012), which can emulate the dynamics of cities related to traffic, mobility, transport, climate, energy, land use, and so on as a result of urban processes and activities (or urban operational functioning), can be used to shape and inform collective decisions in an intelligent manner. This relates to urban intelligence functions as based on simulation models and optimization methods. The kinds of simulation models needed most within smart sustainable/sustainable smart urbanism are those that grapple with the shift associated with our

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increasingly computerized and urbanized world, which is dominated by the flow and manipulation of information instead of material and energy. The rudiments of this kind of models are available in many of the agent-based models constructed for urban sectors (Pagliara et al. 2013). However, they still need to be further advanced and effectively integrated in ways that make them well suitable for advancing smart sustainable/sustainable smart urbanism, thereby increasing their effectiveness and reliability with respect to improving and maintaining the contribution of smart sustainable/sustainable smart cities to the goals of sustainable development. Indeed, this urbanism approach poses great challenges for traditional approaches to simulation models, adding to the fact that the emergence of such cities is pushing for more sophisticated simulation modes (Bibri 2018a), to reiterate. Among the classes of models under investigation include the cellular automata models of urban planning and the agent-based models of spatial behavior in relation to transportation modeling. Batty et al. (2012) are currently working on using and extending the agent-based microsimulation MATSim, which provides a basis for extensive model implementation that strongly links different weakly hitherto connected travel behavior, land use, mobility patterns, and social networks. Also, as stated by the authors, ‘other classes of more aggregate land use transportation models (such as the Simulacra suite of models … will be extended and linked to more disaggregate physical models. These simulate aggregate dynamics of development and we envisage that models of this kind can be linked to models of the MATSim variety. This style of model will be used to demonstrate how new planning and decision support systems can be fashioned for planning the smart sustainable city’ (Batty et al. 2012, p. 510).

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Smart Sustainable/Sustainable Smart Cities as Complex Systems

8.1 Complexity Aspects and Complexity Science Relevance and Usefulness The system concept has gained central importance in smart sustainable/sustainable smart urbanism in terms of design, planning, and development. As an approach to such urbanism, smart sustainable/sustainable smart cities are complex systems par excellence and hence inherently intricate. This complexity can be demonstrated in various ways. It is manifested in the array of multitudinous individual and collective decisions that is taken from the bottom up to the top down to plan, design, and develop them, as well as in the very technologies being used to monitor, understand, and analyze them to improve, advance, and maintain their contribution to the goals of sustainable development in the face of urbanization. It is also obviously manifested in the various ways of describing the whole system of such cities and its constituent parts using such concepts as boundaries, homeostasis, adaptation, reciprocal transactions, feedback loops, dynamics, mesosystem, and chronosystem. In this respect, such cities are characterized by the following: • • • • • • • • • • • • • • • • • •

more than the sum of their parts (sub-systems) that are related directly or indirectly; organized entities made up of interrelated and interdependent parts; encapsulated and defined by their boundaries, thereby being distinguished from other systems in the environment; nested inside (i.e., their components may themselves be complex systems) and overlap with other systems in the environment; bounded in time and space but may be intermittently operational and their parts not necessarily colocated; comprising processes that transform inputs into outputs; open systems in the sense of existing in a thermodynamic gradient and dissipate energy; receive input from and send output into the wider environment; autonomous in fulfilling their purpose through internal functions; change in one of their part affects other parts and the whole system; have unpredictable patterns of behavior produced by reciprocal relationships with implications; their positive adaptation and evolution depend upon how well they are adjusted with their environment; have tendency to resist change and maintain status quo; have tendency to make the changes needed to grow to accomplish their goals; engage in circular interactions such that they influence, and are influenced by, other systems; self-correct themselves based on reactions from other systems in the environment; have nonlinear relationships in that a small perturbation may cause a proportional or large effect; have nonlinear behavior over time based on feedback loops, time delays, flows, and stocks;

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• involve relationships containing feedback loops (negative and positive feedback). The effects of their parts’ behavior are fed back to the system in such a way that these parts themselves are altered; • adaptive by having the capacity to change and learn from experience; • composed of significant life events that can affect their adaptation in the environment. There are other features of complex systems that apply to such cities, namely cascading failures, memory, network of complexity, and emergent phenomena. With respect to the first one, a failure in one or more of their components can lead to cascading failures because of the strong coupling between their components (e.g., energy system and climatic system), which may have catastrophic consequences (e.g., environmental crisis) on the functioning of the whole city system (see Buldyrev et al. 2010). As to memory, the importance of the history of such cities lies in that they as dynamical systems change over time, and prior states may have an impact on present states. More formally, they are most likely to exhibit spontaneous failures and recovery as well as hysteresis (see Majdandzic et al. 2013). As a set of interacting systems, such cities are likely to have complex hysteresis of many transitions (Bibri 2018a). Concerning the dynamic network of multiplicity, the dynamic network of such cities is of import as a topology. Scale-free networks (Albert and Barabasi 2002; Newman 2010) have many local interactions or relationships, and a smaller number of interconnections are often employed (Bibri 2018a). Regarding the potential generation of emergent phenomena, such cities exhibit behaviors and processes that are emergent. This implies that they may have properties that can only be analyzed at a higher level, despite the results being sufficiently determined by the activity of urban systems’ basic constituents. For example, such cities have physical, spatial, social, economic, political, and technological development that are at one level of analysis, but their environmental behavior is a property that emerges from the collection of the underlying systems, which needs to be analyzed at a different level. There are some conspicuous aspects that are applicable to smart sustainable/sustainable smart cities as complex systems. In this regard, the main goal such cities seek to fulfill is to strategically improve, advance, and sustain their contribution to the environmental, social, and economic goals of sustainable development through directing their operational functioning and adaptation as part of their collective behavior toward achieving sustainability as a desired state. Also, such cities represent more than the sum of their sub-systems (i.e., the operating and organizing processes of urban life) that are involved in directing their collective behavior toward the desired state of sustainability due to the other nested interrelationships and interdependencies underlying their sub-systems, as well as to the mutual interaction of their whole system with other systems. Indeed, they involve profound interactions between environmental, physical, social, and economic systems, and as such, they receive input from and send output into the wider natural system where they are embedded. In doing so, they can cause systematic degradation and concomitant perils to natural environment and human well-being. Therefore, smart sustainable/sustainable smart cities should work toward enhancing the underlying environmental, physical, social, and economic systems over the long run by means of sustainable interventions and programs using advanced technologies and their novel applications, with the primary purpose of maintaining predictable patterns of behavior and hence stable reciprocal relationships principally responsible for generating such patterns. In particular, as their positive adaptation depends upon how well they are adjusted with the environment, they need to make changes to protect themselves and grow to accomplish their goals in terms of achieving the ultimate goal of sustainability. One way of doing this is to self-correct themselves based on reactions from the natural/environmental system with respect to climate change and related hazards and upheavals. This feature relates to the adaptive nature of complex systems in that they have the capacity to change and learn from experience. Failure to combat climate change could cause irreversible disruption, a significant life event that can affect their adaptation. In sum, smart sustainable/sustainable smart cities as complex systems are composed of many parts connected and joined together by a web of relationships that interact to generate collective behaviors that cannot easily be explained on the basis of the interaction between the individual constituent elements (Bibri 2018a). In this context, their complexity entails the way a vast number of complicated and dynamic sets of relations, interactions, or dependencies can produce the behavioral patterns associated sustainable development and hence sustainability performance. Systems theory recognizes that the parts and most individual details of complex systems are irrelevant to describe them as a whole while identifying crucial general dynamics (Anderson 1972). For example, a city exists, operates, functions, and evolves even as people change their place and way of living, or urban areas are restructured and redesigned. Regardless, systems thinking remains the most effective approach to understanding complex systems, and also enables to create methods and design interventions for dealing with sustainability problems that are highly likely to produce the desired outcomes while minimizing unexpected consequences. For a detailed account of the complexity of smart sustainable cities, the interested reader can be directed to Bibri (2018a), specifically to Chap. 6 of his book. The aim of this chapter as grounded in complexity science and systems thinking as theoretic approaches is to systematically explore the key structures, behavioral patterns, conditions, relationships, interactions, and dependencies

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underlying such cities as complex systems, and to elucidate the associated principles in terms of methods, mechanisms, and goals. The intent of offering the knowledge to describe and analyze these characteristic features of complexity in this context is to provoke thought, foster deeper understanding, and create fertile insights, with the primary purpose of making visible possible places for actions that improve the contribution of smart sustainable cities to the goals of sustainable development. To understand complex systems and the way they behave, form relationships with their environment, and evolve over time, as well as how their internal parts interact with and affect each other, necessitates the application and use of computationally and analytically sophisticated approaches, e.g., new powerful forms of simulation models and optimization and prediction methods built on top of the patterns and models generated as a result of big data analytics. The science of complexity (i.e., information theory, nonlinear dynamics, networks, pattern formation, collective behavior, emergence, adaptation and evolution, and systems theory), which is utilized to fashion such models, is integral to the understanding of smart sustainable/sustainable smart cities, which is a moving target in that they are becoming more complex through the kind of technologies being used to understand them. This understanding involves tracking the underlying changing dynamics, interactions, reciprocal relationships responsible for generating the behavioral patterns such cities exhibit as complex systems (Bibri 2018a). Its primary aim is to develop solutions that do not create further problems associated with their operational functioning, planning, design, and development, and also to solve existing urbanism problems by focusing on external agents merely because these problems are often embedded in larger systems. Examples of problems in this regard include, but are not restricted to, the following: • • • • • • • • • • •

resource depletion; environmental degradation; inefficient use of natural resources; air and water pollution; toxic waste disposal; traffic congestion; ineffective decision-making systems; public health and safety decrease; social vulnerability and inequality; economic instability; urban isolation.

8.2 Some Essential Tensions There is an increasing consensus that smart sustainable/sustainable smart cities represent the crucible for technological innovations, an environment in which the interaction of different elements leads to the creation of new things (e.g., big data computing and its technological applications), and that larger cities represent the best places where sustainable development and its progress can be made with their invention and application (Bibri 2018a). One of the reasons why many governments are embracing the idea of smart sustainable/sustainable smart cities is that there is now a widespread view that to become and remain smart sustainable and be ahead of the game, cities must mobilize advanced ICT, especially big data analytics and related intelligence functions, simulation models, and prediction and optimization methods to become even smarter in the pursuit of their sustainable advantage, or in realizing their full potential in terms of sustainability in an increasingly urbanized world. Indeed, the idea of cities growing even bigger in terms of their populations and knowledge base (i.e., sustainable development, sustainability, ICT innovation, data-driven urbanism, etc.) epitomizes or lies at the core of smart sustainable/sustainable smart cities. The key to a more sustainable world is held by advanced ICT, and this will be most clearly demonstrated in large cities. There are a number of innovations that the ongoing research on smart sustainable/sustainable smart cities will establish in the years ahead. First and foremost, ICT of pervasive computing as mainly enabled by big data analytics is founded on the application of complexity science to urban problems, and as such, it is immediately apparent that the very subject of its focus within most of the projects and endeavors pertaining to such cities entails that urban systems are in and of themselves becoming even more complex, especially when considering the challenges of sustainability and urbanization. This in turn is due to the invention and application of new modes of urban functioning in such cities using advanced ICT. The endeavor of such cities is at the forefront of understanding complex social systems using the very ICT tools that are fashioning those systems in the first place. Such cities, which apply and use

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ICT in various forms (e.g., infrastructures, platforms, architectures, wireless networks, computational/data analytics, applications, etc.), change the very nature of the application and use processes by means of that same ICT. In a nutshell, the science itself changes the very science that we are applying and using. The great innovation of ICT of pervasive computing as underpinned by big data technology and its application is that the urban problems should be approached in full knowledge of these dilemmas (Bibri 2018a). This includes the concerns for privacy and security and other risks involved in the creation of the deluge of urban big data flooding from urban systems, domains, and networks and concerning the activities of individual citizens and collective urban entities in the context of smart sustainable/sustainable smart cities. Worth noting is that whatever approach we adopt or pursue as to tackling the wicked problems within smart sustainable/sustainable smart urbanism, it should be driven by the premise that the nexus is complex, and it is of critical importance not to overlook this interwoven complexity at human and environmental perils. It is of high relevance to put more emphasis on questions involving organization that imply software development, database administration, and management of large-scale computer resources, networks, and data, in spite of the fact that smart sustainable/sustainable smart cities as programs and initiatives would be strongly focused on hardware in terms of infrastructures and networks. Here comes the pivotal role of complexity science in terms of the aforementioned topics that help connect the biological inspiration with the challenges pertaining to technological and engineering systems. This relates particularly to urban intelligence and planning functions and related urban simulation models and optimization and prediction methods in terms of the integration of data, patterns, and models using advanced technologies. This involves a constellation of issues in ICT that surround the use and development of new varieties of computation and analytics entailing data sensing, data acquisition, data storage, database coupling, data mining, data coordination, and so on. These aspects will be part of new governance structures for new intelligence and planning functions in the context of smart sustainable/sustainable smart cities that utilize real-time construction and use of a variety of simulation models and optimization and prediction methods related to decision support systems designed for enhancing sustainability performance, coupled with much wider participation in decision-making. In this respect, city governments are challenged to find effective ways of governance to manage new complexities as urban systems and domains become more network-centric and intricate to ensure flexibility and resiliency (Bibri 2018a).

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Complex Systems Simulation Models

9.1 Challenges and Driving Forces There is a rapidly growing interest in complexity science within research on complex systems simulation in many fields, including the domain of smart sustainable/sustainable smart urbanism (e.g., Batty et al. 2012; Bettencourt 2014; Bibri 2018a, c; Bibri and Krogstie 2017b). Complex systems simulation tackles some of the most challenging and fundamental questions of science, technology, and engineering related to many diverse disciplinary, interdisciplinary, and transdisciplinary fields. The issues of complexity are at the core of some of the major physical, spatial, environmental, social, technological, and economic challenges pertaining to smart sustainable/sustainable smart cities (Bibri 2018a). Complexity science is crucial to explaining such cities as complex systems, to reiterate. Its importance and relevance in urban planning, design, and development lies in that cities and related ICT networks and infrastructures present some of the most pressing real-world challenges for city governments and industries—in population growth, energy, environment, technology, economics, public health, transport, mobility, traffic, and so on. In particular, complexity science is rapidly gaining momentum and traction due predominantly to new demands in advanced technologies. This is predicated on the assumption that there is increasing awareness that traditional approaches to planning, design, and engineering are failing to keep up with the increasing scale and connectivity of systems nowadays. Due to the management, design, engineering, and modeling problems facing modern ICT in the context of smart sustainable/sustainable smart cities, practitioners and experts are critically concerned with ensuring efficiency, effectiveness, reliability, robustness, resilience, security, scalability, and evolvability in the interconnected ICT systems which almost all urban domains rely on in their related activities. Systems from data storage and retrieval, to data processing and management, to telecommunications are rapidly increasing in scale and growing sophisticated. Also, urban processes and transactions are becoming automatic, and urban systems are being connected together, urban domains coordinated, and urban networks coupled. In the meantime, the increases in scale and connectivity of the technological systems underlying smart sustainable/sustainable smart cities make managing their complex dynamics a daunting task.

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New approaches to design, engineering, management, and control seek to remove complex dynamical behaviors and emergent processes that are intractable through minimizing errors and undesired behavioral patterns and thereby preventing the system from failing or crashing down, with possible irreversibility implications. They are more urgently needed than even for handling complex systems. This calls for a strategic collaboration between industry and academia in that industry should drive new research at universities and seek the relevant engineers and scientists trained to understand and deal with complexity. Indeed, advanced knowledge about complex systems simulation is highly desirable and increasingly gaining central importance within many urban domains, especially in relation to sustainability (Bibri 2018a). The engineers and scientists trained in systems thinking and techniques for handling and exploiting the properties of complex systems are desperately needed. Moreover, one of the open challenges facing the researchers in the complex systems community is to overcome the institutional obstacles to interdisciplinary and industrial involvement in complexity research. Furthermore, the availability of powerful computing power to simulate large-scale complex systems and investigate new ways of approaching related modeling and design is seen as another driving force in the recent upsurge of interest in complexity science. With respect to the latter, Batty et al. (2012, p. 485) point out that exploring many different kinds of models building on and extending complexity science is considered of importance for building ‘many different models of the same situation in the belief that a pluralistic approach is central to improved understanding of this complexity.’ Computational modeling allows new approaches that were not previously conceivable and testable. Formal mathematical methods and traditional design approaches remain inadequate to the design of large-scale systems in terms of their ability to handle complex dynamical behaviors and emergent processes, which are too difficult to predict. Another main driver is the opportunities made available for complexity science to learn from biological systems with regard to gaining new insights into and inspiration for tackling complexity and understanding new approaches to modeling and controlling complexity in engineered and technological systems on the basis of the way biological systems function as well as harness, exploit, and cope with emergent behaviors and processes and other system-level phenomena. Complex systems exhibit emergent properties that are to be discovered and modeled. As our cities become even more interconnected and uncertain, a systems perspective becomes increasingly necessary to understand and deal with them.

9.2 New Opportunities and Future Prospects With respect to the question of where complexity science is headed, the recent trends in complexity science are bringing together research from a variety of established fields, including ICT, computer science, data science, mathematics, complex adaptive systems, systems biology, systems ecology, environmental sciences, systems engineering, physics, and management toward new developments with wide-ranging implications. This is to stimulate new research opportunities and thus create exciting new research directions and innovative cross-disciplinary activities. Indeed, the push from academia and industry to solve complexity challenges pertaining to smart sustainable/sustainable smart cities and other complex systems in a variety of fields has produced a massive response from the academic community and several research funding councils across the globe (Bibri 2018a). One of the most enticing aspects of complexity science is its interdisciplinary and transdisciplinary nature. Of paramount to underscore in this regard is the interface of complexity science with organismic biology, cellular biology, molecular biology, and ecology, in addition to many different disciplines. This involves fascinating possibilities to learn from how complex adaptive systems cope with emergent dynamical behaviors and properties, as well as to adapt to control, harness, and exploit them in every possible way to be thought of. Indeed, a lot of research in complexity science spanning diverse domains, including smart sustainable/sustainable smart urbanism, is seeking ways to understand, analyze, model, and extract the useful properties and behaviors of biological systems using big data analytics for applied purposes related to planning, design, and development. This offers the prospect of better understanding complex systems and gaining inspiration for new approaches into solving technological and engineering challenges and issues associated with human systems in terms of control, management, optimization, resilience, robustness, and prediction. Among the systems from ICT that need new approaches inspired by biological systems to handling complexity in the context of smart sustainable/sustainable smart cities include, but are not restricted to, the following: • large-scale software development; • data processing and management systems; • database management and integration;

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• • • • • • •

sensor networks; infrastructure networks; semantic web; cloud and fog computing; grid and distributed computing; wireless network reconfiguration; telecommunication systems and Internet networks.

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The inspiration can emanate from the various characteristic features of biological systems, including evolution dynamics, DNA and self-replication, metabolic networks, gene regulation networks, ecosystem sustainability, and immune systems and repair. The relevant topics that help connect the biological inspiration with the challenges pertaining to technological and engineered systems include, but are not limited to, the following: • • • • • • • • • • •

network science; dynamical systems; feedback control; machine learning; statistical theory of complex systems; information theory; evolutionary design and algorithms; self-organization and self-regulation; simulation modeling; autonomic computing; data mining and time series analysis.

For example, in terms of complexity and network science, complex systems can be represented by a network where nodes represent the components and links represent their interactions (Dorogovtsev and Mendes 2003; Newman 2010). Examples in this regard relate to ICT networks, infrastructure networks, urban networks, socio-economic networks, climate networks, biological networks, and neural networks. Networks as parts of complex systems can fail and recover spontaneously (see Majdandzic et al. 2013 for modeling this phenomenon). Interacting complex systems can be modeled as networks of networks (e.g., Majdandzic et al. 2016 and Gao et al. 2011 for their breakdown and recovery properties).

9.3 Toward Novel Urban Simulation Models: Incorporating Dynamical Properties of Complex Systems One of the significant challenges and key focuses within smart sustainable/sustainable smart urbanism is to develop new powerful forms of simulation models that embrace the emerging forms of complexity characterizing city systems. This entails the development of a new class of simulation models and their integration in relation to various urban systems and domains that will evolve as the structures and forms of smart sustainable/sustainable smart cities themselves evolve and become smarter in strategically assessing and continuously enhancing and optimizing their contribution to the goals of sustainable development. Such models are to simulate urban dynamics as self-organizing evolution processes with respect to sustainability and urbanization under selective pressures through balancing between selection mechanisms and innovation patterns. Especially, there is a need for a new approach to urban planning and design that can tackle the mounting challenges of sustainability in an increasingly urbanized urban world—pervaded with computer and big data technology and dominated by the flows of computable information and analytical data that leave no physical traces and have no spatial aspects like area, position, location, and shape (Bibri 2018a; Bibri and Krogstie 2017a). Hence, the real challenge is to build simulation models that grapple with these shifts and that have the potential to embrace and inform the new conceptions of the way smart sustainable/sustainable smart cities function, i.e., intelligence functions. There is a need for building simulation models that respond to such cities as complex systems and what this entails in terms of dynamical properties, including how the planning and decision processes might change in ways that enable the functioning of such cities in real time from routinely sensed data. Indeed, as stated by Batty et al. 2012, p. 507), to reiterate, ‘spatial scales and timescales are being collapsed by the emergence of real-time data from the bottom up. Data sets are being created that show immediately the functioning of the

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real-time city but also imply how long-term changes in the city can be detected. In short, if all the data that we collected were in real time, at any instant, we could aggregate the data to deal with change in the city at any scale and over any time period. This prospect is a long way off and will never be reached (for once we reach it we will find more and different data that need to be collected) but what it does promise is an ability to have a real-time view of change at different spatial scales and over different timescales.’ Besides, in terms of the human capacity to predict the behavior of complex systems through modeling, it is believed that the sciences of complex phenomena could not be modeled after the sciences that deal with essentially simple phenomena (Bibri 2018a). This relates particularly to chaotic systems whose long-term behavior remains difficult to forecast with any accuracy. Nonetheless, one can theoretically make accurate (pattern) predictions about the future of smart sustainable/sustainable smart cities as complex systems on the basis of the kind of knowledge that is as good as it is possible as to the relevant equations describing their behavior. This relates to simulations which are intended to translate the structure of such cities into a set of equations characterizing the nature of the underlying relationships and their direction and strength, predicated on the assumption that the set of reciprocal relationships pieced together generate the patterns of behavior exhibited by such cities. The whole idea, though, remains unfeasible in practice at the current stage of research in complexity science and its application. Prigogine (1997) argues that complexity gives no way whatsoever to precisely predict the future, and is non-deterministic. Hayek (1978) notably explains that complex phenomena can only allow pattern predictions using modeling approaches, compared with the precise predictions pertaining to non-complex phenomena. Regardless, many real complex systems, including cities, have the potential for radical qualitative change while retaining systemic integrity. In the context of smart sustainable/sustainable smart cities, the use of computer simulation is primarily designed to stimulate research in the simulation of their adaptive behavior due to the underlying web of ongoing, reciprocal relationships cycling to generate the patterns of behavior to be exhibited as manifested in the interaction between physical system, environmental system, economic system, and social system as nested systems and representing networks of networks. However, the simulation of their behavior on a computer necessitates ensuring that the set of the reciprocal relationships involved generate the patterns of behavior that they exhibit, to reiterate. Important to consider additionally are the mechanisms believed that such cities are using to control themselves. The basic idea is to explore how things are related to and affect each other, and how they are connected to, configured in, and constrained by the diverse systems forming such cities in terms of pressures and expectations. The ultimate aim is to design, manage, and build the control systems of technological and engineering systems in such that the latter can proliferate and increase in size and connectivity in response to urban growth, environmental pressures, changes in socio-economic needs, unprecedented shifts, global trends, discontinuities, and societal transitions associated with such cities. The behavior of smart sustainable/sustainable smart cities as complex systems is inherently difficult to model due to the kind of dependencies, relationships, or interactions between their sub-systems or between them and the environment. Such cities have distinct dynamical properties that arise from these relationships. The commonalities among complex systems as they appear in a wide variety of disciplines and fields, including urbanism, have become the topic of their own independent area of research. A deep level of understanding the dynamical properties of complex systems is crucial to bringing about a drastic change to both the simulation models that we are able to build based on the analysis of big data of various velocities (especially real-time data) and the way in which the underlying technologies can inform the planning and decision processes with simulations and decision support systems. Such properties are at the core of the new conceptions of the way such cities function and thus planned and designed, that is, new intelligence functions that utilize complexity science in developing simulation models and optimization and prediction methods that generate urban structures and forms and thus spatial organizations and scale stabilizations that enhance and maintain sustainability performance, as well as improve resilience, efficiency, equity, and the quality of life. Specifically, as a set of interacting systems, such cities should be built to be scalable, robust, resilient, stable, balanced, and adaptive by incorporating such dynamical properties as self-organization, self-adaptation, self-regulation, feedback loops, self-repair, spontaneous order, nonlinearity, and evolution, thereby, ideally, mimicking biological systems. It is equally important to focus on the components that make such cities function as a social organism. The idea of advanced ICT penetrating wherever it can to enhance and maintain sustainability performance as well as improve resilience, efficiency, equity, and the quality of life is central to this quest. What is crucially important in the pursuit of making such cities function as a biological or social system is a deeper understanding of the main concepts of complex systems and their effective incorporation in the very design, engineering, and modeling of the technological and engineering systems intended to monitor, understand, and analyze such cities. Especially, complexity science brings together deep scientific questions pertaining to sustainability with application-driven goals within the domain of smart sustainable/sustainable smart urbanism. These concepts include, but are not limited to, the following as described by Bibri (2018a):

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Systems Systems concepts play a central role in complex systems due to their interdisciplinary and transdisciplinary applicability. A system is a set of entities determined by its boundary that forms a unified whole. This is manifested in its interactions, relationships, or dependencies in relation to its constituent elements. The entities lying outside a system becomes part of its environment. A system has parts that exhibit properties and behavioral patterns which are distinct from what it itself can exhibit as system-wide properties that generate its collective behavior. This is characteristic of how its parts behave as a whole or how it interacts with its environment. Hence, the processes that take place over time as part of the collective behavior are at the heart of the study of complex systems. Complexity Given the multifarious nature of the concept of complexity, there are many archetypal examples of complexity, including chaotic behavior, dynamical behavior, emergent properties, and computational intractability of modeling. In particular, systems exhibit complexity when the regularly found conditions pose difficulties in modeling them due to the number of parameters involved that grow too rapidly proportional to the size of the system and the nature of the connectively of its parts. This implies that their properties which produce behaviors cannot be understood apart from the very relationship between their properties and behaviors (which almost entirely govern them due to their properties) that make them difficult to model. In this regard, any kind of models generated on the basis of modeling approaches that overlook such difficulties become inaccurate and of less value. Like in many areas, researchers in smart sustainable/sustainable smart cities are attempting to solve them, since as yet no fully general theory of complex systems has thus far emerged to address and overcome these issues. The way forward in this endeavor is that urban researchers view the main task of modeling to be capturing the complexity of such cities as their systems of interest. This also applies to particular domains such as mobility (Giannotti et al. 2011). Emergence Another common concept and aspect of complex systems is the presence of emergent properties and behaviors, which result from the relationships, interactions, or dependencies they form by virtue of being within a system, and are not apparent from their parts in isolation. Emergence is associated with the appearance of such properties and behaviors. It is of high applicability in smart sustainable/sustainable smart urbanism as a field of study. In this regard, it denotes the appearance of unplanned organized behavior (order) pertaining to environmental and socio-economic changes due to urbanization as well as new economic and social properties and behaviors associated with or resulting from the unsustainability of the city systems. Denoting a breakdown of city organization as well, it describes urban phenomena that are difficult to forecast from the smaller entities that make up the city. Networks Network is a core concept of complex systems due to their inherent interacting components. It represents a web of relationships usually depicted as nodes illustrating the components and links illustrating their interactions, or as a graph of vertices connected by edges. In the context of smart sustainable/sustainable smart cities as social organizations, networks can describe the relationships between their constituent entities. Networks describe the sources of complexity in such cities as large networks or networks of networks. The number of relationships between the entities of such cities can quickly dwarf that of the entities in the network as it grows. One way of depicting such cities is through viewing them as constellations of instruments across many spatial scales that are connected through wirelessly ad hoc and mobile networks with a modicum of intelligence, which provide and coordinate continuous data on different aspects of urban systems and domains in terms of the flow of decisions about the physical, infrastructural, operational, functional, and socio-economic forms of such cities. Studying them as networks enables useful applications of network science. Nonlinearity Characteristic to complex systems is the notion that a small perturbation may cause a large effect (e.g., chaos, butterfly effect) or a proportional effect due the nonlinear nature of the relationships between their parts. This relates to their nonlinear behavior caused by the reciprocal relationships over time based on feedback loops, time delays, flows, and stocks. Accordingly, cities as complex systems may respond in varying ways to the same input (e.g., energy) depending on their context. Speaking of context (the current state of a city or its parameter values), a change in the size of the input received by a given city in the form of energy, for example, does not produce a proportional change in the size of the output pertaining to the environment. In other words, a given change in input may generate significantly greater than or less than a proportional change in output. Further, some nonlinear dynamical systems may be associated with chaotic behavior, which is sensitively dependent on initial conditions that a complex system can exhibit (see Bibri 2018a for an account of chaos theory). Here, small changes to initial conditions can lead to drastic results, thereby the difficulty in, if not impossibility of, modeling the chaotic behavior of complex systems numerically. In addition, after a complex system returns to its original state, it may

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behave in a completely different way in response to exactly the same event. Thus, the chaotic behavior of complex systems poses significant challenges when it comes to extrapolating from past behaviors as part of a system’s experience. Spontaneous Order and Self-Organization Spontaneous order is the emergence of unplanned order or organized behavior out of seeming chaos in the social sciences. It refers to self-organization in the hard (physical) sciences. Central to spontaneous order is that the actions of a group of individual elements of a system are coordinated without centralized planning. Thus, spontaneous order is created and controlled by no one. It results from human actions, not from human design (Hayek 1978). However, both concepts are of high applicability to smart sustainable/sustainable smart cities as complex entities due to the kind of systems they involve, namely physical systems, economic systems, and social systems, and how these systems interact with their environment. Regarding spontaneous order, for example, a particular social or economic order in a city may emerge from a combination of self-interested individuals or entities that have no intention to create order through any kind of planning. As to self-organization, it is associated with the environmental changes triggered by a particular intensity of energy use or undertaking economic activity. Examples of systems that have evolved through spontaneous order include ecosystem, language, the Internet, a stock market, the evolution of life on Earth, and the universe. In contrast to organizations which are characterized by hierarchical networks, spontaneous orders are differentiated by being scale-free networks; yet, organizations often constitute an integral part of spontaneous social orders. Adaptation The adaptive behavior of complex systems is about having the capacity to change and learn from experience. This feature relates to complex adaptive systems, which represent special cases of complex systems. Examples of complex adaptive systems are the ecosystem, the biosphere, the city, and any social group-based endeavor in a socio-cultural system, such as sustainable communities and ecological districts. Adaptation involves dynamic evolutionary processes and has a functional role in each organism in terms of sustaining itself and evolving by natural selection. Cities face a succession of environmental challenges as they evolve, and show adaptive behavior in response to the imposed conditions by attempting to apply and use advanced technological and engineered systems or integrating these with sustainable urban design concepts and principles and planning practices. This is intended to boost their resilience to varying environmental conditions, or enable them to withstand adverse environmental conditions and to quickly recover from difficulties.

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Discussion and Conclusion

This chapter examined and discussed the approach to data-driven smart sustainable/sustainable smart urbanism in terms of computerized decision support and making, intelligence functions, simulation models, and optimization and prediction methods. These emerging urban technologies as related to the process of knowledge discovery/data mining were addressed in the context of smart sustainable/sustainable smart cities in terms of a number of related aspects and issues. Moreover, an account of smart sustainable/sustainable smart cities as complex systems was provided, encompassing complexity characteristics, complexity science relevance and usefulness, and some essential tensions. If smart sustainable/sustainable smart urbanism as a system-based approach is to be successful, it needs to design and integrate complex systems and social processes and reflect their synergy on the basis of advanced technologies in terms of innovative solutions and sophisticated methods in ways that are dynamically interactive and fundamentally humane, which calls for cities to become masters of a stable, inclusive, just, resilient, and ecological urbanism. Such solutions and methods must be based on complexity sciences and complex systems for explaining and dealing with such urbanism so as to enable more effective actions necessary for enhancing urban operations, functions, services, designs, strategies, and policies in ways that guide and move urbanism toward sustainability. The underlying assumption is that complexity sciences are integral to the understanding of such cities, which is a moving target in that they are becoming more complex through the very technologies being used to understand them (Bibri 2018a, c). Furthermore, this chapter documented and highlighted the potential of the integration of the aforementioned urban technologies for facilitating the synergy between the operational functioning, planning, design, and development of smart sustainable/sustainable smart cities for the primary purpose of improving, advancing, and maintaining their contribution to the goals of sustainable development. At the core of smart sustainable/sustainable smart urbanism is the interaction or cooperation of these urban practices to produce a combined effect greater than the sum of their separate effects in the context of sustainability and smartness. In this respect, urban planning determines the way urban structures and forms should be designed, which shapes urban operational functioning that in turn drives urban development. This entails using advanced technologies as an enabler for such synergy as well as a determinant of its outcomes. As an advanced form of ICT, big data

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computing and the underpinning technologies allow for a very rich nexus of possibilities in terms of providing new and open sources of the data deluge that is essential to a better understanding of how such cities function and to overcoming the mounting challenges of sustainability and disentangling the intractable issues of unimaginably urbanized areas. This is owing to the underlying powerful engineering solutions as a set of novel applications and sophisticated approaches. In particular, big data analytics as a form of urban intelligence enables to utilize complexity science and urban science to develop new powerful forms of urban simulation models and optimization and prediction methods directed for use in various urban domains in the context of sustainability. As high-performance computers and sophisticated analytical techniques have become an indispensable source of urban information and a salient factor for urban intelligence, respectively, the best way to study smart sustainable/sustainable smart cities as complex systems is by exploiting big data computing and the underpinning technologies. In light of this, embedding more and more advanced ICT in various forms into smart sustainable/sustainable smart cities will undoubtedly continue and even escalate for the purpose of providing the most suitable tools and methods for handling the underlying complexity as systems and thus dealing with the challenges they are facing and will continue to face. Especially, advanced ICT, specifically big data computing and the underpinning technologies, has an instrumental and shaping role in not only enhancing the operational functioning, planning, design, and development of such cities, but also in monitoring, understanding, and analyzing them to improve sustainability, efficiency, resilience, and the quality of life. With that in regard, the broad availability of urban data is pushing research even more into further advancing the core enabling technologies of big data analytics toward realizing and implementing urban intelligence functions and related simulation models and optimization and prediction methods. In terms of implications, big data analytics and related simulation models and optimization and prediction methods hold great potential to completely redefine urban problems, as well as offer entirely innovative opportunities to tackle them as part of new urban intelligence and planning functions, thereby doing more than merely enhancing existing urban practices. In addition, the upcoming developments and innovations in big data computing and the underpinning technologies, coupled with the unfolding and soaring deluge of urban data, provide clear prospects of advancing the different aspects of smart sustainable/sustainable smart urbanism. As long as big data applications are motivated by the goals of sustainable development, and hence not used meaninglessly, as to monitoring, understanding, and analyzing smart sustainable/sustainable smart cities, they will drastically change the way we operate, manage, plan, design, develop, and govern them over the long run. The intensive research being undertaken to advance urban science and thus big data computing and the underpinning technologies, as well as to find, effective ways of integrating and leveraging their scientific potential, has become imperative and worthy given the numerous benefits that are expected to be gained with respect to sustainability, resilience, efficiency, and the quality of life. However, while data-driven smart sustainable/sustainable smart urbanism provides a set of advanced solutions for urban sustainability problems, it does so within some limitations and biases. This is because data-driven urbanism generally is, to draw on Kitchin (2015), selective, flawed, normative, and politically inflected, although it purports to produce a commonsensical, neutral, apolitical, evidence-based form of responsive urban governance. Chapter 6 provides a deep philosophical discussion on data-driven smart sustainable urbanism.

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Toward the Integration of the Data-Driven City, the Eco-city and the Compact City: Constructing a Future Vision of the Smart Sustainable City

Abstract

At the beginning of a new decade, we have the opportunity to look forward and consider what we could achieve in the coming years in the era of big data revolution. Again, we have the chance to consider the desired future of the data-driven smart sustainable city as we are in the midst of an expansion of time horizons in city planning and development. Sustainable cities look further into the future when forming scenarios. The movement toward a long-term vision arises from three major megatrends or macro-shifts that shape our societies at a growing pace: sustainability, disruptive technology, and urbanization. Recognizing a link between these trends or shifts, sustainable cities have adopted ambitious goals that extend far into the future, which relate to the way they should be monitored, understood, and analyzed to improve, advance, and maintain their contribution to sustainability, and hence to overcome the kind of wicked problems, intractable issues, and complex challenges they embody. Indeed, sustainable cities and smart cities as landscapes and approaches are extremely fragmented and weakly connected, respectively. Moreover, there are multiple visions of, and pathways to achieving, smart sustainable cities based on how they can be conceptualized and operationalized. As a corollary of this, there is a host of opportunities to explore toward new approaches to smart sustainable urbanism. The aim of this futures study is to analyze, investigate, and develop a novel model for smart sustainable city of the future using backcasting as a scholarly and planning methodology. In doing so, it endeavors to integrate the physical landscape of sustainable cities with the informational landscape of smart cities at the technical level, as well as to merge the two strategies on several scales, all in the context of sustainability. This chapter is concerned with Step 3 of the backcasting approach being used to achieve the overall aim of the futures study. In this respect, it aims to report the outcome of Step 3 by answering 6 guiding questions. Visionary images of a long-term future can stimulate an accelerated movement toward achieving the long-term goals of sustainability. The proposed model is believed to be the first of its kind and thus has not been, to the best of one’s knowledge, produced, nor is it being currently investigated, elsewhere. Keywords













Smart sustainable cities Sustainable cities Smart cities Data-driven cities data technology Urban planning and design Backcasting Future vision

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Compact cities Futures study




Eco-cities




Big

Introduction

Contemporary cities have a central and defining role in strategic sustainable development; therefore, they have gained a central position in operationalizing this notion and applying this discourse. This is clearly reflected in the Sustainable Development Goal 11 (SGD 11) of the United Nations’ 2030 Agenda, which entails making cities more sustainable, resilient, inclusive, and safe (UN 2015). In this respect, sustainable cities have been the leading global paradigm of urbanism (urban planning and development) (Bibri 2018a; Bibri and Krogstie 2017b, 2019; Van Bueren et al. 2011; Wheeler and Beatley 2010; Whitehead 2003; Williams 2009) for more than three decades. The subject of ‘sustainable cities’ remains endlessly fascinating and enticing, as there are numerous actors involved in the academic and practical aspects of the endeavor, including engineers and architects, green technologists, built and natural environment specialists, and © Springer Nature Switzerland AG 2019 S. E. Bibri, Big Data Science and Analytics for Smart Sustainable Urbanism, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-17312-8_11

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environmental and social scientists, and, more recently, ICT experts, data scientists, and urban scientists. All these actors are undertaking research and developing strategies and programs to tackle the challenging elements of sustainable urbanism. This adds to the work of policymakers and political decision-makers in terms of formulating and implementing regulatory policies and devising and applying political mechanisms and governance arrangements to promote and spur innovation and monitor and maintain progress in sustainable cities. Since its widespread diffusion in the early 1990s, sustainable development has had significant positive impacts on the design, planning, and development of cities in terms of the different dimensions of sustainability (Bibri 2018a; Bibri and Krogstie 2019). It has also revived the discussion about the form of cities (Jabareen 2006). In this regard, it has inspired a whole generation of urban scholars and practitioners into a quest for the immense opportunities and fascinating possibilities that could be enabled and created by, and the enormous benefits that could be realized from, the planning, design, and development of sustainable urban forms (especially compact cities and eco-cities)—i.e., forms for human settlements that will meet the required level of sustainability and enable the built environment to function in a constructive way by continuously improving their contribution to the goals of sustainable development in terms of reducing material use, lowering energy consumption, mitigating pollution, and minimizing waste, as well as in terms of improving equity, the quality of life, and well-being (Bibri 2018a; Bibri and Krogstie 2019). However, new circumstances require new responses. This pertains to the widespread of urbanization and the rise of ICT and how they are drastically changing sustainable urbanism. The transformative force of urbanization and ICT and the role that cities can play have far-reaching implications. By all indicators, the urban world will become largely technologized and computerized within just a few decades, and ICT as an enabling, integrative, and constitutive technology of the twenty-first century will accordingly be instrumental, if not determining, in addressing many of the conundrums posed, the issues raised, and the challenges presented by urbanization (Bibri 2018a; Bibri and Krogstie 2019). It is therefore of strategic value to start directing the use of emerging ICT into understanding and proactively mitigating the potential effects of urbanization, with the primary aim of tackling the many intractable issues and wicked problems involved in urban operational functioning, management, planning, development, and governance, especially in the context of sustainability. Indeed, the rapid urbanization of the world poses significant and unprecedented challenges associated with sustainability (e.g., David 2017; Han et al. 2016; Estevez et al. 2016) due to the issues engendered by urban growth (see Bibri and Krogstie 2019 for more detail). In short, the multidimensional effects of unsustainability are most likely to exacerbate with urbanization (Bibri 2018a). Urban growth will jeopardize the sustainability of cities (Neirotti et al. 2014). Therefore, ICT has come to the fore and become of crucial importance for containing the effects of urbanization and for facing the challenges of sustainability, including in the context of sustainable cities which are striving to improve, advance, and maintain their contribution to the goals of sustainable development. As pointed out by Bibri (2018a, 2019a), the use of advanced ICT in sustainable cities constitutes an effective approach to decoupling the health of the city and the quality of life of citizens from the energy and material consumption and concomitant environmental risks associated with urban operations, functions, services, strategies, and policies. Smart sustainable cities as an integrated and holistic approach to urbanism represent an instance of sustainable urban planning and development, a strategic approach to achieving the long-term goals of urban sustainability—with support of advanced technologies and their novel applications, especially those pertaining to big data computing (Bibri and Krogstie 2019). Accordingly, achieving the status of smart sustainable cities epitomizes an instance of urban sustainability. This notion refers to a desired (normative) state in which a city strives to retain a balance of the socio-ecological systems through adopting and executing sustainable development strategies as a desired (normative) trajectory (Bibri 2018c, d; Bibri and Krogstie 2019). This balance entails enhancing the physical, environmental, social, and economic systems of the city in line with sustainability over the long run—given their interdependence, synergy, and equal importance. This long-term strategic goal requires, as noted by Bibri (2018a, p. 601), ‘fostering linkages between scientific research, technological innovations, institutional practices, and policy design and planning in relevance to sustainability. It also requires a long-term vision, a transdisciplinary approach, and a system-oriented perspective on addressing environmental, economic, social, and physical issues’. All these requirements are at the core of backcasting as a scholarly and planning approach to future studies. This approach facilitates and contributes to the development, implementation, evaluation, and improvement of models for smart sustainable cities, with a particular focus on practical interventions for integrating and improving urban systems and coordinating and coupling urban domains using cutting-edge technologies in line with the vision of sustainability. One of the most appealing strands of research in the domain of smart sustainable urbanism is that which is concerned with futures studies. The relevance and rationale behind futures research approach is inextricably linked to the strategic planning and development associated with long-term sustainability endeavors, initiatives, or solutions. And backcasting is well suited to any multifaceted kind of planning and development process (Holmberg and Robèrt 2000; Phdungsilp 2011), as well as to

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Introduction

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dealing with urban sustainability issues (Bibri 2018a, d; Bibri and Krogstie 2019; Carlsson-Kanyama et al. 2003; Dreborg 1996; Miola 2008; Phdungsilp 2011). This chapter is concerned with Step 3 of the applied backcasting approach as a scholarly and planning methodology to achieve the overall aim of the futures study: to analyze, investigate, and develop a novel model for smart sustainable city of the future. In this respect, it aims to report the outcome of Step 3: future vision generation, by answering the following 6 guiding questions: 1. 2. 3. 4. 5.

What are the terms of reference for the future vision? How does the future sustainable socio-technical system and need fulfillment look like? How is the future vision different from the existing socio-technical systems? What is the rationale for developing the future vision? Which sustainability problems, issues, and challenges have been dealt with successfully by meeting the stated objectives and hence achieving the specified goals? 6. Which advanced technologies and their novel applications have been used in the future vision? The remainder of this chapter is structured as follows. Section 2 provides the background of the futures study, including a review of the area being researched, the issue of the current situation, studies on the issue, and relevant history on the issue. Section 3 outlines the applied backcasting approach, with an emphasis on Step 3, which is the focus of this chapter. In Sect. 4, a summary of the previous backcasting study: Step 1 and 2, is provided. Section 5 delves into Step 3 of the backcasting endeavor: future vision generation, by answering 6 guiding questions in more detail following the applied methodology. This chapter ends, in Sect. 6, with discussion and conclusion.

2

Background of the Futures Study

Sustainable cities are associated with a number of problems, issues, and challenges (i.e., deficiencies, shortcomings, inadequacies, difficulties, fallacies, and uncertainties) when it comes to their management, planning, design, development, and governance in the context of sustainability (Bibri 2018a; Bibri and Krogstie 2017a, b, 2019). This mainly involves the question of how sustainable urban forms should be monitored, understood, and analyzed in order to be effectively managed, planned, designed, developed, and governed in terms of enhancing and maintaining their sustainability performance. The underlying argument is that more innovative solutions and sophisticated approaches are needed to overcome the kind of wicked problems, intractable issues, and complex challenges pertaining to sustainable urban forms. This brings us to the current question related to the weak connection and extreme fragmentation between sustainable cities and smart cities as approaches and landscapes, respectively (e.g., Angelidou et al. 2017; Bibri 2018a; Bibri and Krogstie 2017a, 2019; Bifulco et al. 2016; Kramers et al. 2014), despite the great potential of advanced ICT for, and also its proven role in, supporting sustainable cities in improving their performance under what is labeled ‘smart sustainable cities’ (e.g., see, Bibri 2018a, b; Bibri and Krogstie 2017b, 2019; Kramers et al. 2014; Shahrokni et al. 2015). In particular, tremendous opportunities are available for utilizing big data computing and the underpinning technologies and their novel applications in sustainable cities to improve, advance, and maintain their contribution to the goals of sustainable development. The main strength of the big data technology is the high influence it will have on many facets of smart sustainable cities and their citizens’ lives (see, e.g., Al Nuaimi et al. 2015; Angelidou et al. 2017; Batty et al. 2012; Bettencourt 2014; Bibri 2018a, b, 2019a; Bibri and Krogstie 2017b; Pantelis and Aija 2013; Townsend 2013). In light of the above, recent research endeavors have started to focus on smartening up sustainable cities through enhancing and optimizing their operational functioning, management, planning, design, development, and governance in line with the long-term vision of sustainability under what is labeled ‘smart sustainable cities of the future’ (Bibri 2018a, b, c, d, 2019a; Bibri and Krogstie 2017a, b, c, 2019). This wave of research centers particularly around integrating the landscapes of, and the approaches to, sustainable cities and smart cities in a variety of ways in the hopes of reaching the required level of sustainability and improving the living standard of citizens (Bibri 2018a, 2019a). This integrated approach tends to take several forms in terms of combining the strengths of sustainable cities and smart cities based on how the concept of smart sustainable cities can be conceptualized and operationalized, just as it has been the case for sustainable cities. Indeed, several topical studies (e.g., Angelidou et al. 2017; Bibri 2018a, b; Bibri and Krogstie 2017b; Kramers et al. 2014, 2016; Rivera et al. 2015; Shahrokni et al. 2015; Yigitcanlar and Lee 2013) have addressed the merger of the sustainable city and smart city approaches from a variety of perspectives. This points to the fact that there is a host of opportunities yet to explore towards

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new approaches to smart sustainable urbanism. The focus in this chapter is on integrating the design concepts and principles and planning practices of sustainable urban forms with big data computing and the underpinning technologies and their novel applications being offered by smart cities of the future. The underlying assumption is that the evolving big data deluge with its extensive sources hides in itself the answers to the most challenging analytical questions as well as the solutions to the most complex challenges pertaining to sustainability in the face of urbanization, as well as plays a key role in understanding urban constituents as data agents. In recent years, there has been a marked intensification of datafication. This is manifested in a radical expansion in the volume, range, variety, and granularity of the data being generated about urban environments and citizens (e.g., Bibri 2018a, 2019a; Bibri and Krogstie 2019; Kitchin 2014, 2015, 2016), with the primary aim of quantifying the whole of the city, putting it in a data format, so it can be organized and analyzed. We are currently experiencing the accelerated datafication of the city in a rapidly urbanizing world and witnessing the dawn of the big data era not out of the window, but in everyday life. Our urban everydayness is entangled with data sensing, data processing, and communication networking, and our wired world generates and analyzes overwhelming and incredible amounts of data. The modern city is turning into constellations of instruments and computers across many scales and morphing into a haze of software instructions, which are becoming essential to the operational functioning, planning, design, development, and governance of the city. The datafication of spatiotemporal citywide events has become a salient factor for the practice of smart sustainable urbanism. In the wake of datafication, a new era is presently unfolding wherein smart sustainable urbanism is increasingly becoming data-driven (Bibri 2018a, b, 2019a). At the heart of such urbanism is a computational understanding of urban systems and processes that renders urban life a form of logic, calculative, and algorithmic rules and procedures. Such understanding entails drawing together, interlinking, and analyzing urban big data to provide a more holistic and integrated view and synoptic intelligence of the city. This is being increasingly directed for improving and maintaining the contribution of sustainable cities to the goals of sustainable development in an increasingly urbanized world. We are living at the dawn of what has been termed as ‘the fourth paradigm of science,’ a scientific revolution that is marked by the recent emergence of big data science and analytics as well as the increasing adoption of the underlying technologies in scientific and scholarly research practices. Everything about science development and knowledge production is fundamentally changing thanks to the unfolding and soaring data deluge. The upcoming data avalanche is thus the primary fuel of this new age where powerful computational processes or analytics algorithms burn this fuel to generate useful knowledge and deep insights directed for a wide variety of practical uses, e.g., strategic decisions to create more sustainable, efficient, resilient, livable, equitable, and safe cities. Big data science and analytics possess unparalleled potential to revolutionize society in a way that no one is able to predict in terms of the dramatic change that it will have on our lives. Indeed, it embodies an unprecedentedly transformative and constitutive power manifested not only in the form of revolutionizing science and transforming knowledge, but also in advancing social practices, producing new discourses, creating and catalyzing major shifts, and fostering societal transitions. Of particular relevance to this chapter, this new area of science and technology is instigating a drastic change in the way cities are studied and, hence, in the practice of operating, managing, planning, designing, developing, and governing them in the context of sustainability in the face of urbanization. To put it differently, these practices are becoming highly responsive to a form of data-driven urbanism that is the key mode of production for what have widely been termed smart sustainable cities whose monitoring, understanding, and analysis are accordingly increasingly relying on big data computing and the underpinning technologies. In a nutshell, the Fourth Scientific Revolution is set to erupt in cities, break out suddenly and dramatically, throughout the world. This is manifested in bits meeting bricks on a vast scale as instrumentation, datafication, and computation are permeating the spaces we live in. The outcome will impact most aspects of urban life, raising questions, and issues of urgent concern, especially those related to sustainability and urbanization. This pertains to what dimensions of cities will be most affected; how urban planning, design, development, and governance should change and evolve; and, most importantly, how cities can embrace and prepare for looming technological disruptions and opportunities. In light of the above, at the beginning of a new decade, we have the opportunity to look forward and consider what we could achieve in the coming years in the era of big data revolution. Again, we have the chance to consider the desired future of the data-driven smart sustainable city. This will induce and inspire many urban scholars, scientists, and practitioners to think about how the subject of ‘data-driven smart sustainable cities’ might develop, and about the debates that authors from various interdisciplinary and transdisciplinary fields may want to contribute to in the years ahead, as well as into a quest for the immense opportunities and fascinating possibilities that can be created by the development and implementation of such cities. In this respect, we are in the midst of an expansion of time horizons in city planning. Both sustainable cities and smart cities look further into the future when forming strategies or scenarios. The movement toward a long-term view or vision

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Background of the Futures Study

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arises from three major megatrends or macro-shifts that shape our societies at a growing pace: sustainability, ICT, and urbanization. Recognizing a link between these trends or shifts, both classes of cities across the globe have adopted ambitious goals that extend far into the future. The focus in this chapter is on sustainable cities, more specifically sustainable urban forms.

3

Backcasting as a Scholarly and Planning Approach

As a special kind of scenario methodology, backcasting is applied here to build a future model for smart sustainable city development as a planning tool for facilitating societal sustainability. The process of developing a scenario is concerned with eliciting a desirable future for the purposes of assisting in strategy development and providing decision-making guidance. Backcasting scenarios, which are used to explore future uncertainties, create opportunities, build capabilities, and improve decision-making processes, are constructed from the distant future toward the present by taking the future scenario we want to achieve, and then moving step-by-step backwards toward the present, thereby identifying the strategic steps that need to be taken to attain our desired goal as well as the stumbling blocks on the way and the key stakeholders that should be

Table 1 Guiding questions for each step in the backcasting study

Steps and guiding questions of backcasting as a scholarly and planning approach to strategic smart sustainable city development Step 1: Determine aim, state objectives, and specify goals and targets 1. What are the overall aim and objectives of the backcasting study? 2. What are the long-range objectives declared by the goal-oriented backcasting approach? 3. What are the goals and targets of sustainability the long-range objectives are translated to for scenario analysis? Step 2: Describe and analyze prevailing and emerging trends, clarify problems and issues of the current situation, and identify main expected developments 1. What is the socio-technical system to be studied? 2. Which societal functions/needs are addressed by this system? 3. What are the important trends and expected developments related to this system? 4. What are the problems and issues of the current situation and the underlying causes pertaining to this system? Step 3: Generate sustainable future vision 1. What are the terms of reference for the future vision? 2. How does the future sustainable socio-technical system and need fulfillment look like? 3. How is the future vision different from existing socio-technical systems? 4. What is the rationale for developing the future vision? 5. Which sustainability problems, issues, and challenges have been tackled by meeting the stated objectives and hence achieving the specified goals? 6. Which advanced technologies and their novel applications have been used in the future vision? Step 4: Strengthen the future vision with empirical investigation 1. What category of case studies is most relevant to the future vision? 2. How many case studies are to be conducted and what kind of phenomena do they intend to illuminate? 3. What is the rationale for the methodological approach adopted? 4. What extent can this empirical research generate new ideas and serve to illustrate the theories underlying the future vision and to underpin its potential and practicality? Step 5: Backcasting analysis—developing strategic steps to reach the future vision 1. What structural and technological changes are necessary for achieving the future vision? 2. What institutional and regulatory changes are necessary? 3. How have the necessary changes been realized and what stakeholder groups are necessary? 4. What are the opportunities, potentials, benefits, and other effects of the alternative scenario for the future? Step 6: Specify and merge the components of the novel model for smart sustainable city of the future 1. What specific design concepts and typologies and technological components are necessary for developing the novel model for smart sustainable city of the future? 2. What kind of urban centers and laboratories are necessary? 3. What spatial dimensions and scale stabilizations should be considered? 4. How all of the ingredients can be integrated into a framework for strategic smart sustainable city planning and development? Source Bibri and Krogstie (2019)

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involved to drive change. Accordingly, their primary aim is to discover alternative pathways through which a desirable future can be reached. Following Rotmans et al. (2000), scenarios can be classified into different categories, including projective and prospective scenarios, qualitative and quantitative scenarios, participatory and expert scenarios, and descriptive and normative scenarios. The futures study is concerned with a normative scenario, which takes values and interests (sustainability and big data technology) into account and entails reasoning from specific targets that have to be achieved (Bibri 2018a, d). Bibri (2018d) has recently conducted a comprehensive study on futures studies and backcasting. Its core focus is on backcasting as a scholarly and planning approach to strategic smart sustainable urban development. Later, Bibri and Krogstie (2019) refined and adapted the approach, i.e., made minor changes so as to improve and clarify it in accordance with the nature and aim of the futures study as well as the specificity of the proposed model to be developed. Indeed, a commonly held view is that the researchers’ worldview and purpose remain the most important criteria for determining how futures studies can be developed and conducted in terms of the details concerning the questions guiding the steps involved in a particular backcasting approach to help identify and implement strategic decisions associated with urban sustainability (Bibri 2018a, d; Bibri and Krogstie 2019). The backcasting approach applied in the futures study is based on a synthesis of a number of notable studies done on backcasting methodologies, with a particular emphasis on their relevance to urban planning and development. The synthesized approach as refined and adapted later by Bibri and Krogstie (2019) is presented in Table 1. As the focus in this chapter is on Step 3, it is important to point out that the backcasting approach is traditionally based on one normative vision, but multiple visions can also be used to explore different future alternatives (e.g., Tuominent et al. 2014). In the futures study, Step 3 of backcasting constructs or generates only one future vision based on the objectives and goals specified in Step 1 (see Bibri and Krogstie 2019 for more details), indicating an integrated solution to a set of problems and issues associated with existing sustainable urban forms, with support of advanced technologies. In addition, the development of the future vision is typically performed after the stage of analyzing the current situation and assessing the external factors (Step 1 and 2 of backcasting). While some views defend that a prior evaluation grounds the vision in realism, others argue that it curtails the ability to think of ‘ideal states’ by putting the current circumstances and capabilities at the center of attention. However, this prescribed vision of the future is based on a sequential progression, from one stage to another in a single series of steps, into the future of the current trends and expected developments and the way they intertwine with and affect one another in relation to smart sustainable cities, without sharp transformation, as well as on a combination of technological innovations and sustainability advancements, or on the coevolutionary pathways of social and ecological systems.

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A Summary of the Previous Backcasting Study: Step 1 and 2

The previous study conducted by Bibri and Krogstie (2019) is concerned with the first two steps of the backcasting approach being applied as a scholarly and planning methodology to achieve the overall aim of the futures study. As such, it reports the outcome of Step 1 and 2 by answering the guiding questions listed in Table 1. We present a brief summary of this outcome.

4.1 The Outcome of Step 1 Here, we provide the key aspects of the futures study being conducted, i.e., of the approach to the research topic in question so as to put things into context by summarizing the outcome of Step 1 (see Bibri and Krogstie 2019 for a detailed account). As a research endeavor in its nature, the futures study tries to develop, compile, transform, enhance, and disseminate knowledge of the smart sustainable city of the future. Its emphasis is on the key untapped or unexploited benefits, opportunities, capabilities, impacts, possible routes, and future scenarios enabled by the emerging big data computing paradigm in regard to advancing urban sustainability. And its intention is to create the form of knowledge that can be used to guide sustainability transitions in an increasingly technologized, computerized, and urbanized world. In more detail, the futures study falls within the research field of sustainability transitions where ICT of pervasive computing as underpinned by big data technologies and their novel applications is considered a critical enabler and powerful driver given its transformational, disruptive, synergetic, and substantive effects on forms of urban operation, management, planning, and development, owing to the underlying enabling, integrative, and constitutive nature.

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A Summary of the Previous Backcasting Study: Step 1 and 2

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The urban transition process from one city state (sustainable) to another (smart sustainable) can in this context benefit from being undertaken in a strategic step-by-step manner thanks to backcasting. With that in regard, the futures study aims to analyze, investigate, and develop a novel model for smart sustainable city of the future using backcasting as a scholarly methodology. In doing so, it endeavors to integrate the physical landscape of sustainable cities with the informational landscape of smart cities at the technical level in the context of sustainability. In more detail, it approaches this new integrated model from the perspective of combining the design principles and planning practices of both the compact city (i.e., compactness, density, diversity, mixed-land use, and sustainable transport) and the eco–city (i.e., renewable resources, passive solar design, ecological and cultural diversity, urban greening, environmental management, and other key environmentally sound policies), and then amalgamating the outcome with the data–driven city in terms of the innovative solutions and sophisticated approaches being offered by big data technology and its novel applications for sustainability. Worth noting is that this approach, which is one among several others that have been proposed in the field of smart sustainable cities and are being investigated further and hence not implemented yet (see Bibri 2019b for an overview), encompasses the core elements of urban sustainability, namely planning, design, and technology. As part of Step 4 of the backcasting methodology: four case studies (two compact cities and two eco-cities) are to be performed—with a focus on the design concepts and principles and planning practices of these two models of sustainable urban form, which specifically relates to urban design and planning and constitutes a specialized area of it. The outcome in terms of the physical and ecological components of compact cities and eco-cities is to be firstly integrated and secondly merged with the technological and informational components of big data projects that have been developed and implemented (or being implemented) as part of the data-driven smart city initiatives targeting sustainability across the globe. The rationale for investigating a set of projects as cases in this urban context is because there exist no truly data-driven smart cities or districts: fully developed urban environments on the basis of big data technology and its applications, not to mention in relation to sustainability, but only scattered projects which are yet to find their way to wide adoption across (capital) cities. Moreover, the idea of the data-driven smart city has not been fully conceptualized and operationalized yet in any part of the world in terms of its orientation, focus, and status. Accordingly, related scholarly research remains scant; in particular, no case study has been conducted on the topic of using big data technology and its applications for sustainability purposes in the context of data-driven smart cities—according to a recent literature review carried out by Bibri (2019a). Nevertheless, the data-driven city has become a clear prospect and the new reality with the massive proliferation and adoption of the core enabling technologies of big data analytics (see, e.g., Bibri 2019a; Kitchin 2014, 2015, 2016; Rathore et al. 2018). All in all, the case studies combined in the futures study represent sustainable cities and big data projects and initiatives for sustainability as part of data-driven smart cities as the subject of the inquiry that is an instance of the phenomenon of smart sustainable cities of the future that provides an analytical frame—an object—within which the futures study is to be conducted and which the cases (i.e., cities, districts, and projects) included illuminate and explicate. As far as the development part is concerned, it entails combining the results from the analysis and investigation with insights into how sustainability criteria can be formulated, especially in relation to the goals of sustainable development and what it means for a smart sustainable city as a process-oriented development approach to work with such criteria.

4.2 The Outcome of Step 2 In the second stage, the relevance of describing the broader context within which the analysis will take place lies in defining the different components that could act as direct inputs to the scenario analysis (Step 5).

4.2.1 Long-Lasting Trends The main prevailing and emerging trends identified in the previous backcasting study include the following: • Global shifts: sustainability, ICT, and urbanization; • Intellectual discourses: sustainable urbanism, smart urbanism, scientific urbanism, and sustainable development; • Academic discourses: sustainable cities, eco-cities, compact cities, smart cities, smarter cities, and smart sustainable/sustainable smart cities; • Computing and scientific paradigms: pervasive computing, the IoT, big data computing, and data-intensive science; • Technological innovations: big data analytics, applications, technologies, and ecosystems; The dynamic interplay between these varied forms of trends, which will undoubtedly continue to evolve simultaneously and affect one another in a mutual process for many years yet to come, is the backcloth against which many recent urban

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innovations and transition endeavors or enterprises have emerged and materialized, and hence, numerous opportunities have been created and exploited in the context of data-driven smart sustainable urbanism within both ecologically and technologically advanced nations. In particular, these trends are shaping and driving not only the emergence and materialization of smart sustainable cities as a leading paradigm of urbanism, but also their evolvement, success, expansion, and evolution.

4.2.2 Problems and Issues of the Current Situation This part of Step 2 is rather a comprehensive overview of the current situation, but here we list only the topical subjects that relate to the futures study, which involve the following: • Sustainable cities—compact city and eco-city models of sustainable urban form; – Key benefits; – Limitations, deficiencies, fallacies, uncertainties, challenges, and prospects; • Smart cities; – Realizing the tremendous potential of smart cities of the future for advancing sustainability; • Smart sustainable cities; – The key underlying and driving forces for smart sustainable city development; – Research gaps; – Key scientific and intellectual challenges; • Big data analytics and its application for sustainability; – Big data applications for multiple urban domains or sub-domains; – Research issues and challenges.

4.2.3 Expected Developments The main expected developments identified are believed to be already happening or to arrive soon and include the following: • Sustainable cities embracing big data technologies and their novel applications to improve, advance, and maintain their contribution to the goals of sustainable development toward achieving sustainability; • Smart cities incorporating the goals of sustainable development in their conceptualization and operationalization as part of new pathways toward enhancing their sustainability performance by relying heavily on big data computing and the underpinning technologies; • Big data analytics increasingly pervading urban systems and domains in terms of operations, functions, services, designs, strategies, and policies; • Both smart cities and sustainable cities becoming increasingly datafied to operate properly—and even to function at all with regard to many domains of urban life; • The practice of both sustainable urbanism and smart urbanism becoming predominately data-driven; • Smart sustainable/sustainable smart cities gaining foothold and prevalence worldwide as a promising response to the challenges of sustainability and urbanization in an increasingly technologized and computerized world; • Data-intensive science as a fourth scientific paradigm drastically changing urban analytics and urban studies in the context of sustainability.

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Step 3 of the Applied Backcasting Approach: Future Vision Generation

Developing the vision of the future as Step 3 of the applied backcasting approach entails defining and describing a desirable future in which the problems and issues identified as related to sustainable urban forms have been solved by meeting the stated objectives and thus achieving the specified goals pertaining to the novel model for smart sustainable city of the future. In general, it is about identifying the desired future state, which consists of vibrant descriptions of audacious goals, as well as reflective statements dealing with or addressing the aspired future. It is important to note at this stage that the vision of the future and the proposed novel model tend to be used interchangeably in this chapter because this vision represents a short and concise version of this model, which indeed refers to a desired future state that is supposed to be more detailed in the end of this scholarly backcasting endeavor.

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5.1 On the Visionary Approach The future vision is a result of the concept of urban sustainability as clarified, advocated, and advanced by many scholars, academics, and practitioners in the field, as well as demonstrated in numerous real-world cities across the globe, especially in ecologically advanced nations. Indeed, the development of the novel model for smart sustainable city of the future is supported by several case studies as well as their integration in terms of the strategies and practices through which sustainable urban forms can be achieved. Additionally, however, the future vision involves the way instrumentation, datafication, and computation is opening up dramatically different forms of enhancing the performance of such forms and thereby increasing their contribution to sustainability. This entails ways in which the informational landscape of smart cities as underpinned by big data technologies and their novel applications can be integrated with the physical landscape of sustainable cities, and what this implies in regard to boosting their effects and benefits. The essence of the idea revolves around the need to harness and leverage big data technologies that have hitherto been mostly associated with smart cities but have clear synergies in the functioning of sustainable cities and tremendous potential for advancing their sustainability and need to be steered or directed for this purpose so that many new opportunities can be enabled and realized. The problems and issues that the sustainable city faces today will, especially if its landscape and strategy continues to be extremely fragmented from and weakly connected with those of the smart city at the technical and policy levels, increase in the future with possibly much greater compounding effects due to the rapid urbanization of the world and the mounting challenges of sustainability in a rather increasingly technologized and computerized world. As a scholarly endeavor, the development of the novel model for smart sustainable city of the future as a holistic approach to city planning and design is primarily aimed at bringing together and interlinking the sustainable city and smart city landscapes and strategies so as to address and overcome a set of challenging problems and issues associated with the existing sustainable urban forms as an instance of sustainable cities in terms of planning and design. This requires finding more effective and creative ways of merging sustainability knowledge with advanced technologies to enhance the performance of such forms under new circumstances, i.e., to enable them to continuously improve, advance, and maintain their contribution to the goals of sustainable development in the face of urbanization using cutting-edge technologies. This can be accomplished by, in the context of this scholarly endeavor, amalgamating the compact city with the eco-city into one model of sustainable urban form in terms of the underlying typologies and design concepts as planning practices, and then augmenting this model with big data technologies and their novel applications as a set of innovative solutions and sophisticated approaches to be delivered by citywide instrumented system and urban intelligence labs—these features characterize the data-driven city as described in Sect. 5.4.2. This spans a range of urban systems and domains with respect to operations, functions, services, designs, and policies in the context of sustainability. In this regard, city operating system, operations center, and intelligence labs will handle the activity of generating, processing, and analyzing the data deluge aimed at adopting innovation solutions and sophisticated approaches for improving, advancing, and maintaining the contribution of the smart sustainable city of the future to sustainability. Accordingly, such city is based on the notion of a living city, i.e., one that aims to seamlessly merge urban intelligence with urban sustainability. The objective is to develop and disseminate integrated future-proofed solutions and methods to support the notion of the living city approach to the built environment.

5.2 Combining Urban and Technological Visions This future vision has a high expectation on big data technology to deliver the needed solutions to meet the optimal level of sustainability and enable the built environment to function in a more constructive way than at present in terms of lowering energy consumption, mitigating pollution, and minimizing waste, as well as in terms of improving equity and the quality of life. This is to be determined by whether and the extent to which a given city is currently badging or regenerating itself as, or manifestly planning to be, smart sustainable and following different approaches to urbanism than the proposed one. And what this entails in terms of long-term sustainable development targets as set by each category of such city, in particular in connection with its design concepts, typologies, spatial organizations, and scale stabilizations as planning practices. In the short term, although big data technology could theoretically help meet the optimal level of sustainability and enable the instrumentation, datafication, and networking of the built environment toward purposeful functioning, this would be difficult and expensive. However, this future vision can be feasible because it has to be realized over the long run. The technological vision is based on the assumption of a full development, integration, and deployment of big data computing and the underpinning technologies which exist today and are likely to become widely available in the years ahead to achieve the intended or sought goals. The incorporation of advanced technologies into urban environments is supported by the untapped

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potential and proven role of big data computing and the underpinning technologies in overcoming the challenges and problems of urbanization and sustainability. This entails containing and reducing the negative effects of urbanization, which, as a dynamic clustering of people, buildings, infrastructures, and resources, puts an enormous strain on urban systems by stressing urban life as to the associated operating and organizing processes. The way forward is to continuously enhance and optimize urban operations, functions, services, strategies, designs, and policies, which can at the same time be directed for improving and maintaining the contribution of sustainable urban forms to the goals of sustainable development. In many parts of the world, twentieth-century urban development strategies engendered sprawling, car-centered cities; but accelerating rates of urbanization have made this approach unsustainable, thereby the emergence, materialization, and expansion of smart sustainable urban forms. In this respect, big data computing and the underpinning technologies will be determined in the process of redesigning and restructuring urban places to achieve the optimal level of sustainability. Regardless, policy interventions play a crucial role in supporting big data technology and its evolution. Key policy instruments and political mechanisms relate to technological research, development, and innovation in relation to the domain of sustainable urbanism, which stimulates the use of big data applications for advancing sustainability practices. The topic is to be addressed in more detail in the very last stage of the applied backcasting approach.

5.3 The Future Vision The key goal to be necessarily present in any backcasting endeavor is generating the normative alternative for the future and, as related to Step 5 which is to be addressed in one of the upcoming articles related to the futures study, analyzing its opportunities, potentials, environmental and social benefits, and other effects. Taking the prevailing and emerging trends to the extreme with the main expected developments in mind, we singled out one major societal driver for one scenario: a situation that could exist, or rather is most likely to happen, in the future. A scenario where the advancement of big data science and analytics and underpinning technologies as a disruptive form of science and technology fundamentally or dramatically changes the rules by which society functions on a global level. Accordingly, the futures study envisions the smart sustainable city of the future as: A form for human settlements that will be composed of and monitored by ICT of pervasive computing and that will meet the optimal level of sustainability and enable the built environment to function in a more constructive way than at present as to the underlying design concepts and typologies characterizing existing sustainable urban forms through a reliance on a set of big data technologies as used and applied by urban operations centers, planning centers, research centers, and innovation and living labs. This entails improving, advancing, and maintaining the contribution of such form to the goals of sustainable development in terms of reducing material use, lowering energy consumption, mitigating pollution, and minimizing waste, as well as improving equity, the quality of life, and well-being on the basis of the deluge of big data produced through instrumentation and datafication. Such data will enable to analyze different aspect of urban life and new modes of urban planning and governance, as well as will provide the raw material for envisioning and enacting a more sustainable, efficient, livable, and equitable city.

In light of the above, envisaging the smart sustainable city of the future focuses on the urban and technological components and how they should be integrated that make the city functions as a smart sustainable entity as well as a social organism. Central to this quest is the idea of big data computing and the underpinning technologies as an advanced form of ICT penetrating wherever and whatever it can of the built environment to improve and sustain the performance of what and how urban stakeholders can envision and enact in terms of new forms of cities with regard to sustainability. Furthermore, advanced ICT comes into play as a response to the commonly held view that cities should be conceived in terms of both urban strategies and processual outcomes of urbanization, which involves questions related to the behavior of inhabitants; the processes of living, consuming, and producing; and the processes of building urban environments—in terms of whether these are sustainable. The underlying assumption is that conceiving cities only in terms of, or accounting only for, urban strategies to make cities more sustainable remains inadequate to achieve the elusive goals of sustainable development.

5.4 The Three Strands of the Novel Model for Smart Sustainable City of the Future As hinted at above, the novel model for smart sustainable city of the future, the more detailed version of the future vision, integrates two models of sustainable urban form: the compact city and the eco-city, with the data-driven city. This will result in a holistic approach to urbanism, which is different, to a great extent, from these cities taken separately as existing approaches to urbanism. Worth pointing out, to reiterate, is that the focus of this amalgamation is on the design concepts and typologies characterizing both the compact city and the eco-city and the citywide instrumented system and urban intelligence

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labs characterizing the data-driven city. The nature and scope of this amalgamation are to be determined by how and the extent to which the characteristic features of the data-driven city would dovetail with those of the integrated model of sustainable urban form toward producing what can be described as data-driven smart sustainable urban form. The possible steps to be taken to attain the smart sustainable city of the future as a desired end point or future vision are rather the object of Step 5 of the backcasting approach, which comes after Step 4. This step is concerned with the case studies that need to be performed to strengthen the future vision and thus the novel model with empirical investigation. The guiding questions of these two steps are listed in Table 1. Furthermore, it must be noted that there are neither real examples of a truly smart sustainable city that has actually been delivered and thus no precedents to reference, nor future-proofing of the big data technology to ensure that it is able to be adapted, modified, and built upon in an effective way over the next 25 years or so in response to the dynamic changes of technology and fast-moving hi-tech industry. Therefore, the planned big data technology solutions must be evaluated through actual implementation and its successfulness in order to outline the actual opportunity pertaining to the improvement and advancement of urban sustainability. Indeed, big data computing and the underpinning technologies intended to support the smart sustainable city of the future are currently evolving along with those experts and professionals who are needed to support and operate them; sustainability objectives, goals, and directives are increasingly being, and should continue to be, supported and facilitated using this advanced technology as much as possible across urban domains in terms of operations, functions, services, designs, strategies, and policies; and citizens and communities must be involved and engaged with big data technology and related platforms on a far broader scale. The road ahead promises to be an exciting one as more cities become aware of the great potential and clear prospect of integrating the smart city and the sustainable city as landscapes and strategies. In the sequel, we describe the three strands that comprise the novel model for smart sustainable city of the future as hinted at in the description of the vision of the future above.

5.4.1 Sustainable Cities There are multiple views on what a sustainable city should look like or be, and hence multiple ways of defining it or conceptualizing it. Generally, it can be understood as a set of approaches into operationalizing sustainable development in cities or practically applying the knowledge about sustainability and related technologies to the operational functioning and thus planning and design of existing and new cities or districts (Bibri 2018a; Bibri and Krogstie 2017a, 2019). Sustainable cities represent an instance of sustainable urban development, which is a strategic approach to achieving the long-term goals of urban sustainability. As such, they, as put succinctly by Bibri and Krogstie (2017a, p. 11), ‘strive to maximize the efficiency of energy and material use, create a zero-waste system, support renewable energy production and consumption, promote carbon-neutrality and reduce pollution, decrease transport needs and encourage walking and cycling, provide efficient and sustainable transport, preserve ecosystems, emphasize design scalability and spatial proximity, and promote livability and community-oriented human environments. There are various instances of sustainable city as a meta-model, but compact cities and eco-cities are advocated as more sustainable urban forms and, indeed, the most prevalent and environmentally sound models of such forms. There are multiple definitions of compact cities and eco-cities in the literature (e.g., Bibri 2018a; Hofstad 2012; Jabareen 2006; Jenks et al. 1996a, b; Joss 2010, 2011; Joss et al. 2013; Rapoport and Vernay 2011; Register 2002; Roseland 1997; Van Bueren et al. 2011). These definitions tend to be based on the wider socio-cultural context in which these two models of sustainable urban form are embedded in terms of projects and initiatives. According to Jabareen (2006), the compact city and the eco-city as the most prevalent models of sustainable urban form entail overlaps among them in their concepts, ideas, and visions: the compact city emphasizes density, compactness, diversity, and mixed-land use, whereas the eco-city focuses on renewable resources, passive solar design, ecological and cultural diversity, urban greening, environmentally sound policies, and environmental management. In addition to land use patterns and design features, the compact city emphasizes sustainable transportation (e.g., transit-rich interconnected nodes), environmental and urban management systems (Handy 1996; Williams et al. 2000), energy-efficient buildings, closeness to local squares, more space for bikes and pedestrians, and green areas (Bibri and Krogstie 2019; Phdungsilp 2011). In view of that, Jabareen (2006) ranks compact city as more sustainable than eco-city from a conceptual perspective using a thematic analysis. However, the effects of these models are compatible with the goals of sustainable development in terms of transport provision, mobility and accessibility, travel behavior, energy conservation, pollution and waste reduction, economic viability, life quality, and social equity (Bibri 2018a).

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The Compact City As an idea that is aligned with the goals of sustainable development, the compact city was indeed envisioned as, as first proposed by Dantzing and Saaty (1973) 15 years before the diffusion of sustainable development, a city that enhances the quality of life but not at the expense of the next generation. So, its notion and development were just revived by the popularization of sustainable development. Subsequently, it became a preferred response to the challenges of sustainability since the early 1990s (e.g., Hofstad 2012; Jenks and Dempsey 2005; Van Bueren et al. 2011) sustainable development provides the basis for the argument underlying the compact city (Welbank 1996). As a concept, it entails ‘many strategies that aim to create compactness and density that can avoid all the problems of modernist design and cities. The popularization of sustainable development has contributed to the promotion of the urban compactness idea by enhancing the ecological and environmental justifications behind it’ (Jabareen 2006, p. 46). It was around the mid-1990s when the research led to the advocacy of combining compactness and mixed-land use (Jabareen 2006). Mixed-land use should be encouraged in cities (Breheny 1992). Fundamentally, the compact city is characterized by high-density and mixed-land use with no sprawl (Jenks et al. 1996a; Williams et al. 2000). Accordingly, it is more energy efficient and less polluting because its dwellers can live in close proximity to work and leisure facilities and can walk, bike, or take transit (Bibri 2018a). Therefore, it offers great opportunities for reducing fuel consumption for traveling, as well as for reusing urban land, supporting local facilities, and protecting rural land from further development (Jabareen 2006). Travel distances between activities are shortened due to heterogeneous zoning that enables compatible land uses to locate in close proximity to one another (Parker 1994). Such zoning in turn reduces the use of automobiles for commuting and leisure and shopping trips due to nearby location (Alberti 2000; Van and Senior 2000). The argument is that people are encouraged to cycle and walk due to many services and facilities being within a reasonable distance. Indeed, as concluded by Newman (2000), the compact city is the most fuel-efficient of existing sustainable urban forms. Integrating land use, transport, and environmental planning is key to minimizing the need for travel and to promoting efficient modes of transport (Sev 2009), and population densities are sufficient for supporting local services and businesses (Williams et al. 2000). In this respect, the compact city ideally secures environmentally sound, socially beneficial, and economically viable development through dense, diverse, and mixed-used patterns that rely on sustainable transportation (Burton 2000, 2002; Dempsey 2010; Dempsey and Jenks 2010; Jenks and Dempsey 2005). It can be implemented at various scales, e.g., neighborhood, district, city, and so on, including entirely new settlements. To sum up, the compact city model has been advocated as more sustainable urban form due to several reasons: ‘First, compact cities are argued to be efficient for more sustainable modes of transport. Second, compact cities are seen as a sustainable use of land. By reducing sprawl, land in the countryside is preserved and land in towns can be recycled for development. Third, in social terms, compactness and mixed uses are associated with diversity, social cohesion, and cultural development. Some also argue that it is an equitable form because it offers good accessibility. Fourth, compact cities are argued to be economically viable because infrastructure, such as roads and street lighting, can be provided cost-effectively per capita’ (Jabareen 2006, p. 46). Neuman (2005) enumerates, and adds some of, the characteristics of the compact city, as shown in Table 2.

Table 2 Compact city characteristics

Compact city characteristics 1. High residential and employment densities 2. Mixture of land uses 3. Fine grain of land uses (proximity of varied uses and small relative size of land parcels) 4. Increased social and economic interactions 5. Contiguous development (some parcels/structures may be vacant or abandoned or surface parking) 6. Contained urban development, demarcated by legible limits 7. Urban infrastructure, especially sewerage and water mains 8. Multimodal transportation 9. High degrees of accessibility: local/regional 10. High degrees of street connectivity (internal/external), including sidewalks and bicycle lanes 11. High degree of impervious surface coverage 12. Low open—space ratio 13. Unitary control of planning of land development, or closely coordinated control 14. Sufficient government fiscal capacity to finance urban facilities and infrastructure Source Neuman (2005)

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The Eco-city The idea of the eco-city is widely varied in conceptualization and operationalization and also difficult to delineate. According to the most comprehensive survey of eco-cities to date performed by Joss (2010), the diversity and plurality of the projects and initiatives reflected in the use of the term ‘eco-city’ across the globe make it difficult to develop a meaningful definition. Therefore, the concept of the eco-city has taken on many definitions in the literature. Register (2002), an architect widely credited as the first to have coined the term, describes an eco-city as ‘an urban environmental system in which input (of resources) and output (of waste) are minimized’. Joss (2011) states that an eco-city must be, using three analytical categories, developed on substantial scale, occurring across multiple domains, and supported by policy processes. As an umbrella metaphor, the eco-city ‘encompasses a wide range of urban-ecological proposals that aim to achieve urban sustainability. These approaches propose a wide range of environmental, social, and institutional policies that are directed to managing urban spaces to achieve sustainability. This type promotes the ecological agenda and emphasizes environmental management through a set of institutional and policy tools’ (Jabareen 2006, p. 47). This implies that realizing an eco-city requires making countless decisions about urban design, urban planning, urban governance, sustainable technologies, and so on (Rapoport and Vernay 2011). This in turn signifies that the relationship between sustainable development objectives and urban design and planning interventions is a subject of much debate (Bulkeley and Betsill 2005; Williams 2009). Irrespective of the way the idea of the eco-city has been conceptualized and operationalized, there are still some criteria that have been proposed to identify what a desirable or ideal ‘eco-city’ is or looks like, comprising the environmental, social and economic goals of sustainable development. Roseland (1997) and Harvey (2011) describe an ideal ‘eco-city’ as a city that fulfills the following set of requirements: • • • • • • • •

operates on a self-contained, local economy; maximizes efficiency of energy resources; is based on renewable energy production and carbon-neutrality; has a well-designed urban city layout and sustainable transport system (prioritizing walking, cycling, and public transportation); creates a zero-waste system; support urban and local farming; and ensures affordable housing for diverse socio-economic and ethnic classes; and raises awareness of environmental and sustainability issues, decreases material consumption.

As added by Graedel (2011), the eco-city is scalable and evolvable in design in response to urban growth and need changes. Based on these characteristic features, the eco-city and green urbanism overlap or share several concepts, ideas, and visions in terms of the role of the city and positive urbanism in shaping more sustainable places, communities, and lifestyles. Beatley (2000, pp. 6–8, cited in Jabareen 2006) views, while arguing for the need for new approaches to urbanism to incorporate more ecologically responsible forms of living and settlement, a city exemplifying green urbanism as one that: • • • • • •

strives to live within its ecological limits; is designed to function in ways analogous to nature; strives to achieve a circular rather than a linear metabolism; strives toward local and regional self-sufficiency; facilitates more sustainable lifestyles; and emphasizes a high quality of neighborhood and community life.

The eco-city approaches tend to emphasize different aspects of sustainability, namely passive solar design, greening, sustainable housing, sustainable urban living, and living machines (Jabareen 2006). Worth noting, as a general consensus, the eco-city is formless or eco-amorphous in terms of typologies, although it emphasizes passive solar and ecological design (Jabareen 2006). Indeed, it is evident that urban form specificities are on less focus in eco-city development. That is to say, the built environment of the city in terms of urban design features and spatial organizations is inconsequential or insignificant, unlike the compact city which focuses on the spatial patterns of physical objects and typologies. Rather, what counts most is how the city as a social fabric is organized, managed, and governed. In this line of thinking, Talen and Ellis (2002, p. 37), state, ‘social, economic, and cultural variables are far more important in determining the good city than any choice of spatial arrangements’. In view of that, the focus is on the role of different environmental, social, economic,

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institutional, and land use policies in managing and governing the city to achieve the required level of sustainability (e.g., Council of Europe 1993; Jabareen 2006; Robinson and Tinker 1998).

5.4.2 The Data-Driven City and Its Smart and Sustainable Dimensions Smart cities, big data computing, and data-driven cities have recently evidenced a powerful discourse which is merging with and reshaping the urban environment on many scales. ‘Data-driven smart sustainable cities’ is a term that has recently gained traction in academia, government, and industry to describe cities that are increasingly composed and monitored by ICT of ubiquitous and pervasive computing and thereby have the ability of using advanced technologies by city operations centers, planning and policy offices, research centers, innovation labs, and living labs for generating, processing, and analyzing the data deluge in order to enhance decision-making processes and to develop and implement innovative solutions for improving sustainability, efficiency, resilience, and the quality of life. It entails developing a citywide instrumented system (i.e., inter-agency control, planning, innovation, and research hubs) for creating and inventing the future. For example, a data-driven city operations center, which is designed to monitor the city as a whole, pulls or brings together real-time data streams from many different agencies spread across various urban domains and then analyze them for decision-making and problem-solving purposes: optimizing, regulating, and managing urban operations (e.g., traffic, transport, and energy). As cities are routinely embedded with all kinds of ICT forms, including infrastructure, platforms, systems, devices, sensors and actuators, and networks, the volume of data generated about them is growing exponentially and diversifying, providing rich, heterogenous streams of information about urban environments and citizens. This data deluge enables the real-time analysis of different urban systems and interconnects data across different urban domains to provide detailed views of the relationships between different forms of data that can be utilized for advancing the various aspects of urbanity through new modes of operational functioning, planning, design, development, and governance in the context of sustainability, as well as provides the raw material for envisioning more sustainable, efficient, resilient, and livable cities. Cities are becoming ever more computationally augmented and digitally instrumented and networked, their systems interlinked and integrated, their domains combined and coordinated, and thus their networks coupled and interconnected, and consequently, vast troves of urban data are being generated and used to regulate, control, manage, and organize urban life in real time. In other words, the increasing pervasiveness of urban systems, domains, and networks utilizing digital technologies is generating enormous amounts of digital traces capable of reflecting in real time how people make use of urban spaces and infrastructures and how urban activities and processes are performed, an information asset which is being leveraged in steering cities. Indeed, citizens leave their digital traces just about everywhere they go, both voluntarily and involuntarily, and when cross-referenced with each citizen’s spatial, temporal, and geographical contexts, the data harnessed at this scale offers a means of describing, and responding to, the dynamics of the city in real time. In addition to individual citizens, city systems, domains, and networks constitute the main source of data deluge, which is generated by various urban entities, including governmental agencies, authorities, administrators, institutions, organizations, enterprises, and communities by means of urban operations, functions, services, designs, strategies, and policies. Smart cities of the future seek to solve a fundamental conundrum of cities—improve sustainability, services, equity, and the quality of life at the same time as reducing costs and increasing efficiency and resilience by utilizing a fast-flowing torrent of urban data and the rapidly evolving data analytics technologies; algorithmic planning and governance; and responsive, networked urban systems. In particular, the generation of colossal amounts of data and the development of sophisticated data analytics for understanding, monitoring, probing, regulating, and planning the city is one significant aspect of smart cities that is being embraced by sustainable cities to improve, advance, and maintain their contribution to the goals of sustainable development (Bibri 2018a, b, 2019a; Bibri and Krogstie 2017a, b, c; Bibri and Krogstie 2018). For supranational states, national governments, and city officials, smart cities offer the enticing potential of environmental and socio-economic development—more sustainable, livable, functional, safe, equitable, and transparent cities, and the renewal of urban centres as hubs of innovation and research (e.g., Al Nuaimi et al. 2015; Batty et al. 2012; Bibri 2018a, 2019a; Bibri and Krogstie 2019; Kitchin 2014; Kourtit et al. 2012; Townsend 2013). While there are several main characteristics of a smart city as evidenced by industry and government literature (see, e.g., Hollands 2008; Kitchin 2014 for an overview), the one that the futures study, and thus this chapter, is concerned with focuses on environmental and social sustainability. There has recently been much enthusiasm in the domain of smart sustainable urbanism about the immense possibilities and fascinating opportunities created by the data deluge and its extensive sources with regard to enhancing and optimizing urban operational functioning, management, planning, design, and governance in line with the goals of sustainable development as a result of thinking about and understanding sustainability and urbanization and their relationships in a data-analytic fashion for the purpose of generating and applying knowledge-driven, fact-based, strategic decisions in relation to such urban domains as

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transport, traffic, mobility, energy, environment, education, health care, public safety, public services, governance, and science and innovation (Bibri 2018a, 2019). Therefore, the operational functioning, management, planning, and design of smart sustainable cities as a set of interrelated systems is increasingly being dominated by the use of advanced data, information, and communication technologies. The provision of data from urban operations and functions is offering the prospect of urban environments wherein the implication of the way such cities are functioning and operating is continuously available, and urban planning is facing the prospect of becoming continuous as the data deluge floods from different urban domains and is updated in real time, thereby allowing for a dynamic conception of planning and a scalable and efficient form of design (Bibri and Krogstie 2018). Chapter 5 is concerned with the anatomy of the data-driven smart sustainable city in terms of datafication, digital instrumentation, urban operating centers and strategic planning and policy offices, living labs, innovations labs, urban intelligence functions, data types, data-driven urbanism and urban and data-intensive sciences, as well as an architecture and novel typology of urban dimensions and functions.

5.5 The Rationale Behind Developing the Future Vision: The Novel Model for Smart Sustainable City of the Future In this section, the arguments, a set of reasons given in support of the novel model for smart sustainable city of the future, are compiled and distilled from the outcome of Step 2 of the backcasting study conducted by Bibri and Krogstie (2019). There are many reasons for integrating the existing models of sustainable urban form as a course of action or many explanations of controlling the concepts and principles of such practice in the domain of urban sustainability. This applies also to the integration of the sustainable city and the data-driven city as different approaches to urbanism. Here, we identify the key reasons in relevance to the aim of the futures study. This is accordingly to justify the research pursuit of analyzing, investigating, and developing the proposed model.

5.5.1 Amalgamating the Compact City with the Eco-city • The debate over the desirable or ideal sustainable urban form is a long-standing debate about what and the way things should be done to achieve the required level of sustainability. This has produced a multitudinous array of arguments and insights which have in turn contributed to advancing the existing approaches to sustainable and ecological urbanism by enhancing them with the principles of sustainable development and ecological design, especially environmental rationalization, as well as by exploring and exploiting the opportunities that can be offered by new technologies in relation to sustainability. • The mounting challenges of sustainability, coupled with rapid urbanization of the world, are ever more inducing scholars, planners, policymakers, governments, and civil societies, especially within ecologically advanced nations, to propose new frameworks for redesigning and restructuring urban places to achieve the optimal level of sustainability this time after more than three decades of academically and practically endeavoring to develop and implement sustainable urban forms. • Being one of the most significant intellectual and practical challenges for more than three decades, the development of desirable models of sustainable urban form continues to motivate and inspire collaboration between researchers, academics, and practitioners to create more effective design and planning solutions based on more integrated and holistic perspectives. • New actors have recently joined the academic and practical endeavor of advancing sustainable cities, namely urban scientists, data scientists, computer scientists, and ICT experts. These actors are undertaking research and developing strategies, approaches, and programs to tackle the rather challenging elements of sustainable urbanism. • There are many approaches that aim to achieve sustainable urban forms, emphasizing some concepts over others and using different scales of these concepts, which have been addressed on different spatial scales. A critical review of these approaches demonstrates a lack of agreement about which form is the most sustainable and environmentally sound, or about the most desirable urban form in the context of sustainability. • Different scholars (as well as planners) can combine different design concepts and/or typologies to achieve the goals of sustainable development. This implies that that there are different forms that they can come with, which contribute mainly to the environment for the present and future generations.

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• Different sustainable urban forms contribute differently to sustainability, but vary as to how and the extent to which they perform in this regard. • Sustainable urban forms have many overlaps among them in their concepts, ideas, and visions. While there is nothing wrong with such forms being different yet compatible and not mutually exclusive, it can extremely be beneficial and of strategic value to find innovative ways of combining their distinctive concepts and key differences toward more holistic models of such form for boosting sustainability performance. • The existing models of sustainable urban form are associated with critical issues that are unsettled and largely ignored as to how to overcome the rather complex challenges of sustainability and urbanization, and that are also underexplored from an applied theoretical perspective with regard to how to improve, advance, and maintain the contribution of such form to sustainability on the basis of advanced technologies. Such issues involve limitations, inadequacies, and fallacies. • The existing models of sustainable urban form face significant challenges and pose special conundrums—when it comes to planning, design, and development in the context of sustainability. These challenges pertain to uncertainties, difficulties, and failures, in particular in relation to compact cities and eco-cities. • Compact cities have a form as they are governed by static planning and design tools, whereas eco-cities are amorphous: without a clearly defined form, thereby the feasibility and potential of their integration into one model that can eventually accelerate sustainable development toward achieving the optimal level of sustainability. • Neither real-world cities nor academics have yet developed convincing models of sustainable urban form, and the components of such form are still not yet fully specified. • Sustainable cities can be conceptualized and operationalized in different ways: there are multiple processes of, and pathways toward achieving, sustainable urban development, in addition to a movement toward understanding the interplay between ecological, social, and technological solutions. As a corollary of this, there is a host of unexplored opportunities toward new approaches to sustainable urban development in light of the rise of ICT of pervasive computing, a set of disruptive technologies, as a major societal driver.

5.5.2 Merging the Integrated Model of Sustainable Urban Form with the Data-Driven Smart City • Smart urbanism as being predominately driven by big data computing and the underpinning technologies has recently revived the debate about sustainable cities, and promises to add a whole new dimension to sustainability by enhancing the performance of the design concepts and typologies underlying the existing models of sustainable urban form in ways that enable such form to achieve the optimal level of sustainability. • It is an urban world where the physical landscape of sustainable cities and the informational landscape of smart cities are increasingly being merged. Hence, it is high time for existing sustainable urban forms to embrace and leverage what data-driven cities have to offer as innovative solutions and sophisticated approaches to overcome the rather complex challenges of sustainability and urbanization. • A large part of research within the emerging area of smart sustainable cities focuses on exploiting the potentials and opportunities of advanced technologies and their novel applications as an effective way to mitigate or overcome the issue of sustainable cities and smart cities being extremely fragmented as landscapes and weakly connected as approaches, especially at the technical and policy levels. • There is huge potential for using big data computing and the underpinning technologies to advance sustainable urban forms through novel approaches to, and new processes of, decision-making informed by a high level of applied intelligence enabled by the analytical power of the deluge of urban data. • Tremendous opportunities are available for utilizing big data applications in sustainable cities to optimize and enhance their operations, functions, services, designs, strategies, and policies, as well as to find answers to challenging analytical questions and thereby advance knowledge forms. • As an integrated and holistic approach, smart sustainable cities tend to take multiple forms of combining the strengths of sustainable cities and smart cities based on how the concept of smart sustainable cities can be conceptualized and operationalized. As a corollary of this, there is a host of unexplored opportunities toward new approaches to smart sustainable urbanism. • The quest for finding more effective ways to merge the physical and informational landscapes of the emerging smart sustainable cities in ways that can sustain their contribution to the goals of sustainable development is currently motivating, inducing, and inspiring many researchers, scholars, scientists, and practitioners, as well as real-world cities, to develop innovative, robust models of such cities.

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• Building future models for smart sustainable cities will play a pivotal role in laying the foundation for and spurring the development, deployment, and implementation of such cities. This will in turn stimulate their replication in different places around the world, thereby mainstreaming this drastic urban transformation.

5.6 An Applied Theoretical Approach to Addressing Problems, Issues, and Challenges 5.6.1 An Applied Theoretical Inquiry Approach In the sphere of sustainable urban forms, there obviously are numerous questions that needs to be addressed or problems to be investigated, which relate to the scientific, technological, conceptual, practical, analytical, discursive, technical, socio-technical, futuristic, social, political, institutional, and ethical domains of sustainable cities. The kind of the problems and issues the futures study is concerned with pertains to the theoretical and practical domains of sustainable cities from a futuristic perspective, which suits an applied theoretical inquiry approach to the topic of the futures study. In other words, the novel model for smart sustainable cities of the future to be analyzed, investigated, and developed is a result of the theories of urban sustainability and urban science and their effects on the built environment. In this respect, the futures study combines investigation (case studies) and understanding of theory—a literature-based activity in conjunction with consultations with urban thinkers and experts—to study the application and effects of theories. As such, it involves an interesting and varied set of activities that are suitable for combining thinking with doing (Bibri 2018c). Indeed, with its strong applied focus, it is not alienated or divorced from real-life settings; rather, it is being carried out to inform the planning and design of smart sustainable cities as a holistic approach to the practice of urbanism. The applied theoretical approach to smart sustainable city planning and design entails analyzing and investigating how a set of theories derived from urban planning, urban design, urban sustainability, urban science, data science, and ICT can be applied to the built environment in the context of sustainable urban forms. The relevance and importance of such approach stem from the fact that the research in the field of smart sustainable cities is still in its infancy, and hence, their development should be grounded in a more theoretically and practically focused approach to mitigate or avoid any potential ad hoc progress in this field (Bibri 2018a; Bibri and Krogsgtie 2017a). Besides, the subject of smart sustainable cities draws upon influential theories and principles drawn from a number of academic and scientific disciplines with high integration, fusion, and applicability potential, as well as with wide-ranging implications, as regards the practice of sustainability (Bibri 2018a, c). In short, in this subject, the underlying theories and principles constitute a foundation for actions. In addition, any study whose concern is strategic planning and development, method is investigation and analysis, and orientation is applied theoretical needs to link to what is happening in the world around us (Bibri 2018c) in terms of, for example, knowledge advancement, successful practices, emerging technologies, technological innovations, and so on. All in all, the significance and usefulness of the applied theoretical approach provide a strong motivation for the research being conducted and also dovetails with the ethos of the backcasting approach, which can align the current problems and potential solutions identification for future practices in the domain of smart sustainable urbanism (Bibri 2018c). 5.6.2 Problems, Issues, and Challenges The issue of sustainable urban forms has always been problematic and daunting to deal with. In view of that, the intellectual challenge to produce a theoretically and practically convincing model of sustainable urban form with clear components continues to induce scholars, academics, planners, scientists, and real-world cities even to create a more successful and robust model of such form. As stated in the introduction of this chapter, sustainable cities are associated with a number of problems, issues, and challenges (i.e., deficiencies, shortcomings, inadequacies, difficulties, fallacies, and uncertainties) when it comes to their management, planning, design, development, and governance in the context of sustainability. This mainly involves the question of how sustainable urban forms should be monitored, understood, and analyzed in order to be effectively managed, planned, designed, developed, and governed in terms of improving, advancing, and maintaining their contribution to sustainability. The underlying argument is that more innovative solutions and sophisticated approaches, especially those being offered by big data computing and the underpinning technologies, are needed to overcome the kind of wicked problems, intractable issues, and complex challenges pertaining to such forms. This brings us to the current question related to the weak connection and extreme fragmentation between sustainable cities and smart cities as approaches and landscapes, respectively, despite the great potential of advanced ICT for, and also its proven role in, supporting sustainable cities in improving their performance under what is labeled ‘smart sustainable cities’ (Bibri and Krogstie 2019). In addition, the

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contribution of the existing models of sustainable urban form to sustainability has, over the last three decades or so, been subject to much debate, generating a growing level of criticism that essentially questions its practicality, intellectual foundation, and added value (Bibri 2018a; Bibri and Krogstie 2017a, b). Developing the model for smart sustainable city of the future is aimed at improving, advancing, and sustaining the contribution of sustainable urban forms to the goals of sustainable development with support of big data computing and the underpinning technologies as an advanced form of ICT given the underlying potential for enhancing and optimizing urban operations, functions, designs, services, strategies, and practices in line with the goals of sustainable development, as well as for solving a number of problems, addressing key issues, and overcoming complex challenges in the context of sustainable urban forms. These are distilled and compiled from an extensive interdisciplinary literature review and the outcome of the second part of Step 2 (Bibri and Krogstie 2017a, 2019) (Table 3).

Table 3 Problems, issues, and challenges pertaining to sustainable urban forms What to solve, deal with, mitigate, or overcome

Deficiencies, shortcomings, inadequacies, difficulties, fallacies, and uncertainties

Problems

Not only in practice but also in theory has the issue of sustainable urban form been problematic and daunting to deal with as manifested in the kind of the non-conclusive, limited, conflicting, contradictory, uncertain, and weak results of research obtained. This is mainly due to the use of traditional collection and analysis methods and data scarcity. These results pertain particularly to the actual effects and benefits of sustainability as assumed or claimed to be delivered by design concepts and principles and planning practices Sustainable urban forms fall short in considering smart solutions within many urban domains where such solutions could have substantial contributions to the different aspects of sustainability Deficiencies in embedding various forms of advanced ICT into urban design concepts and principles and planning practices associated with sustainable urban forms Sustainable urban forms remain static in planning conception, unscalable in design, inefficient in operational functioning, and ineffective in management without advanced ICT in response to urban growth, environmental pressures, changes in socio-economic needs, global shifts, discontinuities, and societal transitions Realizing compact cities and eco-cities requires making countless and complex decisions about green and energy-efficient technologies, urban layouts, building design, and governance Divergences in and uncertainties about what to consider and implement from the typologies and design concepts of models of sustainable urban form The systems of sustainable urban forms are in themselves very complex in terms of management, planning, design, and development, so too are their domains in terms of coordination and integration as well as their networks in terms of coupling and interconnection Sustainable cities and smart cities are weakly connected as ideas, visions, and strategies, and extremely fragmented as landscapes at the technical and policy levels Sustainability goals and smartness targets are misunderstood as to their—rather clear—synergies

Issues

In relation to spatial scales, the existing models of sustainable urban forms tend to focus more on the building level and the neighborhood level than on the city level in terms of design and planning due to the uncertainties surrounding the design concepts and principles and planning practices as to their actual sustainability effects and benefits Conceiving cities only in terms of forms remains inadequate to achieve the goals of sustainable development. It should rather be informed by the processual outcomes of urbanization to attain these goals, as this involves asking the right questions related to the behavior of inhabitants; the processes of living, consuming, and producing; and the processes of building urban environments—in terms of whether these are sustainable Cities evolve and change dynamically as complex systems and urban environments, so too is the underlying knowledge of design and planning that is historically determined to change perennially in response to new factors In urban planning and policymaking, sustainable cities have tended to focus mainly on infrastructures for urban metabolism—sewage, water, energy, and waste management while falling short in considering innovative solutions and sophisticated methods for urban operational functioning, planning, design, and development (continued)

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Table 3 (continued) What to solve, deal with, mitigate, or overcome

Deficiencies, shortcomings, inadequacies, difficulties, fallacies, and uncertainties

Challenges

One of the most significant challenges is to, in addition to producing a theoretically and practically convincing model of sustainable urban form with clear components—seamlessly integrate and fully augment such model with advanced technologies and their novel applications—in ways that enable it to improve, advance, and maintain its contribution to the goals of sustainable development There are difficulties in translating sustainability into the built, infrastructural, and functional forms of cities There are difficulties in evaluating the extent to which the existing models of sustainable urban form contribute to the goals of sustainable development. It is not an easy task to even judge whether or not a certain urban form is sustainable One of the key scientific and intellectual challenges pertaining to sustainable urban forms is to relate the underlying design concepts, typologies, and infrastructures to their operational functioning and planning through control, automation, management, optimization, and prediction There will always be challenges to address and overcome and hence improvements to realize in the field of sustainable cities, and this has much to do with the perception underlying the conceptualization of progress concerning cities. This centers around what we think we are aspiring to, what we assess ‘progress’ to be, and what changes we want to make

5.7 Big Data Technologies and Their Novel Analytical and Practical Applications for the Future Vision Big data computing is an emerging paradigm of data science, which is of multidimensional data mining for scientific development and knowledge production over large-scale infrastructure. Data mining/knowledge discovery and decision-making from voluminous, varied, real-time, exhaustive, fine-grained, indexical, dynamic, flexible, evolvable, relational data is a daunting challenge/task in terms of storage, management, organization, processing, analysis, evaluation, interpretation, modeling, and simulation, as well as in terms of the visualization and deployment of the obtained results for enhancing and optimizing operations, functions, services, designs, strategies, and policies across multiple urban domains, such as transport, traffic, mobility, environment, energy, land use, waste management, health care, public safety, planning and design, and governance (Bibri 2018a, b). As a new paradigm, it amalgamates, as underpinning technologies, large-scale computation as well as new data-intensive techniques and algorithms and advanced mathematical models to build and perform data analytics. Accordingly, big data computing demands a huge storage and computing power for data curation and processing for the purpose of discovering new or extracting useful knowledge typically intended for immediate use in an array of multitudinous decision-making processes to achieve different purposes. It entails the following components, of which Chap. 3 provides a detailed account: • Advanced techniques based on data science fundamental concepts and computer science methods; • Various data mining models; • Computational mechanisms involving such sophisticated and dedicated software applications and database management systems; • Advanced data mining tasks and algorithms; • Modeling and simulation approaches and prediction and optimization methods; • Data processing platforms; • Cloud and fog computing models; Big data technologies and their novel applications are increasingly permeating the systems and domains of sustainable cities as an evolving approach to data-driven urbanism (Bibri 2018a, b; Bibri and Krogstie 2017a, b). This can be seen as a new ethos added to the era of sustainable urbanism in response to the rise of ICT and the spread of urbanization as major global shifts at play today. The characteristic spirit of this era is manifested in the behavior and aspiration of sustainable cities toward embracing what big data computing and the underpinning technologies have to offer in order to bring about sustainable development and thus achieve sustainability under what is labeled ‘smart sustainable cities of the future’. This is due to the tremendous potential of this advanced form of ICT for adding a whole dimension to sustainable urbanism in a rather increasingly technologized, computerized, and urbanized world. The range of the emerging big data applications as novel analytical and practical solutions that can be utilized for enhancing the sustainability performance of sustainable cities

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is potentially huge, as many as the case situations where big data analytics may be of relevance to enhance some sort of decision or insight into connection with their domains or sub-domains. A recent interdisciplinary and transdisciplinary review conducted by Bibri (2019a) reveals that tremendous opportunities are available for utilizing big data applications to improve, advance, and maintain the contribution of sustainable cities to the goals of sustainable development by optimizing and enhancing urban operations, functions, services, designs, strategies, and policies, as well as by finding answers to challenging analytical questions and advancing the existing knowledge production forms. This finding is based on identifying and synthesizing the most common big data applications in relation to a number of urban domains or sub-domains, as well as elucidating their sustainability effects associated with the underlying functionalities pertaining to these domains or sub-domains, which specifically include the following: • • • • • • • • • • • • • •

Transport and traffic; Mobility; Energy; Power grid; Environment; Buildings; Infrastructures; Urban planning; Urban design; Academic and scientific research; Governance; Health care; Education; Public safety;

The potential of big data technology lies in enabling sustainable cities to harness and leverage their informational landscape in effectively understanding, monitoring, probing, and planning their systems in ways that enable them to achieve the optimal level of sustainability. To put it differently, the use of big data analytics is projected to play a significant role in realizing the key characteristic features of such cities, namely the efficiency of operations and functions, the efficient utilization of natural resources, the intelligent management of infrastructures and facilities, the lowering of pollution and waste, the improvement of the quality of life and well-being of citizens, and the enhancement of mobility and accessibility.

6

Discussion and Conclusion

Long-lasting and substantive transformations such as sustainability transitions can only come about through the accumulation of several integrated smaller-scale actions associated with strategically successful initiatives and programs. The backcasting approach to futures studies can help to highlight such initiatives and programs and also play a key role in sustaining the momentum in the quest to bring about major transformations. In the context of city planning and development, this approach can be used to illustrate what might happen to cities in order to allow them to adapt to perceived future trends and to manage uncertainty. As such, it aids in dealing with this uncertainty by clarifying what the most desirable possibilities are, what can be known, what is already known, as well as how today’s decisions may play out in each of a variety of plausible futures. Futures studies using backcasting approaches allow for a better understanding of future opportunities and exploring the implications of alternative development paths that can be relied on either to adapt or avoid the impacts of the future. There is a strong belief that future-orientated planning can change development paths. The interest in the future of smart sustainable cities is driven by the aspiration to transform the continued urban development path. Therefore, it is worthy to venture some thoughts about where it might be useful to channel the efforts now and in the future in relation to smart sustainable urban planning and development. The backcasting scenario, a description of possible actions in the future, starts with constructing the vision of the future and then works backwards in time step-by-step to figure out how this future could emerge as a particular ‘desired end point’ or a set of goals. This chapter aimed to report the outcome of Step 3: future vision construction, by answering 6 guiding questions. Important to note, as there are many questions that guide the 6 steps of the backcasting methodology applied in the futures study that need to be answered in a form entailing description, identification, elaboration, explanation, analysis, synthesis,

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Discussion and Conclusion

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investigation, and so on, it is deemed relevant and practical to divide the results of the whole scholarly backcasting endeavor into several articles. Concerning Step 3, however, we described the terms of reference for the future vision under the visionary approach and combining urban and technological visions. These terms entail the scope and limitation of the area of knowledge to be focused on and the description of the structure and objectives of the futures research endeavor. Then, we described how the future vision look like, more specifically, the novel model for smart sustainable city of the future and its role in achieving the optimal level of sustainability and enabling the built environment to function in a more constructive way than at present as to the underlying design concepts and typologies characterizing the existing models of sustainable urban form through a reliance on big data computing and the underpinning technologies. Following this, we detailed how the proposed model is different from existing approaches to urbanism, namely compact cities, eco-cities, data-driven smart cities by describing and discussing the three strands that comprise this model as well as how they intertwine with one another in the context of sustainability. This was justified by providing the rationale for developing the future vision, which represents the short and concise version of the model in question. Of particular importance, we provided a tabulation version of the review and discussion of the sustainability problems and issues that are supposed to be tackled by meeting the objectives stated and thus achieving the goals specified in Step 1 of the backcasting study. In relation to this, we provided an account of the kind of technologies and their novel applications that are intended to be used as part of the proposed model. Working with a long-term image of the future is meant to increase the possibilities of, and accelerate the movement toward, reaching a smart sustainable city. In this regard, the novel model for smart sustainable city of the future will be the boost to new forms of policy analysis and planning in the era of big data revolution, and the greatest impacts of big data technology will be on the way we improve, advance, and maintain the contribution of sustainable cities to the goals of sustainable development in the future by means of integrating urban strategies and technological innovations. The main goal of big data technology is to provide intelligence functions that will make this possible in the most effective ways. Worth pointing out is that smart sustainable cities as an integrated model take multiple forms of combining the strengths of sustainable cities and smart cities based on how the concept of smart sustainable cities can be conceptualized and operationalized, just as it has been the case for sustainable cities: there are multiple visions of, and pathways toward achieving, sustainable urban development (Bibri and Krogstie 2019). As a corollary of this, there is a host of unexplored opportunities toward new approaches to smart sustainable urban planning and development. These future endeavors reflect the characteristic spirit and prevailing tendency of the ICT–sustainability–urbanization era as manifested in its aspirations for directing the advances in ICT of pervasive computing toward addressing and overcoming the challenges of sustainability and urbanization in the defining context of smart sustainable cities of the future. Similarly, in relation to backcasting as a planning approach, multiple visions can be used to explore different future alternatives as to smart sustainable cities. It is important, though, to take into consideration that big data technologies as part of future visions seem to be de-urbanized in the sense of not being made to work within a particular urban context, or to be tailored to different urban landscapes and strategies. Besides, it is unfeasible simply to plop down advanced technologies and force them to work in a given urban space. Cities are so characterized by key specificities that technology systems might work in one city and not be desirable in another one, unless they are dramatically reworked or reshaped to be practical in those cities where they have to be implemented. Hence, there is a need for urbanizing big data technologies and in different directions, we content and advocate, when it comes to generating future visions. Nonetheless, it is far from an erroneous truism to start cultivating the habit of not thinking what we are doing because civilization as manifested in modern cities has already taken the way to express new advances by extending the number and variety of important operations and functions that we can perform without thinking about them. Additionally, cities are becoming more complex through the very technologies being used to understand and deal with them so as to respond to population growth, environmental pressures, changes in socio-economic needs, global shifts, social trends, discontinuities, and societal transitions. This is another form which cities are using to fossilize and convey a wordless message, witnessing and withstanding the turbulences of another generation. We are currently in the midst of a new wave of enthusiasm for scientific urbanism inspired by the big data revolution. Research on scientific smart sustainable urbanism is garnering growing attention and rapidly burgeoning, and its status is consolidating as one of the most enticing and fanciest areas of investigation today, making the relevance and rationale behind the smart sustainable city debate of high significance and value with respect to the future form of urbanism.

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