Smart Cities and Artificial Intelligence: Convergent Systems for Planning, Design, and Operations [1 ed.] 0128170255, 9780128170250

Table of contents :
Cover
Smart Cities
Smart Cities and Artificial Intelligence: Convergent Systems for Planning, Design, and Operations
Copyright
Dedication
Preface
Acknowledgments
Introduction
Convergence theory proposition
Fourth Industrial Revolution
The metamodern turn
Convergence as an approach
Further reading
Description of each section
Descriptions overview
Approach
Architecture
Application
Info system (Fig. 0.0)
1. Evolution of cities/technologies
1.1 Overview of smart city concept and context
1.2 The evolution and integration of technology, AI, and cities
1.2.1 Evolutionary strategies
1.3 City DNA narratives
1.3.1 Beijing—the radiating megacity
1.3.2 London—the cosmopolitan hub
1.3.3 New York—the media metropolis
1.3.4 Dubai—the iconic branded city
1.3.5 Songdo—the new digital city
1.3.6 Masdar—the new sustainable city
1.3.7 NEOM—the future city
1.3.7.1 Summary
1.4 The dimensions of the city and potential for convergence
1.4.1 Physical/environment dimension
1.4.1.1 The city as evolution of space, form and hardware
1.4.2 City systems, infrastructure dimension
1.4.2.1 The network of the city, the spine, and major organs
1.4.3 The human dimension
1.4.3.1 The city as a manifestation of human patterns and constructs
1.4.4 Culture, society, and governance dimension
1.4.4.1 The nuances of human civilization, behaviors, activities, desires, and relations
1.4.5 Digital infrastructure dimension
1.4.5.1 The city as evolution of systems, technologies, and software
1.4.6 The ubiquitous dimension
1.4.6.1 The merging of technology with the natural environment in the form of imbedded and ambient connectivity
1.5 How convergence theory applies to smart cities
1.6 Conclusion
References
Further reading
2. City as living organism
2.1 The city as a living organism
2.1.1 Concepts of space and representation
2.1.2 Dynamic, self-regulating systems in nature
2.1.3 Biomimicry
2.1.4 Biomimicry applied to human anatomy
2.1.5 City as extension of the human body
2.2 Principles of collective intelligence
2.3 City DNA
2.3.1 Cities as global brands/destinations
2.4 The role of data collection and mapping
2.4.1 Mapping the system
2.4.2 Mapping as the basis of smart cities
2.4.3 Real-time behavioral data
2.5 Conclusion
References
Further reading
3. Strategies, planning, and design
3.1 Criteria for planning and design of smart cities
3.1.1 Strategic goals
3.1.2 Outcome-based modeling
3.2 New approaches to innovation for planning and designing smart cities
3.2.1 Cities as living labs
3.2.2 City as hubs of innovation/innovation-driven cities
3.2.3 Co-design
3.2.4 Citizen centric cities
3.2.5 Design thinking
3.3 Convergence methodologies
3.3.1 Human-machine collaboration
3.3.2 Real-time visualization
3.3.3 Information architecture and Philosophy of Information
3.3.4 Real world/virtual simulation
3.3.5 Generative design and metadesign
3.3.6 Convergence Development Method: strategy, planning, design, and operations process
3.3.7 Convergence design method: design thinking/machine learning
3.3.8 Convergence application method: outcome-based AI scenario modeling
3.4 Conclusion
References
4. City Operating Systems
4.1 Overview of operating systems
4.2 The language and representation of systems architecture
4.2.1 The role of meta-architecture, information architecture and technical architecture in the design of smart city operating sy ...
4.2.2 Meta-architecture—principles and guidelines
4.2.3 Operating systems planning considerations
4.2.4 Operating systems design considerations
4.2.5 Information architecture and technical architecture
4.3 Representational hierarchy of cities as operating systems
4.3.1 City ecosystem
4.3.2 Smart city framework—the smart city mandala
4.3.3 OS Behavioral Typologies
4.3.4 Anatomy of operating systems
4.3.5 Smart city operating system flow
4.4 What is the correct OS?
4.5 New constructs—convergence-based city OS
4.5.1 Convergent OS
4.5.2 Co-development/open source/open data
4.5.3 Self-regulating systems
4.6 Conclusion
References
Further reading
5. Connectivity
5.1 Introduction
5.1.1 Connectivity itself will become intelligent
5.1.2 All living organisms are related within a frequency spectrum
5.2 Evolution of connectivity
5.3 The electromagnetic spectrum, frequencies, and bandwidth
5.3.1 Electromagnetic patterns, frequencies, and human energy fields
5.3.2 Electromagnetic spectrum
5.4 The role of machine learning and deep learning in intelligent connectivity
5.4.1 Radio Frequency Machine Learning Systems
5.4.2 The role of evolutionary algorithms in connectivity
5.5 Connectivity anatomy
5.5.1 The human body and neural networks as models of connectivity
5.5.2 The brain
5.5.3 Other organic models of connectivity
5.5.4 The backbone of connectivity—telecommunication networks
5.5.5 The sensorial layer of connectivity
5.5.6 Mobile connectivity
5.6 Integrated networks and services
5.6.1 Industry 4.0—the basis of connectivity
5.6.2 Convergence connectivity
5.6.3 Intelligent connectivity using combination of 5G AI and IoT
5.6.4 Connectivity singularity
5.6.5 Smart objects
5.7 Conclusion
References
Further reading
6. Interface
6.1 City-wide interface—the city is an interface
6.1.1 City interface as an extension of the city OS
6.1.2 The city as an ecosystem—scale, boundaries bridging global and hyperlocal
6.1.3 Infrastructure as interface
6.2 City interface functions
6.2.1 Urban navigation
6.2.2 Urban media
6.2.3 Urban sensing
6.2.4 Urban interaction
6.3 City interface design practices
6.3.1 Theory and method of city interface design
6.3.2 Urban user experience
6.3.3 Urban interaction design
6.3.4 Urban simulation and gaming
6.4 Collective intelligence interface
6.4.1 Collective intelligence
6.4.2 Collective intelligence participation/interaction
6.4.3 Dynamic frames of reference
6.4.4 Human to human, human to machine, machine to machine and machine to nature
6.5 Convergence Urban Interface
6.5.1 Total interface solution—AI/sensors/big data/pattern recognition
6.6 Conclusion
References
Further reading
7. Smart City Scenarios
7.1 Introduction
7.2 Theory of systems change
7.2.1 Multi-level perspective
7.2.2 Convergence application
7.3 Smart mobility
7.3.1 Past–present–future
7.3.1.1 Evolution
7.3.1.2 Challenges
7.3.1.3 Directions
7.3.2 Object–action–outcome
7.4 Smart environment
7.4.1 Past–present–future
7.4.1.1 Evolution
7.4.1.2 Challenges
7.4.1.3 Directions
7.4.2 Object–action–outcome
7.5 Smart people
7.5.1 Past–present–future
7.5.1.1 Evolution
7.5.1.2 Challenges
7.5.1.3 Direction
7.5.2 Object–action–outcome
7.6 Smart governance
7.6.1 Past–present–future
7.6.1.1 Evolution
7.6.1.2 Challenges
7.6.1.3 Direction
7.6.2 Object–action–outcome
7.7 Smart economy
7.7.1 Past–present–future
7.7.1.1 Evolution
7.7.1.2 Challenges
7.7.1.3 Direction
7.7.2 Object–action–outcome
7.8 Smart living
7.8.1 Past–present–future
7.8.1.1 Evolution
7.8.1.2 Challenges
7.8.1.3 Direction
7.8.2 Object–action–outcome
7.9 Conclusion
References
Further reading
8. Smart city functions
8.1 Introduction
8.2 Smart city enablers (hardware infrastructure)
8.2.1 Collection: IoT and low energy consuming sensors
8.2.2 Processing: scalable computing power and storage through edge and cloud computing
8.2.3 Transmission: network infrastructure—5G
8.2.4 OS: AI smart city operating systems
8.3 Introduction to AI, AI applications and capabilities (software infrastructure)
8.3.1 Capabilities-based AI
8.3.2 Functionality-based AI
8.3.2.1 Critical AI capabilities needed to power smart city functions
8.3.3 Computer Vision
8.3.4 Natural language processing
8.3.5 Machine learning
8.3.6 Predictive analytics
8.3.7 Robotics
8.4 The convergence of AI applications within smart cities
8.4.1 Convergent applications
8.4.2 Hierarchy framework for scale and scope of smart city functions
8.5 Smart city functions
8.5.1 Smart environment
8.5.1.1 Macro scale/context
8.5.1.2 MESO scale/content
8.5.1.3 Micro scale/component
8.5.1.4 Strategic functional objectives
8.5.2 Smart government
8.5.2.1 Macro scale/context
8.5.2.2 MESO scale/content
8.5.2.3 Micro scale/component
8.5.2.4 Strategic functional objectives
8.5.3 Smart mobility
8.5.3.1 Macro scale/context
8.5.3.2 MESO scale/content
8.5.3.3 Micro scale/component
8.5.3.4 Strategic functional objectives
8.5.4 Smart economy
8.5.4.1 Macro scale/context
8.5.4.2 MESO scale/content
8.5.4.3 Micro scale/component
8.5.4.4 Functional strategic objectives
8.5.5 Smart people
8.5.5.1 Macro scale/context
8.5.5.2 MESO scale/content
8.5.5.3 Micro scale/component
8.5.5.4 Functional strategic objectives
8.5.6 Smart living
8.5.6.1 Macro scale/context
8.5.6.2 MESO scale/content
8.5.6.3 Micro scale/component
8.5.6.4 Functional strategic objectives
8.5.7 Convergence of smart city functions
8.6 Conclusion
Further reading
9. Smart city business models
9.1 Introduction
9.2 The smart city/Artificial Intelligence market
9.2.1 Business models and risk mitigation
9.2.2 A Marxist analysis of smart cities
9.2.3 Smart city movement marketing
9.3 Innovation-led economics
9.3.1 Innovation as the driver
9.3.2 Intellectual property as the new asset
9.3.3 China–USA race, India rising
9.3.3.1 China—education
9.3.3.2 USA—renewable energy
9.3.3.3 India—biomimicry
9.3.4 Cities as living labs
9.4 The new economy
9.4.1 Planetary accounting
9.4.2 Strategy shift
9.4.3 New forms of digital currency
9.4.4 Blockchain
9.4.5 Holochain
9.5 New forms of business exchange
9.5.1 Flow
9.5.2 Channeling on demand
9.6 Bringing it together
9.6.1 Convergent economies
9.6.2 Collaboration
9.6.3 Self-regulating systems
9.7 Conclusion
References
Further reading
10. Conclusions
10.1 From theory to practice
10.2 East-West Collaboration
10.3 The human factor
10.4 Wide-spread automation
10.5 Consequences of embracing convergence
Further reading
Appendix
Explanations of convergence theories
Convergent evolution
Convergence theory of society
The convergence of science, technology, and nature
Convergence in knowledge, technology, and society
Digital convergence
Organizational convergence
Further Reading
Glossary of Terms
Index
A
B
C
D
E
G
H
I
L
M
N
O
P
R
S
T
U
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Back Cover

Citation preview

Smart Cities Series Editors Tan Yigitcanlar Queensland University of Technology, Brisbane, Australia Nicos Komninos URENIO Research, Faculty of Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece Mark Deakin Edinburgh Napier University, Edinburgh, United Kingdom Untangling Smart Cities (9780128154779)

Smart Cities and Artificial Intelligence Convergent Systems for Planning, Design, and Operations

Christopher Grant Kirwan Visiting Professor Informatics Research Centre Henley Business School University of Reading United Kingdom

Zhiyong Fu Associate Dean China-Italy Design Innovation Hub Tsinghua University China

Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States Copyright Ó 2020 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein).

Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-817024-3 For information on all Elsevier publications visit our website at https://www.elsevier.com/books-and-journals Publisher: Joe Hayton Acquisition Editor: Brian Romer Editorial Project Manager: Michelle W Fisher Production Project Manager: Selvaraj Raviraj Cover Designer: Alan Studholme

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Dedication This book is dedicated to the celebration of international collaboration and friendship between Christopher Grant Kirwan and Dr. Zhiyong Fu initiated as part of an academic exchange that has evolved for over a decade. and to our families, friends, colleagues, and students who have supported us in shaping the vision and material of this book. A special dedication to the memory of Constance and Ernest Kirwan, whose creative, intellectual, and spiritual direction has been instrumental.

Preface There is a reason for convergence in the universe. We live in cycles, ebbs and flows that modulate the rhythms of life evolving towards a state of singularity. Nature is its own architecture with humans dwelling in the house of planet Earth as a living organism, contributing, participating, affecting. As Alan Watts stated in one of his monologues, we are just a bad case of dandruff on the planet. The question is how disruptive is this condition and will we have enough sensibility to reverse this current systemic malady even if benign to planetary evolution. As Norbert Weiner described in The Human Use of Human Beings, Earth has reached its peak in its lifecycle and is now in a state of pre-entropy. The remedy for arresting this process is in the term he uses as resistance. Within our own human lifecycle, resistance means effective measures to slow the inevitable process of decline. Beyond the biotechnical environmental challenges we are facing, the socioeconomic dimension of human sustainability and self-actualization is perhaps an even greater obstacle that may prevent human advancement to a new level of enlightenment. Without a better distribution of wealth and resources to all the inhabitants of the planet, we will not succeed in achieving a higher state and we will continue to accelerate our own demise as a human race. Cities are the new space, playground over battleground, for the distribution of global resources as the world’s population concentrates in cities. Ironically, we have increased the density of populated areas and reduced the spatial footprint, rather than inversely decentralizing people since we now have access to information, markets and resources that can be obtained through digital networks. This signifies a new world order that requires design solutions that solve specific and global problems that cities create. Convergence evolution explains how different species evolve with similar traits. Technology is perhaps the unifying element that brings all cities to a similar level of enablement and operational control. However, each city has unique characteristics that embody the concept of city DNA, determining the rate and type of technological absorption and administration that must be factored in. The world is still divided with the current economic and trade systems that polarize individual and collective interests, obstructing collaboration to solve global challenges. The ability for governments, markets, and societies to develop international standards and regulations to manage resources and design solutions will be even more necessary as the planet is now hyperconnected. This leads to

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the requirement of co-creation and co-design methods to align, integrate and develop universally accepted solutions. Hence, there is a need for us to develop and manage the system architecture through a global collective intelligencedwith East and West convergent understanding of a shared design. This book represents such a collective design process between two hemispheres, cultures, mindsets, political systems and individual understandings. The collaboration over the past 10 years between the authors is an indication of the attempt to obtain shared values, common interests, and motivation to advance ideas and solutions to allow us to more effectively manage the convergence process through transdisciplinary and transcultural efforts. The next stage in the evolutionary process is the ability for AI to accelerate the development of solutions that will absolutely be necessary to resist planetary entropy and the potential collapse of cultures and societies. We pursue a combination of design thinking and machine learning, linking human and technology in an integrated flow of data and problem-solving processes to achieve the most optimal convergence state. The ideal outcome is a combination of human wellbeing, balance of natural systems, and the optimization of technology. This is the search for the obtainment of convergence, as the natural evolutionary process that will again bring humans closer to being in harmony with nature. This entails the formation of a collective intelligence network that will help cities to selfregulate as part of Earth’s natural ecosystem, the ultimate goal and steady state of convergence. Along the path of convergence, many new combinations and hybrids will attempt to determine and design sustainable directions including human machine collaboration, new forms of human-machine behavior, and new patterns languages that move away from a human-centered focus to establishing a higher evolutionary stage of humanemachineenature awareness. We have positioned this book to appeal to a broad audience, ranging from students to practitioners, through a combination of academic research and professional methods in the emerging field of smart cities - still today a somewhat elusive and catchall term. The evolving discourse surrounding smart cities has represented both a marketing hype that has been slow to come to real substance and at the same time a hyper accelerated demand for creative, technical expertise and advancement across multiple industries and professions, all competing to obtain a piece of the projected 1.5 trillion dollar pie. Starting a new decade today, January 1st, 2020, building on the much anticipated 2020 threshold embodying clear vision and future insight, we are now firmly entrenched in the process of the convergence of technology, humans, and nature in a way never before possible in human evolution. This process is both necessary and part of the evolutionary medium that will allow humans to manage the planetary resources and human impact in a more efficient and harmonious manner. January 1st, 2020 Christopher Grant Kirwan

Acknowledgments This book has been formed through a combination of professional and academic experience that has led us to defining a theory and practice of convergence. In the academic realm, this book expands and consolidates research and projects beginning in 1996 and continuing through the present. This includes numerous courses and special projects spanning Design and Technology, Information Architecture, Interface Design, Urban Media, as well as Maker Space workshops, year-end exhibitions, and graduate thesis projects. The underlying aspects in all of these initiatives have been interdisciplinary, crosscultural collaboration, and co-design enabling tried and true and emerging processes. This generative approach fosters the most optimal methods to better understand contemporary challenges and design solutions in the crossover and integration of design and technology and the physical and digital dimensions. On the professional side, this book represents a diversified, multigenerational design practice (based on the authors’ work experiences and backgrounds) spanning product design, architecture, urban planning, graphic design, interactive design, and new mediadrelated to the practice of the development of sustainable global projects and with a special focus on smart cities. The enterprises that the authors have established as professional practices and/or have worked with as consultants in the business of Smart Cities and AI include Newwork International, AI Convergence, VITADIGI, Linkay Technologies, IA New, Empire Asia Holding in London and Bangkok, and other enterprises related to the design and development of Smart Cities and Smart Nations. Many students and faculty collaborators were involved in various parts of the research and application of this book over the past two decades, from Tsinghua University, Parsons School of Design, Henley Business School, Harvard Graduate School of Design, collaboration with Stanford University, Carnegie Mellon, MIT, KAIST, Seoul National University, and other institutions where the authors have taught and/or have established academic research collaboration and partnerships, such as the Beijing Design Lab, Service Design Lab, and Innohub. Other individuals whom we would like to recognize for the guidance and advice include Lady Susan Griffiths, Advisor, for her support in reviewing this book throughout all phases, Edward S. Grant and Joseph Bezzone, Advisors, for their strategic feedback, Tad Crawford, Publisher, Writer, for his instrumental xv

xvi Acknowledgments

technical advice and guidance, Sven Travis, Associate Professor, Parsons School of Design, for our 25 years of media technology collaboration, Sahara Kirwan for assisting in the initial documentation preparation and Sanah Kirwan for ongoing encouragement.

Credits The specific team brought together to assist the authors in the execution of this book include the following: Brent Cooperdresearcher, editor, and contributor Meng Lindgraphic design and technical illustrations Bika Armanddtechnical research, communication engineering, systems architecture and integrationdcontributor Chapter 5 Stefan DobrevdAI research and innovation strategydcontributor Chapter 8 Huan Wangdresearch coordination and graphic design Ling Chyi Chandresearch coordination Jiru Zhaodresearch coordination Jianhua Gudgraphic design and technical illustrations Jeong Eun Songdgraphic design and technical illustrations Songling Gaodgraphic design and technical illustrations. Tommaso Guerzonidresearcher Sahara Kirwanddocumentation administration and proofing Miraal Moazzamdproofing Tsinghua University Student Projects: Xingjian Cuidtechnical illustration City DNA (Fig. 2.5) Citizen Engagement (Fig. 6.4) Junjie Yu, Ke Fang, Yin Li, Yechang Hu, Jieyun Yangdproject creators “Co-Pulse” Junjie Yudproject creator “Sub Scope” Xu Lindproject creator “City Care”

Introduction Until very recently the worlds of Digital and Brick-and-Mortar have remained divided along the lines of the old and new economies. Old economies were based on resource development, industrial processes and human labor. New economies operate on a higher level of abstraction, leveraging computing power and venture capital, such as with Silicon Valley tech startups and IPOs. This separation has influenced the business landscape of how real estate development, infrastructure and cities have evolved. In the physical world, real estate is limited to property market value. It is valued with respect to available land and prices can be relativized on a per square meter basis. The digital economy has no such spatial constraints and is scalable. As such, prices and profits can be unmoored from a physical base. Today we are finally seeing these two different worlds convergingdthe abstract with the concretedbut not without a massive reconceptualization of how we plan, design, and implement smart cities. Defining and building smart cities aimed at the convergence of the virtual and the real will ensure harmonious, sustainable solutions will be developed, allowing for adaptation and change as technology, humans, and the physical environment evolve and are impacted differently. This book provides a comprehensive approach to the planning, design, and operations of smart cities and the significant roles Artificial Intelligence (AI) plays in the convergence of cities, technology, and nature. At this particular moment in time, perhaps one of the several major paradigm shifts in human progress, we have a moment to pause before plunging headlong into the new reality that is at our threshold: a reality led by new technologies that are already beginning to transform every aspect of our contemporary life as we understand and experience it. This new reality, if managed ethically through privateepublicepeople partnerships, guiding the convergence of technology with natural and social systems to form self-regulating governance platforms, will potentially be the solution to what humanity has constructed as our current demise, the overpopulation of cities, socioeconomic inequality and injustice, exploitation of our natural resources and the destruction of earth’s ecosystems. As cities now make up more than 50% of the concentration of the world’s population and are absorbing an increasing share of people, cities are presently the most important point of leverage to focus on as it relates to the optimization of the earth resources through the application of

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technologies to improve efficiencies for a more sustainable planet. Smart cities, therefore, are the key to bringing this all together. In ancient times, humans were more connected to the natural world with direct relational awareness of universal biorhythms. As we have evolved with the use of technology, we have moved away from this direct connection with nature and have lost the capability of sensing nature instinctually. In the process of the mass migration and overpopulation of urban centers, humans have exacerbated the interrelationship with nature and compromised earth’s balance. The concepts put forth in this book, rooted in the theory of convergence, attempt to explain how new technologies enabled by AI, in the present and future stages of human development, can potentially bring us closer to nature, assisting humans to understand and visualize the biorhythms and corresponding patterns of nature and our footprint and impact on the ecosystem. In this new stage of development, AI will enable us to achieve a collective intelligence that has the potential to create a critically needed interface between humans and the natural world. This interface will be formed over the next fifty years through a new hyper-accelerated stage of technology and the convergence of human and machine that will significantly impact our relationship with the planet and the natural world (see Fig. 1). We now have the opportunity to reverse the anthropogenic damages done to planet earth and its biosphere and to maintain “homeostasis,” which Norbert Weiner described, in The Human Use of Human Beings, as the need to “resist the general stream of corruption and decay.” Just as humans are now able to apply preemptive measures to personal health management, AI and related technologies provide us the necessary tools to better manage our natural resources and address anomalies in planetary behaviors in real time. Through the advancements identified as the Fourth Industrial Revolution (4IR) representing next generation ICT combined with ecosystem-based innovation, we have established the basis of new technological processes that will allow us to achieve a simultaneous balance of human well-being and environmental sustainability. In this regard, this book presents a positivistic point of view of how technology is playing a role in human evolution and evolution in general. It neither takes a social anthropological or political approach nor a technocratic one. The concepts presented in this book are a combination of scientific, technological, and humanistic theory, driven by design thinking with a rationale that follows the notion that convergence, within the natural evolution of systems, is inevitable. In this case, the evolutionary convergence of human civilization, the natural world, and the technologies made possible with the advancements in ICT, AI, nanotechnology, biotechnology, design, cognitive science, and other related fields. As part of defining this point of view, the book draws from diverse fields of knowledge and philosophies of the East and the West to achieve a more holistic understanding of evolutionary processes. The schism between eastern and

Digital Evolution

Convergence

Earth Evolution 4,500,000,000 years

6,000,000 years

100 years

Now

Next 50-100 years

Human Evolution FIGURE 1 Evolution/convergence of systems.

Introduction

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Introduction

western philosophy, science and medicine, which remains still to this day an impediment to achieving a more enlightened human civilization, may finally have an opportunity to reach a new dimension of interrelations with the advancements in technology. Thus, the worldly perspective presented in this book may provide a new consciousness to support the process of convergence incorporating both linear and nonlinear processes and in defining a greater cross-cultural collective intelligence. Without a global partnership and a coherent set of internationally recognized standards of city design, energy efficiency, environmental sustainability, labor relations, education, and human rights, the potential for chaos and collapse will be even greater and will potentially speed up our decline. In a counterintuitive way, the evolution of technologydfrom the ability to reorder material and social relations on a global scale to creating general AId also brings about a realization that low-tech solutions will be, in the end, more sustainable in some cases. A return to a natural, holistic approach to managing resources and consuming less may allow humans and nature to achieve a better balance. Nevertheless, it appears that advancements in technology will not slow down anytime soon and embracing technology and steering it toward a more universally equitable application is required in the long run to achieve a unified operating systemdOS Planet Earth. As the natural world follows ebbs and tides, so will the evolution of technology, requiring complexity to lead us to simplicity. In the theory of communication, semantic noise and psychological noise are comprehension barriers between the transmitter and the receiver. The sum of this noise can hold back discourse and drown out the best options. In this way, the complexity of the future advanced technology is speaking to us, but the signal-to-noise ratio makes it hard to hear, so it will require a holistic shift that brings all things into a harmonious state of convergence, where diverse systems become integrated in a unified state of being.

Convergence theory proposition This book proposes the application of a new composite theory of how cities adopt technology over a period of time based on diverse subtheories of convergence, such as for evolution, society, science, media, nature, technology, knowledge, organizations and globalization. One notion of how these intersect at a higher level is the concept of the “smart city” as a “convergent sociocyber-physical complex,” which is optimally adaptive to itself, the state space, and its members. The 4IR is a major theme in these convergences and the development of the smart city. Additionally, in developing a proposed methodology for how cities can best integrate technology within the planning, design, and operations of cities, the Convergence Theory for initial value problems is co-opted and reinterpreted to develop a concept of Net Present Potential that identifies and analyzes the underlying conditions of the state of the city

Introduction

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(current physical, cultural and technological status). Solving initial value problems amounts to predicting the evolution of complex systems, and understanding this process can help show how the Net Present Potential determines the evolutionary process of adaptation and convergence of cities and technology. The following explanation is provided to help understand the foundational theories of our combined interpretation and application of convergence to illuminate the evolutionary process of the merging together of diverse systems including human, natural and technological. In our Appendix, we explore six topics of convergence that informed our theoretical and practical understanding. The basic concept comes from convergent evolution, the observation of similar traits in different species, such as the eyes, hair, organs, appendages, or wings, which did not evolve from a common ancestor. They evolved the same function because it was so adaptive to a particular environment. Convergence theories of society observe common features and patterns across different cultures and states. Within science and technology, different techniques and tools are being combined to accelerate innovation as well as integration with nature. This trend is converging in Nanotech, Biotech, ICT and Cognitive Science (NBIC). There is also a convergence of knowledge, technology, and society that is bringing together common knowledge through consilience, networking and new forms of socialization. Digital convergence has shown exponential growth in computing power according to Moore’s law, as well as prolific social media connectivity and AI assistance. Finally, organization convergence adopts more flexible strategies, holistic ontologies, and best practices to overcome traditional hierarchies, inequalities, path dependencies and pathologies. Methods that are converging in smart city design include Living Labs, Innovation Hubs, Design Thinking, Co-design and Citizen Centric Cities. Machine learning and Generative Design are converging and accelerating, enhancing workflow and computational power and enabling designers to work more intuitively. By setting certain parameters and letting AI do the heavy lifting humans and technology are converging ever closer through the smart design of their own environments. This idea and the table below constitute our convergence methodology. From this research, we intuited and designed six types of convergences that fall into three categories. There is a convergence of nature, with convergent evolution and scientific process convergence. There is a convergence on the human level, where society converges through growth, interaction and social systems, as well as general knowledge converging into collective intelligence; lastly, technology convergence, where digital, ICT and AI systems converge. These form our basic layers of analysis and align with our six dimensions of convergence described in Chapter 1. There is simultaneous convergence in all dimensions, and they are all converging with each other. However, biological evolution is slow, and technology evolves on an exponential curve that quickly outpaces biology. Metaconvergence is the convergence of all of them,

xxii Introduction

which is not linear, but accelerating and recursive because of the faster evolution of human culture and technology. The six theories and convergence and six dimensions of smart city development effected can be seen in Table 1.

TABLE 1 Convergence types. Nature

Human

Technology

Evolutionary convergence

Natural environment and systems

1. Physical dimension

Scientific convergence

Technological and epistemological systems

2. City systems, infrastructure dimension

Knowledge convergence

Human population and behaviors (patterns)

3. Human dimension

Culture, society, governance convergence

Human governance, socioeconomic

4. Culture and society dimension

Digital, ICT convergence

Technology infrastructure, connectivity

5. Technology infrastructure dimension

Artificial Intelligence convergence

Collective intelligence and automation

6. Ubiquitous technology dimension

¼Metaconvergence natureehumanetechnology systems integration and embeddedness

Convergence continuum

Fourth Industrial Revolution The 4IR is the fourth major leap in technological productivity and represents a potent new stage of globalization. The first was the Industrial Revolution of the 18th century characterized by machines, iron manufacturing, textiles, rail transport, urbanization and unprecedented population growth. The second was dominated by steel, oil, electricity, mass production, the telephone and the internal combustion engine. The third is the digital revolution in our recent history: that of the personal computer, basic Internet and ICT. The 4IR is exemplified through cyber-physical systems, breakthroughs in robotics, AI, nanotech, quantum computing, the Internet of things (IoT), 5G wireless, 3D printing, clean energy, smart cities and autonomous vehicles. The idea of convergence can be tracked throughout these stages, pointing us toward a technological singularity in the near future. Our mandate in this book is to use the science of convergence to influence a more sustainable process of the planning, design, and operations of our cities and to play a pivotal

Introduction xxiii

role in bringing OS Planet Earth online. Understanding how convergence works in the 4IR is critical for getting us through the current various ecological, social and epistemic crises. The bureaucratic bottleneck choking out progress will either be the cause of our extinction or the catalyst of our convergence on metasolutions, thereby releasing the pressure valve to liberate creative and productive forces to collaboratively build self-sustaining and self-regulating smart cities.

The metamodern turn The 4IR is one of the several distinct historical markers for the metamodern era, circa the turn of the millennium, 2000, as described by Dutch cultural theorists Vermeullen and van den Akker. Other relevant markers include global social movements, global financial crises, and the coining of the “anthropocene” to mark the new period of humans as the dominant geological force on the planet. Philosopher Hanzi Freinacht develops the concept of metamodernism further into an active social and political philosophy, as well as a theory of (co-)development. Our notion of metamodernism notes these cues but has a deeper root in Albert Borgmann’s philosophy of technology and his definition of metamodernism. As metamodern theorist Brent Cooper explains in his review, Borgmann used the term “metamodern” in an earlier formulation but then switched to “postmodern realism” in Crossing the Postmodern Divide (1992) for practical reasons. Nevertheless, we adhere to the spirit of his “metamodern” vision, which is needed now more than ever, as metamodernism in the broader sense still aligned with Hanzi Freinacht, Vermeullen and van den Akker and others. In the 1992 edited volume New Worlds, New Technologies, New Issues, Borgmann predictively described a bifurcation of postmodernism into techno-social cultures of hypermodernism and metamodernism. While hypermodernism refers to pathological and dominant technoscience, metamodernism refers to an alternative path where deep ecology and technology converge in harmonious ways and people are sensitively attuned to the destructive impact mass consumption and financial capitalism have wrought. Naturally, our book advocates for the latter forms of society and technological implementation. The evolving frame of metamodernism gives a necessary historical context to the technological singularity and the social and political transformations that also must occur. Thus, following on Vermeulen and van den Akker’s act of introducing the term as an “intervention,” this book hopes for a “metamodern turn” in smart city literature as well as wider academia, as the metamodern discourse is also indicative of the convergence of philosophy itself and the converging oscillations between modern and postmodern forms of art, culture, critique and technology. Now more than ever, there is a pressing need to address the schism of postmodernism as Borgmann prescribed back in 1992. Borgmann’s ideas of the postmodern, hypermodern and metamodern can be seen juxtaposed in Table 2.

xxiv Introduction

TABLE 2 Modernity definitions. Postmodern

Hypermodern

Metamodern

Critique of modernism, “postindustrial society,” “striking convergence of economic, intellectual and architectural postmodernism.”

“Pernicious influence of modern technology will become still more pervasive and dominating.”

Technology will be “context sensitive and historically reverent,” attentive to different “voices of reality.”

If the power of technology remains unquestioned, modernism will be succeeded by hypermodernism, that is, modernism by other means. If we come to recognize and restrain technology, however, a genuinely other era may dawn, one called “metamodernism” for the time being. The question, then, is whether postmodernism will turn out to be hypermodernism or metamodernism. Albert Borgmann

Convergence as an approach Convergence is not only a theory but also a reality that has already begun to be part of our everyday lives. Humans are merging with digital devices, attached or imbedded in our bodies, tracking behavior and performance. In the future, we will be implanting devices in our internal organs to monitor our bodily functions and we will have sensors monitoring external phenomena of every aspect of life. Technology and biology are fusing on a molecular level through advancements in nanotechnology and other forms of biological convergence. The combination of different states convergingdnature, humankind, and technologydis leading us to a new direction in the next stage of evolution. This convergence requires new ways of thinking, approaches and methodologies to develop tools to plan, design, and operate our cities and the planet in optimal ways so that we can sustain a balance of these diverse systems. A simple example of convergence can be represented in the smartphone, which brings all different technologies together. Three decades ago it seemed difficult to predict exactly how we would have all of the independent functions of the smartphone previously delivered through different devices and protocols, now in a singular interface. At the same time, while all of the independent features integrated in the smartphones have converged, all of the functions within cities are converging as well. For example, the iPhone sets a new standard for simplification and streamlining, inspiring similar aesthetics, functions and interfaces in other products, services and systems. As such, city planners and administrators already use smart devices to communicate and monitor city systems and are redesigning cities themselves to reflect these trends. This is convergence, happening all around us, and the goals we are striving for in the convergence of nature, humans, and technology, are balance, well-being, and optimization, as shown in Table 3.

Introduction

xxv

TABLE 3 Convergence evolution. Category

Convergent purpose and function

Biology/nature

Balance/homeostasis

Human/city

Well-being/sustainability

Technology/Artificial Intelligence

Optimization/intelligence

However, within this process of convergence, the incorporation of all of the elements into one system where everything becomes integrated in a gestalt operating system like nature itself, there is a reverse process occurring that requires the decentralization and distribution of smart objects and sensors embedded into every dimension of human, natural and technology, to record and transmit the genetic code patterns and activities of each living organism within the ecosystem. The new convergence requires a methodology driven by today’s innovation practices from co-design, collaboration, living lab experimental approach, a fusion of diverse systems of knowledge bringing together resources and using a new algorithm-based methodology that can program variations to simulate, visualize and analyze the best solutions that allow us to remain in the flow. The science and fields of professional disciplines are also merging, slowly eroding the 17th and 18th century reliance on empiricism that classified human knowledge as discrete epistemological bodies influencing the nature of how we think and operate and how our institutions have formed and governed human intelligence. This evolution of human knowledge is now being reconsidered, not as a deliberate act of rebellion but as the natural progression of human development as we understand and construct new realities with the advancements in science and technology. Within the complexity of the process of multiple dimensions converging, some aspects of convergence are occurring as conscious by-products of material change; other dimensions of convergence are representing evolution beyond our immediate perception. The concepts of simplicity and singularity are the light at the end of the complexity tunnel where all things will communicate harmoniously within an integrated system; a new stage of biological, human and technological convergence approximating the laws of nature and an inherent state of homeostasis. Although Ray Kurzweil does not use the term convergence in his book, there is a striking parallel with his notion of the technological singularity. The singularity is a future that became obvious and inevitable when technology began to demonstrate it was on an accelerating and irreversible evolutionary course, such that it would begin to innovate on itself and automate civilization. AI and robotics will become self-reproducing. In a basic sense, our six dimensions of convergence loosely resemble Kurzweil’s “Six Epochs

xxvi Introduction

of Evolution,” which explains stages of information in (1) atomic structures, (2) DNA, (3) neural patterns, (4) hardware and software, (5) human-tech merger, and (6) nature-tech merger. Similarly, in The Social Singularity, Max Borders writes that AI, neuroscience, and collective intelligence will converge. If the process of the convergence of nature, human, and machine relies partially on human input, then indeed the output will continue to incorporate human error that will inevitably be factored in the evolution of the converging system. If nature is perfect and the future of machine learning is based on logic, our fallibility may be the remaining ingredient that retains the state of imperfection. In the Japanese sense of beauty, the tension between harmony and disharmony, like a beautiful improvisational jazz composition or the asymmetry of the branches of a Bonsai tree, may be what keeps things in a state of dynamic equilibrium.

Further reading Allenby, B., 2007. The Convergence of Science, Technology, and Nature. GreenBiz. https://www. greenbiz.com/blog/2007/04/01/convergence-science-technology-and-nature. (Accessed 20 January 2020). Baldwin, R., 2016. The Great Convergence: Information Technology and the New Globalization. Belknap Press. Borders, M., 2018. The Social Singularity. Social Evolution. Borgmann, Albert., 1992. In: Cutcliffe, Stephen H. (Ed.), “The Postmodern Economy.” New Worlds, New Technologies, New Issues. Lehigh University Press. Brio, M., et al., 2010. Convergence Theory for Initial Value Problems. Mathematics in Science and Engineering 213 (C), 109e144. https://doi.org/10.1016/S0076-5392(10)21308-5. (Accessed 20 January 2020). Chartres, B., Stepleman, R., 1972. A General Theory of Convergence for Numerical Methods. SIAM Journal on Numerical Analysis 9 (3), 1972, pp. 476e492. JSTOR, http://www.jstor.org/ stable/2156145. Cooper, B., 2019. Borgmannian Metamodernism. “The Abs-Tract Organization”. https://medium. com/the-abs-tract-organization/borgmannian-metamodernism-8ed5e275f8ae. (Accessed 20 January 2020). Deloitte Insights/Tech Trends, 2018. The Symphonic Enterprise. Freinacht, H., 2017. The Listening Society: A Metamodern Guide to Politics, Book One. Metamoderna ApS. Freinacht, H., 2019. Nordic Ideology: A Metamodern Guide to Politics, Book Two. Metamoderna ApS. Kurzweil, R., 2005. The Singularity is Near. Penguin Books. Roco, M., et al., 2014. The Convergence of Knowledge, Technology, and Society. Springer Science & Business Media. Vermeullen, T., van den Akker, R., 2017. Metamodernism: Historicity, Affect, and Depth after Postmodernism. Rowman and Littlefield. Volkov, A., 2018. Smart City: Convergent Socio-Cyber-Physical Complex. MATEC Web of Conferences 251, 03065. https://doi.org/10.1051/matecconf/201825103065. (Accessed 20 January 2020). Watson, P., 1997. Convergence: The Idea of the Heart of Science. Simon and Schuster.

Description of each section Descriptions overview The book is divided in sections Approach, Architecture, and Application giving equal space to these three dimensions representing the convergence theories and methodologies integral in the planning, design and operations of Smart Cities and Artificial Intelligence (AI).

Section 1

Approach This section of the book covers our approach to the challenge of smart cities and the role of technology and artificial intelligence on a broad scale. Chapters 1e3 are about the perspective and epistemology of the book, laying down the basic definitions and premises about the evolution of smart cities based on supporting convergence theories and system science. It is grounded in applied methodologies related to more open-ended design fields to identify the potential for systems and cities to be autopoietic or self-regulating. Chapter 1 is about establishing theoretical foundations in the evolution of systems and the application of convergence theories to the advancement of cities and their ability to adapt and transform based on their unique DNA. The theory of convergence introduced before is applied to smart cities to understand the accelerating and intersecting trajectories of technology and social organization. Some smart city case studies are explored to illustrate the notion of city DNA, as a speculative measure of a city’s history, cultural code, and socio-technical landscape. The six dimensions of the city are outlined in further detail, explaining how all layers are connected and converging. The six layers fall into three translocal spaces that intersect: the physical domain, the human realm, and the technology sphere. All are convergent. Chapter 2 looks at cities as socio-biological and metabolic systems, rooted in diverse understandings of cities as living systems and the relation to human xxvii

xxviii Description of each section

anatomy. It starts by recalling the history of organic and systemic thinking about cities, from Thomas Hobbes to Niklas Luhmann. This includes discussion of biomimicry and urban biology informing our design principles, down to providing tables analogizing the system functions of the human body to that of the city, the social body, and its needs and technologies. It then outlines the concept of collective intelligence, the living wisdom emergent from informed and educated citizens involved in sense-making and decision-making, and integrated with the technological landscape. City DNA is emphasized as the ability for each city to identify its own uniqueness and integrate universal standards with local needs accordingly. This brings into focus the role of mapping and data collection as foundational mediums of smart city operations, monitoring and coordinating the self-organization of the city and its inhabitants. Chapter 3 enters into the normative, constructive, innovative, and generative aspects of smart city planning. It draws from diverse design practicese Design Thinking, Co-Design and Generative Design, to develop new hybrid methodologies to address a changing world, and adapt to new urban systems and realities. Machine learning is brought forth as a tool to facilitate outcomebased modeling, where the city is generatively designed based on its needs, borrowing from the biomimetic approach introduced in the previous chapters. All these are considered in light of the convergence design method that allows us to now simulate the city in real-time, in public, and open-source ways.

Section 2

Architecture The Architecture section ranges from macro city anatomy to micro user experience. Chapters 4e6 look at the organizational structure, communication spectrum and system behaviors of Smart Cities based on three major elementsdoperating systems (OS), connectivity, and interface. The smart city is in itself a self-forming and self-regulating system that evolves based on its inherent characteristics and formal structure as a living, adapting system. The smart city is a form of neural network interface linking multiple stakeholders and diverse ontological frames of reference or UX. The concept of a smart city information architecture represents different scales and functional typologies that are integrated with the City OS and city-wide Interface. Chapter 4 is about city OS, meaning how the city operating system is a combination of top down, bottom up and middle ground influences. Establishing

Description of each section xxix

the appropriate system architecture for each city requires an understanding of the structure, relationships, rationale and intended outcomes of the system. This is filtered into an aesthetic and intelligent interface, the face of the city as a living lab. We propose a convergent OS where living systems, technical networks, and humane interfaces all interconnect and converge, organizing together hierarchies of governance and infrastructure. Chapter 5 is about connectivity. Connectivity is the medium for cities to achieve a new ambient connectivity based on Artificial Intelligence (AI), neural networks, and machine learning (ML). Connectivity evolves like the city itself, based on different infrastructures through time, from rivers and roads to wireless transmission. Communication technology is evolving along more expansive lines now, becoming more invisible and real time. Connectivity is built into urban architecture. AI and ML allow for more dynamic utilization of different bandwidths. The ubiquitous connectivity allows the convergent urban architecture to self-regulate. Chapter 6 describes the evolving and adapting complex city interface. Interface is what allows people and elements to interact in a new city-wide collective intelligence platform. It posits the ultimate direction of a seamless interface, merging nature and technology in urban architecture. Through this trajectory of the book, there is a thematic ramping up of the technological convergence, toward a totalistic interface and holistic information architecture. Chapters 4e6 combined provide a gestalt view of the hybrid systems architecture that allows the AI driven smart city to be adaptable, sustainable and self-regulating.

Section 3

Application Chapters 7e9 cover the application of smart cities across the established functional typologies identified in the City Mandala model as a continuous wheel of interconnected yet independent functions as basis of the smart city operations. The functions are presented through an evolutionary lens that traces the development and trends of how technologies are reshaping the nature of cities and the convergence of human, machine, and natural environment as a new collective intelligence formation through the lenses of specific scenarios, functions, and business models.

xxx

Description of each section

Chapter 7 describes smart city functions based on evolutional (horizontal timeebased) scenarios describing the relationship between objects (resources), actions (coordinated input), and outcomes (higher state). We use this analytical framework to present the directional relationship between the elements found in each smart city function. The identified states represent the highest level of the functional hierarchy (desired outcome or singularity) needed to balance and support smart cities. The six functions of smart cities (based on the Smart City Mandala) act as the building blocks of an integrated universal city OS customizable to each city DNA. We then present various scenarios for each of the functions through the lens of a process-oriented systems-change framework, the multi-level perspective (MLP) model, and the convergence of natureehumanemachine. This allows us to trace the directional development and convergence that leads to the six states (inputs and development stages). Chapter 8 presents the key technologies necessary for the deployment and development of Artificial Intelligence (AI) (cloud computing, 4G/5G, and Big Data) across the six smart functions. A Scale Hierarchy framework is adopted to present the macro-, meso-, and micro-levels of the smart city functions. A Scope Hierarchy framework is applied to present three levels as well, of how the applications fall into the context (macro convergence), content (application themes), and component (micro convergence & strategy) dimensions across the six smart functions. Presented in sets of three, these combinations allow us to explore new levels of analytical complexity indicative of the broadest application of the convergence of humans, machines, and nature in the context of smart cities. Chapter 9 explores the transforming landscape of business prospects and new economic models related to smart cities and AI. It invokes some themes, strategic implications, and the high-impact components for creating sustainable advantage in the era of AI-driven innovation and explores how these can be embedded in the design language and business models of the future. AI enabled Smart City applications show the niche opportunities for businesses to collaborate more with other businesses and establish public-private models due to the convergence of technology, and the codependency of users and citizens as stakeholders in new commons-based forms of commerce and industry. The convergence is noted in the context of competitive advantage and business strategy, as well as the niche innovations of social movements and open-source technology to challenge the incumbent market-driven paradigm. The lessons learned in Chapters 7 and 8 help us build the bigger picture scope of Chapter 9.

Info system (Fig. 0.0)

Earth / Nature

Humankind

Technology

Balance

Culture

Environment

Eco-system

Population

Infrastructure

Landscape

Regulation

Service

Market

UX

City

Inhabitant

Wellbeing

Optimization

Application

Behavior

Pattern

Scale

Boundary

Frequency

Function

Structure

Context

Management

Strategy

Solution

AI

Network

Data

Connectivity

Smart Object

City OS

FIGURE 0.0 Info System represents a conceptual and technical visual ecosystem encompassing an open-ended spectrum of the converging human-bio-technical systems and processes explained throughout the three sections of the book. In a sense, the book itself has been designed as a form of operating system information architecture with multiple illustrations and a glossary of terms to explain the theories, methods and application of artificial intelligent and smart cities. The info system icons are presented as a flexible, adaptable lexicon structure to assist in the understanding of the system complexity, its components, and the many functions and attributes.

xxxi

Chapter 1

Evolution of cities/technologies Chapter outline 1.1 Overview of smart city concept and context 1.2 The evolution and integration of technology, AI, and cities 1.2.1 Evolutionary strategies 1.3 City DNA narratives 1.3.1 Beijingdthe radiating megacity 1.3.2 Londondthe cosmopolitan hub 1.3.3 New Yorkdthe media metropolis 1.3.4 Dubaidthe iconic branded city 1.3.5 Songdodthe new digital city 1.3.6 Masdardthe new sustainable city 1.3.7 NEOMdthe future city 1.3.7.1 Summary 1.4 The dimensions of the city and potential for convergence 1.4.1 Physical/environment dimension 1.4.1.1 The city as evolution of space, form and hardware 1.4.2 City systems, infrastructure dimension 1.4.2.1 The network of the city, the spine, and major organs

2 4 7 9 10 11 12 13 14 15 16 17 17 18

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1.4.3 The human dimension 1.4.3.1 The city as a manifestation of human patterns and constructs 1.4.4 Culture, society, and governance dimension 1.4.4.1 The nuances of human civilization, behaviors, activities, desires, and relations 1.4.5 Digital infrastructure dimension 1.4.5.1 The city as evolution of systems, technologies, and software 1.4.6 The ubiquitous dimension 1.4.6.1 The merging of technology with the natural environment in the form of imbedded and ambient connectivity 1.5 How convergence theory applies to smart cities 1.6 Conclusion References Further reading

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19 19

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20 20

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Smart Cities and Artificial Intelligence. https://doi.org/10.1016/B978-0-12-817024-3.00001-5 Copyright © 2020 Elsevier Inc. All rights reserved.

1

2 SECTION | I Approach

1.1 Overview of smart city concept and context “Smart city” is now the popular concept driving cities around the world to a new level of technology innovation and quality of life enhancement while simultaneously a term being co-opted for the purpose of attracting investment and stimulating new economic opportunities. This latter purpose is a critical part of the establishment of a sustainable business ecosystem that can support the requirements of the development of a smart city and the next generation of urban growth. Investment is a necessary, but not sufficient, precondition for establishing a smart city. The determination of what is required to make a city smart originates in the unique characteristics of each individual city: its geographic location, physical composition, inhabitants, workforce, government structure and policies. The term “city DNA” is used in this book to express this complex composition specific to each city. Over the last several years as the definition of smart city has emerged, numerous research initiatives, technical studies and reports have been published to create a coherent etymological framework and taxonomy of smart cities. The book Understanding Smart Cities: A Tool for Smart Government or an Industrial Trick? (Anthopoulos, 2017) explains the evolution of the concepts and terminology of smart cities, beginning with the earliest references to digital cities beginning in the 1990s. Since then, multiple interpretations of smart cities are fashioned based on the stages of technological advancements including the Internet of things (IoT), smartphones and various tech fads that are codependent in the sense of establishing a new language representing market-driven innovation. “Smart city” discourse initiated around the requirements of ICT to address urban conditions and adapt to local needs, and has been continuously evolving and converging into more complex schematic representations. In tracing the etymology of the term smart cities through different incarnations, we see that the way we use language to shape and style our social reality obscures the more significant reality of the merging of the physical and digital realms, the convergence of technology and everything else as an evolutionary process. It may be right under our noses, but the scent is elusive. Some true aspect of smart cities is masked by our linguistic comprehension of it. In defining things, much perspective is gained but something is lost in translation; this is akin to the concept of leaky abstractions in programming. This book points to the process of convergence (beyond words) as the key to understanding the evolution of smart cities and paths to adoption. The importance of understanding the evolutionary process of how cities adopt and integrate technologies to transform the nature of the city is critical within the context of determining how and why technology is best utilized to achieve the goals and requirements of each city to remain competitive and cooperative within the global landscape. As introduced in the previous chapter, the convergence theory applied to society in the context of smart cities describes the nature of all systems to develop parallel or similar traits when provided the same resources, opportunities and industrial or technological systems. This deterministic view of the evolution of cities and societies

Evolution of cities/technologies Chapter | 1

3

reinforces the hypothesis that all cities will arrive at a similar stage of technological development if presented with the same technological advancements, leading to the lessening of the disparity between smart cities versus those that are less developed. However, this theory presupposes that cities are similar in terms of their initial starting point, which we elaborate further in this chapter and define as the Net Present Potential. To determine the potential for cities to achieve being defined as a smart city, a major consideration is the historic context of development. It is this criterion that differentiates the unique smart city strategy and implementation plan that is required to achieve the goals of what is “smart.” Around the world, the diverse types of cities and their stage of development, from historical cities to new planned cities, influence which direction the smart city development will take its course. This diversity has made it challenging for professionals and those leading the development of smart cities to agree upon an established language and approach. To solve this issue, initiatives are taking place internationally by diverse stakeholders and professionals to develop a universal language including the establishment of ISO standards and best practices that will eventually govern the development of smart cities. Technology is evolving in teleonomic (undirected) and teleological (directed) ways and converging on AI-driven, autonomous, self-regulating systems. Likewise, our societies and cities evolve in similar ways and by definition are “selforganizing” given that no one person is omniscient. The ultimate goal is for Artificial Intelligence (AI) to assist in the self-regulation of cities as living systems. If we think of traditional forms of city governance like the process of triage, then self-regulating smart cities use AI and machine learning to monitor systems in real time and anticipate problems, thereby saving everyone with fewer resources. Technology has had a clear “automation” function since the industrial revolution and the assembly line was born. In the 21st century, automation is eliminating the final sectors of physical labor and human work is increasingly abstract. The autonomization of smart cities therefore recognizes the imperative to serve the needs of the people who make it up. “A city is a system of systems with a unique history and set in a specific environmental and societal context. In order for it to flourish, all the key city actors need to work together, utilizing all of their resources, to overcome the challenges and grasp the opportunities that the city faces. The “smartness” of a city describes its ability to bring together all its resources, to effectively and seamlessly achieve the goals and fulfil the purposes it has set itself.” ISO/IEC 2015.

As cities developed over many centuries, the physical, socioeconomic configuration has transformed in some cases drastically, while in others the form of cities has expanded in a linear progression that has reflected the organic growth of the city as a direct response to population increase and the need for expanded land areas. In the recent TV documentary series Ancient Invisible Cities (BBC Two, 2018), Istanbul is explored for its reconfigurations over the past 2000 years from Roman outpost, to the seat of the Ottoman Turk empire, from Pagan to

4 SECTION | I Approach

Roman to Christian to Islamic to today’s more secular multicultural city. As explained through digital models and 3D visualization, including Virtual Reality (VR), these massive transformations, as a result of shifts of empires, wars and conquests, are expressed in the complex strata of urban archeology architects define as palimpsest. Meaning the layers of architectural formal language written over time, in this case representing the physical manifestation of Istanbul’s evolution from ancient to modern times as a gateway between the East and the West. In Section 1.3 we develop our concept of six dimensions of the city, which are both layered on top of and throughout each other, culminating in a seamless continuum. These dimensions are based on the fact that the “first distinctive characteristic of smart cities is the central role of technology,” as described in the Angelidou paper Four European Smart City Strategies (2016). Technology enables vast scales of knowledge and information to an increasing number of people, ostensibly to make their own lives and social structures more efficient. As technology gets cheaper and the urban geography gets mapped extensively with sensors, the IoT emerges. Coupled with the rising interest in personal data logging, city operations can also be monitored and audited, so public authorities and citizens can make informed decisions and solve problems. In short, the technology enhances city functions, thereby rendering it smart. We develop our six dimensions to describe how technology bookends the human experience, between nature and the technological singularity and looping through natureelow techehumansegovernanceehigh techeubiquitous tech/reinsertion to nature.

1.2 The evolution and integration of technology, AI, and cities Everything evolves in the basic sense of change over time, but more specifically cities evolve akin to living organisms changing through adaptation, selection and emergence into higher and more extensive forms. Throughout history we have seen empires rise and fall according to discernible patterns, but now we are in an age of globalization and perpetual cities and must understand the evolution of cities and technology to achieve sustainability and avoid systems collapse. The unique evolution of cities as manifested through the combination of the development of physical urban form, societal structure and cultural expression is the foundation of the city DNA and the substance by which the technological dimension of the city will be integrated. How technology will contribute to the city DNA is a result of the ability of individual cities to adopt and integrate technologies based on their unique characteristics, opportunities and constraints within the process of convergence. To explain the Net Present Potential of a city, we have co-opted the “initial value problem” concept and applied this to the evolution of technologies for the purpose of developing benchmarks to understand how each city will evolve over time. This is based on the initial value status representing the combination of factors, including physical environment, population, culture, socioeconomic, political and historical characteristics. This combination is what we

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have termed city DNA, explained in more detail in Chapter 2. Without understanding each cities’ DNA and historical development, it is not possible to plan the appropriate smart city solutions. We must factor in its historical developments and how these will be influencing factors in the future adoption and integration of technology within the specific culture, in the broadest sense, of each city, hence Net Present Potential and city DNA. An initial value problem (also called a Cauchy problem) refers to physics equations that model systems based on a key variable used to predict the unfolding of the system over time. Applied to the evolution of cities, the initial value can help indicate what stage of technological development a city is at and how best to integrate new technologies. For example, underdeveloped cities can leapfrog ahead of older cities with developed infrastructure by skipping an outdated stage of technology. Alternatively, developed cities can often adapt new technologies and applications faster. Put another way, the problem is how to determine the initial “value” or “potential” of somethingdsay, a city, for our purposesdwhen it depends on how the problem is understood. As technology converges in some places more than others, we need a way to determine the prospects of a given city and what stage it is at in its convergent evolution. Why is the determination of the evolutionary characteristics important in the adaptation of technology? Given the notion of the convergence of cities and technology, the historical development of cities coupled with the consideration of the Net Present Potential of cities provides a baseline to evaluate how each specific city will develop uniquely through the application of smart city technologies to serve the specific needs and cultural requirements of that city. Simultaneously, this criterion offers the possibility to understand how individual cities will develop in parallel with other cities of similar characteristics. As time goes on, we anticipate more cities will incorporate these concepts to have an increasingly clear sense of their city DNA, for which machine learning algorithms will be applied to maintain and develop the city as a self-regulating organism. Under our collective guidance, cities will be programmed to constantly improve along prescribed dimensions. We are well into a 50 year Convergence window where nature, technology and humans will deepen their connectivity and resilience. A complex study of history allows us to predict and project this window of the future in terms of key indicators of change such as climate change, renewable energy automation, AI, population stabilization and the implications for smart cities. Through this process, the spirit of Deep Ecology, Deep Learning and Depth Psychology can converge into a profound Collective Intelligence of compassionate humans and environments that are aided by AI. The following graphic represents a hypothetical evolution of the combination of the physical environment, human population growth and technological advancements to illustrate the convergence of these three independent states (Fig. 1.1).

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TIMELINE 50 Year Window of Convergence Innovation Cycles Population Growth

ARTIFICIAL NEURAL NETWORKS

AI

Moore’s Law 6 billion people AUTONOMOUS VEHICLES

Excellerating Cycles of Innovation

400 Years

200 Years

100 Years 50 Years

DEEP LEARNING

Population 350 million people

1200

1400

Convergence

Computing increase in power and decrease in relative cost

1800

Exponential growth in intelligence AI exceeding human capability

SMART CITIES SMART CONNECTED OBJECTS

1600

STRONG AI

ROBOTICS

Moore Law

10 billion people

COLLECTIVE INTELLIGENCE

2000

FIGURE 1.1 Timeline of converging systems.

AMBIENT CONNECTIVITY

2050

2100

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As an example of properties and prospects for city DNA, in the extensive report “Comparative Study of Smart Cities in Europe and China 2014” commissioned by a European UnioneChina collaboration, a multilayered criterion was applied to evaluate and compare 30 cities in Europe and China to determine the stages of development and unique characteristics of each city in terms of technological adaptation and system integration. It also compared and contrasted the impact of best practices for the development of global standards, policies and applications. The report gives us detailed snapshots of characteristics that resemble city DNA. In the recommendations section, they outline two principles of the “smart city staircase roadmap toward maturity.” It advises “no isolated advances,” meaning that trying to improve particular assessment metrics at the expense of others will likely be counterproductive. It also advises against “leapfrogging” faster than the city can handle; however, we unpack the notion and its positive implications in the next section.

1.2.1 Evolutionary strategies Based on a composite of multiple theories related to technology evolution and adoption, we have established the following technology development types. Similar to the concept in technology adoption, certain cities approach and adopt technologies differently than other cities based on each city DNA profile. Below we describe six degrees of technological adaptation that open up different strategies and can result in varied paths to smart city integration (Tables 1.1). TABLE 1.1 Six degrees of technological adoption. Nonintervention

Linear Integration

Dynamic Overtake

Alternative Bypass

Hyperaccelerated

Future Vision

No specific adoption strategy; technology will naturally come when appropriate

Conventional path; middle of the road strategy; stable planning

Skipping inferior technology stages; emerging countries with no legacy systems

Variation of leapfrog; lowtech approach; unconventional methods

Innovation hubs; accelerators; incubators; living labs

Speculative; high-risk, high investment in novel technologies

Non-intervention strategies would be more common among smaller or more peripheral cities, some of which get left behind overall national development. Their industries would typically be oriented around regional opportunities and they can wait for the benefits from more concentrated centers of innovation. Linear integration would follow a safe and predictable technology adoption strategy, mostly following a set path of development. Dynamic overtake is similar to leapfrogging, where there is opportunity to skip stages of

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advancement and at lower costs. Alternative bypass is a variation of leapfrog but a low-tech version, such as slowing growth and reducing impact. This is the case in cities with a highly developed civil society and cultural identity. Scandinavian counties are an example of societies that are now in the process of adapting alternative technological strategies to avoid the buy-in to a consumption-driven culture. While technology is part of the everyday life and is highly developed, Scandinavian cities are prioritizing quality of life and environmental sustainability over rapid technological development. Hyper-accelerated cities establish hubs of innovation to incubate new technologies to provide rapid economic growth that underpins the technological evolution. Cities such as Songdo City, Masdar and NEOM are cities developed with technology as the core feature of their ecosystems. As has proven volatile, accelerated technological progress through new innovations whose rapid application and diffusion cause an abrupt change in society is not always successful. Future-vision cities are envisioned and analyzed for speculative opportunities. In the same way, market analysts and companies develop economic strategies based on future derivatives, cities can potentially invest in future technologies while waiting out a particular state of present technology. In this approach, cities can gain a competitive advantage by-passing current technology lifecycles and investment in today’s technologies. The technology lifecycle is concerned with the time and cost of developing the technology, the timeline of recovering cost and modes of making the technology yield a profit proportionate to the costs and risks involved. Emerging technologies are the focus of future-oriented cities, as they can foster fast growth and coherence, but it comes with uncertainty and ambiguity. A technology roadmap is a necessary plan to help identify and coordinate emerging technologies, build consensus and mitigate unknown challenges or risks. The technology acceptance model describes how users adopt new technologies, influenced by a variety of factors, such as perceived usefulness and ease of use. The technology adoption lifecycle looks at consumer strategies from a more macro level, codifying demographic and psychological traits. This reveals a bell curve distribution with “innovators” leading the pack, followed by “early adopters.” The bulk of people fall into the “early majority” and “late majority,” trailed by a smaller group of “laggards” that do not have either access or interest. We can use these same notions to consider the range of technological adoption strategies of smart cities. The term leapfrogging comes from business and economics and is used when radical innovations permit a smaller newer firm to leapfrog the larger and more dominant firm. This concept applies to cities and countries just the same as businesses. An underdeveloped city may have the opportunity to skip inferior technologies. A village could go from no prior landline telephone service to having WIFI because the former technology is redundant. All countries have different competitive advantages based on resources and social capital, but the concept of leapfrogging undermines that and gives potential

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underdog cities greater opportunity to catch up. Leapfrogging also enables cities and countries to skip harmful or polluting stages of development that industrialized countries had to invent. Another example is the adoption of clean energy (such as solar) directly rather than relying on fossil fuels. If the only systems available are better than legacy systems, then the city will accidently leapfrog, but the ideal is to have a common vision, committed elite, relevant institutions and a labor market ready for rapid growth. Through the convergence of knowledge and technological solutions, all smart cities globally can have access to the greatest means, thereby potentially allowing the most impoverished cities to leapfrog rich and more established cities. In cities where there is a concentration of technological production and high-tech industries, as in the case historically with Tokyo (Japan) and Taipei (Taiwan) or more recently with Seoul (South Korea) and Shenzhen (China), the evolution and adaptation of technology has been accelerated due to the accessibility of technology resources. The concept of early adopters can be superimposed on the stage of technology adoption of cities, where cities that have had the privilege of advanced technology developed alongside either with a concentration of industries or academic research centers, resulting in more linear growth. To the extent that it is both, technological development may be accelerated. As per Ray Kurzweil’s Law of Accelerating Returns, the cities with early technology adoption may have competitive advantages and may undergo technological evolution in a linear progression. An analysis of the history of technology shows that technological change is exponential, contrary to the common sense ‘intuitive linear’ view. On the other hand, cities that have adopted specific technologies early on may have already peaked in terms of technological infrastructure development and have remained locked in a form of dependency with legacy systems. From non-intervention to future vision, various cities are taking approaches within that broad spectrum. Different cities may be pursuing some mix of strategies along different sectors as well. Urban narratives play a role in shaping consumer demand and public support for the best approaches to technology reform and smart city upgrades. This can be explored through smart city case studies and looking at their city DNA.

1.3 City DNA narratives From a long-term historical lens, cities have evolved in wildly different ways and at different rates. Some explode in size, others grow slower, or become very niche. Cities are typically established in a form of ecological and geographical hotspot, and evolve from there. New resource-driven cities may be prone to collapse into ghost towns, while old cities built in the most coveted places stand the test of time. From a macro perspective, cities can be viewed simply as clusters of humans working together, like cells making up living nodes in a network, organizing, pulsing, and radiating together. People work

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and consume, moving themselves and materials throughout the world. Infrastructure grows up around their common routes and the city comes to life. They have convergent levels of different normative states d through networking globally, they find common language and adopt best practices. In these case study vignettes, we look at the unique DNA of several major smart cities to understand opportunities for technological adoption and growth, based on their diverse historical, geographical, socio-economic and technological profiles (Table 1.2). TABLE 1.2 City DNA narratives. Beijing The radiating megacity

London The cosmopolitan hub

New York The media metropolis

Dubai The iconic branded city

Songdo The new digital city

Masdar The new sustainable city

NEOM The future city

1.3.1 Beijingdthe radiating megacity Beijing, the capital city of the East built spanning millennia and the second largest city in the world, behind Shanghai, has grown in a linear procession. Its austere urban form is based on a concentric evolution originating for the centrally positioned walled-in Forbidden City as the stage of the various dynastic rulers, to the expanding “ring roads,” forming a rational network of urban circulation of the present modern megacity, As China’s capital, it has a unique responsibility to balance needs at the local, state, and global level, establishing the character and identity of Beijing as one of timeless stability, while simultaneously directing an unprecedented feat of rapid economic transformation and social advancement in human history. The city’s 20th century urban configuration has been influenced by a combination of soviet-era military-style city planning designed for national spectacles and large-scale mobilization symbolically exerting its power on the world stage. The other influence draws from the modernist concept of “The Functional City” with designated zones and districts as elucidated by the Congre`s internationaux d’architecture moderne (CIAM) planning principles formalized in the European post war reconstruction period. Due to its massive scale, Beijing’s urban space is not pedestrian in nature like many other cities, so it must rely on efficient public transit systems to move its citizens, goods and services between its 16 municipal districts. Incorporating the complex history of China’s Great Leap Forward, the Cultural Revolution, and Beijing’s formidable urban structure, the city is in a perpetual state of balancing its ancient traditions with innovation and change. As part of Beijing’s modern expression, the 2008 Olympics was a catalyst for the city’s major infrastructure upgrade and setting the pace and tone for the new Beijing that has emerged as a leading cosmopolitan city.

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city DNA; Beijing is Top-down/hyper-accelerated, setting the bar high for rapid technological advancement, with China having put forth the goal of surpassing the US as the most advanced technological country by 2050. As part of the plan, the central government has initiated a massive, unparalleled program for smart city development with over 300 smart city pilots. With this objective, Beijing has in the last few years heavily promoted technological development through innovation supported by its influential universities. It seems there are innovation labs and co-working spaces both public and privately-run popping up all over the city, creating a new form of creative-class hybrid workers and stimulating a frenzy of tech-sector entrepreneurs that similarly surfaced in New York in the 90’s. With major tech company successes including WeChat (Chinese name: Weixı`n), Tencent, Alibaba and Baidu, Chinese society and citizens in Beijing have adopted new forms of digital lifestyle and ecommerce services perhaps faster than any other country or city in the world. The QR code embedded in the WeChat App was a key component creating the interface between the physical and the digital realms and has allowed an exponential growth for Chinese consumers to be plugged in. The biggest challenge for Beijing and Chinese cities, is to balance growth between a top-down, centrally controlled system and the power of a bottomup, massive consumer pull. With restrictions on access to global information and more regulated information systems, Beijing will require the metaphorical next stage of development d the 7th ring, as described in the author’s paper Urban media: new complexities, new possibilitieseA manifesto (Kirwan and Travis, 2011), to become the next dimension of expansion, evolving from physical rings of transportation to digital rings of communication, both open and closed, allowing Beijing to radiate beyond its physical constraints.

1.3.2 Londondthe cosmopolitan hub London has been designated as the smartest city in the world, according to the IESE Business School’s 2019 Cities in Motion Index (CIMI), with New York in the number two position. The city also ranks first in human capital but in the same report is 45th in social cohesion with its highly diverse population. London is also a world center of finance and an enormous investor in the technology sector, working closely with universities and research institutes, as well as through smart public-private initiatives. Geographically, London is a city of multiple villages that have merged over the last few centuries to form its present megacity status. The city is an intricate urban tapestry, a living museum combining old and new, from heritage sites to ultra-contemporary architecture and a nexus of tourism. As the city grows and becomes denser, London’s ecosystem is under pressure to maintain a balance with the environment as a predominately green city. The Smart London Board keeps a keen eye towards green innovation as a priority to achieve London’s goal of becoming a zero-carbon city by 2050. London’s famed public transportation

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system (The Underground Tube and Double-decker Buses) is a critical component in the urban metabolism/equilibrium equation. Through the efforts of Transport for London, the city is able to flow through continual upgrades making mobility more efficient and user friendly. Many civic initiatives, including district based WiFi connectivity and bike sharing programs, are also making the city more navigable and supporting mobility alternatives. To ensure operational efficiency, safety and security, these urban systems are highly monitored through extensive data capture via sensors and cameras and at City Hall, the mayor uses an iPad wall to visualize the city performance data in real time. city DNA; London is a hybrid Top-down, Bottom-up/Linear Integration approach to technological adoption. Predominantly a knowledge and service economy, London has no choice but to place innovation at the center of its evolution. It does so by leveraging its world-renowned universities and institutions in knowledge creation and information accessibility, making the city a leader in human capital. London’s smart city economy is expressed through transparent public access information systems and knowledge assets exemplified in the London Datastore, a free and open data-sharing portal. London has also pursued innovation with a global orientation, networking with other countries around the world and exporting technology expertise in many sectors. London’s city-level initiatives are tackling climate change, taking action where governments have failed to implement long-term sustainable policy architecture. The city is optimized for mobility, with a comprehensive transit system, shared bikes and walkable streets. London’s technological path is a mixture of linear integration and hyper-accelerated. Its brand recognition as a cosmopolitan global capital, has evolved from its previous identity as center of the British Empire, to now becoming a global leader in smart cities and human capital development. Brexit has created some social, political, and economic turmoil, but smart cities advancement may actually be key to offering a path forward through the UK’s challenges.

1.3.3 New Yorkdthe media metropolis New York has a fabled geography and history as a key port of entry to the East Coast and its five distinct boroughs. The importance of its position was realized early by Dutch settlers who established New Amsterdam and soon after by the English, renaming it New York as the center of the New World. It is also the most populated city in the US with one of the highest densities. New York’s history is complex and unique, shaped by various immigrant waves that have culturally transplanted their roots in the city’s diverse neighborhoods creating the melting pot synonymous with NYC and the American Dream culture. As a pioneer in engineering at the end of the 19th Century, the modern city has inherited more than a century-old infrastructure from previous generations, much of which now needs major regeneration. To solve this

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challenge, New York is slowly adopting novel upgrades to improve the urban experience and quality of life for its inhabitants, such as greening of the city, more public spaces, bicycle lanes and pedestrian zones. The contrast of characteristic neighborhoods, dense skyscrapers, city subway lines, Central Park, and consumer driven 24/7 hyper-urban lifestyle, exemplified by Wall Street market behavior, has made New York one of the most featured locations in popular culture, from films to photography, musicals, books, paintings and experimental art. New York is a city of different identities and perceptions, together making up a vibrant social tapestry and global brand. city DNA; New York City is a Bottom-up/Non-Intervention model as the pure capitalist arm of the western world, driven predominantly by the private sector, big business and its own market behavior. This ethos is embodied in the survival of the fittest motto “If you can make it in New York, you can make it anywhere.” Due to New York being a city as an innovator or early adopter of new systems, this has in some way put New York in a quagmire. The burden of inheriting and maintaining both physical and digital legacy systems require massive investment to upkeep these systems, while simultaneously fostering self-preservation traits of corporations in order to recover their investments at the expense of adopting new systems. This is further exacerbated without the benefit (or non-benefit) of New York being a center of government (as does Beijing, London, and Dubai), where the free radical and Laws of the Jungle nature of the city makes all technological advancement driven by competitive forces of the market and consumer behavior. In this regard, the media, advertising and digital immersion culture reflects a totally unregulated commercial reality driven by how consumer citizens interact with technology. Sneaker stores stay open 24 hours a day and energy consumption is not factored on efficiency of systems, but on consumption levels and return on investment. Technology is therefore not about the optimization of systems, but the exploitation of technology to achieve higher yields. The dichotomy between old and hyper-new is what spins the DNA of NYC.

1.3.4 Dubaidthe iconic branded city Dubai, a branded destination city, is a linear city-state situated along the coast of the Persian Gulf as part of the United Arab Emirates. Born from a fusion of nomadic desert and fishing village cultures, the city-state has evolved symbolically and physically with Sheikh Zayed Road as the physical and symbolic spine connecting the dispersed desert city districts and linking its neighbors Abu Dhabi to the southwest and Sharjah to the northeast. Initially built alongside a simple airstrip to bring people to the region, the city is now a fullfledged aerotropolis with Emirates Airlines as one of the most successful airlines in recent history, serving as a catalyst for Dubai’s miraculous success as a global tourist destination and the envy of the world. Dubai’s marketing

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strategy is based on price competitive destination tourism, attracting people from all over the world to bask in the warm dry weather, tolerant Arab society, and visionary metropolis. Contrastingly, Dubai’s resident demographic is segregated in three predominant groups: local Emirati, South Asians and European expats. English is the neutral language and most social interaction between Dubai’s inhabitants takes place through work environments, while their social and cultural lives are divergent due to different habits, religion and native languages. Unprecedented growth drove up property values and cost of living until 2008 threatened to burst the dream bubble. Since then, Dubai’s growth has stabilized allowing the potential to reflect on the balance between preservation of the local cultural heritage, as well as continuing to create a new sense of collective identity and a hyper-modern city. city DNA; Dubai is an example of Top-down/Dynamic Overtake. Despite the market crash in 2008 and the long and winding road back to recovery, Sheikh Mohammed bin Rashid Al Maktoum, the revered leader of Dubai and the visionary figurehead behind Dubai’s global brand recognition, has continued to drive Dubai’s aggressive real estate development with awardwinning contemporary architecture, extreme engineering feats and construction innovation. This has included better, taller, more striking building forms and smart building technologies, including the 830m-tall Burj Khalifa tower that dominates the Dubai skyline and is the site for the world’s most dynamic and large scale multidimensional, multimedia show (lasers, media, sound and water). It seems almost every day a new iconic tower or building continues to surface in architectural journals and online media. This continuous innovation in the constructed environment at times seems like an ego-driven pursuit of global recognition, but has actually been the fulcrum for accelerated development, giving Dubai its leading edge as a global city. In this context the adoption, application, and integration of technology play an accompanying role in transforming Dubai as a smart city in the desert and the leading newlybranded city.

1.3.5 Songdodthe new digital city Songdo bills itself as the “smartest city” in the world, and it may very well be in terms of integrated technology and urban design as a pioneering ubiquitous city. The 1500-acre International Business District (IBDI), located an hour from Seoul and adjacent to Incheon Airport, was built from scratch starting in 2001. Envisioned to be a low-carbon, high-tech utopia, the project set new standards in sustainability, with innovative approaches to urban density, green infrastructure, community planning and building performance. Designed by the New York architectural firm Kohn Pedersen Fox (KPF), the new city has been programmed to promote a high quality, balanced lifestyle with “live, work, play” urban features comprised of themed cultural districts like American Town, abundant parks and open space, efficient waste management,

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and embedded smart systems throughout the buildings and streetscapes. As a speculative private-sector real estate development, Songdo has taken time to fill in as a complete city with some challenges including affordability and market demand, with the district only partially populated. Nevertheless, the city is active and slowly taking shape as an entirely new smart city experience for its inhabitants. city DNA; Songdo is a Hybrid/hyper-accelerated model born out of the ascendance of South Korea and Seoul as a new capital of hardware and software technology, surpassing Japan and Taiwan as a leader in chip manufacturing, smart phones and the digital media revolution. Seoul needed a new geographic area to expand on the island nation and created a new massive city-region on reclaimed land in the sea near the new Incheon International Airport, reinforcing the potential of creating a mega-aerotropolis region. The achievement of smart is not only a factor of technological pervasiveness and smart buildings, but also a factor of human adoption, absorption and proliferation. As a predominately homogenous population of 52 million people with one common language and culture, shared set of values, strong middle class and a 98% literacy rate, the potential for one society to adopt and sustain a common technological evolutionary framework may be the most advantageous in South Korea, with Songdo being the brain child of the real estate and tech industry symbiosis.

1.3.6 Masdardthe new sustainable city Masdar (Arabic for “spring” or “source”) is a new city planned from scratch with a mission to spark innovation and a technological revolution in the 21st century and beyond, and to position Abu Dhabi and the UAE as pioneers in new forms of renewable energy, green building design and manufacturing. As an initiative financed by the government and rulers of Abu Dhabi to reinvest funds underwritten by its 92-billion-barrel oil reserves, and with a forwardlooking strategy, Masdar would appear to be a viable platform to secure Abu Dhabi’s economic stability for the infinite future. The plan was the most ambitious in its time, begun more than a decade ago, bringing in the world’s leading architects and engineers, and designed by the British architectural firm Foster and Partners. The highly innovative urban design and technological framework, including a zero-carbon footprint strategy, was a combination for success. Unfortunately, the project was perhaps ahead of its time and the entirely newly-fabricated city has not been able to scale up in an accelerated way as the project was designed to accomplish. But, in other ways, it has served as a new model of cities as centers of innovation and R&D. Ironically Masdar was born as a child of Dubai and Abu Dhabi, each able to grow from humble beginnings to world icons within a short life-span of 50 years. As one of most expensively designed city in the world, Masdar can hopefully continue to serve as a living example of a bold, new model of a zero-carbon city vision

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that one day we will be able to achieve, learning from each incremental step forward. city DNA; Masdar is a Top-down/alternative bypass example rooted in the research and development of new technologies as a catalyst for a new city like a frontier space station. As an experimental smart city driven by the concept of itself being a Living Lab, Masdar brings academic research, engineering, and manufacturing together, and by partnering with leading universities and tech companies, it creates a tech innovation hub and ecosystem as the core of the city operations. As the first anchor university, The Masdar Institute of Science and Technology, a graduate-level research university focused on alternative energy, environmental sustainability, and clean technology in partnership with MIT, has established a knowledge industry and technology IP footprint. One of the major ideas of Masdar has been to use biomimicry to design the city as a self-developing urban environment, where education, technology, manufacturing, and social dynamics can thrive as a living eco-system.

1.3.7 NEOMdthe future city NEOM (meaning “new future” in Arabic), the new energy city, is a planned smart city in The Kingdom of Saudi Arabia (KSA), slated for completion in 2025 with an estimated budget of $500 billion. NEOM is conceived as a crossborder city in the Tabuk Province of north-western Saudi Arabia near the Red Sea and borders that KSA shares with Egypt and Jordan. The city and region are intended to be a form of market showcase, incorporating smart city technologies while doubling as a new travel destination. The innovative ideas it promises include a vast renewable energy array (including solar, wind, and smart grids), all-green transport, biotech, vertical farming and advanced manufacturing. Hosting a major global forum in 2019 to jump start the project, NEOM invited the world’s leading corporations, technology companies, advisors and consultants to join the development of a new tech and energy business ecosystem to solidify global industry partnerships and attract investment. NEOM has had a few challenges subsequent to its start, however the project will likely continue to evolve in different directions as the new vision of The Kingdom emerges under the leadership of Prince Mohammad Bin Salman (MBS) and as the Middle East as a whole, appears to collectively be committed to putting their oil investments into incubating new smart cities and sustainable/renewable energy solutions to solidify wealth for future generations. city DNA; NEOM is a Top-down/Future Vision city and region. Its speculative future is balanced on long-term investment and short-term, tech market-driven opportunism, making it both an attractive initiative and a high-risk venture. Conceived as a new technological marketplace and special trade zone strategically located at the Gulf of Aqaba in the Red Sea, NEOM can potentially avoid the challenges of Masdar as an innovation-driven city.

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Functioning as a geo-political and cultural bridge between the non-secular world of the Middle East and secular worlds beyond, including an initiative to create potential partnerships with Israel, and by building on the momentum of a new era of modern reform, NEOM is a socio-economic driven city. Formed as a company wholly owned by The Public Investment Fund (PIF), the sovereign wealth fund of Saudi Arabia, NEOM adopts technology not as its central strategy, but as a catalyst for the creation of a new economic model and hub of global influence.

1.3.7.1 Summary These case study vignettes demonstrate how different some of the world’s megacities can be. Through the simplified urban narratives portrayed, each city has a unique raison d’etre, a reason to be, that influences the way it will adopt and integrate new technologies to reinforce its identity, its DNA. The stage of historical development and the socio-economic landscape in each city influences the decisions of which technological solutions best apply.

1.4 The dimensions of the city and potential for convergence There are many different ways to model how smart cities and the world are and/or should be coordinated and layered: ISO (International Standards Organization), ITU (International Telecommunications Union), City Protocol/ City Anatomy, the World Government Summit’s nine layers (Bridgwater, 2016) and GIS City Layers are a few useful examples. They all have their strengths and weaknesses, and we choose not to compete or comment directly on them. Rather, the six dimensions we present in this section are based on a more intuitive approach of human development. They are less about particular aspects of the smart city and more about the evolutionary process giving rise to the whole of human reality, to tell a complete story from nature to technology and back again. While other models may use the term layers, levels, domains, or stages, we describe six dimensions that are coextensive. One emerges out of the previous, but they occupy the same space and are nested within each other. They are interrelated in the sense that each dimension overlaps and intersects with all others. All are interdependent and therefore must find harmonious coordination. We propose six dimensions, presented sequentially, where the last dimension connects back to the first forming a continuum: Physical, City Infrastructure, Human, Governance, Digital Infrastructure and Ubiquitous Technology. First, the base is “nature” itself, the bountiful earth, and providing the ecosystems and all the minerals, timber, animals, and fuels therein; second, concrete urban infrastructure, encompassing all basic services and structures; third, the behavior and patterns of humans, their language, culture, beliefs and

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Ubiquitous Technology

TECHNOLOGY SPHERE

Digital Infrastructure

Culture, Society, Governance

HUMAN REALM Human Population

Physical Infrastructure

PHYSICAL DOMAIN

Nature, Landscape, Environment

FIGURE 1.2 City dimensions.

so on; fourth, the socioeconomic and political institutions going beyond the functional relationship between the previous two dimensions; fifth, the ICT infrastructure that characterizes the Internet and 21st century technological convergence toward a singularity; and sixth, a ubiquitous technology dimension that (e)merges and converges seamlessly with the environment (the first dimension), including through embedded systems, nanotechnology and permaculture planning. The dimensions emerge from concrete to abstract and reinsert back into the beginning. This is shown as a stack in Fig. 1.2, but the layers are actually coextensive dimensions forming the loop.

1.4.1 Physical/environment dimension 1.4.1.1 The city as evolution of space, form and hardware Before considerations of technology and smart city applications is the need to fully understand the physical city including its broader context and environment, geographic location, orientation and linkages, physical composition and climate. These aspects must also be seen in relation to the historical evolution of the city and the stages of its physical development. The physical city forms the basis of how all objects and activities function within the geospatial framework and it is the first layer required in the data collection upon which all data is referenced. The physical city embodies the cultural pathos and ethos as the direct translation of human expression in the form of the urban composition and building architecture. This combination of geospatial positioning, city orientation, and building language comprises the physical form and hardware of the city as the

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base or first dimension. Through this foundational dimension, cities are seen to manifest in strategic or niche regions, usually capitalizing on some convenient port, hospitable climate, or proximity to resources.

1.4.2 City systems, infrastructure dimension 1.4.2.1 The network of the city, the spine, and major organs The second dimension of the city is the physical infrastructure and utilities system that supports the entire operations of the city, from road networks, utility pipelines and telecommunication networks. It enables interaction with the nature and resources of the first dimension, including utilities (water, power) and transportation of materials and waste. Infrastructure and the physical city are intertwined and have typically evolved over time in a symbiotic manner while not always working in harmony with each other or the city as a whole. In the example of ancient Rome, roads and aqueducts served as the first communications and transportation networks, networks being the operative term. From this acceleration of exchange, modern cities emerged and wealth was generated to develop further technologies and infrastructure. These networks of utilities and transportation parallel early versions of cyberspace in that they give birth to emergent properties that quickly transformed the nature of society and their own properties. 1.4.3 The human dimension 1.4.3.1 The city as a manifestation of human patterns and constructs The third dimension represents human activity in relationship to itself and all other dimensions. In the 2017 book, Urban Being, the identity of each city is characterized by the behavior of its inhabitants, but the lower dimensions of the city constitute the potential expression at the human level. Human activities interact with both the physical environment while humans interact with each other. In his influential book Pattern Language (Alexander, 1977), architect Christopher Alexander describes how the dynamic forces of urban life can be perceived as patterns which can be employed to establish the design criteria or program for the planning of structures at any scaledfrom a small structure to a larger city or metropolitan region. 1.4.4 Culture, society, and governance dimension 1.4.4.1 The nuances of human civilization, behaviors, activities, desires, and relations This dimension covers governance and political systems, typically where elected representatives are obliged to serve the community. Furthermore, interaction between citizens and city organizational structures are framed by the relationship between governance and urbanism. As such, in this dimension, people are not just

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represented as humans but as citizens who play a role in governance. In various ways, governments coordinate and guide the actions and outcomes of people interacting in a top-down way, but bottom-up approaches and feedback enabled through ICT are also necessary. This dimension includes laws, policies, and institutions at the local, international and global levels. Needless to say, there is always room for improvement across the board and best practices should converge. Generally, there is a move toward open-source knowledge, much of which is produced collectively. and open-source governance. Smart cities also attract the right (smart) labor force for this reason; people who want to be a part of innovation utilize technology in sustainable ways. A smart city is only smart as its most intellectual people, and you cannot have competent governance without intelligence at the human level of society first. As such, free open-access education is a prerequisite for any smart city. Non-governmental organizations and businesses also play vital roles, determining what drives the urban economy. All these interactions are enabled and mediated by ICT, or digital infrastructure, which is the next dimension. As analogous to city anatomy, government processes can be modeled as the Operating System of the human organism.

1.4.5 Digital infrastructure dimension 1.4.5.1 The city as evolution of systems, technologies, and software The fifth dimension is the digital infrastructure which is all the communications and computing power networked in a given city and globally, including wireless and satellite technology bouncing information all over the world. This dimension also includes the hardware we interface with, such as computers, smartphones, televisions, radio, etc. By extension it includes software and code, with their multitudes of layers of programming abstractions. As technology becomes cheaper, these systems become more widespread and ubiquitous. Urban media also has a role to play in disseminating knowledge and culture and renewing narratives that create community and city brand identities. This helps create a host of reasons to attract smart citizens for work, play, and investment. The case studies of New York, Dubai and Beijing show the productive potential of new media to highlight their rich cultural identities and marketing opportunities. 1.4.6 The ubiquitous dimension 1.4.6.1 The merging of technology with the natural environment in the form of imbedded and ambient connectivity Ubiquitous technology is increasingly everywhere and yet unseen. This final dimension emerges after the others have evolved and developed to a new threshold. It has been a historically slow process, but the gap has been

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shrinking and the time between the last dimension and this one is a matter of mere decades. The ubiquitous city is integrated top to bottom, embedded with sensors to collect data to optimize city functions and human well-being. Nanotechnology is a game changer in that it has the ability to mechanically alter the fabric of our experienced reality at the micro scale, from medical procedures to material construction and beyond. Over the past 10 years, we see a pattern of technologies trending and converging toward more holistic ICT tools and architecture across the domains of Digital, Analytics, Cyber, Business of IT, Cloud and Core. This technological infrastructure gathers, organizes and analyzes vast quantities of information (hence ‘the world at your fingertips”). Useful data are spontaneously accumulated by various tracking algorithms and metrics that people produce and utilize. It is a confluence and convergence of technology that extends itself through the IoT and into this final dimension.

1.5 How convergence theory applies to smart cities “Smart city” is a convergent socio-cyber-physical complex, the management parameters processes of which are optimally adaptive to their own state space. In the popular science sense, a “smart city”is a city that is optimally flexible to human beings and society. A “convergent socio-cyber-physical complex” is a finite set of open convergence systems, including functional components (elements, objects, computing resources integrated into the included physical processes), and their relationships, human being and society, allocated in accordance with a certain goals system on a specific time interval. Smart City: Convergent Socio-Cyber-Physical Complex (2018), Andrey Volkov.

All cities evolve throughout their history and we can use the concepts of City Anatomy (this chapter) and city DNA (Chapter 2) to understand their unique character and to map smart systems throughout. As smart cities evolve, technology and AI become increasingly integrated. Our criteria for understanding the viability over time is based on the Net Present Potential, which includes considerations of environment, population, culture, socio-economics, governance and history. The emergent total is what we call city DNA. A city’s potential for smart city status is first determined by its historic stage of development (including where there is none). The diversity of current versions makes it difficult to abstract a common language and approach. Movements toward a standard and universal language (such as ISO, ICU, City Protocol, or GIS CIty Layers) are converging. For illustrative purposes, we invoke City Protocol and its proxy City Anatomy to help discuss our own approach. The City Protocol Agreement includes a model for smart cities called City Anatomy, an analogy between cities and living systems that helps to map the city systems and connections between them. City Anatomy conceives of an “anatomy of urban habitat” with four main qualities: timeless, acultural,

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scalable and generic. This ensures that it is compatible with any city throughout history, applicable to any culture or type of city, valid for any size city from village to metropolis, and every city would be a part of the metaarchitecture. City Anatomy presents three layers (Structure, Interactions, and Society) of the city with nine conceptual subsystems overall. It is a holistic integration of the city ecosystem. We describe it here only to extend the analogy of cities as living systems, the focus of Chapter 2.

1.6 Conclusion The concept of Smart Cities has evolved a great deal in the past 30 years and will continue to accelerate innovation into the near future. However, in some ways the language commonly used to describe smart cities and their evolution is problematic and does not reflect the reality unfolding. The IoT and smartphones have revolutionized society, but not necessarily in ways that we would consider “smart.” By understanding processes of evolution and convergence, informed decisions can be made about when and how to adopt new technologies to realize the goals and ambitions of each city in a global context. The concept of leapfrogging is promising for underdeveloped countries that have suffered disproportionately from geopolitical strife and harmful international monetary policies. By some measures, the BRIC countries have leapfrogged the United States and Europe through rapid economic development. The concepts of convergence and Net Present Potential work together to promote predictable and sustainable city evolution. So, while cities may be different in many ways, their developmental trajectories in convergent technological terms are shared. To this effect, we presented seven brief case studies highlighting the respective strengths and weakness of uniquely branded smart cities: Beijing, London, New York, Dubai, Songdo, Masdar and NEOM and how their unique DNA is the platform for the determination for the role technology should play within their urban policies, development strategies, and city narratives. In contrast to the more schematic models of smart city layers and design, we proposed six dimensions emerging from the base layer of earth and its resources: Physical, City Infrastructure, Human, Governance, Digital Infrastructure and Ubiquitous Technology. The final dimension blends nature, urban design and embedded technology fluidly and seamlessly, creating a circuit connecting all dimensions. Technology becomes ubiquitous, everywhere and always, and the dominant factor in our lives and on the planet. In the ubiquitous sense, it is ideally not only normatively nonintrusive and invisible but also functionally easy to call-up any information, on demand, to serve personal, civic and or natural systems requirements.

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Humankind Nature

Technology

ARTIFICIAL INTELLIGENCE

Balance

Optimization

Wellbeing

FIGURE 1.3 Convergence goals.

Our broader goal is to see not only the convergence of technology to promote more effective smart city operations but a convergence of smart people, smart policy, and smart technologies necessary to ensure wellbeing for all citizens and the protection of the physical environment and natural world. In order to do this, each major category has an ideal measure to achieve its highest state of evolutionary convergence. For nature, it is the idea of homeostasis, or balance. For humankind, it is actualization, or well-being. And for technology, it is efficiency, or optimization. If we can converge on these principles in a coordinated way, smart cities will be on track to becoming sustainable realities rather than remaining only as concepts and marketing hype (Fig. 1.3).

References Alexander, C., 1977. A Pattern Language: Towns, Buildings, Construction. Oxford University Press. Angelidou, M., 2016. Four European Smart City Strategies. International Journal of Social Science Studies 4, 18.

24 SECTION | I Approach Anthopoulos, L.G., 2017. Understanding Smart Cities: A Tool for Smart Government or an Industrial Trick? Vol. 22. Springer International Publishing, Cham. BBC Two, 2018. Ancient Invisible Cities, Series 1. Istanbul, Palace of Bucoleon. https://www.bbc. co.uk/programmes/p06lp8yf (Accessed 19 December 2019). Bridgwater, A., 2016. Counting to Nine - The Nine Layers of a Smart city. World Government Summit. https://www.worldgovernmentsummit.org/press/news-press-releases/the-nine-layersof-a-smart-city. (Accessed 21 September 2019). China Academy of Information Communications, 2016. Comparative Study of Smart Cities in Europe and China 2014, 1st ed. Springer. GIS City Layers Map j GIS Mapping System. https://zenduit.com/product/gis-city-layers-map/. ISO/IEC, 2015. ISO/IEC JTC 1 Information Technology. Smart Cities Preliminary Report 2014. ISO, Switzerland. Kirwan, C., Travis, S., 2011. Urban media: new complexities, new possibilitieseA manifesto. In: Foth, M., Laura, F., Christine, S., Martin, G. (Eds.), From Social Butterfly to Engaged Citizen: Urban Informatics, Social Media, Ubiquitous Computing, and Mobile Technology to Support Citizen Engagement. The MIT Press. McArdle, M., 2018. Is Masdar City a Ghost Town or a Green Lab. Popular Science. https://www. popsci.com/masdar-city-ghost-town-or-green-lab/. (Accessed 21 September 2019). Miller, M., 2016. A Rare Tour Of Masdar, The Failed Smart City In The Arabian Desert. Fast Company. https://www.fastcompany.com/3061187/a-rare-tour-of-masdar-the-failed-smart-cityin-the-arabian-desert. (Accessed 21 September 2019).

Further reading Bechtel, W., Richardson, R., 2010. Discovering complexity. The MIT Press. Calcott, B., Sterelny, K., 2013. The Major Transitions in Evolution Revisited. The MIT Press. Capra, F., Luisi, P., 2014. The Systems View of Life. Cambridge University Press. Minsky, M., 2007. The Society of Mind. Simon & Schuster Paperbacks. Brock, J., 2000. The Evolution of Adaptive Systems. Elsevier. Preston, C., 2018. The Synthetic Age. The MIT Press. Tegmark, M., 2017. Life 3.0. Alfred A. Knopf. Watson, P., 2017. Convergence. Simon & Schuster, LTD. Wiese, W., 2018. Experienced Wholeness. The MIT Press.

Chapter 2

City as living organism Chapter outline 2.1 The city as a living organism 2.1.1 Concepts of space and representation 2.1.2 Dynamic, self-regulating systems in nature 2.1.3 Biomimicry 2.1.4 Biomimicry applied to human anatomy 2.1.5 City as extension of the human body 2.2 Principles of collective intelligence

25 26 28 29 30 31 33

2.3 City DNA 2.3.1 Cities as global brands/ destinations 2.4 The role of data collection and mapping 2.4.1 Mapping the system 2.4.2 Mapping as the basis of smart cities 2.4.3 Real-time behavioral data 2.5 Conclusion References Further reading

36 37 39 39 41 42 43 44 45

2.1 The city as a living organism In the 17th century, political philosopher Thomas Hobbes wrote Leviathan (1651) to describe the way in which state power was brought to life through a relationship between the sovereign and the people. The striking book cover featured a giant sword-wielding monarch whose body was constituted by the people and various religious and civil society actors. The notion that the collective had a life of its own was in full force throughout the next few centuries, for better or worse. In the 20th century, systems theorist Niklas Luhmann (1995) wrote about how social systems reproduce and maintain themselves in similar ways to biological systems, and so described them as autopoietic or self-generating. In the 21st century, the concept of social organisms is acknowledged and it is speculated that they even have quantum properties insofar as there is quantum mechanical phenomena in the brain, observer effects in the world, and probabilistic relations between people (Wendt, 2015). The city, like the human body, is a living organism comprising of multiple functions working together as a system operating in real time. Using the metaphor of the human body, it is possible to conceptualize the city as a series of co-dependent systems, each providing a critical function within the whole. Smart Cities and Artificial Intelligence. https://doi.org/10.1016/B978-0-12-817024-3.00002-7 Copyright © 2020 Elsevier Inc. All rights reserved.

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In the human body, live information representing operations of these multiple functions can be tracked and managed with the assistance of biometrics and diagnostic equipment. In the city, real-time data can be collected by sensors, cameras, tracking algorithms and user input. The major challenge so far has been the ability to link all of these systems into a comprehensive operating system that is able to create a holistic view, a dashboard so to speak. With the augmentation of Artificial Intelligence (AI), there is now the possibility to collect, filter and analyze massive real-time data sets to transform city operations into a self-regulating ecosystem. Through the process of demonstrating the concept of the city as living organism, we have developed a conceptual framework and vocabulary expanded through examples, visual illustrations and a glossary of terms to describe the living city. The notion of the city as living organism particularly applies to the role that technology and AI will play in the future operations of cities, allowing cities to potentially become self-operating in the future. However, this can only be achieved if there is good leadership ensuring that the appropriate technologies and management approach will allow the optimal initial conditions to set in motion this next stage in the evolution of cities. The theoretical overview of city as a living organism addresses three key concepts. The first concept is the notion of systems in nature as dynamic, self-regulating systems. This relates to how smart city operating systems can regulate the activities within the city in real time and can respond to the multiple functions of the city simultaneously as a unified, harmonious system. The second concept presents the idea of the design and the functions of systems as a form of biomimicry. This concept illustrates how humans have designed and constructed cities as extensions of themselves and to imitate the patterns and flows of nature. As technology becomes more prevalent, cities and nature have the potential to become more interconnected. The last concept addresses the application of the city as a metaphorical body including the human comprehension of systems as an extension of human anatomy in the broadest sense (circulation, digestion, energy). Cities operate similar to the human body with multiple functions that work together as a holistic system responding to internal and external stimuli and conditions requiring an orchestrated reaction. Given the right balance of diet, exercise, and meaning, the body takes care of itself. The city, however, depends on the cooperation and collective intelligence of its people and its smart systems.

2.1.1 Concepts of space and representation The renowned Japanese architect Yoshio Taniguchi, responsible for the redesign of the New York Museum of Modern Art, made a provocative realization around the time cyberspace and the internet were emerging. He observed that the early digital designers were using metaphors borrowed from other areas

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like physical architecture, for example in the case of Sim City, and constructing virtual worlds as a parallel reflection of physical worlds rather than seeking to define a new innovative language. One of the reasons why it is so difficult to create representations of an emergent system is that we attempt to portray the new system using the tools of the present. It is clear that there are both the constraints of current and past models of representation and opportunities to the challenging search for new descriptions to represent emergent worldsdyet it is this collision that can produce new interpretations that give richness. To solve this discrepancy, we need to build tools that continuously change and adapt, embodying the nature of the evolving system. This is a big question for us as we embark on describing emergent systems and their behaviors: Mapping new frontiers and finding innovative and appropriate ways to visualize and communicate systems that may not yet be fully evolved in terms of language. But perhaps most interesting in Taniguchi’s challenge for us is to think in new dimensions and to develop both a new sensitivity and ability to comprehend and represent new languages. The significance of Edward Tufte’s research and publications including his book Visual Explanations (1997) is the fact that he has selected unique illustrations that embody the essence of a particular data experience or phenomena that the visualization attempts to describe rather than imposing a predetermined model of representation and then attempting to mold the experience or phenomena within the visual explanation. Beyond this, science fiction can be both speculative and normative, from the interactive holographic computers in Minority Report (a 2002 vision of the year 2054) to the holodeck and food replicators on Star Trek: The Next Generation 400 years in the future, to name a few of the sci-fi speculative representations of the future. AI-driven dynamic data visualization now has the opportunity to simulate complex patterns in real time, lessening the gap between data representation and actuality. As we are able to create simulations that accurately reflect and mimick reality and patterns of behavior, data visualization is becoming closer in characteristic to the actual data it is representing rather than imposing a predesigned format on the data. This advancement is the first step in creating a real-time operating system that is able to embody the city as a living organism. Through such a platform, we can begin to design operating systems that mirror and complement natural systems, rather than imposing structures of control that organize behavior in pathological ways. Contrary to military-minded top-down organization, enforced by threat of violence, convergent technology for the living city can respond and reflect the organic nature of emergent society. In this way, the operating system is able to function as a self-regulating system, biomimetically analogous to the anatomical natural state of the city.

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2.1.2 Dynamic, self-regulating systems in nature Cities are complex organisms with multiple individual subsystems operating simultaneously with a requirement to function harmoniously. Considering how many diverse components are interacting in real time, it is remarkable that cities are actually able to function as they do without more chaos especially as many cities are continually expanding. If we apply the second law of thermodynamics, the natural state of things is that any isolated system left to itself moves toward greater disorder and chaos (Fig. 2.1). In the book Dealing with Complexity: An Introduction to the Theory and Application of Systems Science (Flood and Carson, 1993), the fundamental concepts of systems are identified, including the composition of systems and their behavior to adapt through positive and negative feedback that allows systems to self-regulate and to perpetuate as a living organism, avoiding chaos or the state of entropy. Fortunately, in nature, chaos is managed by this natural feedback, the dynamic state of equilibrium in a system. self-regulation. Homeostasis is achieved by the ability of a system to allow the constant flow and exchange of new energy allowing the system to evolve. This is part of evolution itself, which is why closed systems contradict nature and are prone to collapse. Homeostasis can be promoted in human systems by governments with an open policy that continues to bring in new ideas to advance and enhance the nature of the system. As operations of cities evolve, technology enables cities to operate more complex systems simultaneously including multiple city functions that allow the city to self-regulate the flow of information. Traffic systems and automated or responsive traffic signals are a simple example of self-regulation. However, centralized city management systems have not yet developed true selfregulating operating systems that route traffic flow automatically to avoid traffic jams. On the other hand, new GPS-based applications are providing drivers with real-time data to assist in navigating routes with less traffic

Order

Chaos

FIGURE 2.1 Entropy and the second law of thermodynamics.

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congestion. In the future, traffic management systems will self-regulate by processing real-time data and modifying the flow of traffic based on AI pattern recognition applications. Driverless vehicles will be routed along the most efficient routes. A comprehensive system solution such as this requires a combination of physical and geospatial data (roads, vehicles in motion, traffic lights) combined with digital networks and data processing.

2.1.3 Biomimicry Biomimicry is the process by which technology imitates nature, inspired by its mysterious functions and efficient problem-solving. For example, through observing photosynthesis in plants, we mirror the idea with photovoltaic cells harnessing solar power. Planes achieve flight by copying bird aerodynamics. Even more advanced forms of biomimicry today are combined and embodied in very functional insect drones. Biomimicry itself is converging, as technology imitates biology with increasing verisimilitude. Due to advances in robotics, 3D printing and a variety of new synthetic materials for muscles and human organs, we are on the verge of creating very compelling humanoid robots. These principles can be applied to smart city design, guided by the idea of neural networks and connectivity, which efficiently map energy and information flows in systems. Biomimicry was popularized by scientist and author Janine Benyus in her 1997 book Biomimicry: Innovation Inspired by Nature. It is an approach to innovation that seeks sustainable solutions to human challenges by emulating nature’s time-tested patterns and strategies creating products, processes and policies representing new ways of living well adapted to life on earth over the long haul. Biomimicry has been applied to engineering structure and particularly in the advancements of solar power development by providing smart panels that adjust their position to the evolving angle of the sun to optimize solar gain (Fig. 2.2). Additionally, new building fac¸ade smart glazing systems automatically control the amount of light to enter the room while creating more energy efficiency in the building envelop.

Flower

Solar Array

Nature

Bio-Mimicry

FIGURE 2.2 Biomimetic models.

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2.1.4 Biomimicry applied to human anatomy Humans have constructed the built world primarily through the understanding of the laws of nature based on the unique human perception expressed metaphorically through the self-realization of the human anatomy. In other words, we build things in the physical likeness of ourselves and we are seemly constrained by our human understanding of the universe. Expanding on this notion, in the conference and proceeding publication, The Technological Extensions of the Boundaries of the Body, issues related to the philosophical, cultural and ethical nature of how new technologies influence our understanding of the representation of the contemporary human condition were debated by a diverse group of academics and professionals across different fields of science and art. In ancient civilizations, temples and other special buildings were often constructed to reflect the golden mean and other sacred geometries. Ultimately the purpose was to mirror the majesty of the cosmological order at once mapping its functions and transmuting its aesthetics into spacious beauty. This tradition was profiled in The Temple in Man (1981), by R. A. Schwaller de Lubicz, in which the Luxor Temple is said to embody human anatomy and geometry. This pursuit of universal form has continued throughout diverse historical periods and cultures. From Leonardo da Vinci’s Virtruvian Man representing the divine proportions of the human body, to Santiago Calatrava’s engineered structures in the form of living organisms and skeletal structures, each one is an expression of human attempts to reveal and construct our world in harmony with nature. Extending this to the planning of cities, Le Corbusier considered cities to be akin to biological phenomenon. In 1942, architect Jose Luis Sert declared that “Cities [are] living organisms . city planning has become obsolete. In its place must be substituted urban biology.” (Batty and Marshall, 2009). In The New Science of Cities, Michael Batty (2013) extends the story of city as living organism, describing the ebbs and flows through various networks, from energy to traffic to social life, etc. He argues that understanding the true nature of city as organism requires appreciating how networks and flows operate. This type of thinking finds resonance in Eastern philosophy. Although the distinctions are largely a reified social construction, there is a need to integrate Eastern and Western thought to cancel out any contradictions. Typical associations with Eastern thought include circles over lines, contextuality over universality, Confucianism versus Aristotelianism. Other recognizable values include interdependence, collectivism, harmony and holism. Thus, the concept of Qi as a metaphor for vital life force and energy flow is useful for thinking about living cities. Their abstract representations reveal patterns of connectivity across nodes in networks, as visualized in parallel human meridians and cyber networks (Fig. 2.3).

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GB GV BL GB

SI

LI LU ST KD

LV

HT PE GB SP

LV

ST SP GB

SP

BL

Human Body Meridians

Cyber Networks

FIGURE 2.3 Meridians and the flow of energy.

Along these lines, the term panpsychism is undergoing a renaissance in parts of academia and intellectual circles. It is useful for consolidating multiple metaphysical positions such as emergentism and holism. From here it is a small step to the quantum turn in social science, public policy and cybersecurity, for which a symposium of international scholars called “Project Q” have been convening for over five years. Similarly, data scientist Hardy Schloer advocates a relational approach that recognizes the process-oriented nature of reality: Just like systems theory and Whitehead’s process ontology, the Quantum Relation Principle moves away from the Western classical ontological premises of the independent existence of a knowing subject and a knowable object. It postulates that nothing exists independently in our universe and that reality arises primarily not as objects and entities, but as dynamic networks of relations among such objects and entities, which are in a state of continuous flux. Hardy Schloer, Quantum Relations presentation, November 1999

2.1.5 City as extension of the human body It has long been theorized that technology is an extension of the human body, from ancient times through to Marshall McLuhan, Ernst Kapp and David

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Rothenberg. Tools convert our strength and ingenuity through specific functions that we cannot otherwise perform, such as drilling a hole or sawing a piece of wood, for example. All technology extends physical and mental abilities in obvious ways, from driving a car faster than you can run or communicating over large distances farther than you can shout. Brey (2000) argues that this perspective, first, improves understanding of the evolution of technology and, second, improves how to evaluate the role of technology in society. The real-time city is not unlike the brainebody relationship in the way that the city responds to empirical data and then adapts and executes actions based on the system logic. This cybernetic process has expanded the physical dimensions of the city to a vast virtual network in which a combination of data sources and public interaction provide real-time feedback contributing to both the empirical and experiential phenomena of the living organism. The developers of the early Internet (ARPAnet) understood that centralized and corporeal-based systems can be easily attacked and corrupted, hence the advancements of decentralized systems that led to the Internet as a vast interconnected web and decentralized network. Early scientific representations of human anatomy and their corresponding functions were often disconnected. One type of representation depicted a rich, complex organism while the other typically a simplified mechanical interpretation of its process. In Fig. 2.4, the brain and brain functions of optical processing are illustrated in both anatomical and mechanical representations. As computing power exponentially increases along with advances in AI, Machine Learning, data visualization, and high definition imagining, the cognitive functions of the brain and neural networks can be understood and represented in real time combining both anatomical and mechanical processing in a unified simulation.

Neocortex

Left Side of Brain

Basal Ganglia

Right Hemisphere

Optic Nerve

Left Eye

Left Hemisphere

Prefrontal Cortex Image

Object

Corpus Callosum

Cerebellum Hippocampus Amygdala

Brain Anatomy

Right Side of Brain

Optic Nerve

Right Eye

Optical Process

FIGURE 2.4 Brain sensorial representations.

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An example of the brain system metaphor is the recently launched Alibaba Cloud’s ET Brain (2009), an ultra intelligent AI platform for solving complex business and social problems offering users multidimensional perception, global insight, real-time decision-making, and continuous evolution under complex situations to rapidly form optimal decisions. The platform is composed of four main functions including (1) cognitive perceptiond multidimensional awareness; (2) strategic decision-makingdsituational intelligence; (3) reasoningdreal-time decision-making; and (4) machine learningdperpetual innovation. One of the first applications of the ET Brain is the Hangzhou City Brain, assisting the Hangzhou government to address the city’s urban living challenges and traffic management by utilizing image recognition technology to analyze videos of over 3000 traffic cameras in Hangzhou in real time, significantly increasing video utilization and efficiency. City Brain draws on data from videos, AutoNavi, WiFi probes, carriers and other sources to effectively monitor passenger delay rates in certain areas and estimate capacity needs. It adjusts and plans bus frequencies based on travel supply and demand, determines shuttle routes and controls taxi dispatches to minimize the delay rates at key venues and transportation hubs. In Table 2.1, systems of the human body correspond to particular technological systems in contemporary society. It is obvious that just as all physiological systems are basic and vital, so are the equivalent societal systems enabled and managed by technology. They all have a specialized function that plays a holistic role. If any of them falter, all systems suffer and have to compensate. By understanding the analogy between individual human and social systems, we can constantly monitor all data and optimize outcomes. To further illustrate how humans understand the natural world through an anthropocentric lens, the following table details the more specific and unique functions of a smart city to represent dynamic systems and how the metaphor of the human body and anatomical construct can be applied to describe and organize different aspects of the smart city operating system including the interface with the exterior world through the human framework whether mental or physical. We have further applied the human organs to roughly equivalent functions in smart cities, in Table 2.2.

2.2 Principles of collective intelligence Collective intelligence is the positive emergence of group wisdom from crowdsourcing intellectual capital. It is a synergy between (1) knowledge/information, (2) software/hardware, and (3) technical experts. Considering the city as a living organism from a gestalt perspectivedthe whole is greater than the sum of the partsdallows us to better comprehend, visualize and construct

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TABLE 2.1 Human/technology systems. Human system

Technology

Control, programming

Cognitive/ brain

Operating system, metagovernance

Circulation, nutrient diffusion

Circulatory

Data/information/ knowledge flow

Food consumption, nutrient processing, energy conversion

Digestive

Food and land systems, biomass power

Metabolism, regulation, homeostasis

Endocrine

Ecology, environmental systems

Spatial, boundary sensing, interfacing, protection

Integumentary

Sensors, user feedback, AI/ AR interface

Immunological

Lymphatic

Healthcare, emergency services, security

Posture, movement

Muscular

Transportation/transit, mobility

Sensory input/ouput, brain

Nervous

Energy, communications

Waste management

Renal

Recycling, upcycling, materials processing

Species continuation, population health

Reproductive

Arts, education, sexuality, genetics

Gas exchange

Respiratory

Wellbeing, spirituality, community

Structure

Skeletal

Urban architecture

Function

a comprehensive model or simulation of the city in which technology is dedicated to optimizing city functions and citizen well-being, where citizens are akin to healthy cells in an organism. This gives rise to the need for a higher consciousness that governs the overarching system and individual elements within the system, necessitating the concept of a collective intelligence that regulates the flow of information with a purpose of creating a harmonious ecosystem. This implies a large role for humans as smart citizens or epistemic nodes contributing to the collective intelligence. Collective intelligence represents this state of unity enabling all elements within the ecosystem to share a common purpose while operating individually with their unique characteristics and functions. AI provides real-time processing of complex information; a collective intelligence is required to set broad rules and make complex decisions. This enables us to monitor and

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TABLE 2.2 Human functions applied to smart cities. The brain

The command and control center

The brain, considered the central operating system, is the command and control center of the city where decisions are made and actions are deployed throughout the system.

Eyes and ears

Data collection and processing

Real-time video recognition and automated inspection and augmentation through citizen participation and action through the use of personal mobile phone and devices.

Blood system

Flow of information

Data flows to the brain informing the system of all activities, considered as the neural transmitters that provide the command and control center the critical information to inform actions and reactions.

Senses

Sensors

Imbedded at every scale of the city are sensors to monitor the behavior of the various functions of the city including security, transportation and utilities. The sensors are literally the sensorial preceptors that transmit data to the command and control center that filters the data and determines if the system is running smoothly or not.

Liver

Data filters

Data filters allow the data of the system to be streamlined and contextualized to provide more efficiency in data processing. Artificial Intelligence contributes to allowing massive data sets to be processed and filtered detecting anomalies in patterns.

Immune system

Security

Applied to community and public safety improves the efficiency of security and emergency response times, allowing for preventative public security and safety measures.

Muscular system

Mobility

Urban transportation systems that support mobility and of people, goods, and services.

Digestive system

Waste management

Waste and waste disposal as representation of city consumption and processing.

Food

Energy/utilities

Utilities including electricity and water provide the source of energy to keep the city nourished and operating.

Equilibrium

GPS

Within the human body is the requirement for the system balance based on a combination of functions and organs that allow the body to maintain equilibrium. The city requires georeferencing to orient the functions and flow of resources within the system and therefore relies on the geospatial relationship of components within the whole.

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regulate the flow of information from multiple data sources, creating a macro scale feedback system designed to evolve gradually and to link more and more networks until there is a virtual reproduction of organic states. This would also combine management of both environmental and human-made systems. Ultimately, as the simulation and reality converge, management and regeneration become more streamlined, and so do theory and praxis become merged in a more emancipatory, ecological and transformative operating system. Collective intelligence treats the city as a living organism and so ensures that homeostasis is maintained and that all the parts are working together seamlessly. As an “Operating System,” it enables all operations of the city in a user-friendly platform for holistic systems maintenance. Within the resulting collaborative framework, diverse cultures, environments and life forms can be harnessed together as a technological extension of both natural processes and human consciousness. On an organic level, such a new system would allow unions of collective intelligence to form a holistic web that would no longer be constrained by the divisions and boundaries of human creation, such as borders or cultural barriers. A collective intelligence no longer constrained by the divisions and boundaries of nations, governmental policies and technology IP or imposed security restrictions must be a bridge between diverse industries, fields of knowledge and cultural backgrounds to facilitate this critically needed information sharing and decision-making process. The more our collective intelligence and AI converge, the more it is like a Rosetta Stonedthe ancient slab inscribed in three languages that was a key to deciphering hieroglyphicsdso we are able to translate and share best practices for algorithms, self-regulation, governance, innovation, user experience, automation, etc. The goal will be to serve not only our leading urban centers but also rural communities that must remain viable to maintain social, economic and environmental balance and growth at local, regional and global scales. To realize the city as harmonious, self-regulating ecosystem, we will need to work together to find equitable ways to share economic opportunities, create new publiceprivate partnerships, incorporate new technologies rather than allow the protectionist interests of large corporations to inhibit their use, and to move away from our current fossil fuelebased economy toward a new era of AIenabled sustainable development.

2.3 City DNA To achieve the smart city status, each city needs to understand its unique characteristics, advantages and constraints. To determine these qualities, we introduce the concept of city DNA to facilitate the process of mapping out each city based on their unique history, culture, and present assets. In the same way, no two people’s DNA are alike (except for twins), no two cities would

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have identical city DNA. Just as biological DNA is a rich and precise blueprint of the genome revealing the many composite characteristics of a person, so too does city DNA attempts to map a living city’s memetic code across a broad range of considerations including geography, history, population, culture, technology, etc. Throughout the world, cities have been defining their unique identities while simultaneously competing on multiple levels to attract trade, workforce, and other resources. The strategy, planning, and design of smart cities is based on many factors that must be incorporated to optimize the unique position, resources and identity that each city offers to achieve its full potential to deliver a higher quality of life for its citizens. Simultaneously, the strategy and ultimate implementation must constitute both short-term successes to build momentum, attract investment, win public support, etc. and long-term solutions that will reinforce the goals of city leaders and stakeholders in the process of defining sustainable development programs to achieve the vision, meet the requirements of growth, and strengthen the unique identity of the city. Each city has its unique characteristics, including but not limited to geographic location, physical layout, infrastructure, population, culture, governance, industries, resources and workforce. Building on Harvard University Professor Michael Porter’s Competitive Advantages model (O’Shaughnessy, 1996), each individual city can be evaluated for the combination of its attributes to derive a city DNA profile. Like the concept of brand DNA, this profile allows a clear identity and positioning of the city within regional and global contexts. Based on the city DNA model, the development of a comprehensive strategic plan enables the appropriate planning, design, and operations program to be established. The illustration represents a research project conducted by the authors with students at Tsinghua University to explore the competitive advantages of major Chinese cities in relation to the creative industries that have been promoted by the Chinese government to bolster economic growth in cities Fig. 2.5.

2.3.1 Cities as global brands/destinations Today cities around the world are being branded and revitalized through many different strategies. Each city looks to create a unique position and identity in the global market. For example, the regeneration of historic districts in Shanghai, green design policies in Berlin, and the rebranding of Las Vegas to be more tourist- and corporate-friendly are efforts that were planned to capitalize on the unique consumer appeal of each destination. The city DNA concept can be used to facilitate this renewal process by developing strategies that capture the inherent cultural, geographical and informational characteristics of a city or region, employing various strategies to channel these assets into a comprehensive integrated approach that reflects the city as a desirable place to live, work, play and invest.

38 SECTION | I Approach Architecture Landscape

Art

Advertising

Broadcast

Film / TV

Fashion

Publishing

Performance

Music

SHANGHAI

Media

Museum

IT

Education

Exhibition Tourism

SHENZHEN

BEIJING

FIGURE 2.5 City DNAdcomparison of Chinese cities. Original diagram created by Xingjian Cui.

Networking is the key to collective critical thinking about smart cities: for regional cooperation, coordinating identity formation, best practice sharing, differentiation of productive means and economies of scale. As knowledge economies are the norms in developed countries, cities are placing emphasis on innovation of services and public engagement, inviting both diversity and collaboration. Through marketing, this extends to communicating visions of the future, especially for urban development and the idea of smart cities. The Internet (such as websites and social media) has already transformed networking and city operations, as Angelidou (2016) explains in Four European Smart City Strategies, and we expect it to be revolutionary again as AI governance systems, City OS, embedded technology, and AR/VR interfaces become available. Overall, it seems that we are finally heading towards a true integration of the digital with physical and institutional dimensions of the smart city. Physical planning and social policy, then, can and should underpin the digital or “smart‟ dimension of the city and promote its integration upon them. Four European Smart City Strategies: Margarita Angelidou.

Additionally, many cities have finally realized the importance of soft resources and have invested in the creation and development of major art and culture events and festivals such as expos, design weeks, and industry forums to differentiate themselves from other cities and to spark design, tech and innovation culture as drivers. Cities around the world like London, Miami, Dubai and Shanghai have established ongoing major cultural events that have indeed created highly significant global recognition and economic benefits.

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Furthermore, the Cities of Opportunity Report published by PWC ranks cities based on multiple indices that provide a wide spectrum of criteria to measure the success of cities. The report analyzes the trajectory of 30 major cities, all capitals of finance, commerce and culture through their current performance, seeking to open a window on what makes cities function best. As a major part of the city character, technology, intellectual capital and openness to the world through telecommunications are some of the key drivers of the growth and prosperity of cities, according to the Pricewaterhousecoopers report (2011).

2.4 The role of data collection and mapping 2.4.1 Mapping the system Cities, like the human body, are dynamic systems that exist within physical contexts and also in a state of constant change. The symbiotic relationship of the constructed environment of the city and the natural environment including geographic configuration and climate requires the collection and processing of data to be flexible, scalable and adaptable to the changing conditions and growth of the city, its environment, the flows and needs of people, and the dynamic relationship between these diverse systems Fig. 2.6.. The reality and experience of the city is therefore influenced by a combination of forces that make it up: the characteristics of the physical surroundings, the city as a constructed environment, and the behavior of the inhabitants of the city comprise data sources that each play a role in the formation of the city as a living operating system. This base information includes the underlying evolution of the city in terms of its growth in physical, demographic and technological development. Different types of data including static, dynamic, quantitative and qualitative are necessary to measure and depict the biological, human and digital dimensions of cities, their environment, and the inhabitants that live and interact within the city and its context.

Biological

Human

FIGURE 2.6 Types of data.

Machine

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Data comes in infinite varieties and countless ways to be presented. The trend is that all biological, human and machine data is converging into a collective information stream representing the life flow of smart cities. The data may operate on different scales and life cycles, but they are moving toward an integrated platform, where nature and technology collide, fuse and converge with the city. Mapping is the process of gathering base data upon which all objects are referenced in space and the basis of how cities and city system are organized between the digital and physical realms Fig. 2.7. Multiple types of mapping techniques have evolved from traditional surveying to high-tech solutions utilizing satellites, drones, lasers and big data analytics representing different types of data collection that supports the creation of maps and geospatial intelligence Table 2.3. Mapping, therefore, must incorporate multiple types and sources of data that contribute to the composite view of the particular system that is being described. Data collection includes the gathering, processing and analysis of diverse data sets to understand and describe the boundaries, composition and behavioral patterns of systems and can be applied to diverse scales from regions, cities and neighborhoods to individual structures or objects, each element forming a part of the composite.

Pattern

Frequency

Context

Experience

User

Digital Realm

Scale

Boundary

Landscape

Function

Inhabitant

Physical Realm

FIGURE 2.7 Mapping criteria.

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TABLE 2.3 Data collection and mapping technologies. Satellites

Satellites are artificial objects intentionally placed into orbit to collect geographic data and to transmit data to multiple sources and applications including global positioning systems (GPS), satellite imagery, geographical information system (GIS) and forming the basis of maps in government census and survey operations.

Lidar

Lidar is a surveying method that measures distance to a target by illuminating the target with pulsed laser light and measuring the reflected pulses with a sensor.

Drones

Unmanned aerial vehicles providing data capture are components of an unmanned aircraft system including a UAV, a ground-based controller and a system of communications between the two.

GPS

GPS use satellites that orbit earth to send information to GPS receivers that are on the ground. The information helps people determine their location.

GIS

GIS is frequently applied to geographically oriented computer technology, integrated systems used in substantive applications. GIS is also a software that helps people use the information that is collected from GPS satellites.

Sensors

Sensors collect data in the form of images and provide specialized capabilities for manipulating, analyzing and visualizing these images. Remotesensed imagery is integrated within GIS. There are two types of remote sensing instrumentsdpassive and active.

2.4.2 Mapping as the basis of smart cities Smart cities require the correlation between base data that describe the physical properties of the environments of the city and the people and objects that engage, move and interact within the urban environment. Smart cities must also factor in the environmental conditions that impact the functioning of the city including climate, seasonal fluctuations and other variable environmental data that influence the operations of the city. Mapping comprises the layering of different scales and types of data that are geolocalized within boundaries of the defined system (see Fig. 2.8). Smart cities require mapping to assist in the amalgamation of these various data sources that are linked to the core operating system and subcomponents including hardware, embedded software, connectivity, security protocols and gateways for external

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Nano

Object

Building

District

City

Environment

FIGURE 2.8 Scales of mapping.

information sources. This multilayered technology ecosystem enables the collection, analysis and sharing of the data within a coherent unified platform to serve the needs of operating and managing the city. To make this ecosystem sustainable from both a financial and technical point of view, cities require a partnership between city governance, private sector and the public to build and support the shared technology infrastructure, especially as more and more opportunities for co-development and citizen participation are being made possible by open-source platforms and engagement campaigns.

2.4.3 Real-time behavioral data Dynamic living systems require data to record the behavior and performance of systems in real time. With the recent advancements in AI, the opportunity for real-time data processing allows us to move even closer to creating a realtime simulated model of different types of human-made and natural systems from cities, transportation infrastructure and utility networks to biological systems. This real-time living modeling allows us to better manage and optimize our resources by allowing us the ability to track performance of systems and by making them more intelligent. We see many important applications for real-time data in our cities, providing public sector agencies with the capability to monitor, filter and direct activity in the areas of public safety, health, environment, transportation and energy. It is also vital for private sector business, industry, banking and real estate organizations using real-time information on global and local political, financial and marketplace trends. The ultimate assemblage of this real-time data is the development of a very advanced yet user-friendly software interface, which will help many cities and communities, providing mayors, city councils and planning boards with multisourced information to assist in making critical decisions in compressed timeframes. Additionally, real-time data management will allow us to better respond to the many natural disasters being experienced around the globe by monitoring the causes of global warming and effects on human, animal and plant populations through deploying a network of sensors linked to data filtering and visualization applications. By utilizing a combination of real-time data and pattern recognition (Fig. 2.9), we will be able to better understand and respond to both long-term environmental change and short-term effects due to natural and human-made factors.

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Behavior

Pattern

Scale

Boundary

43

Frequency

FIGURE 2.9 Patterns.

Real-time data is the life blood of the living organism, but not in the sense of tracking trivial personal activity or online purchases. Rather, the real-time modeling of the city as a living system, serving humans as constituents of it, allows for a synergistic relationship between environment and organism. Humans and cities are highly evolved extensions of the environment as a living system, and as such must provide behavioral feedback to the planet in a healthy, regenerative way. This organic analogy of holistic systems is in tension with contemporary consumer society, hence the urgent need for convergent evolution of smart cities to innovate on consumption and upcycling, climate change mitigation and restoration, and clean energy production and distribution.

2.5 Conclusion This chapter articulates the notion of the city as a living system, a macroscopic organism, linked through its collective consciousness, communicative structure, and relationship to natural resources and cycles. Through advanced methods and technologies, we can monitor the health of systems in real time, and over time, just as we might measure biometrics in individuals. To an extent, this is only possible with AI, and so it is critical that we identify and explore the complex relationship and interoperability of cities and technology. Three key concepts are essential for understanding cities as living organisms: (1) nature as self-regulating systems; (2) urban design via biomimicry; and (3) the city as metaphorical body. The first allows us to understand the nature of closed-loop systems and how our cities’ materials and energy cycles should be in harmony with nature’s ecological metabolic functions. Traffic systems and the rules of the road exemplify a self-regulating system, which is

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constantly being made more efficient through AI modeling and driverless vehicles. The second concept teaches us how to design smart cities that imitate the self-regulation of nature in the first concept. Through biomimicry, we can discover and replicate nature’s brilliant problem-solving. This has already achieved enormous gains in technology, and we are converging on the synthetic realization of life in various ways: tissue growing, robotic prosthetics, biotech implants and more. The third concept is to understand the city as analogous to a body and brain, organized in such a way that mind and body are holistically integrated. We discussed the concept of city DNA, which helps create a profile for each city, to determine its strengths and opportunities to develop and progress in a smart and sustainable way. Just like DNA analysis and consulting a person’s health history allows for more effective diagnosis and treatment, so too does city DNA help determine the best policies for planning and implementing smart city technology. This means there is no one-size-fits-all solution, but there is a universal methodological approach that will help all cities find the optimal and most harmonious smart city urban design for them. Branding is part of city DNA, as each city promotes its own unique character to attract investment, stimulate activity, and give meaning to the residents and tourists. Finally, mapping has been introduced as the basis for envisioning smart cities. Mapping includes layers of data sets and virtual models of city systems, often combining different types of geographic maps with other data sets. This includes the ability to map all the physical qualities, buildings and objects within. Weather and seasonal changes can be tracked and predicted to optimize operations that interact with these natural systems. Traffic systems can be analyzed and streamlined. AI advancements are making real-time mapping and monitoring the norms, and the process is enhanced through considerations of the city as a living organism overseen by the collective intelligence of its smart-enabled citizens.

References Alibaba Cloud, 2009. Alibaba Cloud’s ET Brain. https://www.alibabacloud.com/et. (Accessed 17 September 2019). Angelidou, M., 2016. Four European Smart City Strategies. Int. J. Soc. Sci. Stud. 4. Batty, M., 2013. The New Science of Cities. The MIT Press. Batty, M., Marshall, S., 2009. Centenary Paper: The Evolution of Cities: Geddes, Abercrombie and the New Physicalism. Town Plan. Rev. 80 (6), 551e574. Benyus, J.M., 1997. Biomimicry: Innovation Inspired by Nature. Governance International J Policy Administration. Harper Perennial. Brey, P., 2000. Theories of Technology As Extension of Human. Res. Philos. Technol. 19, 1e20. Flood, R.L., Carson, E.R., 1993. Dealing With Complexity: An Introduction to the Theory and Application of Systems Science, second ed. Plenum Press.

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Hobbes, T., World Public Library Association, 1651. Leviathan. Internet Sacred Text Archive (ISTA). Luhmann, N., 1995. Social Systems. Stanford University Press, Stanford. Calif. O’Shaughnessy, N., 1996. Michael Porter’s Competitive Advantage revisited. Manage. Decis. 34 (6), 12e20. https://doi.org/10.1108/00251749610145889 (Accessed 19 September 2019). Project Q, 2014. Project Q: Peace and Security in the Quantum Age. https://projectqsydney.com. (Accessed 19 September 2019). PwC, 2011. Cities of Opportunity. Partnership For New York City, New York. Schloer, H.F., 2013. The Quantum Relations Principle. Rom J. Inf. Sci. Technol. 16 (2e3), 155e176. http://www.romjist.ro/content/pdf/04-schloer.pdf (Accessed 19 September 2019). Schwaller de Lubicz, R.A., 1981. The Temple in Man: Sacred Architecture and the Perfect Man. Inner Traditions. Tufte, E.R., 1997. Visual Explanations: Images and Quantities, Evidence and Narrative. Graphics Press, Cheshire. Conn. Wendt, A., 2015. Quantum Mind and Social Science, Quantum Mind and Social Science. Cambridge University Press.

Further reading Boomen, T., Frijters, E., Van Assen, S., Broekman, M., 2017. Urban Challenges, Resilient Solutions. TrancityxValiz. Kitchin, R., 2014. The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. Sage Publications. Seto, K., Reba, M., 2018. City Unseen: New Visions of an Urban Planet. Yale University Press. Thatcher, J., 2018. Thinking Big Data in Geography: New Regimes, New Research. University of Nebraska Press. White, R., Engelen, G., Uljee, I., 2015. Modeling Cities and Regions as Complex Systems: From Theory to Planning Applications. The MIT Press. Winkless, L., 2016. Science and the City: The Mechanics Behind the Metropolis. Bloomsbury Sigma.

Chapter 3

Strategies, planning, and design Chapter outline 3.1 Criteria for planning and design of smart cities 3.1.1 Strategic goals 3.1.2 Outcome-based modeling 3.2 New approaches to innovation for planning and designing smart cities 3.2.1 Cities as living labs 3.2.2 City as hubs of innovation/ innovation-driven cities 3.2.3 Co-design 3.2.4 Citizen centric cities 3.2.5 Design thinking 3.3 Convergence methodologies 3.3.1 Human-machine collaboration 3.3.2 Real-time visualization

47 48 51

52 53 54 56 56 59 60 60 61

3.3.3 Information architecture and Philosophy of Information 3.3.4 Real world/virtual simulation 3.3.5 Generative design and metadesign 3.3.6 Convergence Development Method: strategy, planning, design, and operations process 3.3.7 Convergence design method: design thinking/ machine learning 3.3.8 Convergence application method: outcome-based AI scenario modeling 3.4 Conclusion References

62 62 63

64

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65 66 66

3.1 Criteria for planning and design of smart cities Before the full spectrum of smart technology solutions can be applied to the planning, design, and operations of cities, each city must be analyzed and evaluated from the multiple dimensions that represent the city as a complex, dynamic living organism. This chapter seeks to define a methodology for planning and designing cities that represent the city as a complex whole, composed of various units and dimensions that work both independently and in unity as a gestalt formation. Each part can be considered for its uniqueness but not in isolation from the interrelated, co-dependent processes that make up the city. Without understanding the special nature of each city and its unique characteristics, technology can only provide localized solutions that may or Smart Cities and Artificial Intelligence. https://doi.org/10.1016/B978-0-12-817024-3.00003-9 Copyright © 2020 Elsevier Inc. All rights reserved.

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may not build a broader collective intelligence that serves the purpose of realizing a sustainable smart urban ecosystem combining the physical, human and technological realms. The planning, design, and operations of smart cities must reinforce the raison d’eˆtre of the city by embodying the expression of its origins, present state of being, future aspirations, and potential to expand based on new smart technologies. Each of these stages of its development function as the tools that shape the unique city DNA, unlocking the true potential by applying specific processes that weave together the diverse dimensions of the city as a unified real-time operating system. In a sense, planning, design, and operations can be conceptualized as a form of multi-dimensional, seamless integration supporting the city as a living organism. From historical cities to ground-up cities, from megacities to neighborhood communities, no singular planning approach, design solution, or technology application can solve the complexity that each of the world’s diverse cities require to solve their individual circumstances. Part of the future success and sustainability of cities is dependent on how cities will adopt technologies to enhance their internal operations as well as bolster the competitive advantages that each city offers. To do so, cities must develop strategies that simultaneously consider both macroeconomic and localized technological solutions in their plans. To develop a viable, sustainable plan, the appropriate strategy, planning methodology and design approach must represent the multiple factors that comprise each city and its regional position including historical evolution, geographic orientation, natural and human resources, social fabric, governance, policies and leadership style to develop a viable master plan and road map for success. This chapter presents how strategy, planning, design and operations can be linked together as a seamless process that can potentially unlock the unique city DNA while allowing the city to become a unified self-regulating entity.

3.1.1 Strategic goals The development of smart cities has typically been made by city leaders and key stakeholders with more recent inclusion of the public depending on the structure of governance and leadership. In cities with a top-down culture, government officials and key stakeholders determine the direction of how cities develop and manage the agenda. In other cases, where there is an open decision-making process, consensus is reached with public participation and input. In either scenario, each city requires an agenda that establishes the principles of how that city will prioritize the drivers of growth and development. Thus, the general objective for smart cities is growth and development toward self-regulation, so all types of decision-making should be guided by that principled end. As discussed in Chapter 2, each city is unique and requires a different solution to how technology can enable cities to become smarter. Therefore, smart technology solutions are not all the same. Nevertheless, there are certain common issues facing the world’s major citiesdenvironmental degradation,

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traffic congestion, inefficient energy utilization and many more urban problems facing cities. Indeed, there are common challenges to be solved but what makes each city different is what makes each smart city solution unique. Therefore, cities around the world each need to define their own unique set of requirements to determine what is the best approach, but with the help of a common rubric that includes global risks that all cities face, and interdependencies that all cities rely on. Without a coherent system of standards and best practices yet to be fully available and with the diversity of stages of development and requirements of each city, it has been very difficult for city leaders to determine what is the best solution for their smart city. An additional factor that has created challenges for city decision-makers is the selection of the appropriate technology partners and operating systems. In some cases, the proprietary technology platforms made available by leading technology companies may create a form of dependence that can potentially limit opportunities for an open-source ecosystem. Therefore, the final smart city platform selection is a difficult process and can finally impact the success or failure of the city to advance both for its own immediate needs and remaining competitive within regional and global contexts. One of the means to lessen the gap between the vast array of technology solutions available and steering cities in the right direction with technically knowledgeable leadership is the establishment of city CTO positions. Borrowing from private sector IT culture, this new position allows cities to be run more as corporate entities and has the benefit of driving the technologies of cities as a priority for growth and competitive advantage. In 2017, London hired its first CTO which has led to the successful development of the London smart city initiative and has underscored London’s ranking as the second smartest city globally. In the immediate future, more and more cities will recruit Artificial Intelligence (AI) specialists and managers to work in-house to build the necessary capability to manage complex technology platforms and solutions. The following list is an example of principles to steer the development of smart cities, outlined in the publication Comparative Study of Smart Cities in Europe and China 2014, Table 3.1. TABLE 3.1 Principle objectives of smart cities. l

l l l

l

l l

To achieve a unification and smart application to information infrastructure and public infrastructure (e.g., the management of green resources, a dynamic monitoring of environment and the classification of wastes) To create a sense of sharing for environmental protection and a low-carbon lifestyle To establish a transparent, fair and inclusive mechanism of incentives To achieve equal access to basic public services, as well as a universal sharing of knowledge and information To drive forward the modernization of both the system and the capability to govern the city, with greater and fuller social participation To cultivate the plurality and innovation in business models To open up the data of public sectors, to bring down barriers and limits between departments and to ensure the entitlements of all stakeholders

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Another example of guiding principles that can be applied to smart cities is the Sustainable Development Goals (Fig. 3.1) established by the United Nations Development Programme (2015). According to these goals, “The United Nations shall promote higher standards of living, full employment, and conditions of economic and social progress and development.” The UNDP uses five quantitative modeling tools to assess policy options for sustainable development. These tools are open source based, use cutting-edge knowledge, and are in continuous development by a global community: l

l l l l

The Climate, Land use, Energy and Water Systems analysis and model (CLEWS) Economy-wide models socio-economic micro simulations Energy systems models Geospatial electrification access modeling

AFFORDABLE & CLEAN ENERGY

QUALITY EDUCATION

NO POVERTY

GOOD HEALTH & WELLBEING

GENDER EQUALITY

CLEAN WATER & SANITATION

ZERO HUNGER

CLIMATE ACTION

PEACE, JUSTICE & STRONG INSTITUTIONS

RESPONSIBLE CONSUMPTION & PRODUCTION

REDUCED INEQUALITIES

Variation on UNDP Development Goals

FIGURE 3.1 Development goals.

City Protocol, a collaborative innovation framework for smart cities, draws from the United Nations’ three pillars for sustainable development e economic, social, and environmental e to establish a new taxonomy based on the city ecosystem containing three main system elements: the physical Structure, the living entities of Society, and the Interactions between them, helping to

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map city interconnections and flows. Rather than focusing on sustainability as the goal, which can be a vague directive, City Protocol have developed standards to foster “self-sufficiency,” akin to our usage of self-regulation. For a general assessment framework, common characteristics across smart city projects are measured in a consistent way. This is not to compare pilot smart cities hierarchically, but rather to find best practices, understand new challenges, converge on standards, and evaluate projects. The assessment method used in the Comparative Study of Smart Cities in Europe and China 2014 drew on nine key characteristics to compare cities in both China and Europe: strategy, stakeholders, governance, funding, values, business models, ICT infrastructure, smart city services and legal structure, to define emerging trends and establish universal standards. This assessment framework and the additional standards presented above serve as a basis of future criteria to be continually woven into the smart city self-evaluation process and a basis of a self-learning methodology.

3.1.2 Outcome-based modeling Another strategic planning approach applied to smart cities is outcome-based modeling, which seeks to reverse engineer the solution by identifying the desired outcomes defined by the key stakeholders. Christopher Choa, Director of AECOM’s Cities Consulting group, has developed a strategic planning methodology based on an outcome-based approach. The process begins with a strategic workshop with key stakeholders invited to contribute multiple points of view. During this workshop, the smart city development program defines outcomes that the city would like to achieve over a determined period of time. Rather than starting with infrastructure or technology inputs (e.g., transport capacity), the outcome-based method allows for a broader consideration of influences while at the same time enabling decision-makers to identify key priorities and drivers of smart city programs (e.g., educational achievement or social mix). This method is also important to provide a solution-based approach to determining the appropriate technologies rather than letting technology solutions drive the strategic planning and implementation of smart cities. While technology can provide the appropriate solution, in many cases technology can exacerbate the problem by imposing a technology framework, to promulgate itself, which may not be the best solution. An example of this might include the development of smart city strategies to drive a cultural shift, for example, toward environmental awareness, a technology savvy society, a participatory society, creative and innovative thinking, etc. The following model was developed to present four key drivers for the City of Newark Aeroportropolis project based on a competitive analysis report produced by Michael E. Porter and team for Opportunity Newark with the intention to drive public awareness about the project’s holistic planning approach and strategy. Fig. 3.2.

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AERO-PORT CITY

GO GREEN

LOGISTICS HUB

MULTI-MODAL LIVING

FIGURE 3.2 Development drivers.

3.2 New approaches to innovation for planning and designing smart cities The subsections below are different methods of innovation that will spur smart city convergence. We explore five, which are living labs, innovation hubs, codesign, citizen centric cities and design thinking Table 3.2. In the last chapter, cities were considered as living organisms. Now we move to cities as living labs, open urban design experiments that engage citizens as the end-users. This process enhances the lifecycle of products, increases user satisfaction, fosters economic growth, and most importantly, promotes learning. It allows the city to become a real-time social and environmental experiment. Next we show how every city can be a hub of innovation, and why many cities have demonstrated that pursuing innovation in itself is fruitful and can be expressed differently in each context. The guiding thread is the technology sector, with sustainability as an attractor point to converge on. This process draws talent and intelligence to work on complex problems, which begets better innovation. Several examples are discussed, such as Amsterdam, Cambridge and Beijing. The principles of co-design and co-development are adopted from Scandinavian countries. This is spreading based on the stark realization that it works. Co-design takes more input than traditional urban planning

TABLE 3.2 New approaches. Living labs

Innovation hubs

Co-design

Citizen centric

Design thinking

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methods and as a result governments are able to execute better policies. This leads into the idea of citizen-centric cities, of which Scandinavian countries are exemplars, as are cities like Amsterdam and Barcelona. Finally, we discuss how design thinking is at the root of broad transformations, as it brings together different disciplines with modeling, simulation and schematic tools to create urban and social visions and bring them to life. The convergence of AI and design technology makes this process more intuitive and user-friendly.

3.2.1 Cities as living labs “Living labs” is a concept born out of MIT that is an open-innovation ecosystem for a city or region and is centered around the user experience. It consolidates all research and innovation methods within a publiceprivatee people partnership. A living lab involves everyone as co-creators of research and innovation, not just users of a service. This enables more real-time experimentation and evaluation of ideas, concepts and scenarios. Stakeholders in living labs are able to engage and use information to test the idea, product, or service against global markets and users for potential adoption. Such considerations can be made for a product’s lifecycle from ideation to recycling. There are a whole host of research and design methods out there that fail to engage users as co-creators, but Web 2.0 demonstrates the potential of mass collaboration and crowdsourcing intelligence. The idea of the smart city as a living lab allows the opportunity for the city to become a living experiment that can be tried and tested in real time. This more flexible approach provides the space for technologies and end users to construct real scenarios for how technology can be used to improve the functions of the city. Through this process, the natural selection of which technology applications are more feasible and sustainable and the possibility for comparing different solutions can be possible before necessarily investing in one platform. This living lab approach can also be applied to experiments using VR and AI to model “what if” scenarios on larger-scale systems and applications that require major decisions and investments. By rapid prototyping potential design solutions, city leaders and stakeholders can weigh advantages and disadvantages of different technologies before determining the best approach. Therefore, the city as a living lab has become a more mainstream concept and approach that is allowing cities to develop more flexibility and organically, reflecting a more natural progression and adoption of technology rather than being forced to select a single solution or proprietary platform that may or may not support the required solutions specific to that unique city or community. There are diverse scales and functions of living labs, from macro policy focused to user-centric. The Dutch are pioneering in many ways, with varying public private partnership formats being experimented with throughout the

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country. An example of a policy focused lab is the Governance Lab Utrecht. In collaboration with Utrecht School of Governance, Utrecht University School of Economics and the Copernicus Institute of Sustainable Development, the initiative supports four types of experimental research: lab experiments, living labs, design and innovation labs and simulation labs. On the UX level, ExperienceLab was established by Philips Research at their tech campus in Eindhoven in partnership with a wide selection of universities and companies in Europe and around the world. Led by multi-disciplinary teams including psychologists, sociologists and designers, new technologies and applications are tested, monitored and experienced from a user centric, consumer behavior point of view.

3.2.2 City as hubs of innovation/innovation-driven cities The major challenge for the advancement and sustainability of smart cities is the requirement for cities to attract investment to enable the design and implementation of potentially complex and expensive systems. To solve this problem, some cities and governments have created broader initiatives combining economic development frameworks with technology advancement as the driver. An example of this is the vision of Masdar City in Abu Dhabi introduced in Chapter 1 that was planned as a new center of innovation and R&D with MIT as a founding academic partner to anchor the new city and its technological development in academic research and the business of innovation. To realize the future of smart cities, this combination of innovation-led economic development is a powerful laboratory to attract technology companies and talent to participate in the total ecosystem of the city development and sustainability. In this respect, major cities around the world are competing to create unique environmentsdtech clusters, smart campuses, special purpose districts, underpinning the establishment of smart city programs and projects that will ultimately change the nature of the city and their global competitive advantages. Without a broader innovation model, cities will most likely face challenges to attract investment, spark technological development, and manage the integration of new systems that is required to compete globally in the race to become smart. Additionally, the benefits of innovation-led cities to attract global tech companies, incubate local businesses, and develop human resources to participate in the co-development of the city are motivating cities and governments to create new policies and incentives to stimulate economic and technological development with direct impact to the advancement of smart cities. In this process, each city must identify their unique position both globally and regionally to design programs that reinforce the underlying strengths and opportunities that the city offers. The concept of city DNA elaborated in Chapter 2 defines a framework to position each cities’ unique identity and competitive advantages and how these qualities are marketed to attract global and regional resources.

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A key aspect of innovation-driven cities is the link to technology research and development. Cities at the forefront of advanced technologies typically are hubs of science and technology research with universities and business clusters linkages. In many cases, cities with a critical mass of advanced educational institutions have an advantage with direct access to knowledge workers and new tech IP development. These ingredients are more seamlessly woven into the establishment of innovation-driven cities with tangible resources at the basis of social capital and economic growth. These initiatives can lead to actual development with economic and social impact, creating opportunities for the government, private sector, and the public to interact in a form of real living lab experiment. Advantages of this approach foster development that can both test the viability of innovative ideas and technology solutions while simultaneously providing a platform for a business connectivity. Below, three case studies are discussed: Amsterdam, Cambridge and Beijing. Amsterdam has a bustling innovation scene and was recognized as the 2016 European Capital of Innovation for leading the way in connecting citizens, organizations, academia and business as part of the city’s social tech revolution with initiatives including Startup Boot (Local Meetup Network), Makerversity (Maker Lab), FreedomLab (Co-working Space) and many others. This unique tech identity is due to Amsterdam’s position as one of the top knowledge economies in Europe with innovation as a core sector. It’s compact size and diverse population also makes it a fertile testing ground for social innovation projects. At the heart of Amsterdam’s tech culture is the Knowledge Mile, a vibrant business innovation district that runs from the Amstelplein to City Hall connecting multiple universities where 60,000 students interact, has startups running living lab experiments to address urban problems and to optimize the collective experience. Amsterdam leads in the impact of its research and is second only to Copenhagen in research output per capita, according to a 2015 Elsevier study “Mapping Research and Innovation: Understanding Amsterdam’s Competitive Advantage.” As their website states, they have “innovation in the city’s DNA.” Cambridge, one of the oldest university cities in the world, is also one of Europe’s oldest tech hubs, having created an innovation center in 1987. While it specializes in engineering and deep tech (big problems) research, Cambridge previously struggled to brand and market itself as a global R&D center. However, with recent surges in investment from IQ Capital, its ecosystem of over 6000 tech companies, including a dozen unicorns, is poised to make broader impacts. Recently, Cambridge has teamed up with the Urban Data Project to create a “digital twin” that maps the cities lighting, traffic, and air and noise pollution. Beijing is another example of a hub of innovation with its unique combination of concentration of government, academic, and industry activity. The South China Morning Post (SCMP) reports that “Beijing’s innovation hub is at the center of China’s aim to become a tech powerhouse.” The Beijing

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government started 30 years ago with the intent to replicate Silicon Valley and now have about 9000 high tech companies and 10 major AI labs. It is a testament to how government policy can intervene and take initiative to create innovation hubs. It is a matter of taking initiative. The SCMP also reports that Beijing has plans to become the world leader in innovation by 2050, and by extension, a driver of global economic growth. China is already on track to becoming the frontrunner in a decade.

3.2.3 Co-design For smart cities to be more innovative, city governments need to learn to think like their customers/citizens. Constant innovation in design thinking moves us beyond choosing the best option to creating a better set of options. Smart cities are coming to understand that engaging and including everyone in the process creates the best ideas. Co-design is the practice of co-creation and participatory design between stakeholders, companies and end users. A great example of co-design is the Sidewalk Labs initiative in Toronto, Canada, which began with a mission to tackle the challenges of urban growth and to make the underutilized Eastern Waterfront an experimental zone for urban regeneration. This project has led to a completely new approach to the urban development combining people-centered urban design with cutting-edge technology, to achieve new standards of sustainability, affordability, mobility, and economic opportunity, and has inspired other cities globally to develop similar co-design, co-development initiatives. The Meeting of Minds, another co-design initiative started in California and now widespread throughout the United States, is a nonprofit organization promoting community-driven, multi-stakeholder smart city development. As part of their effort, many communities are creating their own forums, workshops, and co-design efforts to address current challenges that cities and towns are facing and how smart technologies can solve these problems with publiceprivate partnership models. We have traced Scandinavian trade unions in the 1960s and 70s. In the book Seven Cultures of Capitalism (1993), Sweden is used as a case study of economic success which seemed to defy capitalist logic by spending so much on socialist programs. They fostered a culture of “workfare” over welfare for the able-bodied, while also providing world class health care to those who could not work for mental or physical reasons. By valuing and including everyone in their society, they enabled much friendlier and more efficient work environments, where all employees were incentivized to contribute new ideas and improve the company or community as a whole.

3.2.4 Citizen centric cities Smart cities present the opportunity to directly engage and collaborate with citizens via a citizen-centered approach to build and plan ‘smarter’ cities, developing tech-enabled urban ecoystems. As a bottom-up model, city governments can learn from its constituents as a broader participatory

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environment, creating a more inclusive and responsible urban culture. Again, Scandinavian and Northern European cities have been at the forefront of citizen-driven models and have been exemplary at managing a more horizontal society that promotes an underlying culture of collective intelligence. What is called the Nordic model or the “Nordic Secret” (Andersen and Bjorkman, 2017) includes the comprehensive welfare and workers’ rights protections for the people, often ranking the highest in the world along developmental and happiness metrics. The Nordic countries are already “smart states,” as it were, and are striving to export their model globally. The Nordic Smart Building Convention argues explicitly that future cities should be citizen-centric. This is to make life easier, save time, and make citizens healthier and safer. It works so well that it is ironic that some countries including the United States continue to force competition under unnecessary conditions of scarcity. Social problems manifest and grow in the absence of living wages, healthcare education, employment opportunities, where government policies and outcomes are often at the behest of special interests and directly against citizens. The second quality of smart cities is the advancement of human and social capital through knowledge creation and dissemination, advanced participation and digital inclusion, and the establishment of new forms of innovation (open, social). In smart cities, a large fraction of the available knowledge is produced collectively; knowledge is an asset that stems from everybody’s contribution. Smart cities attract highly qualified people and a skilled labor force because of their openness and their eagerness to use technology in effective and innovative ways. They attract creative people who build creative cultures and industries, which in turn foster the development of knowledge ecosystems that bring prosperity to the city. Four European Smart City Strategies: Margarita Angelidou (2016).

North America has also played an important role in establishing community-centric culture and approaches. Rooted in the postwar movements in America, pioneer Jane Jacobs and others developed community-based action communities that inspired citizens to become involved in the development of the New York City and to bridge the gap between autocrats and the community voice. The Village Voice, the famous newspaper, was an outcrop of this movement to have a grassroots approach and involving participation and collaboration by sometime disenfranchised members of society (Shuster, 2016). Jacobs was a co-founder of The Center for Living Cities, dedicated to making cities vibrant, resilient, and just places to live and participate in social life and problem-solving. A 2017 article on Newcities.org (Feder, 2017) invokes Jacobs’ citizen-centered ethic to enable engagement through AI and social media datadwith their direct participation. Jacobs would appreciate the concept of city DNA for her understanding of the importance of historic

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character and local constituents in urban planning. She put emphasis on place and local residents’ experience. Through her activism, Jacobs helped place the citizen and the center of decision-making processes, exactly the type of usercentric innovation experiences being showcased in places like Amsterdam, discussed above in “innovation hubs.” An early example of citizens’ participation was established in the project The Center for Choice, an exhibit at the 1963 Boston Arts Festival. The concept was to provide the citizens of Boston with in-depth information about challenging civic matters and provide them with alternative solutions to select through the use of voting machines and other means of choice. Expanding on this analogue participatory public space is a hypothetical Infosphere comprised of immersive holographic data visualizations and interactive surfaces to entertain and inform citizens and visitors about quotidian Parisian affairs and happenings (see Fig. 3.3).

Center for Choice, Boston 1963

Infosphere, Paris 2023

FIGURE 3.3 Citizen participation. Left, Designed by Ernest E. Kirwan. Right, Designed by Christopher G. Kirwan.

To give another example, Barcelona is mitigating climate change through a bottom-up approach, by enabling citizen-centric projects. In 2015, they invited 800 organizations including schools, nonprofits and businesses to participate in workshops to identify mutual goals and potential outcomes (Brown and Sako, 2016). Among the results were a bike sharing program to reduce traffic, training for energy-efficient home renovations, and a plan to decrease CO2 emissions and increase green space by 1.6 km2 by 2030. Barcelona is also transitioning from a traditional (hegemonic) smart city to an experimental new paradigm. “Smart” citizens are becoming more decision-makers than data providers. In 2016, they hosted the Smart City Expo World Congress focusing on “the role of smart citizens in a smart city (Van Ransbeeck, 2016).” New forms of citizen participation are enabled by ICT and social media. Beyond the simple act of voting occasionally, citizens can use the internet to stay engaged with news, volunteer online, dialogue with diverse people, participate in surveys and research, and organize politically. South Korea is an example where

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social media has enabled and encouraged informal modes of participation. Other forms can include direct engagement with the people around you, such as neighborhood or community building initiatives. In this context, one could participate by organizing litter clean up, starting a local co-op, or attending city council meetings. City governments can play a supporting role in all of these activities and do successfully as shown in Amsterdam and Barcelona, making it a collaborative process. Fostering innovation amplifies these possibilities, which is why hubs and citizen-centric approaches are strategic long-term investments in knowledge and social capital. Through ICT, city and community-based apps enable diverse stakeholders and citizens to participate in the co-management of the city ecosystem.

3.2.5 Design thinking Design thinking refers to the practical steps and techniques involved in the creation of products, buildings and machines by designers and also employed by artists, inventors, managers, etc. It has evolved to include co-design (participatory design), but it is still important in itself because not all stakeholders are designers per se and can contribute to the design process itself in a constructive way. Through technology design thinking has become more reflexive and recursive; it can improve upon itself exponentially. It can be used to solve more complex issues like “wicked problems.” It involves mental and physical tools to reframe problems, think creatively, model, prototype and test. Design thinking also includes solution-focused thinking, abductive reasoning and co-evolution of problemesolution scenarios. Tech culture is capitalizing on design thinking through living labs, hackathons and start-up incubators to name a few. These open source environments and iterative processes foster interdisciplinary collaboration. Design is flexible and adaptive to different contexts and yet has the ability to act as a universal language, to bridge gaps both figuratively and literally. The complex nature of the planning and design of functional urban systems and smart cities requires a methodology that draws from the key strategies across different disciplines including urban planning, sustainable design, ecology, sociology and behavioral sciences, computer programming, media and interactive design. Design thinking has the potential to explore ideas and solutions on both macro- and microscales and can assist in the development of urban strategies providing a broader conceptual understanding of how and why specific technology solutions serve to enhance the experience of cities. The convergence of design thinking, AI and business innovation is now at the core of new educational models and has led to the emergence of hybrid professional fields and careers such as Urban Interaction Design, Information Architecture, User Experience, Data Visualization and other new media related

60 SECTION | I Approach fields that have drawn from diverse areas of knowledge and are now influencing the nature of how cities are rethinking their planning and design approaches. Rau, Cross-Cultural Design: 8th International Conference, CCD 2016.

3.3 Convergence methodologies The traditional planning and design methodology of establishing criteria, collecting data, and developing strategies and design solutions has relied on a linear process. This is being reconsidered, remixed and reevaluated because of the possibility to co-develop the city in real time as a living experiment in which city stakeholders can collaborate in more horizontal, organic ways. As cities become more and more collaborative and open platforms for innovation, co-development and new forms of shared economic development are now possible. This will ultimately benefit the various stakeholders of the city and the public at large and enable the opportunity to introduce AI-driven methodologies to assist in bringing the various dimensions of the city together via a collective intelligence design interface. In traditional city development, city leaders and key stakeholders have been the primary decision-makers with a top-down approach. As we have described in the citizen-centric cities model, grassroots movements have achieved more influence over the last several decades and communities have become much more active in the co-design process of city development, particularly as smart cities have been able to facilitate digital platforms to collect, process, and publish public data in open-source, cloud-based platforms. This process can be further expanded with the assistance of machine learning to program diverse scenarios, modeling and rapid prototyping options that can be utilized in the co-design process allowing stakeholders to visualize potential solutions. This can be highly effective when applied to determining best case scenarios and outcomes which can then be reverse engineered into feasible solutions.

3.3.1 Human-machine collaboration Within machine learning, expanded co-design processes, humans and computers work together to create an enhanced capability combining both linear and nonlinear design and programming methods to tackle all sorts of design development challenges and opportunities. In the future, superintelligence (beyond human) may be achieved as the fusion of human and machine learning. A new design process will emerge as the natural progression of codesign, where the convergence of human and machine intelligence will facilitate a new consciousness emulating natural processes to allow the next stage of the evolution of cities to achieve a self-regulating, self-sustaining

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urban life. This possibility, enabled by new interactive technologies, has multiple benefits, including the following: l

l

l l

l

Reduces the risk of developing solutions that do not work for that citydno one solution fits for each city Harnesses the IoT and social inclusion in the process of defining, shaping, and building the appropriate solutions Engages all stakeholders in the co-creation, co-development of the city Builds organically as the demand for solutions expands and links together into a business ecosystems that underpins the development of the system Employs real-time data, data analytics and visualization to inform and drive real-time processing

In the theory of Collective Intelligence introduced in the previous chapters, the developments in AI will allow cities to eventually be self-regulating through bio-feedback-enabled via sensors, data analytics and real-time processing of city operations. The city will indeed become a digital simulation of real life and the two dimensions of digital and physical will converge as one hybrid bio-digital operating system. The role of AI, data visualization and data analytics will enable this convergence. Information Architecture (IA) carries broad meaning across different disciplines, but refers to the evolving capabilities for using design to structure information for various human needs. Smart cities require the latest IA methodologies, the evolution of which can be considered through the following stages: Stage Stage Stage Stage

1: 2: 3: 4:

Top-Down Decision Making/Linear Process Human Enabled Co-Design Design ThinkingdMachine Learning Convergence Superintelligence Design

Data visualization, prototyping, VR, and data analytics are all used in the strategy, planning, and design of smart cities. How can these tools serve to not only plan cities in real time but also become a living operating system? Part of the advancement in the developments of new technologies indeed is blurring the traditional boundaries between planning, design and operations that can now be accomplished as a continuous process in real time.

3.3.2 Real-time visualization As information visualization becomes more relevant in the operations of cities because of the power and speed of data visualization to simulate living, operating systems in real time, the potential to introduce a combination of information architecture and system visualization to assist in describing the system structure and functions is now available to assist city leaders in rapid prototyping scenarios and diverse options. Part of this understanding conceptualizes the city as a living, dynamic experiment that is in a constant state of change and development. This requires the ability to remain agile to address changing criteria and relationships especially as technology perpetuates the possibility of

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virtual system morphology that may lead the evolution of city operations. The concept of the living lab can be applied to facilitate this living city scenario.

3.3.3 Information architecture and Philosophy of Information The fields of Information Architecture (IA) and Philosophy of Information (PI) are converging to form a new discipline named Philosophy of Information Architecture (PIA). The Oxford Internet Institute hosted a workshop to explore the relationship between these two fields and to establish mutual practices. The workshop prompted a greater need to understand general principles and the way in which abstract thought mediates and presents information. Two major themes were explored: The first is the need for better ways to perceive information, the design process that produces information and the requirements that shape the design process, all of which “presupposes a convergence” between informational artifacts and the conceptual approaches that frame them. The second theme is the human interface and information flows between levels of abstract thinking and conceptual language describing complex issues. This suggests that a meta-architecture is needed to embody the convergence of Information Architecture and Philosophy of Information described as: A set of high-level decisions that will strongly influence the structure of the system, but is not itself the structure of the system. The meta-architecture, through style, patterns of composition or interaction, principles, and philosophy, rules certain structural choices out, and guides selection decisions and trade-offs among others. By choosing communication or co-ordination mechanisms that are repeatedly applied across the architecture, a consistent approach is ensured and this simplifies the architecture. The Visual Architecting Process by Ruth Malan and Dana Bredemeyer, Bredemeyer Consulting, 2005.

Enterprise architecture and sociotechnical transitions are in a renaissance phase, becoming more interdisciplinary between art and science, leading to the emergence and convergence of self-improving systems. We build toward the idea of meta-architecture: The meta-architecture collects together decisions relating to your architecture strategy. It sets direction for your architecture effort, with high-level decisions that will shape the architecture and guide the architects. These include architecture principles, statements of philosophy, metaphors and organizing concepts that will guide system decomposition and design of architectural mechanisms. Meta-Architecture by Ruth Malan and Dana Bredemeyer, Bredemeyer Consulting, April 2004.

3.3.4 Real world/virtual simulation Gaming culture and the simulation of reality plays a significant role in modeling diverse behavior, especially games like The Sims and Sim City,

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which model human behavior at the micro- and macro levels, respectively. As VR and AR become more available and realistic, the simulation will interface with the real world, helping the user navigate and co-design smart city environments.

3.3.5 Generative design and metadesign Metadesign is yet a higher level of co-design, inspired by living systems, where emergence is nurtured to foster new unthinkable solutions. Metadesign aims to address metaproblems, such as designing for basic human existential needs such as food, shelter, clothing, assembly, communication and co-living. With generative design, algorithms supercharge traditional workflow, such that designers have a new systemic role rather than top-down origination on paper. It enables designers to set initial criteria, such as ideal sunlight configuration in an urban space, and allows algorithms to generate design possibilities. Preconceived notions of design may impede creativity, so generative design could be a breakthrough that overcomes such fallibilities. Algorithmic processes, harnessed through the medium of code, allow creators to generate complex forms and organic structures by the application of elementary but carefully tuned sets of rules. Digital fabrication systems, such as computer-controlled laser cutters, 3D printers and machining systems, offer a nearly instantaneous way of exploring ideas in new spatial and material formats. The combination of these two approaches represents a growing niche in art and design, wherein algorithms can generate different aesthetic expressions and prototypes can be generated and tested on short order. Some ways in which meta-design proceeds by intuitive variation and experiment are illustrated in Fig. 3.4.

Digital Variations

Physical Prototypes

FIGURE 3.4 Generative design.

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An example of generative design is the KPF Urban Interface, a highly visual and schematic design platform that uses a computational methodology of “generative design” that automatically creates potential designs and collects feedback. Based on a combination of criteria as input and algorithms that expand the parameters of the design requirements, the system allows rapid prototyping of multiple variations that would normally be a painstaking design development process. This includes factoring in concerns about human experience and functional efficiency using computational intelligence to experiment with an accurate simulation of the city. Generative design promises to be the future of urban planning and design. KPF is working with Sidewalk Labs in Toronto to make this a reality. Client, city, and public participation are enabled largely through apps like Scout, Citybot and Haystack. The platform is a step toward visualization and modeling of cities that makes urban design and experience more inclusive.

3.3.6 Convergence Development Method: strategy, planning, design, and operations process Converge Development Methodology explores the opportunity made possible by a combination of AI, machine learning, and generative algorithms, by streamlining the traditional linear iterative process that begins with defining the objectives through the development of a strategy that then defines the planning approach and design criteria. After the design solution is in place, the operations take over. In the case of Convergence Development Methodology, these processes are integrated through AI allowing the strategy, planning, and design to achieve a unified logic that continually builds on its behaviors and establishes a self-generative and self-regulating iterated process establishing the process as integral to the system as in a living organism rather than an external process. The traditional approach is linear, while the convergent method is networked and reflexive, as shown in Fig. 3.5.

OPERATIONS STRATEGY

PLANNING

DESIGN

OPERATIONS

DESIGN

PLANNING

STRATEGY

Traditional Development Method - Linear Progression - Post Feedback

New Development Method - Non-linear Progression - Built-in Feedback

FIGURE 3.5 Convergence development method.

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3.3.7 Convergence design method: design thinking/machine learning Convergence Design Methodology builds on the combination of design thinking and machine learning to develop a hybrid open and closed methodology that brings the power of human conceptualization and collaboration and expands this process through the scalability of computation. In this form of convergence, human input provides the catalyst for identifying and framing ideas, design concepts, and potential solutions. These ideas are then expanded upon, through a generative design process with the help of algorithms to determine the best match for desired outcomes based on initial criteria. Each design stage is based on strengths of the combination of the designer and AI through a collaborative process. As AI capabilities increase, the proportion of humanemachine collaboration and input adjusts. Through this powerful combination, potentially infinite possible variations and iterations can be developed that can then be further expanded and perfected to meet the requirements of the system as it evolves. An example of a converging methodology is the combination of design thinking and machine learning bringing the power of an open-ended collaborative process expanded upon through the augmentation of machine learning with the possibility to create multiple variations on the core themes. The potential to program the ability to build on sequences of patterns and additive behavior of diverse solutions provides the potential to continually expand, improve, and modify based on a fusion of human and machine skill sets. Through the iterative stages of exploration, integration, ideation, implementation and evaluation, optimal solutions can be derived from this collective intelligence to meet complex, dynamic challenges. Fig. 3.6.

DESIGN THINKING Human-centered

TEST DEFINE EMPHASIZE

MACHINE LEARNING Machine-centered Machine-centered

PROTOTYPE IDEATE

SYNTHESIZE SYNTHESIZE

Exploration

Integration

VALIDATE VALIDATE TUNE TUNE

ANALYZE

Ideation

Implementation

Evaluation

FIGURE 3.6 Convergence design methodology. Based on diagram by John Morley & Associates.

3.3.8 Convergence application method: outcome-based AI scenario modeling Convergence Application Methodology is a further expansion of data visualization and analytic processes that harnesses diverse data types and sources brought together and filtered by AI driven contextual scenarios. Through this process, diverse outcomes are modeled that lead to the advancement of applications and solutions. The following combined elements form a convergent

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smart city solution: urban data generated by citizens; computing power supported by smart IT infrastructure, algorithms based on behavior and patterns of urban management and operations, and AI based application scenarios, as shown in Fig. 3.7.

AI Scenario

Computing

Algorithms

Pattern / Model

Urban Data

System / Infrastructure

People / Behavior

Application

Solution

FIGURE 3.7 Convergence application methodology.

3.4 Conclusion Traditional planning and design methodologies can now be augmented by new innovative tools and processes enabled by AI and smart technologies that can facilitate a more open-ended, multidimensional approach that incorporates diverse stakeholders to shape the potential of a collective intelligent operating system that best reflects the inherent nature of each unique city and urban condition. By applying new AI-enabled methods, cities can also create a new system of co-creation and co-generation that builds on the current state of the city designing a seamless integration of technologies and technological management into the system. The ultimate convergence of smart city system development is the combination of human and machine that works together to establish a higher collective intelligence that achieves a state of self-operations inherently born out of the natural evolution of the convergence of cities, technology, humans and the environment.

References Andersen, L.R., Bjo¨rkman, T., 2017. The Nordic Secret. Scandbook, Falun. Angelidou, M., 2016. Four European smart city strategies. Int. J. Soc. Sci. Stud. 4 (4), 30. Brown, J., Sako, D., 2016. Cities100: Barcelona - Citizen Initiatives Drive Climate Action. https:// www.c40.org/case_studies/cities100-barcelona-citizen-initiatives-drive-climate-action. (Accessed 16 March 2020).

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China Academy of Information Communications, 2016. Comparative Study of Smart Cities in Europe and China 2014, fitst ed. Springer. Choa, C. https://www.aecom.com/blog/author/christopherchoa/. (Accessed 19 September 2019). Elsevier, U.I.N., 2015. Mapping Research and Innovation: Understanding Amsterdam’s Competitive Advantage. Elsevier Research Intelligence. Feder, E., 2017. How AI and Social Media Data Can Help Build Citizen-Centric Cities. NewCities. https://newcities.org/perspectives-how-ai-and-social-media-data-can-help-build-citizen-centric-cities/. (Accessed 20 September 2019). Hampden-Turner, C., Trompenaars, F., 1993. The Seven Cultures of Capitalism. The Seven Cultures of Capitalism: Value Systems for Creating Wealth in the United States, Britain, Japan, Germany, France, Sweden, and the Netherlands. Doubleday. House_n Research Group, 2004. The PlaceLab. http://web.mit.edu/cron/group/house_n/placelab. html. (Accessed 19 September 2019). Jing, M., 2018. Zhongguancun: Beijing’s innovation hub is at the centre of China’s aim to become a tech powerhouse. South China Morning Post (SCMP). https://www.scmp.com/ tech/start-ups/article/2172713/zhongguancun-beijings-innovation-hub-centre-chinas-aimbecome-tech. (Accessed 20 September 2019). Malan, R., Bredemeyer, D., 2005 Meta-Architecture Action Guide. Software Architecture Action Guide 1e10. http://www.bredemeyer.com/pdf_files/ActionGuides/MetaArchitectureActionGuide. PDF. (Accessed 21 September 2019). Malan, R., Bredemeyer, D., 2005. The Visual Architecting Process. Bredemeyer Consulting, pp. 1e14. http://www.bredemeyer.com/pdf_files/WhitePapers/VisualArchitectingProcess.PDF. (Accessed 30 January 2020). Philips Research Eindhoven, 2020. ExperienceLab. https://www.philips.com/a-w/research/locations/eindhoven.html. (Accessed 20 January 2020). Shuster, R., 2016. Jane Jacobs at 100. The Village Voice. https://www.villagevoice.com/2016/09/ 21/jane-jacobs-at-100/. (Accessed 20 January 2020). United Nations Development Programme (UNDP), 2015. UNDP Sustainable Development Goals (SDGs). https://www.undp.org/content/undp/en/home/sustainable-development-goals.html. (Accessed 20 September 2019). Utrecht School of Governance. The Governance Lab Utrecht. https://www.uu.nl/en/organisation/ governance-lab-utrecht. (Accessed 20 September 2019). Van Ransbeeck, W., 2016. 5 Citizen-Centric Sessions You Cannot Miss At SCEWC16. https://www. citizenlab.co/blog/civic-engagement/citizen-centric-talks-scewc16/. (Accessed 16 March 2020).

Chapter 4

City Operating Systems Chapter outline 4.1 Overview of operating systems 4.2 The language and representation of systems architecture 4.2.1 The role of metaarchitecture, information architecture and technical architecture in the design of smart city operating systems 4.2.2 Metaarchitecturedprinciples and guidelines 4.2.3 Operating systems planning considerations 4.2.4 Operating systems design considerations 4.2.5 Information architecture and technical architecture 4.3 Representational hierarchy of cities as operating systems

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4.3.1 City ecosystem 4.3.2 Smart city frameworkdthe smart city mandala 4.3.3 OS Behavioral Typologies 4.3.4 Anatomy of operating systems 4.3.5 Smart city operating system flow 4.4 What is the correct OS? 4.5 New constructsdconvergencebased city OS 4.5.1 Convergent OS 4.5.2 Co-development/open source/open data 4.5.3 Self-regulating systems 4.6 Conclusion References Further reading

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4.1 Overview of operating systems This chapter attempts to define the language and formal structure of smart city operating systems (OS). Owing to a combination of urban growth and rapid technological development, traditional systems architecture and representations of OS now need to be reenvisioned. Opportunities for systems to become self-generating is now possible based on living behavioral intelligence informing a new language embodying states of adaptation, transformation and evolution in the system design. Top-down models of system architecture may no longer be able to describe the dynamic flow of the convergence of human, environmental and technological patterns. As described in Chapter 1, this new architecture depends on the stage of evolution of the city as a living organism and its ability to adopt and integrate Smart Cities and Artificial Intelligence. https://doi.org/10.1016/B978-0-12-817024-3.00004-0 Copyright © 2020 Elsevier Inc. All rights reserved.

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new technologies within the operational framework and unique culture of the city. In parallel, the physical and socioeconomic development of each city, the OS architecture and the evolution of Artificial Intelligence (AI) can be superimposed to create an analytical methodology to interpret the ability for each city to transform, thus altering the nature of the city. City OS, in their own way, are representations of the city. Therefore, the OS architecture is a reflection of the structure and behavior of the city it is developed for and should be appropriately designed to embody the unique characteristics of the city. Consequently, the design, development and operations of the OS is a complex process that must factor in multiple criteria including the stage of technological evolution, city governing approach, physical urban configuration and socioeconomic status. Additionally, OS must be scalable, flexible and efficient to adapt to the dynamic nature of cities and their growth patterns while remaining vital and sustainable within the competitive global landscape. The major challenge for smart city managers and OS developers is how to develop an OS architecture that embodies these factors that each city must define in order for it to perform at its maximum potential before it can effectively determine the appropriate technology configuration and OS solution for the city. As presented in the previous chapters, city OS must build on the unique DNA and stage of evolution of each city including whether it is an existing or new city, the scale of the city from mega cities to local towns and geographic areas and the technological status. Does the city have existing legacy systems and telecommunication networks or is there an opportunity to leapfrog with new technologies? These fundamental questions must first be addressed to determine the appropriate OS design configuration. Owing to the complexity of OS, without a comprehensive, theoretical understanding of the system, decisions made in selecting the appropriate solutions have the potential to become technocratic and may not factor in the greater purpose and requirements for the technology to serve its purpose other than a narrow function. Collective intelligence, introduced in Chapter 2, emphasizes the need for smart city systems to operate like the human body as a harmonious, interconnected organism with each organ providing specific functions within the OS. This includes a range of considerations from the design to the implementation and management of the city OS. To achieve an OS that is in sync with the organization structure and culture of the city, the OS typology may represent a top-down, centralized configuration where the city government may have a strong influence on the system operations to bottom-up, open-source platform where individual companies or stakeholders including citizens may have more influence on the operation of the system. Therefore, depending on its unique characteristics, each city necessitates an OS solution that is customized to meet its special requirements.

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4.2 The language and representation of systems architecture As presented in the previous chapters, the city can be conceptualized as a complex living organism in a state of constant evolution, balancing internal and external factors to achieve sustainable growth. Expanding on the metaphor of the human body, city OS can be visualized as an anatomy of interoperative subsystems, i.e., skeletal, nervous, circulatory, etc., enabling the various functions of the city including energy, security, transportation and communications to be harmoniously incorporated within the city organism. A key question for system architects, engineers and managers is how should city OS be developed to factor in these critical functions while simultaneously being designed to evolve and adapt over time, enabling the integration of new technologies that embody and reflect the unique nature of each city that we have described as city DNA. Studying the human anatomy and organic patterns found in nature allows the creation of a visual language and code to describe complex states such as process, organization and connectivity in the development of new biomimetic system architectures (Fig. 4.1). In the compelling research paper, Urban Operating Systems: Diagramming the City (Marvin and Luque-Ayala, 2017), the authors challenge the representation of system architecture that has historically been conceived as predominantly top-down, linear, Tayloristic, serving military, government or big business purposes and therefore not reflective of the emerging types of city OS

Process

Organization

Connectivity

FIGURE 4.1 System Architectures based on Human Anatomy.

manifesting a broader spectrum of user requirements and participation. This critique, focused on the limitation of the technical, visual language employed to diagram city OS and the subsequent power of information and control, elicits the need for information architects and system designers to investigate diverse representations of OS architecture. Through the exploration of new approaches to the formal language of information architecture, new and alternative system configurations can be created to representing cities as dynamic living organisms made possible by the fusion of technological

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innovation, IoT, social media, ubiquitous computing, ambient networks, and new human and machine convergent design methods described in Chapter 3. This argument can be further supported in the paper Chaos, Order, and Sense-Making (Dervin, 2000) that presents a theory and application identifying seven states of information including “1. Information describes an ordered reality; 2. Information describes an ordered reality but can be found only be those with the proper observing skills and technologies; 3. Information describes an ordered reality that varies across time and space; 4. Information describes an ordered reality that varies from culture to culture; 5. Information describes an ordered reality that varies from person to person; 6. Information is an instrument of power imposed in discourse on those without power; 7. Information imposes order on a chaotic reality.” Using this analogy, information architecture is a tool to impose formal constructs on a system whether that be a system of industrial production, a business enterprise, or a smart city OS. The concept of sense making is the process of understanding and adapting the frame of reference to correspond to the dynamic nature of human systems rather than imposing a preconditioned information architecture that may not appropriately reflect the nature of the system it is attempting to describe. An example of diverse types of system architecture based on system behavior can be explained in the comparison of three types of OS: centralized, decentralized and distributed (Fig. 4.2). Centralized OS combine all functions in one platform seeking to simplify the operations of the system in a more efficient configuration by compressing the various dimensions of the system in a gestalt formation eliminating subcomponents that may have the potential to malfunction and undermine the integrity of the system. Decentralized systems represent a deconstruction and dispersal of system functions into broader

Centralized

Decentralized

Distributed

FIGURE 4.2 Centralized, decentralized, and distributed systems. Based on Paul Baran’s diagrams.

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platform with the benefit of establishing redundancies in the case of system compromise or failure. Distributed systems combine both centralized and decentralized characteristics forming a new hybrid configuration than can be in the form of a distributed network model (Fig. 4.2).

4.2.1 The role of meta-architecture, information architecture and technical architecture in the design of smart city operating systems The planning and design of smart city OS requires a multidimensional process conceived and expressed in terms of a meta-architecture that describes the intentionality of smart city architecture. It requires developing sets of principles, guidelines, ideas and axioms for an abstract framework in which to design information architecture and technical architecture in different ways based on the city DNA and various factors of the smart city. On the Microsoft blog, some developers have been thinking the same way, proposing that enterprise architecture needs to be thought of in terms of meta-architecture to address organized complexity. Alan Hakimi writes, “[Enterprise] Architecture within the context of sociotechnical organization is going through what I believe is a renaissance period.” He describes the enterprise as a self-organizing autopoietic system, which calls for the need to develop a meta-architecture in this way. Our approach is informed by transdisciplinary systems engineering (Madni, 2017) which capitalizes on the convergence between disciplines including STEM and philosophy and the arts. The need for this is prompted by the inefficiency of conventional engineering and typical enterprise architecture. It enables seeing intractable problems from multiple perspectives to find solutions necessary with complex systems. In the research of Brandt Dainow (2017) and others, the smart city is conceived as a sociotechnical, autopoietic system. In his paper, Smart City Transcendent, we are moving towards a model of smart cities as an “integrated domain” where all humans and devices are networked and interact. Through technology, we become “social machines” that are the lifeblood of the city as an autopoietic system. The meta-architecture of the smart city OS embodies the fusion of the physical and digital city in the form of a simulated autopoiesis of these two diverse yet converging realities. In designing the smart city and its operations, we consider three types of architecture: meta-architecture, information architecture and technical architecture, perhaps analogous to wetware, software and hardware. In our interpretation, metaarchitecture defines the high-level principles and sets the strategic drivers of the city OS, as a real-time simulation monitoring the life signs and health and wellbeing of the city as living organism. Information architecture describes the organizational and functional hierarchy of the system as a dynamic visual and codified language representing the system complexity. Technical architecture is

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the material infrastructure and engineered environment in which information about the system is stored and the hardware and software operating requirements are specified. While architectures will evolve new formal, technical and aesthetic languages reflecting emerging systems’ behaviors as a result of new technological realities, the basis of the operations of cities is solidly rooted in the functionality of urban systems that have been developed and engineered over centuries, establishing the historical and physical composition of cities. Consequently, cities are a result of overlapping and integrated systems, as explained in Chapter 1, that form the basis of the city OS as a series of independent and integrated dimensions. Information architecture, used as an organizational language to describe OS, seeks to compose these functions in a logical hierarchy of systems and subsystems while reflecting the unique city DNA.

4.2.2 Meta-architecturedprinciples and guidelines Building on the definition of meta-architecture in Understanding Smart Cities (2017), the following considerations attempt to define the purpose of a metaarchitecture that sets high-level criteria to guide the design of systems applied across the whole of the system architecture. The meta-architecture establishes underlying principles that influence the structure of the system, ensuring a logical, coherent architecture can be achieved, but it is not actually the organizational structure of the system. Put another way, it is the high-level thoughts, discourse, styles, patterns and philosophy that guides trade-offs, rules out certain outcomes, improves on itself, etc. The following key considerations represent the significant of meta-architecture principles that influence the planning and design of the city OS.

4.2.3 Operating systems planning considerations l

City DNA City OS are dynamic, living organisms in their own right that must reflect the evolution of the city, the changing political, socioeconomic landscape, physical growth and technological development. To determine the best solution, a comprehensive understanding of the key drivers of each city is required to plan, design and implement an appropriate OS. The design must simultaneously factor in a combination of top-down and bottom-up considerations within the development of an open platform. A multi point of view solution is necessary to drive the development of the smart city marketplace that stimulates the economy of cities, creates new opportunities for enterprises, allows governments to provide better

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services, improves quality of life and better engages citizens in participating in the management of cities. Macro/MicrodGlobal/Local A major factor in the selection and design of city OS is the consideration of different scales of operations from macro systems including interconnected and overlapping functions to micro applications within the city. To accommodate these different scales, the planning and design of city OS must consider the city from both city management and operations while simultaneously designing for citizen participation and individual user experience. Physical/Digital/Human Smart city OS are a combination of physical and digital elements and comprised of hardware and software that facilitates the interconnectivity of various city systems. To create a seamless flow of operations and information management between the various subsystems, technologies, software applications and equipment are imbedded within the city fabric and are linked by diverse interface(s) to the human labor force and management personnel that is required to collect, receive and publish city operational information, which is now also augmented by robotics and smart connected objects at diverse scales. Citizen Participation By connecting citizens and designing solutions incorporating citizen participation within city OS, cities can greatly benefit by the necessary human augmentation that is required to “sense” beyond the current technological limitations. While AI is now in many cases playing the role of humans and human perception, the present stage of technology still requires human participation within the city OS to generate real-time data on the ground and with the dual purpose of promoting and motivating citizen engagement with all the benefits this brings in co-managing the city. PublicePrivate Partnership The challenging task of building integrated OS, in most cases, requires the collaboration of public and private sectors. This is necessary because of the extensive investment, technology implementation and operational management required to build, own and operate city OS. The publice private partnership model has been an effective means to create a more open development platform to deliver a wider marketplace and sustainable ecosystem beneficial to all. Community, Accessibility, and Privacy In the age of Big Brother and demographic profiling, the balance between community engagement, public access and privacy is a key issue

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for how the OS and system architecture can provide a platform that solves the duality of public/private. A new formal system hierarchy is required to create the hybrid public private OS that at once provides access to the designated stakeholders while protecting the privacy of individual users. OS Management Critical to the architecture of the OS is the people and processes that manage the system. These have traditionally been city managers and IT professionals that select and administer the city OS either through the selection of a legacy OS or a combination of subsystems for each city system including transportation, energy, security, etc., and where city services are managed by the public domain rather than a private company. As city systems are becoming more sophisticated and integrated through smart city platforms, the role of humans will become less and less as automatized systems and AI allows OS to be autonomous.

4.2.4 Operating systems design considerations As part of the dialog about human versus machine management, the need for constant upgrades, improvement and optimization of the system necessitates the requirement of a feedback system built into the OS. In the discourse of metadesign, a related branch of meta-architecture, there are four universal conceptual tools: abstraction, diagramming, procedural games and emergence (of the unknown). In metadesign, the consideration for how systems will evolve and adapt has been factored into the design process as a givendthat future uses and problems cannot be completely anticipated during the design process. Therefore, metadesign promotes system design that must have the ability to be adaptive, interactive and self-generative. Like the human body and living organisms, city OS are in constant a state of change, requiring the ability to be self-regulating. Both humans and smart cities are autopoietic systems with many orders of complexity that are living and breathing simultaneously. To address the complexity of cities, the design of city OS must factor in the specific requirements depending on the scale and application of the OS from megacities to small communities; the evolutionary timeframe from historical cities with existing legacy systems and telecommunication infrastructure to emerging cities with limited infrastructure and the possibility to implement new technologies. Elaborating on the role of information architecture and technical system architecture play in describing systems presented in the book Understanding Smart Cities (2017), the following definitions represent the key aspects that must be factored in the design of smart city OS (Table.4.1).

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TABLE 4.1 Smart city characteristics. Layered

Provides visualization of various coextensive stacks

Interoperable

Adapted to different city circumstances

Scalable

Scaled as fractal

Modular

De/reconstructible into components

Flexible

Adopt advanced technologies

Fault tolerant

Redundancy systems, resilient to fallibility, failure

Available

Logistical resilience, disaster recovery

Standardized

Commensurable, contestable, upgradeable

Tech-agnostic

Open to diverse technologies and innovation

4.2.5 Information architecture and technical architecture Smart city architecture aims for a pragmatic and coherent arrangement of parts that support the functions of the users. That is why we propose a meta-architecture over and above information and technical architecture to understand the city as a complex autopoietic system and to achieve those ends. Enterprise ICT architecture is typically a broad concept including information, business, technical and software architectures, with our focus being on information and technical. A general ICT architecture begins with these aspects: l

l l l

Delineates and describes the whole system and functional attributes of the system Describes the organization structure of the system Defines inter- and intra relationships within the system Establishes guidelines and principles for systems design and evolution

Information architecture defines the organizational structure and flow of information within the city in alignment with the metaarchitecture’s strategic and operational requirements. In general, information architecture has different ways of presenting large information systems. Through a meta-architecture, we can adapt a wide range of potential architectures. The technical architecture is the technology and infrastructure ecosystem that stores and relays information. The technical architecture includes the following features: l l l l l

Creates the structure of business ecosystems Manages core technology standards Composed of people with strategic technical expertise Develops a technology roadmap Promotes good governmentality

From the level of meta-architecture, information and technical architecture can be considered subsets. Together, they can achieve a synthesis with the

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meta-architecture that has to achieve both hard (utilities, resources, transport) and soft (social and human capital) forms of urban infrastructure, compatible with both old and new cities.

4.3 Representational hierarchy of cities as operating systems Smart cities and city OS can be comprehended from diverse scales and means of representation. We have developed the following taxonomy to visualize the different ways of understanding cities as OS (Table 4.2). This includes from the broader context and environment in which the city exists as ecosystem to the OS framework representing the individual categories of urban development to typologies of OS, the anatomy of OS, and finally the flow representing the system behavior. In the following descriptions and diagrams, the representation of each of these OS dimensions are not intended to be a definitive solution but as models to facilitate the understanding of diverse scales and forms of representation. In fact, based on the previous section in this chapter on representation of systems, the arguments that have been made are to underline that we have not yet been able to fully depict the true nature of dynamic systems as these systems are in a state of evolution as cities, people and technology continually change. TABLE 4.2 Smart city representations. Smart city ecosystem

Smart city framework

The totality of city elements, people and environment

The metaarchitecture, planning and design functions

Smart city OS typologies

Smart city OS anatomy

Smart city OS flow

The types of city OS configurations

The technoanatomical structure

The relationships and flow of the system

4.3.1 City ecosystem The city is made up of internal systems and subsystems that are interconnected with external entities and environmental conditions that expand from the urban core to the boundaries of the city and beyond. This city ecosystem incorporates networks and flows that are manifest through a combination of physical, environmental, human and technological dimensions described in Chapter 1 that coexist within the three-dimensional space of the dynamic city and its broader context (Fig. 4.3). Therefore, the city can be conceived both as a self-contained closed system and an open system that relies on external forces that interact and influence the city as a living system. This space expands in many cases via regional transportation,

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power and telecommunication networks to interplanetary satellite networks. External factors include environmental conditions surrounding the city, cross-border activities, people, goods and services flowing in and out of the city. These factors are relevant to city OS in so much as they affect the boundaries, patterns and behaviors of the city that in turn influence the organization structure and operations of the system.

FIGURE 4.3 Smart city ecosystem.

As simulation, GIS and high-definition mapping assist in representing the city at different scales and frames of reference, there is no one form of representation that can capture the complexity of the city in single composite view. There are too many operating dimensions from macro to micro. The classic film Powers of 10 produced by Charles and Ray Eames debuted at the IBM Pavilion at the Montreal Expo in 1968 presented the expansionary view of a square meter exponentially increasing by an order of magnitude into the universe and back into the DNA and submolecular level. This illustrates that systems must be contextually understood in nested relationships. The visual explanations of Bifurcations Urban Metabolism Albania, shown as graphic maps, overlay complex patterns of urban activities, flows of energy, lights and movement, creating a living simulation of cities and regions. The goal is to create a realtime representative simulation to track all urban flows augmented with the use of algorithms to improve such processes. According to a 2018 study from the Journal of Cleaner Production, there have been “prolific” studies on urban metabolism, but not much on the industrial ecosystem, so we aim for a balance to fully comprehend the impact of cities, industry and technology on the environment. The smart city ecosystem is a holistic architecture that connects all parts to every other part, accounts for the cycle of matter and energy and provides housing, transportation and resources to the users. Sustainable energy sources such as land-based and offshore wind farms, biomass, limited nuclear and

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megasolar plants can be linked to a smart grid, with advanced energy storage and smart building design. Autonomous vehicles for personal and logistics transport and intelligent public transit systems will also be linked to the city OS for optimizing and regulating flow.

4.3.2 Smart city frameworkdthe smart city mandala The mandala is an ancient concept. In classical Southeast Asian history from the 5the15th centuries, the mandala (circle) was used as a political model. This perspective of concentric radiating centers of power with fluid boundaries would be disrupted by cartography in the 15th century and the modern notion of the nation state. A paper presented at the American Association of Geographers (Estrada-Rivera, 2018) stressed that urban design and Buddhism can inform each other, specifically with the mandala, through Yi-Fu Tuan’s concept “topophilia,” which accounts for the emotional and perceptual values projected into space along with gentrification or revitalization. The Newcastle city council has adopted a strategy developed by the firm Smart City Strategies and Solutions that is guided by their Smart City MandalaTM concept. Artist Neal Peterson has used imagery from cities to create artistic Urban Mandalas that pay tribute to the mix of architecture, art and landscapes from around the world. Borrowing from the visual, spatial and organizational language of the mandala, the city framework can be conceived as a complex set of elements combined within a harmonious organizational whole. Originally, a mandala is the ritualistic creation of an artistic “circle” intended to represent the universe; a symbolic tribute to the complexity of systems within systems. Creating mandalas, as Buddhist monks do with sand, is at once a spiritual practice and meditation, as well as a practical way to envision complex living systems and cycles. In principle, a mandala is universe and form combined uniting microcosm and macrocosm, the user’s experience and the smart city itself. In the same way, the human body not only is bounded but also has input/output mechanisms; a mandala typically has “gates” that function as the interface between inner and outer worlds. City mandalas look nothing like traditional ones, but they serve as abstract formal constructs to educate diverse stakeholders by making the potentially technocratic nature of smart OS more universally understandable. While there is a direct correlation between the city as an ecosystem and the city explained in the form of the mandala, what is different about the representation of the city through the mandala framework is the simplification of the complexity of the ecosystem. In the case of the mandala shown below, the smart city framework consists of six main components: smart economy, smart people, smart governance, smart mobility, smart environment, and smart living. These idealized forms of smart city operations permit the city to be understood as compartmentalized units representing the multiple city functions and their corresponding sub-functions and attributes comprising a holistic, abstract representation. Radiating from the center, the symbolic heart or

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FIGURE 4.4 Smart city framework. Based on Smart City MandalaTM developed by Smart City Strategies and Solutions.

brain of the city and the second ring of primary functions, these sub-functions extend outward as individual “application gates.” Using the mandala as a form of metaarchitectural framework, city leaders and stakeholders can better comprehend, organize, develop and communicate the key smart city drivers as an abstraction of both information architecture and technical architecture that describes the actual system design and components. In the idealized form of a simple pie chart, the limitation of the city mandala, as currently graphically depicted, is the inability to express the overlapping systems and interdependencies that allow cities to operate. The solution to improving the smart city mandala is the potential to use motion graphics to animate the city mandala as a real-time kaleidoscope of city life (Fig. 4.4).

4.3.3 OS Behavioral Typologies The determination of a city OS typology requires a holistic understanding of the unique physiology of each city in terms of the special combination of city governance, structure and culture, socioeconomic environment and physical resources that define the city DNA. Its management system and relationship to the public influence the system configuration and operational intelligence. It must also factor in the evolution of the city and its ability to adopt technologies described in Chapter 1 including planning for future growth. The OS typology is also highly influenced by whether the city has moved toward privatization of city resources or is government controlled. Therefore, the type of OS a city adopts must factor in these considerations to best inform which system appropriately represents the inherent nature of the city and its potential to transform as the city evolves. See Fig. 4.5.

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Top-Down

Bottom-Up

Hybrid

Emergent

FIGURE 4.5 Smart city OS typologies.

Whether top-down, bottom-up, hybrid, or an emergent system not yet fully developed as a result of new organizational structures made possible by the influence of AI, each city OS must reflect the inherent nature of the city. As political power changes hands and governance structures shift, and businesses and citizens influence the system, the type of self-organization can vary. Below we define top-down, bottom-up, hybrid, and emergent typologies of city OS.

Top-Down City OS have their origins in the context of military operations based on early cybernetic models of command and control and have been predominantly developed as top-down, centralized management systems built on legacy platforms that typically are accessible and managed by specialized software engineers, proprietary operators of the system including city government IT managers. Bottom-Up On the opposite side of the spectrum, bottom-up OS have been made possible by the ubiquitous nature of IoT and the proliferation of mobile phones providing not only city officials but also multiple types of users to be connected as both producers and consumers of data and a direct extension of the OS. Hybrid Hybrid OS allow a fusion of both top-down and bottom-up models to be developed and managed by a combination of public and private interests as a shared platform that can be accessible to municipal workers, corporate entities and citizens. Emergent systems Emergent systems are created by new technological formations representing converging human and machine systems enabled by AI that have the ability to be generative and self-regulating as does the human body and living organisms.

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4.3.4 Anatomy of operating systems The city OS, like the human body, has discrete organs that function independently within a gestalt formation. The individual functions are comprised of complex subsystems that provide services to the city through a combination of physical and digital infrastructure, hardware and software and human and machine actions. These systems flow together through procedures and protocols that govern their actions and behaviors, synchronized within the overall OS architecture. To understand the structure of OS, the purpose of the system must be identified. As a basic rule of thumb, the OS structure should be designed according to “form follows function.” Like biomimicry, form follow function, embodies the inherent behavior of the system. While there are many overlapping functions of the citydtransportation, security, communication, and so ondthe unique nature of each city and their individual requirements to develop a suitable OS and information architecture depends greatly on many factors identified in the city DNA. Therefore, the structure of the OS must be related to the unique set of requirements established when defining the MetaArchitecture of the OS. The idea that systems can be developed as form follows function can support the case for autopoietic systems as opposed to imposing formal structures on systems. The following diagram is an attempt to illustrate a hybrid centralized and decentralized city operating system architecture that is neither top town nor bottom up (Fig. 4.6). Drawing on the metaphor of human anatomy, the system includes a city brain, that serves as the command and control center of the system, linking a tripartite functional hierarchy: applications, network and data. The Applications Function runs the hardware, software and middleware

Hardware Middleware

Applications

Software

City Brain

CITY OS

Urban Media

Open Data

Smart Objects Sensor Platform

Dynamic Data

Network

Data

FIGURE 4.6 Smart city OS anatomy.

Archival Data

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as the skeletal system and primary organs. The Network Function is embodied in the IoT delivered via the Cloud connecting smart connected objects, sensors and urban media as the nervous system and sensorial organs. The Data Function, in the form of open, real-time dynamic and archival data, collects, processes, stores and disseminates data throughout the system for both human and machine operational requirements as the blood and circulatory system.

4.3.5 Smart city operating system flow The city OS has three levels - System, Network, and Interface - representing how the principal functions of the city OS relate to the management and operational flow of the city and serves the diverse users within the system. The importance of this aspect of the smart city OS design is required to align with the strategic positioning of the city to provide a clear identity for each city to differentiate itself from other cities and to provide a solution tailored precisely to the unique combination of political, socio-economic and technological stage of development. Within the system architecture the three levels each relate to the diverse system operating functions and classification of the OS. System Level refers to the macro level that includes the city management, rules and regulations and the brand strategy influencing how the city manifests its identify and modus operandi. Network Level refers to the structural connectivity of the system including the physical and digital hardware and software that facilitate communications and collect, store and disseminate information within the smart city. Interface Level refers to the objects and media channels by which all users including human and machine are provided access to interact and experience the smart city in all its dimensions. The city OS must provide a system hierarchy and flow of communication that harmoniously allow the city to operate with all of the complexity of the city functions both as combination of independent systems and subsystems, and as interoperable and co-dependent systems. The system is running at its best when all of the subsystems are operating at their optimal state based on the successful flow of information and data, the blood of the system. The Theory of Flow, further elaborated in Chapter 9, describes how individuals perform at the optimal state when able to work at their highest capacity, combining motivation, applied skills and personal satisfaction. Applied to OS, flow would enable the city to perform at its optimal state from city management to user experience through a seamless integration of the three operating levels - System, Network and Interface. (Fig. 4.7).

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SYSTEM LEVEL

City Management

Regulations

Brand Strategy

Infrastructure

Connectivity

Bandwidth

Urban Media

UX

Smart Objects

NETWORK LEVEL

INTERFACE LEVEL

FIGURE 4.7 Smart city OS flow.

4.4 What is the correct OS? In the race for developing advanced city OS, many cities are turning to the world’s leading IT companies including Alibaba, Baidu, Cisco, Huawei, IBM, Microsoft and Siemens to provide comprehensive solutions. These companies and others are responding to the dynamic nature of cities as living organisms and are developing total platforms based on the mission and objectives of each company to promote their products and services. With the combination of the complexity of OS and the range of solutions and platforms, those determining what system is best for their city must fully understand the requirements of their city in every aspect, including existing conditions, to be able to intelligently select the appropriate solution that allows both near term implementation and longer sustainable growth. Making this decision more complicated, there is a perception that many large enterprises dominating the industry create a dependency on a singular solution rather than allowing an open-source approach. It has been argued that allowing proprietary legacy systems as the basis of smart city OS is detrimental to the development of adaptable and evolutionary operating platforms by constraining the use to specific OS and technology solutions. In the white paper Framework for SMART City Deployment, the author Paul Goff (2013) argues in favor of an open system approach as the underlying architecture of the smart city OS. “A legacy approach to the deployment of information technology systems within the city will be a constraint . Integration and

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convergence of physical and logical systems is the key to a successful SMART deployment.” On the other hand, the open-source world has not fully been able to provide a solid, reliable platform for developers and is often overlooked by city managers for being too complicated. Therefore, the selection of smart city OS has remained a real issue for city leaders and stakeholders to decide on what is the best solution for managing their city and its citizens. Nevertheless, IoT has provided us with an amazingly accessible medium for the open exchange of information and open source code and we must capitalize on it to the fullest extent. Governments and corporations must aggressively adopt and support new models for information sharing, otherwise the ability for co-development will be limited and typically end up under the control of specialized groups and companies. For sensitive cross-border collaboration to become a reality, all public and private participants must also strive to meet clear standards of transparency and reliability. Another major challenge cities face is the issue of funding and commercialization of OS. The limitation of city budgets to introduce new stateof-the-art OS is the real constraint for cities to upgrade and adopt new technology solutions that could make these cities “perform” better. The role of the private sector is critical to provide market-driven momentum and entrepreneurial solutions to link the top-down governance of smart city OS while connecting and harnessing the power of bottom-up citizen participation. This supports the justification of open source, co-development within a development ecosystem. This allows for the city OS to be able to adapt to user needs and consumer patterns. The system is flexible to the changing technological environment and marketplace. Additionally, city leaders and stakeholders have lacked global and local standards for developing smart city OS. This has been due to the complexity of OS as a whole, the wide array of technology platforms and applications available, as well as the range of technology lifecycles. Fortunately, as discussed in Chapter 3, these challenges have been identified by diverse groups around the world currently working to establish universal principles and ISO standards to begin the process of developing a coherent global system and marketplace. To compare existing OS models, it is first necessary to establish a series of functional criteria to evaluate the diverse architecture and applications. The IDC Government Insights: Worldwide Smart Cities and Communities Strategies (Brooks and Yesner, 2020) business research advisory service is an example of a measurable approach that provides a useful checklist to assist city leaders and stakeholders to navigate potential challenges. The IDC analyses how cities and communities, with ICT suppliers and other partners, are leveraging technology to improve operations and better serve residents, visitors and businesses. The continuing evolution of cities along IDC’s Smart City Maturity Model, the creation of new partnerships and funding models, and the use of innovative technologies to create smart city industry and community ecosystems are all required to support the sustainable development of smart city OS.

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The determination and selection of a system architecture and technology solution is greatly impacted by the form of city government and sociocultural factors. In cities where the government has greater control, a top-down system architecture may be more relevant. In citizen-driven communities, a more bottom-up solution would be a better fit. However, cities and systems change and adapt, so while one approach may appear the correct solution at present, this may not be the appropriate solution in the future. Therefore, developing a multidimensional, hybrid convergent platform may provide cities with more options for present conditions and future sustainable growth. The ideal OS would transcend government structure, individual political parties, or ideologies. Whether change comes from top-down or bottom-up or both, what matters is that the solutions work to solve contemporary problems and plan for a more sustainable future that benefits all. In the following section, we have defined potential new constructs to enable OS to embrace the convergence of human management and machine learning: to understand the future trends of how OS will develop into intelligence, self-learning platforms that can adapt to the changing circumstances and upgrade the system on a demand basis. Additionally, another convergence criteria that we have measured is how the system is able to link to the various layers of the system including natural, human and technological states of the system. As we move toward a greater direction of convergence where we begin to appropriate behaviors of natural systems, the more the OS must adapt to changing, organic states as in the case with biomimicry.

4.5 New constructsdconvergence-based city OS In the latest stage of city OS evolution including bottom-up, open-source platforms, new potential AI-driven bioorganic systems based on a multiuser, multimodal communication networks enable the potential to create a topdown/bottom-up hybrid OS that is able to allow city government to continue in their role as custodians of the system while enabling citizens to have more direct participation and influence in the city operations. An example of a hybrid open OS is presented in the paper Civitas: The Smart City Middleware, from Sensors to Big Data (IEEE, 2013). The authors propose an openmiddleware model that builds a flexible operating platform for multiple stakeholders to engage, share and build out the system in a more organic framework. The main feature of Civitas is a flexible cloud-based IT platform that bridges city OS with third-party enterprise developers and app developers with end users. The authors state “The ecosystem notion of a smart city is an abstraction that comprises the IT infrastructure deployed by governmental institutions all over the city, such as semaphores, traffic sensors, cameras, public Wi-Fi networks, etc. The Civitas platform counts on all these sources of information and actuation as its raw elements from which smart city operations can be articulated.”

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The new OS construct, therefore, should be flexible, adaptable, scalable, and inclusive. At the same time, each city must define its own specific combination of urban characteristics, spatial construct, urban functions, user types, and behaviors to determine the appropriate system architecture and OS for that city in the present and in the future. For such a hybrid OS to properly function and evolve, the OS must be developed to accommodate both existing proprietary technologies and open-source hardware and software. The resulting OS must be a holistic solution, incorporating a collaborative approach, cross-disciplinary teams and open-source technologies. As these sciences and systems converge, it is important to demilitarize and remove lethal capacities of AI systems, whether direct (drone assassinations) or indirect (profiling, criminalizing certain identities or behaviors) in order to protect the interests and rights of all living things, as the primary focus of collective intelligence. Below are various aspects or principles of operating systems that will converge in order to create the most efficient and just citizen-serving City OS (Table 4.3).

4.5.1 Convergent OS Convergent OS are intelligent platforms that simultaneously manage city operations, execute city applications, and act as an interface between the users/ stakeholders of a city and the urban infrastructure. The objectives can be observed to be holistic, balanced, and sustainable. The characteristics of convergent OS are the following: At the end-user sphere, convergent OS can comprehensively consider the needs of different stakeholders and support city managers to control the global and local self-regulating city brain system. OS developers can build a new self-renewing system that can be continuously optimized and improved as a self-learning system, while OS designers can collaborate with end users to learn from and implement in a combination of top-down and bottom-up models. A collective intelligence can be built into the OS enabling the system to reflect and incorporate the needs and desires of the broader community. At the hardware/infrastructure sphere of convergent OS, the application of IoT and AI technology enables the city’s functional modules to have rich interfaces, realizing intelligent connectivity through data exchange and form a smart neural network, which constitutes the urban ecosystem. The network can make the city more flexible and sustainable. The city as a living organism, its cultural and social DNA, can better promote the collaboration between people and cities. At the software/application sphere of convergent OS, the city is treated as a living lab, which supports global collaboration, upgrades, evaluations and iterations for a wide range of smart applications, and collecting and processing real-time data generated by users and physical infrastructure. It promotes the accessibility and interoperability of the OS in an open source

TABLE 4.3 Convergent operating systems characteristics. Co-development Open source Open data City as shared platform

Biomimetic Functions Sustainable

Real-time data Real-time simulation OS as virtual representation

Multiuser Multimedia channels Dynamic frames of reference

Intelligent connectivity Smart connected objects On-demand anytime anywhere

Human machine Collaboration Self-learning

The brain Neural network ebased command and control Self-regulating

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User / Stakeholder

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as

Liv in b La

CONVERGENT OS

Software / Application

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ks or tw

Hardware / Infrastructure

-time Data Real

lligent Connectivity Inte

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g

Sm ar t

User End

Ope rato r

Des ign er

ce ur tom Cus

ized Experienc e

tu frastruc re e In wid tyCi

Biom ime tic

Ne

FIGURE 4.8 Convergence OS.

manner, makes various applications more secure and scalable, and can be customized to suit the characteristics of different cities. The diagram below visualizes the overlapping spheres that form the convergent OS (Fig. 4.8).

4.5.2 Co-development/open source/open data The city and the city OS are in a state of constant change and development as a living experiment. The living lab model allows the city OS to develop in a more flexible way rather than being constrained by a singular configuration or solution. Additionally, as cities are becoming increasingly more sophisticated with cloud-based open data frameworks, there is a potential to identify a hybrid middle ground that connects all stakeholders and delivers a shared platform that (1) allows city leaders to make better decisions on system solutions; (2) creates an open development environment to encourage private enterprises to infuse capital, technology, and innovative business solutions; and (3) engages citizens by participating in the operations and management of cities.

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This hybrid solution integrates a combination of government services, private enterprise solutions, and end user applications that allow citizens to participate more directly in the overall smart city framework. The pushepull interaction allows the users to both access applications within the smart city framework while providing valuable user feedback data that can be incorporated within the smart city OS to understand and improve the quality of life of the city. The data provider can be a range of entities from government agencies, corporations and small businesses to individual users. As part of this co-development environment, the potential for open source to influence the structure and behavior of the OS permits the OS to be shaped organically with the benefit of creating an open environment and expanded market place for codevelopment opportunities.

4.5.3 Self-regulating systems What is the new system architecture of a self-regulating emergent dynamic intelligent system? Whether it is realized consciously or not, we are moving towards selfregulating systems that use biomimicry to imitate natural systems. We now have the tools to design a biomimetic meta-architecture that governs the overarching system behavior of smart cities that are in a constant state of development and transformation. Through simulations that track real-time data, patterns and behaviors and by utilizing Machine Learning and generative design to continually improve efficiencies, a new dynamic, intelligent operating system is emerging. Machine learning is already making great strides in the most basic sense of AI evolution: driverless cars and robotics. Rather than meticulously program every movement and consideration, humanoid robots can teach themselves how to walk in simulations and explore their own dexterity with objects in the real world. Automated vehicles sync up with networks that analyze vast amounts of traffic data, as well as real-time local sensing and analysis, to literally nullify accident potential. This has already proven to be superior to human driving, both in the individual sense and also reducing traffic congestion in the macrosense. Thus, machine learning, given the right guides and inputs, can effectively achieve zero risk and inefficiency in certain respects. As in the case of communication spectrums, the ability of the system to self-regulate based on bandwidth availability and communication optimization is an example of the need for a collective intelligence on a broader global basis. We have explained the system architecture as a transformation from the initial stage of configuration where the design and format of the OS reflects the nature of the city as city DNA and the current state of technological and urban development to Stage 2 where the OS is designed based on appropriate combination of city spectrums representing the inherent characteristics and evolution of the city present in Chapter 1. Stage 3 builds on Collective

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TABLE 4.4 System evolution. Stage 1 Configuration

Stage 2 Adaptation

Stage 3 Self-learning

Stage 4 Self-regulating

The design and format of the OS depends on the current state of development System architecture reflects the nature of the city

The OS is designed based on appropriate combination of city spectrums representing the inherent characteristics and evolution of the city

Collective intelligence is enhanced via machine leaning and generative design and underpinned by blockchain

As collective intelligence enables the realtime operations, OS can develop as self-regulating systems

Intelligence that can be enabled by machine learning and block chain management to create an underlying system evolutionary logic. Finally, in Stage 4, as real-time operations and simulation are established, the city OS can develop as autonomous and intelligent self-regulating systems (Table 4.4).

4.6 Conclusion This chapter has focused on the diverse representations and anatomies of smart city OS and how specific organizational and functional constructs are supporting the evolution of smart city operating system architecture to become more adaptable to each city’s unique requirements and special configuration that we have described as city DNA. This chapter has also attempted to define the language and formal structure of a new emerging smart city system architecture enabled by the advancements in AI. Traditional systems architecture and representations of OS now need to be reenvisioned by the opportunities for systems to become themselves open, living systems, based on co-creation, co-development models as explored in the concept of the city as a living lab and the ideas of open source models. City OS need to slowly evolve as self-generating systems based on living behavioral intelligence that informs the formal nature of architecture into a new autopoietic language of adaptation, transformation and evolution. Top-down, models of system architecture may now longer be able to describe the dynamic flow of the convergence of human, environmental and technological patterns. As described in the previous section Approach, this new architecture depends on the individual stage of evolution of the city as a living, dynamic organism and its ability to adapt and integrate new technologies within the operational framework and unique culture of the city.

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References Anthopoulos, L.G., 2017. Understanding Smart Cities: A Tool for Smart Government or an Industrial Trick?, Vol. 22. Springer International Publishing, Cham. Brooks, A., Yesner, R., 2020. IDC Government Insights: Worldwide Smart Cities and Communities Strategies. https://www.idc.com/getdoc.jsp?containerId¼IDC_P23432. (Accessed 30 January 2020). Dainow, B., 2017. Smart City Transcendent-Understanding the Smart City by Transcending Ontology. Orbit 1. Dervin, B., 2000. Chaos, Order, and Sense-Making. In: Jacobson, R. (Ed.), Information Design. MIT Press, pp. 35e57. Estrada-Rivera, G., 2018. Creating Cities of Sanity: Using the Mandala Principle as a Urban Planning Tool. https://aag.secure-abstracts.com/AAG%20Annual%20Meeting%202018/ abstractsgallery/14174. (Accessed 30 January 2020). Goff, P., 2013. Framework for SMART City Deployment [online] Available at. https://tomsanchez. files.wordpress.com/2013/12/paul-g.pdf. (Accessed 30 January 2020). IEEE, Villanueva, F., Santofimia, M., Villa, D., Barba, J., Lo´pez, J.C., 2013. Civitas: The Smart City Middleware, from Sensors to Big Data. In: Proceedings - 7th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing. IMIS, pp. 445e450, 2013. https://ieeexplore.ieee.org/document/6603713. (Accessed 30 January 2020). Madni, A.M., 2017. Transdisciplinary Systems Engineering: Exploiting Convergence in a Hyperconnected World. Springer. Marvin, S., Luque-Ayala, A., 2017. Urban Operating Systems: Diagramming the City. International Journal of Urban and Regional Research 41 (1), 84e103.

Further reading Andersen, C., Pold, S., 2018. The Metainterface: The Art of Platforms, Cities, and Clouds. The MIT Press. Bailenson, J., 2018. Experience on Demand. W. W. Norton & Company. Batty, M., 2013. The New Science of Cities. The MIT Press. Berger, A., Kotkin, J., Balderas-Guzma´n, C., 2017. Infinite Suburbia. Princeton Architectural Press. Bostrom, N., 2016. Superintelligence. Oxford University Press. Ferra˜o, P., Fernandez, J., 2013. Sustainable Urban Metabolism. The MIT Press. Kurgan, L., 2013. Close Up at a Distance. Zone Books. Thatcher, J., 2018. Thinking Big Data in Geography. University of Nebraska Press. White, R., Engelen, G., Uljee, I., 2015. Modeling Cities and Regions as Complex Systems. The MIT Press.

Chapter 5

Connectivity Chapter outline 5.1 Introduction 5.1.1 Connectivity itself will become intelligent 5.1.2 All living organisms are related within a frequency spectrum 5.2 Evolution of connectivity 5.3 The electromagnetic spectrum, frequencies, and bandwidth 5.3.1 Electromagnetic patterns, frequencies, and human energy fields 5.3.2 Electromagnetic spectrum 5.4 The role of machine learning and deep learning in intelligent connectivity 5.4.1 Radio Frequency Machine Learning Systems 5.4.2 The role of evolutionary algorithms in connectivity 5.5 Connectivity anatomy 5.5.1 The human body and neural networks as models of connectivity

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5.5.2 The brain 5.5.3 Other organic models of connectivity 5.5.4 The backbone of connectivityd telecommunication networks 5.5.5 The sensorial layer of connectivity 5.5.6 Mobile connectivity 5.6 Integrated networks and services 5.6.1 Industry 4.0dthe basis of connectivity 5.6.2 Convergence connectivity 5.6.3 Intelligent connectivity using combination of 5G AI and IoT 5.6.4 Connectivity singularity 5.6.5 Smart objects 5.7 Conclusion References Further reading

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5.1 Introduction At the root of being smart is the ability to connect all entities within the ecosystem. Without the capability to connect to the system, entities within the system will not be able to supply and receive data required to establish a state of collective intelligence. The ability to process complexity requires a connectivity architecture analogous to the most complex system known: the human brain. This has led to the extensive study of the function of the human brain and neural anatomy in the fields of Artificial Intelligence (AI) and smart Smart Cities and Artificial Intelligence. https://doi.org/10.1016/B978-0-12-817024-3.00005-2 Copyright © 2020 Elsevier Inc. All rights reserved.

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city design. As cities become smarter and more people, objects and devices are added to communication networks, the need for managing the vast complexity of the system compels new forms of connectivity and more channels of communication for each of the elements within the network. This will be deployed from smart infrastructure to nanomembranes, each with the built-in attributes of being flexible, adaptable and self-generating as connectivity is in a constant state of evolution.

5.1.1 Connectivity itself will become intelligent In the model “Hierarchy of IoT Thing Needs,” (Hunter, 2015) connectivity is a critical middle layer wedged between physical needs, security needs, data needs and smart needs. This hierarchy reinforces the basis of organic life and the relationship of survival of the organism within the environment. Connectivity, therefore, is a key function within the IoT system hierarchy. A common confusion is understanding the difference between connectivity and communication. Connectivity enables the exchange of data through system compatibility, implying a constant state of connection, while communication is the medium that sends and receives data. Communication is a subset of connectivity. It is important to differentiate between these two functions because this symbiosis forms the basis of the ability of systems to operate and the flow of information to occur. Connectivity is how devices communicate with each other and the network. Connectivity ensures that data or content is efficiently collected and routed to be analyzed and used. Examples include citywide WiFi networks, radio frequency mesh networks and cellular networks. Connectivity occurs on different scales from global to very localized connectivity. As we develop smart objects and advance technology at a nanoscale, the communication spectrum will have an immense range as the diversity of connectivity requirements will continually expand at different scales and serve diverse functions. To manage this complexity, AI-enabled connectivity will route information and resources where they need to be allocated within the specific communication spectrums. The development of a collective intelligence communication will establish a superconnectivity that understands the overall behavior of the system and allocates bandwidth on demand. Through biomimicry, this model can be made analogous to a living organism or the human body where the nervous system sends signals to the brain to relay data back and forth throughout the body as it responds to external stimuli. As the physical environment is made up of electromagnetic fields and frequencies form the basis of connectivity, the flow of data within the city occurs all around us through diverse communication spectrums. The need to organize and allocate these spectrums requires new models to allow multidimensional communication to occur in a harmonious orchestration. This includes a bandwidth architecture based on organic, functional, contextual, spatial, environmental and temporal parameters.

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5.1.2 All living organisms are related within a frequency spectrum The human body exists within this electromagnetic environment and all living entities are composed of vibrations. We are part of a giant electric field, which holds our atoms together and which uses other electric fields to communicate within ourselves and beyond. As humans and machines merge in terms of functionality of systems, humans and machines will begin to share the same frequencies creating a new hybrid intelligent organism. This will also influence the evolution of Industry 4.0 combining IoT and cyberphysical systems where virtual networks set up to control physical objects require continuous and instant communication between various elements within the supply chain ecosystem.

5.2 Evolution of connectivity For millennia, human settlements and communities were strategically built up around lakes, rivers, deltas and seashores, where boats could cover greater distances and be used for fishing. This connected social groups over large geographic areas otherwise impossible giving them access to expanded resources and experiences. With the invention of the road and wheeled vehicles (horse drawn carriages mostly), great distances over various terrain seemed to shrink as suddenly far off lands were more accessible. Roads enabled empires to rise, but were not enough to maintain connectivity and leading to eventual decline. We track the evolution of connectivity marked by major stages from rivers and roads, through copper and fibre optics, to WiFi and the future of ambient connectivity (Table 5.1). TABLE 5.1 Evolution of connectivity. Rivers

Roads

Copper

Fiber optics

WiFi

Ambient

The scientific and industrial revolutions deepened connectivity with the advent of larger steam powered ships and rail transportation networks. When electricity was discovered and developed in the late 19th century, copper wiring and electrical grids would connect cities even more. The science behind fiber optics goes back to the 1840s but the technology became operative in the 1960s and in the 1980s when, it became cheaper than copper wire and has continued to increase in speed and cost-efficiency since then. Fiber optics increase connectivity several orders of magnitude with higher bandwidth, greater signal integrity over long distances, immunity to electromagnetic interference and better security. With the addition of WiFi going commercial in the late 90s, connectivity could be total, instant and ubiquitous in limited

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areas. Finally, we come to ambient connectivity, the future of the Internet as it has evolved up until now. Thus, connectivity has evolved from physical, environmental constructs beginning with rivers and roads as physical connectivity moving people and goods to copper wires transmitting electrical currents to fiber optics sending pulses of light to the emergence of new forms of ambient connectivity, “the ability to assume connectivity anywhere and anytime” (Frankston, 2009). Not only is connectivity maximized between user and Internet but also objects and devices will be connected with each other perpetually. Further into the future, we will decommission physical forms of connectivity as we will merge with electromagnetic fields. This dematerialization of the physical communication infrastructure will form new constructs that are no longer constrained by current linear and hierarchical models. New systems of connectivity will represent emergent forms of communication behavior and will reflect the convergence of diverse communication mediums and spectrums. An example of this convergence today is the trends in power sharing in which data transmission has merged with electrical conduits. This will expand further into organic, nonlinear forms of connectivity, embodying communication behavior that is found in nature such as represented in biomimetic connectivity that no longer relies on linear communication sequencing. Characteristics of new connectivity systems: l l

l

Nonlinear processing of stimuli Routing of information in complex networks and clusters rather than linear sequences Multi-bandwidth spectrums enabling intelligent on-demand communication and noise reduction.

Connectivity is the backbone of technology and the IoT that has enabled the rapid expansion of smart cities. Connectivity begins with communication networks and protocols. Within the system architecture, each system and subsystem utilizes its own form of connectivity to link individual entities in relation to the body as a whole. The system does not always cognitively manifest the boundaries of the system; however, intuitively it does. In other words, systems understand their own finite boundaries while they have built-in functions to connect with both internal and exterior systems. Consider a computer which is self-contained and can operate independently of network connection, yet is typically connected to the Internet where its computational power is vastly extended and multiplied. For instance, our brain has physical limits and internal coherence, but it is also a part of its external environment, communicating with other brains. Your greater selfawareness is either limited or enhanced by the extent of your connectivity. There is a massive difference in types of systems connectivity, with each city having its own distinct format influenced by the stage of technological evolution of the city as a connected system, organizing and determining what technologies it can adopt more easily. One of the major issues cities and countries face in the developed world is the challenge to move forward with new technologies that

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undermine past capital investments to build and operate previously developed telecommunication infrastructure that will become at some point obsolete. This is called path dependence. As discussed in Chapter 1, underdeveloped countries can leapfrog legacy systems to adopt wireless communication while cities still reliant on copper may evolve from land-based to becoming fully wireless over decades in the process of decommissioning existing systems. This relates back to the concept of city DNA presented in Chapter 2, however, within a different dimension of connectivity. Dynamic systems require the ability for connectivity to adapt to diverse states of the system. The system is always in a state of transformation; therefore, connectivity needs to adapt to meet the communication requirements of the system. With the advancements in next-generation networks (NGNs), the connectivity framework will be able to adapt to the flow of communication similar to the way the brain processes complex information, routing communication via a self-regulating mechanism. Convergence connectivity is the integration of diverse functions including services, networks, policies and user experience within a unified connectivity framework that is able to construct an expanded self-regulating ecosystem. Within this framework, smart connected objects from smart infrastructure to autonomous vehicles and smart objects at diverse scales including nanoscale objects will operate at different frequencies and bandwidth. To support the processing of the complexity, high-performance computing (HPC), machine learning (ML) and deep learning (DL) are key drivers of the ever-increasing deployment of AI. As a form of ML, DL exploits deep neural networks that enable sophisticated processing of data across multiple layers simultaneously, essential to enabling the required processing of the huge unstructured datasets within the connectivity complexity. The core of virtualization is the virtual machine (VM). A virtual machine is a software abstraction that allows multiple software environments, including operating systems and their applications, to run together on shared hardware. Deploying high-performance virtual machines for ML/DL workloads as part of a high-performance ML/DL platform offers a host of administrative benefits that increase efficiency, flexibility and agility.

5.3 The electromagnetic spectrum, frequencies, and bandwidth 5.3.1 Electromagnetic patterns, frequencies, and human energy fields For thousands of years the Chinese, Indian, Egyptian and many other cultures have studied energy systems within the human body and as part of the external natural ecosystem. These include the delineation of energy systems and flows of energy including meridians, chakras, Qi and other representations each tapping into the subtle forms of energy, usually practiced through breath and

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body awareness and how the human body channels this energy in a live sustaining force and comprehensive system architecture connecting all parts of the body. While ancient civilizations may not have employed scientific methods by today’s standards, they attempted to map a complex reality that the modern scientific reductionist gaze precludes. Extending the metaphor of human energy fields, frequencies and networks opens the possibility to understand cities in a parallel way. As we discussed in Chapter 2, drawing on Batty, cities are not just places in space but systems of networks and flows. These flows embody the diverse frequencies of urban life (Fig. 5.1).

Time [s]

FIGURE 5.1 Frequencies/waves/bandwidth.

For centuries, researchers have attempted to describe all the fundamental forces of nature and how they interact in a single theory. This unified field theory stumped the likes of Albert Einstein, who worked on the theory for many years. In physics, a field is an area under the influence of some force, such as gravity or electromagnetism. A field theory refers generally to why physical phenomena happen and how these phenomena interact with nature. As human and machines merge within a unified electromagnetic field, connectivity will occur on new levels throughout human, machine and environment combinations. A group of researchers at the Purdue University School of Electrical and Computer Engineering led by assistant professor Shreyas Sen have discovered a new way to use the human body as a robust communication medium for networking electronic devices in and on the body. It promises to be far more secure and low-energy than any wireless system. “We can achieve secure connectivity within devices in or on the body and even among devices on different humans and machines, at orders of magnitude lower-energy than wireless,” Sen explained (Sen, 2017). Among the possible uses for the human body as a communication network are implanted medical devices, wearable devices, secure payment technology, authentication applications and the millions of devices that fall under the Internet of things (IoT) and the Internet of Medical Things (IoMT).

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5.3.2 Electromagnetic spectrum The electromagnetic spectrum is the range of frequencies of electromagnetic radiation (EMR), which divide into seven types: radio waves, microwaves, infrared radiation, visible light, ultraviolet radiation, X-rays and gamma rays. The radio spectrum goes from 30 Hz to 300 GHz, the very low end being used for communication in submarines and on the high-end wireless networks. AM, FM, TV, cellular and WiFi are common uses in the spectrum (Fig. 5.2). As the frequencies become more crowded, technology is also evolving and finding new ways to increase bandwidth and share frequencies. VISIBLE LIGHT

MICROWAVE

RADIO 1KHz

1MHz

1GHz

INFRARED

ULTRAVIOLET

X-RAYS

1THz

Twisted-pair Wire Optical Fiber Coaxial Cable

FM/TV Broadcast

Terrestrial Microwave

Telephone AM Radio Satellites Shortwave

Cellular Telephone

FIGURE 5.2 Electromagnetic Spectrum.

5.4 The role of machine learning and deep learning in intelligent connectivity 5.4.1 Radio Frequency Machine Learning Systems In a new area of frequency and bandwidth allocation and management, new developments allow diverse communications to be able to find suitable bandwidth and frequency spectrums. As more and more devices from smart vehicles to nanoscale objects require diverse frequency spectrums and bandwidth to operate, AI capabilities are required to manage this on demand. Building machine learning and deep learning inherently with connectivity will enable systems to develop self-learning capabilities to identify the appropriate communications channels and allocate bandwidth in real time. One of the solutions to this issue is the development of Radio Frequency Machine Learning Systems (RFMLS) to create the new basics of automatic learning centered on the parameters of the RF spectrum. Leading this research and development effort is Paul Tilghman (2017) at DARPA, developing technologies to understand spectrum sharing, with the goal of significantly

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increasing wireless communication capacity within the electromagnetic spectrum. This R&D forms the basis for a new wave of signal processing technologies to address the performance limitations of conventional radio frequency (RF) systems such as radar, intelligence and communications. This is a new paradigm of shared spectrum usage rather than the current practice of exclusive attribution governed by licensing agreements for specific frequencies. With RFMLS, there is a greater possibility to provide much more agile and versatile capabilities and the ability to identify a wide range of known and unpublished data RF waveforms (Fig. 5.3).

RF Data

MACHINE LEARNING

Task Function Feedback

Model

Waveform Synthesis Learn from waveform modulation that allows for more effective discrimination

Feature Learning RFMLS PROGRAM Uniquely identify a wide range of devices in a large population

Autonomous RF Sensor Configuration Ability to exercise control over a hardware receiver and extend awareness over 5-GHz bandwidth

Attention and Saliency Identify all signals of a given type across 500 MHz bandwidth / Identify and characterize anomaly signals

FIGURE 5.3 Radio frequency machine learning systems. Based on diagram by Bika Armand.

5.4.2 The role of evolutionary algorithms in connectivity Within the multidimensional space of connectivity, algorithms play a key role performing sequences of specified actions and instructional sets to solve precise connectivity problems. Owing to the rapid expansion of networks, Internet services and software, cellular mobile radio systems, fiber optics and broadband networks are continually expanding to meet the global demand. Structural communication engineering design solutions related to the routing of network information is required to facilitate this rapid communication of data, new services and applications. Similarly, as the size of existing telecommunications infrastructure continues to grow, the underlying problem of optimization often poses a challenge to traditional algorithms with low efficiency and unrealistic assumptions that often render the algorithms useless for solving real problems of great magnitude and in a reasonable timeframe.

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To create more efficient communication networks, research efforts based on the principles of natural evolution have led to a category of Evolutionary Algorithms (EAs), based on the theory of Charles Darwin. The evolutionary mechanisms that are implemented vary considerably; however, the basic concept behind EAs is characterized by the existence of a population of individuals exposed to environmental pressure, which leads to a natural selection process or the survival of the fittest and in turn the increase of the average fitness of the population. Fitness is the measure of the degree of adaptation of an organism to its environment; the bigger the fitness is, the more the organism is fit and adapted to the environment. In general, EAs focus only on a subset of mechanisms defined over the biological evolutionary process. By employing EAs, the probability of finding a near optimum in an early stage of the optimization process is very high. In the context of evolutionary cybernetics, a “global brain” (Heylighen, 2007) has been theorized in terms of the emergent collective intelligence coordinated through humans and knowledge systems. The Internet acts as a nervous system with increasing levels of reflexivity and selforganization. The collective intelligence emerges through these interactions, just as consciousness emerges from the activity of the brain. EAs applied to communications and connectivity will transcend human limitations in these matters and improve connectivity faster than humans can innovate.

5.5 Connectivity anatomy 5.5.1 The human body and neural networks as models of connectivity Building on the analogies of human systems and the city as living organism, connectivity can be conceptualized like the human nervous system linking the brain via the spinal cord as central nervous system to the various peripheral systems within the body including the sensorial apparatus. Akin to the human body, smart city connectivity can be conceived as a similar system of organization to the brain, as the command and control center and the nervous system, the conduit linking the various functions of the city. Since the early experiments in the creation of analogue computers and later with the development of mainframe computing in the 1950s, the human brain and cognitive science have undergone research in parallel with computer science and network engineering, which has led to a symbiosis that continues to advance human knowledge in many fields. This is largely the field of cybernetics. A part of this relationship, to understand and take advantage of complexities in information processing and networking, the field of neuroscience, has been a key area of scientific investigation focusing on artificial neural networks adopting the principles derived from human and animal brain functions. While computers and networks are highly sophisticated, the human brain is still an unmatched operating system. Researchers, scientists, and computer and

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network engineers have been working steadily to crack the structural connectivity, communication dynamics and functional connectivity of neural architecture and human information processing. This enables them to develop better and more agile networking systems to meet the increasing demand for computation processing power and speed.

5.5.2 The brain The brain may be the most sophisticated natural operating system, but technology is quickly catching up and aiming to supersede our intelligence, hence the singularity. Metaphorically, the brain has been co-opted for systems design and branding with names like Alibaba City Brain, Baidu Brain and other technology companies building intelligent system based on human neural architecture and functionality. The reason for this is that as humans we conceive the world through a human lens, as described in Chapter 2, and we have designed many systems based on the study of the human body and its relationship with the environment throughout history. It turns out that the human system architecture is efficiently designed. Our brain and neural networks are innate firmware that includes hardware, software, memory, database and a network of active components that move information within the body and interact with external stimuli. Studying the network topology of the brain inspires biomimetic models for designing information architecture (Fig. 5.4).

Neural Networks

Structural Connectivity

Communication Dynamics

Functional Connectivity

FIGURE 5.4 Neural networks. Based on diagram published in: Communication dynamics in complex brain networks.

Alan Turning proposed the “Turing Test” to determine whether or not AI could match the standard of human intelligence. If a machine (computer) could convincingly communicate in real time, it would have passed the Turing Test. Machines were a long way off in his day, but we are much closer now. The idea of “connectionism” uses artificial neural networks to explain mental phenomena. The central principle describes how mental phenomena can be reduced to units (i.e., neurons) and connections (i.e., synapses). Practical engineering applications that use the concept of neural networks are many including airport security bomb detection, signature verification, financial forecasting, robotics, vision and

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speech processing. Neural signaling underpins virtually all aspects of brain function. Artificial neural networks do not necessary have to perfectly mirror biological templates but emulate the patterns of cognition and can “learn” by studying examples.

5.5.3 Other organic models of connectivity While the human brain may be one of the most developed systems, other models of connectivity networks may be more aligned with the idea of ambient connectivity. Jellyfish are studied for their complex network of cells called a nerve net that lines the inside of the jellyfish and provides sensory feedback. Scientists have discovered that this neural configuration is highly sophisticated and generates complex behaviors. There are countless examples across nature of this level of cognition and intelligence, which act as an endorsement for biomimicry as well as nature as a model of sustainable development of systems. The figure below illustrates the difference between Fat Tree and Jellyfish architectures, which may each be optimized for different contexts (Fig. 5.5).

Fat Tree Network Architecture

Jellyfish Network Architecture

FIGURE 5.5 Networks architectures.

In most mental states, human cognition is most like the fat tree network, with some focus on prioritization and decision-making that structures the information filtered through the senses. For connectivity in general, the jellyfish topology is the most efficient. Mushrooms are also a good model for connectivity, with their network of mycelium substrate transporting nutrients and information. The way psilocybin (magic mushrooms) affect the brain to enhance neural activity also reflects their efficient abilities to network information and create emergence.

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Through the development and applications of artificial neural network AI functionally, we can manage the complexity of interconnected systems and expanding configurations of system architecture providing an organic selfgenerating network that is able to process massive data and systems connectivity as the basis of cities as living organisms. Building on the ability to learn, machines are able to develop self-regulating capabilities that assist in managing operations, efficiency in the operating systems and optimization of connectivity.

5.5.4 The backbone of connectivitydtelecommunication networks As in the human body, there are structures of hard-wired networks and softwired connectivity linking both physical organs as well as nonphysical functions. Traditional telecommunications infrastructure and protocols remain part of the landscape of the city and function as the backbone of the connectivity framework and nervous system of the smart city framework. In many cities, the existing telecommunication infrastructure transporting information through copper and more recently fiber optic cables has been the stable element of the system while new wireless technologies have grown around them and added new dimensions of connectivity. Hard-wired network infrastructure, therefore, provides the stable connectivity, linking the brain with the internal command and control center and to the vital organs of the city, while providing the bridge to other cities and regions. Telecommunications architecture can be divided in three functions transport, backbone and access e each integrally operating as a continuous system (Fig. 5.6). Transport is the infrastructure that carries the telecommunication signal. The Backbone routes and disseminates the communication bandwidth and Access is the interface protocol contextualizing the communication within specific user environments. From wide-area networks to ACCESS

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FIGURE 5.6 Communication networks and hierarchy.

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personal area networks, the telecommunication architecture serves the same purpose to provide end-to-end connectivity and context specific adaptability in order to achieve the most stable and efficient communication. Communications technologies have undergone rapid development and obsolescence of diverse forms of communication from the telegraph to telephone, radio, television and the Internet. These technologies have advanced from centralized broadcast models with one transmitter and a multitude of receivers to decentralized distributors of information (Internet), with many source emitters and receivers. Supporting these systems is the telecommunications infrastructure, including cables, IP networks, voice, video, wireless and quantum telecommunications ensuring continuous operability and citylevel applications and services. As telecommunication systems converge with other communication systems and form a broader connectivity platform, traditional telecommunication infrastructure and systems will slowly dematerialize within wireless networks and use new forms of connectivity to expand the overall connectivity framework. Nevertheless, traditional telecommunication infrastructure, for the foreseeable future, remains the backbone of smart city connectivity and serves the purpose of providing stable system architecture and connectivity.

5.5.5 The sensorial layer of connectivity Sensors form a layer of connectivity as the sensorial input responding to environmental stimuli and connecting the core operating system with the environment. More and more cities will place and imbed sensors in every conceivable dimension of the city operations with sensors forming a massive network collecting all forms of human, machine and environmental data. These networks capturing and transmitting real-time data form the basis of a simulacrum of urban and planetary behaviors. As these sensor networks increase in size and reach, the connectivity supporting the sensor network must be self-sufficient both in terms of connectivity functionality with all sensors within each network linked together and with built-in energy supplies. Sensors need a way to stay connected and continuously transmit information, creating a vast, self-sustaining and self-configuring stream of information. An interesting question is how and where will the sensors be located and positioned to optimize data capture to best reflect the inherent nature and behavior of the systems they are monitoring. To solve this challenge, machine leaning, generative design and evolutionary algorithms are used to create and analyze the best bio-deterministic options to refine sensor network configurations. Solutions developed and commercialized by several companies including Orbis Mesh have developed independently functioning networks with autonomous connectivity and energy sources. Unlike a normal cellular or WiFi connection, the more sensors that are within the network, the stronger the network becomes. In the case of Orbis Mesh, a radio frequency (RF) mesh is utilized to

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build wireless networks connecting devices together completely independent of the presence or failure of cellular or WiFi networks, as well as being free of usage or cellular charges. Additionally, the Orbis Mesh utilizes a low-energy and low-cost wireless solution for transmitting data, collecting information and automating equipment. Data are securely relayed by up to thousands of selfconfiguring local devices and can self-heal without a single point of failure. By creating an efficient, low-cost, wireless system which renders cables, satellite, or cellular-based networks redundant and using custom firmware, Orbis Mesh is an example of a new class of self-sufficient smart sensor networks that will be required to build the vast environmental collective intelligence needed to monitor cities and global patterns at diverse scales and contexts.

5.5.6 Mobile connectivity At one time, certain connectivity was based on a stationary location, with point-to-point communications as was the case with landline telephones. As mobile connectivity further advances, so is the behavior of systems to respond to the dynamic nature of objects, devices and people in motion. In the transition from traditional physical communication platforms to mobile dynamic networks, the requirement to track and describe entities or nodes moving within the system has established Mobile Ad Hoc Networks (MANET). Allowing for new connection opportunities in the absence of fixed connectivity infrastructure, data exchanges can take place on impromptu networks created by encounters with other devices, effectively piggybacking on each other. This provides a seamless mobile connectivity platform that adapts to the environmental conditions and creates an on-demand solution. As humans move and exchange data on a variety of platforms and in multiple locations and environmental contexts, the ability to develop flexible intelligent connectivity is critical. As part of the advancement of new mobility models, recent R&D efforts have explored how social relations between people influence mobile dynamic networks with the logic that humans carry mobile devices and human mobility is influenced by the social relations mediated by those devices. Therefore, bridging social networks and connectivity networks based on human social behavior and patterns will advance new mobility models and solutions.

5.6 Integrated networks and services 5.6.1 Industry 4.0dthe basis of connectivity The expansion of IoT to the industrial sector is a major factor fueling the growth of smart supply chains and interconnected industrial processes. Connectivity is the backbone harmoniously linking all of the components throughout the industrial ecosystem. Within Industry 4.0, wireless communication technologies are utilized due to their advantages of flexibility, low cost and ease of

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deployment. Wireless networks also provide easy access to broadband Internet services, allowing multiple external devices including mobile phones, laptops and other communication devices to connect. Additionally, wireless equipment is able to continue to operate in harsh environments or adverse weather conditions with extreme temperature, humidity, vibration and obstacles challenging system functionality. Industry 4.0, combining IoT and cyberphysical systems (i.e., virtual networks used to control physical objects), requires continuous and instant communication between various equipment and integrated workstations in production and supply chains. To support the connectivity of multidimensional systems, the development of industrial wireless networks have deployed diverse architectures and industry protocols including Wi-Fi, ZigBee, Bluetooth and RFID creating more flexible wireless devices and peripherals remotely controlled and better integrated with the overall production system. Within the system architecture, sensors allow production equipment and tools to be selfdiagnostic with predictive maintenance, improved decision-making in real time, inventory forecasting based on production and improved coordination of tasks. These features become imbedded within the connectivity of the system, allowing the system to develop industry-wide collective intelligence and optimization. Another challenge of ubiquitous technology, the deployment of smart objects at diverse scales within Industry 4.0, is the issue of energy and mobility. Industry 4.0 is on the path to developing renewable energy solutions integrated with industrial production and powering smart connected networks of objects, people and resources. Industrial facilities and machinery are becoming powered by wind, solar and geothermal power based on biomimetic functions to meet this challenge.

5.6.2 Convergence connectivity Convergence connectivity is a phenomenon made possible by the combination of the digitization of content, the shift to Internet Protocol (IP)ebased networks, the generalization of broadband access and the evolution of multimedia. As the Internet has enabled more ubiquitous computing and the connection of multiple devices on demand, the expansion of the network in terms of speed, bandwidth and multimodes of communication including VoIP, video chat and real-time gaming has created new opportunities and new demands. The following diagram explains this convergence of the multiple functions that include services, networks, government regulations, user experiences and industry marketplace ecosystems. In this state of convergence, everything over IP comes together in an integrated service platform that provides diverse users including public agencies, businesses and individuals a more content rich, multimedia diverse and user-friendly one-stop solution See Fig. 5.7.

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Convergence Services

Convergence Networks

Convergence Regulations

Convergence User Experience

Industry / Market Convergence

CONVERGENCE CONNECTIVITY

FIGURE 5.7 Convergence connectivity. Based on diagram by Bika Armand.

This convergence will continue to develop more services that will come together as broadband improves real-time, high-definition media experiences and diverse communications mediums become seamlessly woven into service offerings. Media content and communications will merge into rich experiential composites of content and communication delivered across end-to-end protocols. Users will become increasingly savvier with simpler user interfaces that connect diverse devices and types of content. As smart cities become open-source platforms for the repository of information, convergence connectivity will provide an integrated multichannel, multimedia platform for a variety of content, social services and payments, where all city services can be offered through a standardized and optimized interface. Expanding on this platform, connectivity becomes a space for the exchange of information, goods and services and can incorporate B2B and B2C channels supporting new services and applications stimulating the growth of industry business ecosystems and marketplaces.

5.6.3 Intelligent connectivity using combination of 5G AI and IoT A key advancement in developing a new stage of connectivity is the introduction of 5G harnessing AI technologies to new levels and enabling the vision of intelligent connectivity. The ultrafast, low-latency connectivity provided by 5G networks combined with big data collected by billions of devices through the IoT and the contextualization and decision-making capabilities of AI has enabled new processing capabilities in virtually all sectors of smart cities including autonomous transportation, healthcare and enhanced public services. With very high flexibility and speed to promote optimal communication management in networks, this combination marks the beginning of a new era

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defined by highly contextualized and personalized experiences, delivered on demand. Within the intelligent connectivity platform, digital information collected by the machines, devices and sensors comprising the IoT is analyzed, contextualized and presented to users in a more meaningful and useful ways. As with the mesh network model, networks can become self-diagnostic and self-healing. With the advent of the IoT and the smart city, this intelligent connectivity consists of bringing out a new method of convergence with all the integral parts of the network enabled through the integration of AI, machine learning and deep learning. In the future, all connected objects must communicate like the human brain as it organizes itself to understand all the essential and deep needs of the networks and users.

5.6.4 Connectivity singularity Peter Diamandis of Singularity University describes “the coming era of connectivity” (2018) that will give everyone in the world web access at high speed and minimal cost by 2024. The institution has projected that between 2017 and 2025, 4.2 billion “new minds” will join the Internet, making 100% of the global population connected, as well as the IoT. New networks are being established in three forms: 5G, balloons and satellites. 5G outpaces 4G by 100x and broadband by 10x. Large-scale deployment will begin in 2020. Meanwhile, Google is putting balloons and drones into the stratosphere, creating a 4G LTE wireless network, replacing the function of cell towers. The balloons themselves are guided by machine learning to use and adapt to weather patterns. Space X and OneWeb are putting thousands of satellites into low earth orbit to provide 50e500 Mbps download speeds globally by 2021. These advancements represent the massive digital infrastructure and global system development that is underway to create and connect OS Planet Earth as a unified ecosystem serving the entire population whether in cities or rural areas.

5.6.5 Smart objects Smart connected objects are physical objects equipped with sensors or a chip that interact with people and other smart objects. They are electronic devices capable of communicating with computers, smartphones, tablets, or other objects via a wireless network (WiFi, Bluetooth, mobile phone networks, longrange Sigfox or LoRa radio network, etc.) and linked to the Internet or local network. We commonly distinguish two primary groups of smart objects that are connected: (1) objects for the collection and analysis of data, the main purpose of which is to collect and transmit information; and (2) objects that respond to a Command and Control logic and can trigger a remote action or

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perform a function. The sensors installed on these connected objects are more or less intelligent, depending on whether they themselves integrate data analysis algorithms and are able to be self-regulating as in the case with Orbis Mesh sensors that form Smart Networks providing the needed technological infrastructure while combining data analytics, security and self-sufficiency. Smart connected objects can be applied to diverse object types and functions and can be deployed at different scales embedded in large infostructures, smart buildings, wearable human devices and at a nano-technology scale including forming smart membranes (Fig. 5.8).

People

Object

Nano

FIGURE 5.8 Smart connected objects.

AI is not reserved for robotics or science; it infiltrates everywhere. Services and smart connected objects of everyday life become intelligent. They understand human behavior and the environment and anticipate needs. Cloud computing, machine learning, big data and technological progress are changing the relationship between the human and the object. Connected objects are smart objects that fit perfectly into a total ecosystem. An example of this is the maximization of the use of production machinery in industry 4.0 where the manufacturing supply chain allows all devices to communicate with each other forming an integrated manufacturing process that in turn seamlessly links to project management and operations‘ functions, overseeing and adapting the process on demand. Expanding on the smart connected objects platform, machine-to-machine, or M2M, refers to the telecommunication process by which machines connected to a wireless network interact without human intervention. A more general definition of “machine-to-machine communication” is the association of ICT with intelligent and communicative objects, with the aim of providing objects the means to interact without human intervention within the information system of an organization or company. The machine-to-machine model has developed due to the convergence of three families of technologies: smart objects, communication networks and centralized computation with AI.

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5.7 Conclusion In this chapter, we presented diverse types of connectivity from the initial understanding of the evolution of connectivitydthe utilization of natural resources to large-scale physical infrastructure and from analogue to digital networks. And finally, to harnessing the entire electrometric spectrum as the basis of a potentially unlimited bandwidth allocation supporting the concept of ambient connectivity. To efficiently manage these diverse communication spectrums, machine learning and AI-enabled processes have incorporated evolutionary algorithms capable of the optimization of bandwidth allocation and management of bandwidth sharing. Additionally, to understand and take advantage of the complexities of information processing, the field of neuroscience has been a key area of research with the concept of the human brain and neural network introduced to assist in comprehending, designing and programming new communication networks and processes. As our combined knowledge of neuroscience and city operating systems increases, and the physical and digital worlds converge further, the city and brain will form a new complementary and mutually reinforcing union (Fig. 5.9). CITY

DIGITAL

PHYSICAL

BRAIN

FIGURE 5.9 Intelligent connectivity.

In this regard, the function of the human brain is crucial to elucidating how neurons and neural networks process complex information in real time and has guided our reflection on the theory and application of convergence connectivity. Advanced technology converges and connects everything together holistically akin to basic brain functions and networks connect to each other akin to higher

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levels of brain activity. Other possible connectivity models include evolutionary algorithms supporting mobile networks and smart connected objects. Connectivity begins as an external system of communication, connecting independent nodes, but it is evolving to be a living system itself, where algorithms continually adapt and refine new processes that form the technical basis for collective intelligence. Ambient connectivity is permanent state of being online enabled by ubiquitous technology, whether directly or through background communication between devices. Ambient connectivity will move us away from traditional ISPs to ubiquitous open access, where on-demand services are offered through diverse media channels. As all mobile and stationary objects are connected within ambient networks, data will be routed via the most efficient means and stable path by self-learning, evolutionary algorithms supporting a new superintelligence connectivity architecture.

References Diamandis, P.H., 2018. 4 Billion New Minds Online: The Coming Era of Connectivity. Singularity Hub. https://singularityhub.com/2018/07/27/4-billion-new-minds-online-the-coming-era-ofconnectivity/. (Accessed 21 September 2019). Frankston, B., 2009. Ambient Connectivity: An Introduction [online] Rmf.vc. Available at: https:// rmf.vc/IntroAmbient. (Accessed 30 January 2020). Heylighen, F., 2007. 13 Accelerating Socio-technological Evolution. Globalization as Evolutionary Process: Modeling Global Change, p. 284. Hunter, J., 2015. Hierarchy of IoT “Thing” Needs. https://techcrunch.com/2015/09/05/thehierarchy-of-iot-thing-needs/. (Accessed 30 January 2020). Sen, S., 2017. Purdue discovery clears way for human body to work as robust communication network for electronic devices. https://www.purdue.edu/newsroom/releases/2017/Q4/ purdue-discovery-clears-way-for-human-body-to-work-as-robust-communication-networkfor-electronic-devices.html. (Accessed 21 September 2019). Tilghman, P., 2017. RF Machine Learning Systems (RFMLS). https://www.darpa.mil/attachments/ RFMLSIndustryDaypublicreleaseapproved.pdf. (Accessed 30 January 2020).

Further reading Avena-Koenigsberger, A., Misic, B., Sporns, O., 2018. Communication dynamics in complex brain networks. Nature Reviews Neuroscience 19 (1), 17. Fed Biz Opps, 2017. Radio Frequency Machine Learning Systems (RFMLS). www.fbo.gov/index? s¼opportunity&mode¼form&id¼496c944ced6a0434e613bee86a238a6c&tab¼core&_cview ¼1. (Accessed 21 September 2019). Martyna, J., 2013. Performance Modeling of Opportunistic Networks. In: Kwiecien, A., Gaj, P., Stera, P. (Eds.), Computer Networks. CN 2013, Communications in Computer and Information Science, vol 370. Springer. O’Brien, D., 2018. The Urban Commons. How Data and Technology Can Rebuild Our Communities. Harvard University Press.

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Pentland, A., Shrier, D., Shrobe, H., 2018. New Solutions for Cybersecurity. MIT Connection Science and Engineering. RSB blog, 2013. The Peculiarities of the Jellyfish Nervous System. https://blog.rsb.org.uk/thepeculiarities-of-the-jellyfish-nervous-system/. (Accessed 21 September 2019). Siegwart, R., Nourbakhsh, I., Scaramuzza, D., 2011. Introduction to Autonomous Mobile Robots. The MIT Press. Stanford.edu, 2003. The Turing Test. https://plato.stanford.edu/entries/turing-test/. (Accessed 21 September 2019). Vikhar, P.A., 2016. December. Evolutionary algorithms: A critical review and its future prospects. In: 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC). IEEE, pp. 261e265.

Chapter 6

Interface Chapter outline 6.1 City-wide interfacedthe city is an interface 6.1.1 City interface as an extension of the city OS 6.1.2 The city as an ecosystemdscale, boundaries bridging global and hyperlocal 6.1.3 Infrastructure as interface 6.2 City interface functions 6.2.1 Urban navigation 6.2.2 Urban media 6.2.3 Urban sensing 6.2.4 Urban interaction 6.3 City interface design practices 6.3.1 Theory and method of city interface design 6.3.2 Urban user experience 6.3.3 Urban interaction design

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6.3.4 Urban simulation and gaming 6.4 Collective intelligence interface 6.4.1 Collective intelligence 6.4.2 Collective intelligence participation/interaction 6.4.3 Dynamic frames of reference 6.4.4 Human to human, human to machine, machine to machine and machine to nature 6.5 Convergence Urban Interface 6.5.1 Total interface solutiondAI/sensors/big data/pattern recognition 6.6 Conclusion References Further reading

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6.1 City-wide interfacedthe city is an interface An interface is a point or place where two entities (a person and a computer) meet and interact. It is typically a surface, like a screen, that is a boundary between our reality and that mediated by the device. It implies there is some functional or meaningful connection and exchange. In computing, it is a program or device that connects hardware or software through a common communication platform. In the case of a citywide interface, the city itself forms the common platform by which diverse operations occur and communicate within an integrated system. The citywide interface in many ways is an oxymoron being that the city is already a unified operating system (OS) with its own inherent connectivity and linkages, integrating hardware and software, and both human and technical dimensions. This multidimensional interface is

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a highly complex system of subsystems including all forms of city functions, each individually operating within the whole. In addition, the concept of the city as interface builds on the notion of the city as a living organism developing an inherent OS based on the unique city DNA. In this sense, the city interface functions as a composite representation of each city’s specific characteristicsdpopulation, social structure, technological stage of advancement, etc. All aspects of the city as a living system must also function as a communication bridge simultaneously linking internally within and externally with other cities, central governments and global systems. In this way, the information architecture is highly holarchical, scalable and ubiquitous. Citywide interface or urban interface is a concept that has been explored by different professionals from urban planners, sociologists, to user experience designers to name a few groups and has different interpretations and applications. The definition we would like to give to urban interface is that the city is in itself an interface between the various urban dimensions that we have described in Chapter 3 including physical, human and digital dimensions of the city. We can already get a basic sense of this through Google Maps. The city is both a physical and digital interactive and navigable environment. In the author’s previous paper on Urban Phenomenology (Kirwan, 2013), the notion of how the city is experienced depends on the frame of reference of the user. Therefore, the city as interface depends on who and why a specific type of interaction or experience is being offered and delivered. In this regard, the philosophical and design criterion of city interface is a space of possibility combining universal versus personal needs, absolute versus finite values and is context-sensitive, on demand and adaptable. Lastly, we have introduced the concept of collective intelligence that has been articulated throughout the book, representing the composite intelligence of diverse urban dimensions and stakeholders of the city within a unified, living OS. These dimensions include natural, human and machine intelligence, each of these with their own unique behaviors and requirements to achieve the states of balance, well-being and optimization. Such outcomes are described as the ultimate objective in our search to define and achieve convergence through the creation of a new humanemachine interface applied to cities and global scales.

6.1.1 City interface as an extension of the city OS The citywide interface is an extension of the city OS, representing the connective tissue that allows diverse components to coexist and cooperate within the city OS. The difference between a citywide interface and other types of specific interfaces is that citywide interface is comprised of multiple interfaces that collectively form a gestalt interface. In the recent past, before the ability for subsystems to communicate within a holistic cloud-based platform,

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systems remained independent and localized with no connection to other systems except when there was a need to relate or mediate a specific circumstance, such as a natural disaster or a citywide event in which systems may have been connected on demand by a forced intervention rather than a continuous integration. Structurally, the city is an OS that is made up of diverse functions of the city and the city interface links these together as a common platform accessible to diverse users. Traditional OS based on top-down organizational hierarchy have been managed by centralized command and control centers. In the citywide interface concept, the city operates more as a holistic multidimensional organism similar to the human body with subsystems operating independently within a unified framework. As the system becomes more intelligent, these independent functions can develop more autonomous capabilities while operating harmoniously within the composite system. This does not imply that there is no longer a need for a central control mechanism like how the brain functions in the human body. However, there is a potential for the system to be comprised of intelligent subsystems and subcomponents each contributing to a super intelligent network. The concept of smart connected objects is an example of independently connected monofunctional objects forming an intelligent network that can transmit and receive data within broader ecosystems. Similarly, cities are a combination of independent yet interconnected systems that require many types and levels of interface to allow the city to operate seamlessly, on demand and in real time. Edge computing reinforces the decentralization and distribution of networks creating more autonomy and efficiency by allowing computing to be near the physical location where data are being collected and analyzed. Like the smartphone as an aggregate of diverse functions and applications, the city is a composite of multiple functions that each needs a common medium or space to align and process the massive complexity of human, environmental and technological dimensions, each operating on diverse system platforms with the constant potential to move toward chaos and dysfunctionality. Artificial Intelligence (AI) enables the city interface to be a seamless OS, generating predictive scenarios and outcomes based on user profiles and predetermined sequences, aligning divergent subsystems in an autopoietic state. The best interface is no interface at alldthe city is an interface and the interface is the organic living city. The author Steve Krug (2006) explains how AI and user input will make user experience design (UX) more intuitive: “We are seeing a growing complexity within the various interfaces that we need to interact with every day. AI promises to relieve us of this cognitive load. Interfaces will become simpler and fewer. In much the same way you can now set a reoccurring payment through your bank, many tasks will be managed and decided upon in our stead by AI. We then will have realized that axiom of UX: ‘Don’t make me think!’

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6.1.2 The city as an ecosystemdscale, boundaries bridging global and hyperlocal As explained in the previous chapter, the city as a living organism represents a broader ecosystem that interrelates and is reliant on both internal and external elements that allow the city to flow in a state of dynamic equilibrium. This symbiosis requires the city interface to expand beyond the physical limits and boundaries of the city enabling external linkages with global systems. This can include the interface with a central government or other cities and states. The reality that cities have been part of global networks has existed for many centuries through trade, colonization and other forms of human expansion. Cities, like living organisms, are open systems requiring external resources, energy and nutrients to maintain homeostasis and are regulated by positive and negative feedback loops. With the advancements in new communication technologies and networks, global connectivity has simultaneously exponentially increased and dematerialized the physical boundaries of cities. As a result, the city interface is no longer self-contained and is co-dependent on both physical and metaphysical influences. Simultaneously, localized subsystems have become more robust and interconnected as smart connected objects form localized networks supported by edge computing. With people, objects and devices linked via IoT, the city interface must manage the vast complexity created by diverse entities operating at different scales, from macro urban infrastructure scale to micro nanotechnology scale and by connecting global, regional, citywide, local and hyperlocal networks and applications as a seamlessly integrated OS (Fig. 6.1).

Global Systems

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FIGURE 6.1 City ecosystem interface.

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Parallel to the city interface is a combination of syntactic and semantic dimensions that flow within and beyond the city as both independent and dependent systems. The syntactic dimension is represented in the organizational structure of the multifaceted citywide interface and the semantic dimension as the projection of the city interface as diverse urban media channels that stream real-time information linking diverse stakeholders and entities internally and externally. The city interface, therefore, is not only embodied in the physical connectivity and hardware of the city but also the software and media content that manifests the unique characteristics and behaviors of the city as a living organism.

6.1.3 Infrastructure as interface In the corporeal realm of cities, the physical infrastructure is what connects diverse systems within cities and externally from transportation, energy and telecommunications networks to other specific forms of the built city. As cities are now becoming a union of the physical and the digital, the physical infrastructure is evolving into a hybrid infostructure, a term the authors introduced in their class on information architecture to explore the opportunity of infrastructure to become a new information medium to record and transmit critical data representing the real-time operations of cities. In the infostructure concept, bridges, tunnels, highways, telecommunication and energy infrastructure function as urban interface that are mined for relevant data to monitor and manage the infrastructure itself. At the same time, these data can be converted or recycled into useful and engaging information that can support the various users of the infrastructure whether operators or end users, allowing interaction to occur at different levels and serving different user requirements. The composite infostructure data collected from the entire city infrastructure would allow a comprehensive view of the city as a living organism. Given this is all real-time data, a multisystem simulation can be developed, visualizing patterns that can assist in decoding citywide behaviors and functionality with the goal to transform what is currently static unban infrastructure hardware into dynamic living data and interactive media content that can expand the city as a physicaledigital hybrid interface.

6.2 City interface functions In the traditional definition of user interface (UI), there are four principal components: Navigation, Presentation, Content and Interaction. In our concept of city interface, these definitions have been expanded to incorporate a new set of characteristics: Urban Navigation, Urban Media, Urban Sensing, and Urban Interaction, which allow the city to be realized as a real-time, streaming, living entity in the form of a composite urban user interface we have termed urban user interface (UUI) (Fig. 6.2).

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Urban Navigation

Urban Sensing

Urban Media

Urban Interaction

URBAN EXPERIENCE

USER EXPERIENCE

UI

UUI

FIGURE 6.2 Urban interface elements.

6.2.1 Urban navigation As described above in the concept of city as interface, the city is a complex landscape of multiple dynamic systems operating simultaneously. In this potentially chaotic space, there is a need for individual and collective means to navigate diverse systems each requiring a different system of data capture and data presentation to provide critical real-time coordinates to enable navigation. Traditionally people have relied on static paper maps to guide them, even up until the late 20th century, but as we transition through the digital age we are now able to navigate cities via dynamic, GPS-based interactive maps that provide a multitude of metadata that augment the navigational experience and provide a multimedia representation of space to the user. Augmented reality further enhances this experience via expanded sensorial modalities including visual, auditory, haptic and voice delivered information (Fig. 6.3). The navigation of the urban landscape also depends on who is driving and what system of transportation is being utilized. Therefore, the definition of urban navigation is related to a combination of the user of the system and how the user is navigating the system. The potential for humans and machines to self-navigate builds inherently on the spectrums of neuroscience and psychology of wayfinding with preprogrammed routes built into navigation systems over time and the identification of symbolic spatial identifiers within the landscape as triggers of navigational intelligence. Current key attributes of urban navigation include kiosks, signage and wayfinding, maps and

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VOICE

Urban Environment Map

VISUAL

AUDITORY Interaction

GPS Media Content

Kiosk

HAPTIC

FIGURE 6.3 Urban navigation.

landmarks. Map services are becoming iteratively better and faster, including finer detail and greater location precision such that one can never truly be lost. In some ways overreliance on GPS and smartphones has made humans dependent on technology with the trade-off being that humans may become less intuitively able to navigate real space. With the development of augmented reality, the navigation of real space and virtual space is creating a new hybrid experience of reality and the environment. As urban navigational interface moves away from touch-triggered interface screens, voice- and retinal-activated navigation will allow humans to more freely move through urban spaces with a further long-term technological objective being brain telepathy activating navigational sequences. An example of an imbedded citywide urban navigation interface is Urbanflow Helsinki (2011). The objective behind the initiative is to provide a contextually driven interactive interface for people to access different dimensions of the city as they transition between the physical and digital experience of Helsinki. Situated urban screens can be both locally oriented and general purpose, with options for hyperlocal, contextual content and citywide information. Through the touchscreen interface, maps, directions, businesses and different information layers such as air quality, cycling information, energy consumption and municipal works are provided. From each terminal map,

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concentric rings expand outward with overlays of customer-driven reviews and ratings of the various shops and amenities. Placed all over the city, they act as waypoints, ideal for exploration and/or used as a guided tour. A main focus of Urbanflow Helsinki is to synthesize digital information within the urban environment as accessible and useable qualitative and quantitative information for residents and visitors to operate safely and efficiently while appreciating the unique urban characteristics of Helsinki and creating a more transparent relationship between citizens and city administrators. As smart cities will incorporate autonomous transportation systems, the system will in itself become intelligent and self-navigating. The more selfdriving cars added to the road, the more they communicate with each other, and the more efficient the system becomes as machine learning identifies and determines optimal sequencing. Public buses are one of the key modes of transportation that are the most cost-effective and will benefit cities directly if better real-time information can be imbedded in the system especially for users who need additional support, such as tourists, novices, the elderly and disabled people. Urban Bus Navigator (UBN), an example of optimized public transit described in the paper An Internet-of-Things Enabled Connected Navigation System for Urban Bus Riders, is an IoT-enabled navigation system for urban bus riders (Handte et al, 2017). UBN has two attractive features: (1) fine-grained navigation guidance on the specific route and (2) route recommendations based on crowd and traffic flows. Madrid conducted a 6-month trial to remove barriers to public transit use and it has shown a positive impact. Self-driving taxis will also supplant Uber or become integrated into their business model, lowering cost and improving traffic, making cities more accessible.

6.2.2 Urban media Today’s cities are comprised of multiple media channels and content types, each communicating a unique message about the city as a form of multidimensional media itself. These media channels influence the living nature of the city as they represent the behavioral patterns of the city and its inhabitants. Urban media, therefore, defines the city as a living organism from a specific media point of view. Content-driven media including TV, radio and Internet combined with living urban operational data, a unique spectrum of real-time data embodying the living city, contribute to the composite nature of the urban media landscape. Urban media can be a physical building (building as signage), lighting and outdoor advertising, to streaming data from a variety of urban operations and physical infrastructure that the authors have defined as infostructure described above. It is not difficult to imagine urban physical reality converging with its digital representation such that the map is becoming the territory. But the

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implications of this actually happening are quite astounding, if one considers the massive amounts of global data in all its multiple streams and forms. This amalgamation is the formation of a new reality that cities are themselves media. To understand the diverse typologies and applications of media experiences of this amalgamation, media must be deconstructed. Marshal McLuhan, in Understanding Media (1964, 2015), describes the unique character of each type of media communication and how these form individual languages for human consumption. In the same manner, the concept of urban media represents the individual media communication channels and their unique characteristics, broadcast frequencies and content within the holistic interpretation of media as a living orchestral composition combining diverse overlapping media streams. In the book Ambient Commons (McCullough, 2013), the author expands on the idea of the city as urban media through making an analogy to ambient music. In this definition of ambient, the city is a composite of sensorial impressions forming a new environmental media language representing various urban dimensions. Relating this to our concept of city DNA, each city has its own ambience and feel communicated by the architecture, navigation and infostructure, creating a background impressionism of the living, operating city. The convergence of technology in the context of urban media lends to a phenomenological experience of the city, via the ambient interface. The psychological dimension is finally becoming more understood by a cross-disciplinary research and collaboration between architects, planners and behavioral psychologists to optimize the potential for urban spaces and buildings to better support mental and physical well-being.

6.2.3 Urban sensing Underpinning urban interface is the requirement to stream live data from multiple sources across all spectrums of the living city including environmental, human and machine data. Each function of the city must allow information to be collected via diverse means to process and monitor activities and patterns. Urban sensing, a key part of this process, is a concept that goes beyond the technical aspects of collecting data via sensors and implies a deeper intelligence, defined in the previous section of collective intelligence interface. Sensing is a function of the organism. In the case of the human senses, this would be equivalent to the five human senses, capable of a full multidimensional awareness in real time. Expanding on the human senses, the potential to include a broader collective sense including the fusion of the natural, human and technological dimensions is the goal of creating a new collective intelligence able to augment human and machine understanding.

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In Chinese and Buddhist philosophies, this augmentation is represented by a broader awareness of the flow of energy and a melding of mind, body and spirit into one unified flow. In the case of urban sensing, the city itself becomes a sentient being, collecting, processing and responding to the flow of data that is the inherent state of being. The idea is to imbue the city with not just collective intelligence, but higher consciousness, sentience, such that the urban sensing is a natural extension of the living system. Currently, the infrastructure for this new dimension is still very limited. It begins in earnest with nextgeneration technology where new forms of sensing can be prototyped. A more literal example of imbedded urban sensing is the Pulse Smart Hub “street furniture” interface in Belfast, a touchscreen platform shaped like a large smartphone that provides free phone calls and WiFi, contains a defibrillator, air quality monitoring and provides access to local services. In this way it is directly contributing to the living systems (people) that benefit from it, in some cases explicitly saving lives. In this way, the hub has its own base level sentience. It’s a next-generation platform, 5G-compatible, developed to provide connectivity and environmental feedback and is tailored to local needs and promotes well-being of people and vitality of the city itself. The smart hubs, deployed at no cost to the user or taxpayer, are the first of their kind deployed in the United Kingdom and helped to put Belfast on the map globally while setting an example for other cities to create innovative, playful solutions. The hubs are also coordinated with ambulance and police services and locals are pleased to see measurable effects in crime reduction. Sensing can be scaled up to the planetary level, such as with “Earth observation,” to monitor and assess the status of and changes in natural and built environments through gathering of information about the physical, chemical and biological systems of the planet via remote sensing technologies. Such equipment includes satellite-based remote sensing and airborne data collection, supplemented by land-based surveying techniques, encompasses the collection, analysis and presentation of data. The technology continues to evolve and converge. The website Worldometers is a useful tool that incorporates various data inputs to track global metrics in real time, such as population levels, energy produced, transactions made, etc. At this stage it is more of a very neat gimmick, but the next level is advanced platforms like this for global management of real-time climate change and city operations management. All efforts converge toward a dashboard for looking at the big picturedmacro and at the opposite end the microdlocal and hyperlocalized data and corresponding user experiences.

6.2.4 Urban interaction The capacity today to connect all segments of global populations through mobile phones and other IoT devices and to collect and apply a multitude of behavioral big data allows both public and private sectors (governments,

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NGOs, companies) to access critical information to better serve the needs of the end user. At the same time, end users are able to contribute and participate directly as part of the collective intelligence interface by self-publishing realtime information and providing valuable performance feedback to assist city operations. Creating the opportunity for citizens to shape the experience of their city as a collective space for development was established in the principles of citizen participation exemplified through Jane Jacobs’ theoretical writings and social activism in the 1950s in New York City and the Center for Choice project presented in Chapter 3 as two important initiatives to make the development of cities inclusive. It has been proven that giving citizens the opportunity to influence the day-to-day operations of the city stimulates individual and collective motivational behavior from better resource management to quality control aspects that can have real impact on the sustainability of cities. Copenhagen is both one of the smartest and happiest cities in the world, and the overlap is not a coincidence. One of the main reasons Copenhagen has been so successful is citizen engagement. City administrators listen to their people, gain consensus, and meet the public’s demand with pilot projects to demonstrate the ability to achieve collective success through inclusive measures. Copenhagen’s City Data Exchange provides free data access, so citizens can view various statistics and reports on environmental air quality, demographics, and business and market opportunity analysis. This information sharing is intended to catalyze efficient smart city development, assisting citizens to optimize their everyday patterns by monitoring fuel consumption, commuting time and other conditions that impact both individual and civic realms. These measures have encouraged wider implementation of smart design solutions, including developing more public park spaces, extensive bike mobility and novel neighborhood features like outdoor food markets, all contributing to a higher quality of life and reinforcing Copenhagen’s inclusive co-design culture. As diverse stakeholders begin to interact in real time enabled by IoT, smartphones and open data, the city has the possibility to be self-operating through citizen participation in the management of cities as a collective responsibility. In our academic research and design projects over the last decade, our students have explored how citizens can be engaged directly in the operations, maintenance, and creative input of cities as a whole, while enhancing and shaping the identity and characteristics of each city and its unique attributes. The following three projects represent diverse user engagement from individual participation to collective experiences (Fig. 6.4). In addition, as citizens become more and more active in the virtual dimension of the city, the Internet and social media are shaping new urban frames of reference. In the paper Crowdsensing in the Web: Analyzing the Citizen Experience in the Urban Space (Pereira et al., 2011), the authors elaborate on emerging social media frames of reference of the city in which

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FIGURE 6.4 Citizen engagement. City care, Xu Lin; Sub scope, Junjie Yu; Co-pulse, Junjie Yu, Ke Fang, Yin Li, Yechang Hu, and Jieyun Yang.

cites are beginning to construct a virtual representation of urban experiences with consequential reactions that in turn influence the evolution of the physical city. Here, the conceptual and phenomenological potentials of the linear, spatial, physical city converge with the nonlinear, rhizomatic virtual city, creating a new hybrid urban reality and dynamic frames of reference. Many cities are experimenting with new forms of urban interfaces. Naturally, while each is tailored to the specific needs of the city, they have convergent properties, and the knowledge and technology developed can be transposed across cities globally, such as citywide kiosks and free WiFi hotspots enabling the city to operate as a real-time flow of information accessible to all. As these technologies become more efficient and ubiquitous, city services and businesses, such as tourist information centers and internet cafes, will become redundant as will physical maps and wayfinding systems. As machine learning improves exponentially, the greater challenge becomes less about enabling smart city via AI but rather citizens adapting to and keeping up with constant innovations to the real-time, interactive city.

6.3 City interface design practices To establish a professional practice and methodology to plan and design the city as an interface, there is a need for new disciplines to address the changing nature of cities due to rapid technological advancements. Building on a combination of established and emerging fields including information architecture, UX, interface design and urban interaction design, a transdisciplinary approach is required to be adaptable and transformative to meet the challenges of contemporary urban living and operations. In addition, as new forms and combinations of physical and digital experiences are created every day, traditional disciplines are not always able to describe these new realities. As discussed in the chapter on city OS, preconceived ideas about systems architecture and information visualization may limit the opportunities of defining emergent language and behaviors. Therefore, new approaches are needed to parallel new hybrid humanemachine and physicaledigital realities and experiences within the context of smart cities and urban life.

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6.3.1 Theory and method of city interface design Over two decades researching smart cities, the authors explored diverse ideas and applications of city interface through various research and design projects given to the students in their course on information architecture. As part of this course, the final assignment had students propose an intervention by defining two or more diverse urban systems that required an interface. To determine the appropriate interface solution, it was necessary to first define the boundaries, behaviors and typologies of the systems and to design a system architecture where the interface played a specific role in the intervention. From this background understanding, the proposed interface was described through four dimensionsdform, function, time and cost. These exercises allowed the understanding of interface as a vehicle to optimize performance, achieve harmony and integrate diverse elements within complex frameworks. This included how human, machine and natural systems can find commonalities and achieve diverse forms of convergence. Below are some themes that such research has evolved into in terms of the extension of interface design. The urban user experience (UUX), urban interaction design, simulators and gamification are all aspects of the converging urban interactive environment that gives us the focal points and tools to create the self-regulating city.

6.3.2 Urban user experience The proliferation of apps that create new forms of mobility and networking that interface with different agents of the city or society is beta testing for more holistic interfaces in the future convergence. The UX is incorporated in apps such as Yelp, Uber, or Shapr providing usability feedback to them. As they evolve, the interface and protocols become more seamless and convenient. With the ability to publish real-time data and document real-time experiences, the city is now a virtual platform for the repository of living information in a state of dynamic evolution. As described earlier, the concept of palimpsest is the layering of information over time within a given context. The city is a space for the recording of individual and collective experiences, layered on multiple media channels and linked together through geo-referenced data. This collective experience requires guidelines that communicate a unified set of user experiences and design decisions that promote harmony across various media inputs and outputs. As urban sentience evolves, more nuances are collected that were previously neglected, adding to the overall higher definition and context awareness of the city interface and user experience. Past research supports both universal and personalized approaches. During the mid 1990s, the author with students developed a program to map and record the unique experiences of New York City based on individual user types. Introducing the term multiple identities based on a variation of Howard Gardner’s book Multiple Intelligences (2006), 12 individual user types were

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explored to understand the unique perception and to map the specific user touch points that each individual and diverse types of people would interact with as they navigated and experienced the city. We imagined 12 user-type scenarios to study UUX: “Our interactive installation, Multiple Identities, presented at Siggraph 1997, was organized via individual perceptions of the city using virtual avatars as tour guides (elderly, commuter, homeless, student, rap artist, drag queen, child and tourist). The entire exhibition area was covered with soft material functioning as a screen for multiple projected images of people and places. Individual stations allowed individual users to select avatars and navigate the city by selecting various points of interest, each determined by the specific avatar’s unique perspective of the city. In the process of using the installation, we discovered that the resulting image of the city became a composite of a random selection of human personality types, and visitor reaction was diverted from the ‘real’ content by the effect of the virtual simulation.” (Kirwan and Travis, 2011)

In this research project, other theories were explored including the nature of human motivation both on an individual level and on a collective societal realm to understand and define user typologies and behaviors. The take away from this research was the awareness that there are many different realities to how people and collectives perceive and interact within the urban experience. The idea of UUX design must be able to relate and incorporate the unique characteristics of all types of users to achieve a universally applicable city interface. Everybody needs to have a fair, equitable, optimized, healthy and complete UX whether applied at the public or personal scale across all urban lifestyle dimensionsd living, working and playing. The following illustration is an example of a user journey map and that allows the mapping of specific user touch points to understand and visual how individuals and collectives relate, interact, navigate, feel, and experience the city as the basis of UUX (Fig. 6.5). User Profile Student 12 years old

ENVIRONMENT

USER ACTIVITIES

AT HOME

IN TRANSIT

IN SCHOOL / AT CLASS

Smart Science Class

AT GYM

IN TRANSIT

Smart Art Class

EMOTIONAL RESPONSE

Scenario: Tracking Student Daily Activities within Smart Classroom Context

FIGURE 6.5 User journey map.

AT HOME

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6.3.3 Urban interaction design As a multidimensional complex environment, the city offers a compelling platform to develop urban scenarios in which users can interact through virtual avatars simultaneously within the physical environment as well as person-toperson interaction. The combination of urban planning, interaction design and user experience design forms urban interaction design as an emerging field that can play the role of intermediator between public and private realms. As citizens develop the ability to contribute to urban media and as government agencies, organizations and businesses develop diverse constructs to mediate the flow of information, the social fabric becomes more rich and alive. The infostructure of the living city merges the world of human, nature and technology. Urban interaction design provides a hybrid approach to design solutions connecting the various dynamic layers between the “brick and mortar” and digital realms and the city government as the platform and the citizen as end user. In the pop-up book Urban Interaction Design: Towards City Making (Brynskov, 2019), the authors attempt to define this new space in the form of a mini manifesto outlining the new emerging collaborative framework between public and private entities and academic research and professional practices, supporting the concept of the city as a living experiment. In this new framework, each city must develop new approaches that bridge diverse stakeholders including city government, institutions and a public that is increasingly enabled via IoT to contribute to the operations of the city. The potential role of urban interaction designersdto steer this organic processdis to uncover and exploit creative ways to serve human needs above all else (granted the environmental baseline is also secured). This requires emphasizing the convergent approach that brings together as much knowledge, resources, stakeholders, designers and problem solvers as possible in a massive co-design, co-creation process. This process stresses a relational approach, fostering positive interaction between people in all situations.

6.3.4 Urban simulation and gaming The city has been a continual source of inspiration for the design of multiple virtual city-building simulation games including SimCity, Cities: Skylines, the Anno series, the Tropico series and others. In parallel, we are seeing the acceleration of the convergence between the physical and simulated worlds. World building both in movies and in gaming is becoming more immersive and explicitly converging in films like Speilberg’s Ready Player One. Microsoft is coming back out with a new Flight Simulator after 10 years undeveloped. Multiple media technologies have reached a new plateau of convergence such that an ultrarealistic experience is possible. We still have a

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long way to go, but the design innovation in games will eventually converge with interfaces for city OS and the lines between simulation and real will be blurred. User experience and urban interaction design have developed organically over time and have been inspired by advancements in the digital gaming industries. Drawing from these tools and techniques, city mapping and visualization has directly benefited from the gaming industry developments. Singapore, in partnership with MIT, has been a pioneer in developing and applying tools to simulate the built environment, visualizing urban patterns and flows to optimize the urban operations and to design better UUX. Simulation enables testing the virtual system to determine optimal outcomes before physically developing solutions, reducing the risk from trial-and-error approaches and ensuring sound technological and infrastructure investment. In addition to the practical aspects of simulation, gamified city design allows the potential to include experiences to imbue understanding, narrative and meaning, which can reinforce positive emotional states and public wellbeing. Gamification, as a medium, can enable the re-enchantment of society after the disenchantment over the past century. Sociologist Max Weber wrote about the process of disenchantment of modernity, the trend of rationalization and demystification of public life and bureaucratic modes of organization. As David Rose explains in Enchanted Objects (2014), we can somewhat counter this trend, albeit in a potentially rather dystopian fashion, where machines tell us stories. His idea of machine enchantment refers to the increasing scale of personal relatability to technology, starting from connection, moving up to personalization and socialization. The next step is gamification which entrances the user through games and reward systems. Finally, storification allows the machine algorithms to construct a narrative story that meets the user’s wants and needs. Through machine learning tied to ambient connectivity, the user can share data that move them up the levels of enchantment to deliver the desired experience. Through all the diverse types of gaming, players are oriented according to certain sets of rules enabling them to accomplish certain tasks. Games in more objective educational contexts can increase the collective intelligence to work on more complex tasks. Even games that have no practical application demand a certain level of computational intelligence from the user/player. The networked relationship and flows of information between user and interface add to the collective intelligence of the system. City interface design can build on the best practices developed by the gaming industry to recreate the living city experience as a simulated reality that is able to monitor urban systems in real time. The interface includes all stakeholders, users and participants in an ongoing research experience, enabling the best combination of essential smart city functions and imaginary scenarios to be played out.

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6.4 Collective intelligence interface 6.4.1 Collective intelligence In defining the city as a holistic interface, the concept of collective intelligence offers a potential new awareness through intentionally designed interface(s) that allow cities to operate in a harmonious, efficient and sustainable process. Collective intelligence in itself can function as the interface for how cities operate by providing an AI-enabled, self-learning platform that incorporates the functional dimensions described in Chapter 1. It will be able to learn from the diverse users and behaviors of the city dynamically in real time. In this way, collective intelligence can become the new interface that relates all of the various components and subcomponents of the OS as a unified city interface and collective experience. In the context of the urban environment, the collective intelligence interface relates multiple sources of data within a holistic platform that collects, streams, analyzes and presents real-time feedback to all of the users of the platform. Collective intelligence itself is the real-time collective consciousness of the urban operations and the unique urban experiences related to the users of the system. In the convergence theory, collective intelligence can represent the new fusion of natural, human and technological systems. For this to be achieved, the collective intelligence interface must collect data from a diverse spectrum of environmental, human and technological sources building a collective awareness that provides a real-time flow of data that can be tapped into as required to allow the OS to achieve a state of autopoiesis. Building on the convergence methodologies presented in Chapter 3, the function of AI and machine learning is to extend human intelligence to develop evolutionary algorithms to read patterns and adjust the flow of information dynamically and organically to maintain equilibrium between all states and dimension of the ecosystem. In addition, the collective intelligence interface incorporates diverse conditions and builds on urban scenarios to model alternative solutions via applications including generative design and rapid prototyping to allow the entire city system to continually upgrade and improve operations. This also includes filtering data and delivering information to the appropriate end user and within specific contexts.

6.4.2 Collective intelligence participation/interaction The city as collective intelligence interface has the potential to achieve a state of higher consciousness by representing all of the diverse points of view of the city including stakeholders, users and the physical natural environment itself as a living intelligent system. Within this space, all life forms are participants contributing to the living nature of the city. In each of their own ways, these participants produce and consume energy, information, generate data, and define patterns that are the clues to unraveling the complexities of our

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multidimensional urban reality. Understanding and processing these patterns with the assistance of AI-enabled real-time feedback allows the city to become autopoietic through the participation and interaction of all entities operating within the urban ecosystem. Collective intelligence is the ultimate state of being, incorporating all life forms within the urban interface equation.

6.4.3 Dynamic frames of reference City OS are typically organized based on functional categories including security, transportation, energy, healthcare, etc. Within this top-down approach, users are not the focus of the system interface. The city OS should be designed from the perspective of the end user, as well as from the urban function frame of reference establishing a top-down/bottom-up hybrid. Considering the user’s needs as the center of the city operations requires understanding diverse stakeholders and incorporating their needs and perspectives. This hybrid approach contributes to a phenomenological experience of the city, one in which the functionality, aesthetics and content of the urban OS architecture create a multidimensional composite of diverse user experiences. Dynamic frames of reference, introduced by the author in the paper Urban Phenomenology: Incorporating Dynamic Frames of Reference in the Design of Urban OS (Kirwan, 2013), explores this rich layering of UUX. Based on situational or contextual awareness enabled by AI, dynamic frames of reference develops adaptable solutions that serve the needs of specific individuals, collectives and functions within the city. It allows a pluralistic approach that is customized to represent each requirement within the city by allowing information to be targeted to users in a highly effective and efficient manner. Dynamic frames of reference simultaneously allows the potential to filter information on location and on demand making information manageable as we become more and more inundated with media content. In this regard, the function of dynamic frames of reference provides a more streamlined solution for how information is framed and delivered to the end user and consumer related to the specific context and subject area the user requires the information to be applied to within the city experience.

6.4.4 Human to human, human to machine, machine to machine and machine to nature Originally the concept of a computer interface enabled the ability of one system to communicate with another. In early computer interface models, humans were able to communicate with the OS through a desktop interface that permitted computational tasks to be performed based on human abilities. As the human-to-machine relationship expands beyond the realm of individual human to desktop interface, multidimensional city interfaces will be required to mediate diverse OS attributes from human to human, human to machine,

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machine to machine and finally as the city interface reaches higher levels of convergence, the mediation of machine to natural systems will bring the city interface full cycle back to a humanemachineebiological interface. In the research paper Machine behavior, published in Nature.com, Iyad Rahwan et al. (2019) identify the need for a new understanding of the relationships forming between human and machine and the emerging intelligence this merger necessitates through the establishment of a new branch of psychology addressing new hybrid behaviors. Likewise, as machines interface with other machines and develop complex behaviors, this will require a new collective intelligence interface that adapts to each of the unique behavioral patterns that form within the evolution of new unions of connectivity convergence. The following diagram attempts to visualize new relational and behavioral combinations within the convergence of humans, machines and nature (Fig. 6.6).

2 2 2 2 2

Human

Machine

Nature

FIGURE 6.6 User typologies.

6.5 Convergence Urban Interface Convergence Urban Interface (CUI) is the next stage of development of the city as interface and the ultimate structured urban interface that combines all aspects of the city as an OS. In the convergence model, the various functions of the city become interoperative and provide a new form of streamlined service platform expanding and synthesizing all of the diverse aspects of the city as interface. This process of convergence is not unlike the integration of multiple functions that have taken place within smartphones where once diverse

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functions including cellular phones, daily planners, calculators and media players were all independent hardware and software devices. CUI is parallel to convergence connectivity descried in Chapter 5, in which disparate functions including telecommunication networks, data transmission and multimedia content are now converging through 5G networks. Building on this trend of convergence, CUI integrates a combination of elements including city OS functions, telecommunication networks, media channels, smart buildings and smart infrastructure as a real-time, on demand interface that adapts based on the collective intelligence behavior of the city as a whole and the dynamic frames of reference model facilitated by specific end-user requirements and lenses into the reality of the city. As is depicted in Fig. 6.7, a progression is realized from service interface where the organization provides services as interface to clients parallel to how cities provide dynamic experiences as urban interface to citizens delivered via the city OS. CUI adds a final converging dimension integrating city services into the urban interface as a total solution. Urban Navigation Services

Services

Urban Interaction

Organization

Customer

Urban Media

Urban Sensing

City / Infrastructure

User / Citizen

City / Infrastructure User / Citizen City OS

Service Interface

Urban Interface

Convergent Urban Interface

FIGURE 6.7 Convergence urban interface.

The specific service dimension of CUI incorporates all of the smart city macro functions described in the Smart City Mandala model including Smart People, Mobility, Economy, Environment, Governance and Living in a holistic delivery platform. For example, within the Smart Living platform, public healthcare services will be integrated and delivered to citizens within the convergence interface and can overlap with other urban services, while creating generative feedback processes into the system related to healthcare and related functions. This brings us back again to the metaphor of the human brain and human body as a self-regulating OS, delivering diverse functions and services to serve the needs of the body to operate in the most optimal waydeach of the human organs and subfunctions of the body controlled through both a centralized OS and individual interfaces between the various functions and senses.

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6.5.1 Total interface solutiondAI/sensors/big data/pattern recognition The new collective intelligence underpinning the convergence theory is at a stage of human development where new applications of technology enabled by AI can bring humans closer to nature. It can assist us to understand and visualize the patterns of nature and the impact we have on the physical environment including biorhythms, balance and sustainability of the ecosystem. AI is a viable solution allowing humans to monitor the natural world using sensors to feed the metadata substrate of the interface. As the world becomes more and more complex due to population and urban sprawl, it is necessary to monitor planetary activities especially related to human intervention in the physical environment. The total interface solution is a combination of human, machine and naturaleenvironmentalebiological systems combined as one large collective AI-driven interface that links all elements of the city OS as a harmonious living organism including sensors, big data, pattern recognition and data visualization. This higher dimensionality and complexity of the city as interface requires a combination of the principles identified as collective intelligence and CUI as the ultimate interface that is a situational, on demand, multistakeholder, real-time, multispectrum communication, adaptive, responsive system.

6.6 Conclusion The city itself is a highly complex and multifaceted interface connecting all aspects of the operations of the city and presenting them in a UUX. Each function of the city’s operability is a form of interface between diverse subsystems and users allowing the city to flow from engaging citizens with all forms of urban operating functions. This complexity necessitates a common language and platform that enables diverse stakeholders and entities to interact, produce and consume information. In addition, the city as an interface relates to both internal operations as well linking beyond its borders, connecting larger systems and ecosystems. This includes linkages with other cities and central governments as well as global networks and infrastructuredto extend the technological, sociological, physical and biological dimension of cities through a new interconnected hybrid global OSdInterface Planet Earth. The concept of CUI is the integration of multiple systems within a unified real-time collective intelligent platform that adapts and configures the city interface to align with the city users based on the dynamic frames of reference model, delivering user experiences on demand. Simultaneously, each city can be described as a form of ambient media that is a representation of all living city operationsdmanifest as either harmonious or cacophonous orchestrations. In this sense, the user of the city will be able to, in effect, tune in to the symphony

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and conduct it to meet their needs and demands or just listen and go where the music takes them. The citywide interface is not limited to specific boundaries or scales and is in a state of constant adaptation and expansion. Therefore, an AI-driven self-learning interface is needed to allow the system to continually morph and regenerate the city as a whole, while diverse types of subinterfaces mediate between different systems. The citywide interface co-constitutes the city as a living, adapting system, symbiotic with its diverse ecosystems.

References Brynskov, M., 2014. Urban Interaction Design: Towards City Making. https://issuu.com/urbanixd/ docs/urbanixd_towardscitymaking/. (Accessed 17 March 2020). Gardner, H., 2006. Multiple Intelligences. https://howardgardner.com/multiple-intelligences/. (Accessed 19 December 2019). Handte, M., Foell, S., Wagner, S., Kortuem, G., Marron, P.J., 2017. An internet-of-things enabled connected navigation system for urban bus riders. IEEE Internet of Things Journal 3 (5), 735e744. Kirwan, C., 2013. Urban Phenomenology: Incorporating Dynamic Frames of Reference in the Design of Urban OS. In: Cross-Cultural Design. Cultural Differences in Everyday Life. Springer. Kirwan, C., Travis, S., 2011. Urban Media: New Complexities, New Possibilities - A Manifesto. In: Foth, M. (Ed.), From Social Butterfly to Engaged Citizen. The MIT Press. Krug, Steve, 2006. In: Don’t Make Me Think, Revisited: A Common Sense Approach to Web Usability. New Riders. McCullough, M., 2013. Ambient Commons. The MIT Press. McLuhan, M., Gordon, W., 2015. Understanding Media The Extensions of Man. Gingko Press. Pereira, F. C., Vaccari, A., Ratti, C., 2011. Crowdsensing in the Web: Analyzing the Citizen Experience in the Urban Space. Rahwan, I., Cebrian, M., Obradovich, N., et al., 2019. Machine behaviour. Nature 568, 477e486. https://doi.org/10.1038/s41586-019-1138-y. (Accessed 19 December 2019). Rose, D., 2014. Enchanted Objects: Design, Human Desire, and the Internet if Things. Simon & Schuster, enhancement hierarchy, Scribner. Urbanflow Helsinki, 2011. Building an Operating System for Everyday Life. http://helsinki. urbanflow.io/. (Accessed 19 December 2019).

Further reading Bavarian Motor Work(BMW), 2019. Innovation. Smart Cities, Copenhagen. https://www.bmw. com/en/innovation/smart-cities.html?bmw¼sea-goog-smci-bra-miy-com1-sear-SmartCit20180625-.-cc_uk. (Accessed 19 December 2019). Comparative Study of Smart Cities in Europe and China, 2014. Springer. https://link.springer.com/ book/10.1007/978-3-662-46867-8. (Accessed 19 December 2019). Current Chinese Economic Report Series, China Academy of Information and Communications Technology EU-China Policy Dialogues Support Facility II. Earth Observation Miners, 2019. Earth Observation introduction. http://www.eo-miners.eu/earth_ observation/eo_introduction.htm. (Accessed 19 December 2019).

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Griffin, G.P., 2015. The City as Interface, by Martijn de Waal. (book review). Urban Geography 36 (4), 625e627. Kirwan, C., 2011. Urban Media: a Design Process for the Development of Sustainable Applications for Ubiquitous Computing for Livable Cities. In: ACM Symposium on the Role of Design in Ubicomp Research & Practice. ACM. Pulse Smart Hub, 2018. Home Page. http://pulsesmarthub.co.uk. (Accessed 19 December 2019). Sawchuk, Peter, 2019. Peter Sawchuk UX. https://petersawchuk.com/. (Accessed 19 December 2019). Vaughn, C., 2019. Urban Mind: Mental health in the city and beyond. PlaceTech. https://placetech. net/analysis/urban-mind-mental-health-in-the-city-and-beyond/. (Accessed 6 February 2020). Worldometers, 2019. Worldometers. https://www.worldometers.info/. (Accessed 19 December 2019).

Chapter 7

Smart City Scenarios Chapter outline 7.1 Introduction 7.2 Theory of systems change 7.2.1 Multi-level perspective 7.2.2 Convergence application 7.3 Smart mobility 7.3.1 Pastepresentefuture 7.3.1.1 Evolution 7.3.1.2 Challenges 7.3.1.3 Directions 7.3.2 Objecteactioneoutcome 7.4 Smart environment 7.4.1 Pastepresentefuture 7.4.1.1 Evolution 7.4.1.2 Challenges 7.4.1.3 Directions 7.4.2 Objecteactioneoutcome 7.5 Smart people 7.5.1 Pastepresentefuture 7.5.1.1 Evolution 7.5.1.2 Challenges 7.5.1.3 Direction 7.5.2 Objecteactioneoutcome

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7.6 Smart governance 7.6.1 Pastepresentefuture 7.6.1.1 Evolution 7.6.1.2 Challenges 7.6.1.3 Direction 7.6.2 Objecteactioneoutcome 7.7 Smart economy 7.7.1 Pastepresentefuture 7.7.1.1 Evolution 7.7.1.2 Challenges 7.7.1.3 Direction 7.7.2 Objecteactioneoutcome 7.8 Smart living 7.8.1 Pastepresentefuture 7.8.1.1 Evolution 7.8.1.2 Challenges 7.8.1.3 Direction 7.8.2 Objecteactioneoutcome 7.9 Conclusion References Further reading

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7.1 Introduction This chapter identifies the six major functions of smart cities that have been visualized in the form of an abstract mandala that our team has created on the back of various resources and research. These 6 topics are further divided into 18 subareas (3 per section), each with a specific purpose to describe and model a broad understanding of the impact of technology and Artificial Intelligence (AI) on the structural and compositional dimension of cities. While each of the six functions represents independent systems of the city, these systems are in many cases codependent and interoperative. For this reason, the graphical use of the mandala allows the metaphor of a continuous systems as both an Smart Cities and Artificial Intelligence. https://doi.org/10.1016/B978-0-12-817024-3.00007-6 Copyright © 2020 Elsevier Inc. All rights reserved.

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En lig ht m en Edu t ca tio n

ss ne ve si u rm cl efo In /R icy l Po ers old eh k ta

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n tio iza al tu Ac ing be ell W

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FIGURE 7.1 Smart City outcomes.

internalized and self-contained form while simultaneously radiating as an extending, growing system (Fig. 7.1). Each section follows a format, starting with an outline of the past, present and future of the technological trajectories, subtitled Evolution, Challenges and Directions. This is followed by a Convergence subsection for each smart city function. Convergence describes how the human, machine and environment levels of the smart city are becoming intertwined, through advanced humanedigital interfaces, to AI and biomimetic architecture. Following that, a table is presented that describes the levels deep of execution of the smart city function, framed as Object, Action and Outcome. These three subheadings are structured by three questions: What is the object? What is the action? What is the outcome? This highlights a key priority (the object), the process for prioritizing and affecting the object and the expected and desired result as part of a goal-oriented process. The methodology used in this chapter draws from several modeling techniques, including multi-level perspective (MLP) modeling to visualize diverse causes and effects that are directing the development of cities and are influencing new trends. We have described each of the six smart city functions

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through a multidimensional framework, incorporating past, present and future evolutionary stages within the domain of the convergence of human, machine and nature. In addition, the stages are contextualized within the process of adaptation and configuration of technology within city operating systems and how this influences the unique city DNA described in the previous chapters.

7.2 Theory of systems change 7.2.1 Multi-level perspective The MLP is a theory of systems change useful for understanding sociological and sociotechnical transitions, as well as complex issues such as climate change and peak oil, which compels investment and development in alternatives. The MLP crosscuts the past, present and future with three main levels of analysis: niche, regime and landscape. This allows the potential to map and plan strategies at all levels to disrupt and generate new markets, fields and systems to definitively solve sustainability and social issues. In the case of smart cities, living labs and innovation hubs constitute some of the core niche level developments. To foster entrepreneurship, small actors (individuals or groups) and agents in systems of business and government pursue novel technologies that accelerate change and put pressure on regimes, which is the broad mesolevel that includes infrastructure, policy and knowledge ecologies. A change in the regime level affects the landscape, which are the macro factors shaping the horizon of possibility, including environmental and geopolitical factors. The new landscape puts pressure on existing regimes as well, creating new opportunities for innovation and change. The function of the MLP here is to understand that the transitions to global sustainable smart cities are a monumental task. The timing is also critical, as taking systems-level action has already been avoided for decades and delaying further exacerbates problems. The necessary shift is one from the complicated industrial mechanistic city to the complex living, adaptive smart city ecosystem. Governments and civil society are key actors in creating the groundswell and vision for such changes and must collaborate with private businesses and industry to develop the technological base. The MLP concept has gone under thorough critique and revision over the past 15 years, such that it now takes a more metaperspective of systems change, accounting for variables such as politics, ideology, institutions, agency and top-bottom interactions. This is summarized in the latest paper by Frank W. Geels (2019). The MLP sociotechnical transition goes through 4 phases over time. Phase 1 is experimentation and prototyping, where living labs and innovation hubs workshop ideas and make plans. Phase 2 is stabilization, where products are deployed to early adopters and it creates a sustainable cycle and opportunity for growth. Various models and practices converge on a standard set of guidelines and specifications. Phase 3 is diffusion and disruption, where

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Science Industry

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Time Stage 1

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FIGURE 7.2 Multi-level perspective model.

economies of scale enable what was radical to become mainstream. Complementary technologies and supportive actors converge in Phase 4, institutionalization and anchoring, where the new regime influences the landscape level. See Fig. 7.2. A product (such as a car or computer) is not a single entity but a link in an infinite process and supply chain. The technical landscape evolves interdependently with social, technical and institutional connections. The interrelations in the economic web trap us on path dependencies with the dominant technological regime. Private motorized cars is the dominant regime, whereas autonomous socialized transportation is the insurgent industry, also being a more sustainable and efficient regime that benefits more people. Getting to the better system proves difficult as the current regime is resistant to radical displacement and obsolescence. The future of transportation is one of sustainable clean multimodal transports, from walking in nature to streamlined flight travel experiences. The smart city functions must all converge toward such higher organizing principles to disrupt the regime of old decaying cities and to renew vital systems. Approaches must come from the top and bottom, the niche roots and routes and the landscape level shifts that both challenge and influence the regime level hegemony. We are generally somewhere in Phase 2 where the stable but still novel deployment of things like electric vehicles become normalized and challenge existing business models. In her book The Great Mindshift, Maja Gopel (2016) gives the MLP a thorough treatment and shows

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how it applies to citizens themselves understanding and aiding in the sociotechnical systems transition, of which the smart city is a key attractor.

7.2.2 Convergence application As discussed in the Introduction and throughout the book, convergence plays an active role in the development of smart cities. Some current aspects of the dominant sociotechnical regime seem to oppose or inhibit ideal mobility, a clean and sustainable environment, educated healthy people, efficient and fair government, stable and prosperous economy and psychospiritual well-being. On the whole, the current system supports incremental progress in these dimensions, but not the absolute provision of such rights and not without decimating parts of the planet. This situation is both unjust and unsustainable. Switching to a new sustainable sociotechnical system is imperative and requires the MLP approach combined with theories of convergent evolution and the trends of convergence between knowledge, society, technology, nature and organizations. The current landscape has put enormous pressure on the people and the planet. Our linear growth has run up against terminal limits and must transition to collective intelligenceemediated sustainable smart cities. The regime is locked in and exerts hegemony on other levels, resisting niche innovation and ignoring landscape level pressures. Consumption is a prerequisite of the modern economic system, consequently leading to a widening of socioeconomic inequality and increasingly extreme political polarization. Without a doubt, powerful stakeholders supporting the old system will stand in the way of reform for the wider public benefit. Our goal is to optimize the six smart city functions in an interdependent and sustainable way to achieve complete transformation of sociotechnical transitions across all smart cities’ models globally. We apply our understanding of convergence to develop an analytical framework and methodology that maps the scale and scope of convergence, defined by its scenarios, functions and solutions, some of which we explore in this section of the book. In Fig. 7.3, we have structured the convergence of humans, machines and nature in three levels of convergence as relates to smart cities. The first is the convergence on the evolutionary development based on patterns and trends. The second level is the convergence of technologies and cities and within the context of each city’s unique DNA explained in earlier chapters. The final level is the convergence on the operating system level and how humans, machines and nature influence the organizational structure of smart cities. Each transition is a delicate one. For mobility, it is from the current mode of independent privately owned vehicles and commutes to workplaces to one of driverless, publicly owned vehicles with more options for remote work (no commuting) and quite possibly a lot less work due to automation. Therefore, it is a very stark contrast and one hard to imagine happening as

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Machine Technology

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Evolution of Systems Evolutionary Development Trends

City DNA

Input

Output

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System Architecture Operating System / Connectivity / Interface

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FIGURE 7.3 Convergence application.

quickly as a decade. Current niche developments (walkable smart districts) to landscape level changes (public demonstrations) are converging on ushering in this mobility revolution. For environment, the status quo is an unfolding ecological crisis of resources depletion, biodiversity loss and climate change. The next system is one of regenerative design, sustainable food and energy production and climate preparedness and resiliency. Getting from A to B is up against the entrenched interests of the fossil fuel industry, which are resisting some change while investing in others. China is still dependent on coal as the predominant source of energy until 2050. The niche innovations come in many forms: political organizing, public education, alternative media and renewable technology. They all converge on putting pressure on the regime level to support more rapid growth of systems change.

7.3 Smart mobility 7.3.1 Pastepresentefuture 7.3.1.1 Evolution Spanning thousands of years, the evolutionary trajectory of mobility has certainly been one of humanity’s major accomplishments and technological advancements. From primitive forms of movement to the futuristic Transporter in the Star Trek series, humans have imagined an increasing range and speed of travel. Early civilizations harnessed animals for their brute strength and

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endurance and fashioned wheels and axels to ratchet up our capacity for transportation. We have studied and learned to imitate nature to create new ways for humans, goods and services to be transported around the globe and into space, such as by copying birds’ wings to generate lift and achieve flight. From ground, sea, air and space, humans have mastered the art of mobility, based on nature’s patterns and planetary movements themselves being clues toward gravity resistant forms of transport.

7.3.1.2 Challenges Despite the major progress in mobility, humans have not yet solved some of the most basic issues of urban mobility and in most cities around the world; traffic has been one of the most crippling and negative aspects of self-inflicted damage to the natural environment and human quality of life. Fossil fuele based vehicles, carbon dioxide emissions and burning fuels have contributed to our greatest common disaster on the planets ecosystem which desperately needs a solution that will manage the impact of patterns of human movement around the planet, while reducing the resulting negative carbon footprint. Cities have been victims of human development and urban mobility is still one of the major problems to solve with the aid of new technologies and AI. While traffic and other mobility challenges cities face have many common factors, each city has its own unique set of problems to solve that relates to the physical, spatial configuration, urban layout, the growth and population fluctuations and socioeconomic stage of development. As discussed in the first chapters, the combination of the evolution of the city, urban planning factors, and the ability to adopt technology solutions represent the city DNA and is a major influence for how cities will adopt new technologies and applications to solve their own unique problems. As urban lifestyle and mobility design and innovation become more intertwined, the development of new forms of transportation and mobility applications is influencing new experiences. The good city life becomes predicated on unfettered access to movement, mobility and smart city interfaces. This includes walkability, bike-sharing and more comprehensive public transit coupled with taxi services. The ultimate aim is for urban mobility to become all about mobility experiences in which the rider is able to have multiple choices and types of experiences as they travel customized routes to reach their destinations. In a sense, the travel experience between locations will in its own way become the destination. We can apply the issue of mobility to the Multi-Level Perspective, to gauge the levels and the relationships of all factors over time, converging from niche innovation to transforming the future landscape, as depicted in Fig. 7.4.

Future

Major Challenges Asia Growth Consumer Spending

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AI Driven Solutions Machine Learning Efficient Energy Storage

FIGURE 7.4 MLP Mobility.

Robotics Urban Interface

Deep Learning Self Navigation Self Generating Energy

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Present

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7.3.1.3 Directions As auto manufacturers have moved to electric vehicles, this has been one of the first steps in eliminating the reliance of fossil fuels and carbon polluting vehicles. The electric car movement has also spurned a revolution in new automotive design and navigation that has paralleled the evolution of new forms of technology-driven mobility and the advancements in autonomous mobility solutions. In the near future, self-driving electric vehicles will be rolled out as an integral part of smart cities operating systems supported with big data, pattern recognition and AI at once lowering the cost and environmental footprint on mobility while creating a seamless self-regulating network. Currently, ride-hailing and car-sharing companies (Car2Go, Uber) are making the intermediate steps driving progress towards smart mobility solutions, as well as increasing scale of fleet electrification. Smart mobility, especially electric-based transport, also depends on smart grids and clean energy. Smart grids distribute the power more efficiently and sustainably, so there is minimal loss and electricity available at more access points. Smart grids also are able to better respond to peaks and valleys in usage during different periods. AI and machine learning will continue to facilitate the efficiency and resilience of these systems, especially as they combine with intermittent sources of energy such as wind and solar. 7.3.2 Objecteactioneoutcome Object Mixed Modality Smart mobility mixes modalities such as walking, cycling, driving, busing, flying, etc., to integrate all available modes of travel to plan the most efficient routes. It also makes for dynamic and enjoyable options for mobility. Additional positive effects of mixed modalities include decreasing traffic congestion and improving health and happiness (due to walking or cycling).

Action Transportation and Logistics The action of smart mobility is to move an object or person from one location to another as efficiently as possible. Fully automated and autonomous transportation is the future of mobility and logistics alike. The notions of a “driver” or “pilot” are obsolescent and will soon be historical artifacts of the 20th century and early decades of the 21st, limited exclusively to recreational roles, as we replace our active participation with AI navigation and driving functions.

Outcome Freedom The outcome is a fully integrated supply chain and fully autonomous travel capability. You enter a destination in your smart device and are whisked away by the next available vehicle. You order something and it is delivered to your coordinates. Freedom via advanced logistics will be increasingly coordinated by AI, rather than overseen by humans.

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7.4 Smart environment 7.4.1 Pastepresentefuture 7.4.1.1 Evolution As Norbert Weiner described in The Human Use of Human Beings, the Earth, like all living organisms, has a life cycle. Some stars are suns that have died billions of years ago and yet are still visible to humans. Perhaps we take for granted that the earth is not eternal, although within our lifetime and since the dawn of humanity our experience of planet earth is a very short segment of earth’s life cycle. What Weiner speculates, however, is that earth may have already reached its peak and is now in a downhill stage of existence. In systems science, it is identified as entropy when the system finally corrupts because of imbalances and irregularities. As in human aging, finally the human body breaks down and death occurs. Weiner explains that all living systems can potentially slow this inevitability, through the application of resistance; resistance being the ability to slow, regulate, or limit the degradation of the system. In nature, this is accomplished through biofeedback mechanisms that self-regulate the system. Organisms tend toward stability and homeostasisdthey become resilient and antifragiledrather than adding to the entropy of the system, so they are part of a complex ecology. Biodiversity is better for the stability of populations to contribute to homeostasis of the ecology. As explained in Chapter 1, cities are an amalgamation of diverse layers or dimensions including the physical environment, human population and governance and technology which has over the past centuries had an increasing impact on the state of equilibrium through the industrial revolution and its extreme levels of resource extraction and consumption. This pushes the system to the brink of collapse, but the upside is our ability to address it also increases. As postmaterial values have become more prominent, environmentalism can become a more common cause for people to unite behind. As technology and the Internet enhance our understanding of the world, we get a clearer picture as to the environmental entropy and the necessity to improve conditions through the potential to interact and contribute to the collective operating system. 7.4.1.2 Challenges Organizing toward a smarter environment often challenges or competes with imperatives of business and politics. As a result, business and politics co-opts environmental movements, thereby softening their impact. This can be viewed as a “dumbing down,” which is antithetical to smart environment. Another challenge is incentivizing people, businesses and industry to consume less and have a smaller ecological footprint. Hunger and poverty are persistent problems that need to be solved to qualify as a smart environment in smart cities. Smart cities must prepare for climate change, which will pose a unique threat to

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each city based on their individual city DNA and other factors, such as coastal cities at risk of flooding. Preparedness, real-time action alert, and resilience measures will become integrated into all smart environment policies.

7.4.1.3 Directions The future requires us to take command of our global scale systems. Terraforming traditionally refers to making other planets like Earth, but in the case of the Strelka Institute it refers to how we have accidently already terraformed Earth through global scale processes of extraction and construction, and now we must consciously terraform it to be sustainable. Benjamin Bratton (2019) advocates terraforming the planet as a way to cope and take conscious control of our accidental transforming of the planet because of expansion and pollution. Bratton calls for an urbanism that is “proplanning, proartificial, anticollapse, prouniversalist, antieantitotality, promaterialist, antieanti-Leviathan, antimythology and proegalitarian distribution.” Russia’s expansive territory is both the challenge and opportunity to put these visions into practice. A carbon tax is a necessary policy instrument to help wean states and smart cities off fossil fuel dependence. It works by taxing the carbon content of burnt fuels, incentivizing reducing greenhouse gas emissions and creating funds to direct to renewable energy. Carbon offsets are a method of offsetting carbon emissions elsewhere, by purchasing “offsets” that are equivalent to offsetting a certain amount of greenhouse gases, but they are a weak form of intervention. Forbes (2019) magazine reports that many organizations are developing blockchain for carbon markets, although still in the early stages. The promise is that it brings more trust and scale to carbon markets, while expediting our transition to a postcarbon future. Sensors will be embedded in the environment to provide real-time feedback about weather and ecology patterns and levels to be monitored through the smart city interface. Each city will be linked to global barometers regulating ecosystems. Management of environmental processes will be enhanced through data visualization and planetary simulations. With a combination of generative design and machine learning, continual modeling can be produced to find optimal states of human, nature and technology convergence to support balanced and sustainable ecosystems. 7.4.2 Objecteactioneoutcome

Object Resources Resources are the object of every smart environment, providing both energy and

Action Energy Energy is the medium and the means for resource extraction and utilization.

Outcome Sustainability Sustainable terraforming. There is no environment without sustainability. The

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material for construction of urban environments. Global and local resource management will have to be integrated into smart city protocols so that all cities can be more resilient in the face of climate change and market disruptions. The smart city tracks resource flowsdfrom commodities to water, air and powerdas part of its self-organization function.

Energy also brings the city to life through powering transportation, communication, heat and lighting. Constant energy flow will be monitored in real time via city operating system interfaces. An efficient energy generation and distribution platform is needed to reduce reliance on resources and to transition away from fossil fuel dependency.

opposite of sustainability is self-termination, which is the course we are on due to exponential technology causing existential risk, i.e., the proliferation of people and technology that puts increasing pressure on natural ecosystems. Sustainability is the necessary outcome of effective resource and energy management.

7.5 Smart people 7.5.1 Pastepresentefuture 7.5.1.1 Evolution The history of education varies according to time and place. Occasionally we see regression, but the general trend is to expand access to education, knowledge, and participation. As recent as the early 19th century, child labor still existed across the Western world and continues to this day in many developing countries. High school is really only a phenomenon of the 20th century. Along with our institutions and theories of education, we slowly evolve to make better sense and order of the world. Over time, labor largely becomes more abstract and intellectual, which is why knowledge workers and data are increasingly economically valuable. As advancements in AI and technology outpace human progress, societies worldwide must redefine their role and relevance in an AI-driven information age. In addition, as human learning and machine learning fuses, new forms of learning and knowledge creation are emerging. We are at the threshold of a new way of interacting with information that is completely revolutionizing how humans’ access and relate to it. Machine learning and deep learning will anticipate human requirements and process data before it plays out, consequently allowing humans more time to experience rather that compute. But this advancement poses many questionsdwill this new leisure be rewarding and will we still long for opportunities to problem solve for ourselves? 7.5.1.2 Challenges Historically, the delivery of education has always been stratified. Access to knowledge was very exclusive and elitist, typically keeping the working

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classes ignorant of their conditions. The idea of “smart people” for smart cities requires epistemic equality so that there is no deception and deficiency in the information ecology. It is only in the 20th century that public school systems became a normal mandated institution and the foundation of the new modern society. This institution needs to be continually updated and expanded globally to prepare new generations for the complex problems presented by the 21st century. One of the major political challenges is convincing skeptics that education is a worthwhile investment. In times of austerity, it often gets defunded and politicized, thereby jeopardizing the collective intelligence, both in the present and future. Socioeconomic factors also play a role, as teacher’s wages and job security have declined relatively over the past decades. Perhaps no other time in human history has knowledge advanced so rapidly. This acceleration is leaving a deep separation between those with access and those without. There are epistemic gaps between groups or classes of people that results in uneven and unhealthy information ecologies and diets. Access and education need to be made universal so that society can selforganize better and the collective intelligence can function with individuals and collectives on equal playing fields. As cities continue to attract people, capital and technology, rural areas in many countries are being left behind. Technology can either connect and divide the human race, but the process of digitalization holds the key to collective intelligence, as it makes the process of gathering, formatting, processing and analysing data vastly more efficient and impactful. Connectedness, active participation and inclusion are key elements to cultivating smart people.

7.5.1.3 Direction Without participation, people will not be able to contribute to the city operating system in an active way, either producing or consuming information that has an influence on the behavior of the system. In the collective intelligence model, all data are relevant as they provide behavioral insights into the collective organism. In our current stage of technological evolution, human contribution is still relevant in both establishing a collective intelligence of shared information, while participating in the operations and maintenance of the ecosystem. As discussed in the previous chapters, citizen participation is crucial to defining the experience of the city as an environment that can serve the needs of all people and provide a higher quality of life. By allowing all participants to co-own and cooperate, cities can come closer to a shared model of self-preservation. Augmenting this culture, AI has the potential to enhance functionality for the city to be sustainable in all aspects as a self-regulating operating system. The future of education is promising if we can successfully navigate the current “post truth” climate and deliver the next equilibrium of social development. Smart city technologies can foster self-education and lifelong learning

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practices by incentivizing and directing people to libraries and informative experiences as part of their city exploration such that the city itself is a form of living, interactive library serving the needs of its inhabitants. Stanford University offers a program of hands-on-community-based education that merges the academic space with real needs. It’s a powerful new form of engaged research that involves diverse people and resources to solve community problems in real time. This is a collective intelligence in action and what is needed for smart people in smart cities to organize bottom-up; empowering people to learn and make better decisions based on data collection and feedback from the city interface. The living labs and innovation hubs discussed in this book are similar organizational frameworks for scaffolding such practical action research. Education is ultimately an organizing principle for new smart societies and Dr. Zak Stein is a leader in education reform, particularly systematized in his book Education in a Time Between Worlds: Essays on the Future of Schools, Technology, and Society (2019). Universal education in the new millennium is a massive undertaking and a necessary project to include in smart cities, as free access to education is a codified human right. It must go above and beyond access to information and knowledge and include nurturing and guidance on personal and cultural matters as well. In the Scandinavian countries, this type of program is termed “Bildung,” meaning “formation,” as it is allencompassing as a lifelong learning process that forms ones character as a reflective being and proactive citizen. This idea is best represented in Tomas Bjorkman’s book The World We Create (2019).

7.5.2 Objecteactioneoutcome Object Society/Culture The basis for the notion of smart people is nested within culture and society. People cannot be educated in a vacuum, for they come with history, traditions, social norms and tribal relations which are part of the education process. “Smart people” honors society and culture as the objects of an informed, educated and wise population, for people must know

Action Education Systemically educate the people, not simply by rote learning or job-based skills development, but rather by fostering self-learning, personal development, socialization, empathy and critical thinking. The German word for this lifelong cultivation of a citizen ethic is Bildung; it has been deployed in the Scandinavian countries for almost a century and is proven to result in more

Outcome Enlightenment Enlightenment is the process and outcome of education and spiritual development, enabled through knowledge and experience about society, culture, education, innovation and creativity. Technology accelerates this enlightenment, but only the self-directed user/citizen can experience it. The world is at our finger tips through smart devices and the Internet, but only if we understand that power and responsibility. Every smart city requires

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themselves to know anything meaningful.

prosperous, happy, and peaceful societies. The living lab concept and innovation hubs generate continual innovation and prototyping of knowledge hubs.

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such a foundation of social and cultural embeddednessda formal yet open-access education.

7.6 Smart governance 7.6.1 Pastepresentefuture 7.6.1.1 Evolution As the world recognizes diverse political organizational structures and systems of governance, so do the corresponding operating systems reflect the different possibilities. These include top-down and bottom-up forms of governance, explained in the earlier chapters, which have a significant influence on the structure and behavior of society and the ways in which technology can be adapted within the operations of each city. While achieving transparency in systems and open data is a common goal of many cities and countries, this may not be suitable for all forms of government and therefore is not always promoted within the technological framework. This also brings up the most critical challenge of balancing public versus private data, the subject of countless publications, debates and political machinations. This is established in the timeless book Community and Privacy: Toward a New Architecture of Humanism, written for urban planners, architects and policy makers. Regardless of the political and organizational system prevailing within cities and nations, as technology becomes ubiquitous and all citizens can potentially be connected, the evolution of city governance is influenced by the ability of citizens to engage in more meaningful ways. 7.6.1.2 Challenges Governance is still largely subject to special interests that distort reality and create a democratic deficit. Governments around the world have to rise to the challenge to do better, united by the collective threat of climate change. Smart governance is predicated on curbing extremist tendencies while also reigning in utopian dreams to ensure the progress and development are steady. As technology is deployed in different parts of the world at different rates, smart city implementation of smart governance will not be universal. Many governments are in crisis and have boldly taken us into a world of uncertainty and existential risk. Smart people combined with metagovernance solutions are the

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key to successfully achieving sociotechnical transition because people drive the niche innovations which ultimately achieve change.

7.6.1.3 Direction Metagovernance is the future, as we distil and crystallize universal laws and governance frameworks that are adaptable to each situation, guiding policies within planetary and ethical boundaries. The moral basis is to underpin all regulations with strict human rights and environmental guidelines like the UNDP principles that will become embedded in legal frameworks and provide access to the benefits and protections guaranteed by global standards. Metagovernance includes tools that governments and users can opt in to, such as common policies and technologies to make cities more self-regulating and efficient. Software platforms and technologies such as blockchain and holochain are instrumental in promoting trust and security in governance, business and social media platforms by creating underlying, traceable evolutionary records. Open source platforms help accelerate innovation over the private sector and prevent protectionism. Convergence will influence governing as universal systems will emerge from local governing situations and will scale to global standards and vice versa. 7.6.2 Objecteactioneoutcome Object Stakeholders A government is only as good as its collective intelligence and that requires involvement of all stakeholders and users. Public consultations are a key to renewable political models, rather than secretive special interests prescribing policies that only benefit them. The entropy of these systems over time has led to a democratic deficit, in which voters are misinformed, governments are dysfunctional and elites are corrupted. The result is a downward spiral

Action Policy/Reform The action through which smart leadership and institutions are fostered is through reform, diagnosing and reconstructing the necessary social transformation. As automation comes online both in terms of job replacement and metagovernance, human-centered policies and regulations will shape how the city self-organizes effectively. Reform must take place system wide and give every individual the tools for community and self-

Outcome Inclusiveness The final key to smart government is its outcome, which is measured by inclusiveness and the consent of the governed. Citizen participation, user feedback and democratic processes secure inclusion, collaboration and access to services. The user/ citizen is the final arbiter of smart governance, so that it can continually improve its processes and meet the needs of all people while evolving as a selfdetermining, selfregulating society

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and erosion of institutions and ideology. Smart government has to counter these pathological trends to renew institutions, publics and stakeholders.

development. As consistent with stakeholders, transparency through all processes ensures efficiency and integrity in the reform process.

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through the power of inclusiveness.

7.7 Smart economy 7.7.1 Pastepresentefuture 7.7.1.1 Evolution Human and economic development have gone hand in hand for centuries. In this process, the innovation of systems of exchange of goods and services and the creation of new industries have accelerated the progress of human’s ability to exploit earth’s resources and the human use of human beings, as the title of Norbert Weiner’s book relates. Economic systems have been developed to enable a medium of value and exchange to allow society a common interface. This medium is now expanding from physical forms of exchange to new forms of value based on digital representations of currency and exchange. These new forms of value systems have challenged traditional forms of economics and may lead human systems to return back to systems of direct exchange and barter of resources rather than physical manifestation of money. In this new value system, innovation and intellectual property become forms of currency to be accumulated and traded. Smart economies must promote new forms of exchange that lead to the innovation of new economic models to drive innovation, create new commonwealth and allow new forms of economic exchange and marketplace. Economies today operate like delicate living systems, but lack the selfawareness required to find a healthy equilibrium for its constituents. Capital flows in and out of economies like blood circulates. In this flow, the predominate modern global economic system (free market capitalism) produces an abundance of wealth, but the system has not resolved the issue of achieving a more even distribution. Smart economies must be sustainable, just and inclusive to ensure solid foundations are laid for social development, well-being and prosperity. 7.7.1.2 Challenges Vested interests are an obstacle to making a smart economy, as their coste benefit analysis is not incentivized to transition to smarter economies. Where

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businesses pursue their own self-interest, competition and innovation can create the next dominant system, but government intervention is necessary to break up monopolies and regulate hypercapitalist imperatives that threaten other smart city functions, i.e., environment, people, etc. The next system must be a balance between businesses free to pursue value and protecting the global commons and the public interest. Increasing human rights seems sometimes opposed to business interests, even in the workplace, so it is a major challenge to overcome. Coordinated top-down and bottom-up pressure on to the system can transform it. We propose simultaneously building a commons-based economy (a commonwealth) in tandem to the capitalist system (discussed further in Chapter 9).

7.7.1.3 Direction New forms of payment such as PayPal make it easier to do business online. Cryptocurrencies like bitcoin and the many more provide novel ways to “mine” money, conduct business and speculate on prices, but it is also an uncertain and risky landscape. Companies such as Amazon and Alibaba are rapidly developing integrated global supply chains to create higher efficiency in business operations. AI is a key component driving these processes while human input will still need to be factored in the equation to balance the distribution of labor between humans and machines. Most importantly, public services must turn into free access models, towards a moneyless smart city experience. It begins with things like transit and libraries, but it ends with things like emergency food and shelter. Healthcare must be universal and single-payer as well. 7.7.2 Objecteactioneoutcome Object Market The market is the main mechanism for economic activity, as it provides a forum for trade and prices to be negotiated. A smart market is efficient and avoids market fundamentalist beliefs (that all markets are inherently good). Some things, such as water or healthcare, are public resources and human rights and should not be

Action Transaction What takes place across markets is transactions, trades, exchanges, purchases, investments, income, taxes, etc. In a smart economy, transactions must minimize costs and be effortless. A smart economy must ensure fair trade and equitable transactions, including insurance issues,

Outcome Commonwealth The outcome of a healthy market with fair transactions is a commonwealth, another way of saying public prosperity, common goods, distributed income, derived from value the economy generates and manages. In theory, a smart economy is just and equitable, but in

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privatized and left to markets. Traditional monetary systems are being challenged by alternative innovations such as digital banking and cryptocurrencies built on blockchain or holochain. The Internet helps achieve what Bill Gates calls “frictionless capitalism” by innovating time-saving techniques or technologies that reduce unnecessary and unproductive overhead costs.

contract resolution and consumer protection. Fair trade policies are but a precursor to a more comprehensive and effective economic system that ensures labor rights, just compensation and sustainable interaction between employers and people and the environment.

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practice it tends toward stratification (poverty and hoarding) and frivolous waste (conspicuous consumerism). Extreme inequality undermines prosperity as it weakens the overall system and accelerates toward a crash or collapse. The smart economy has the ability to gauge and coordinate prosperity at all levels and scales of society, so that no person or environment is underfunded, arbitrarily devalued, or exploited.

7.8 Smart living 7.8.1 Pastepresentefuture 7.8.1.1 Evolution The high standards of living we enjoy in the developed world today are largely a relatively recent phenomenon. As recent as the early 19th century, there was still slavery and child labor in Western countries and appalling living conditions for most people in general. Today, people in developed countries often take for granted having access to clean drinking water, variety of foods, healthcare, technology, entertainment and recreation options.. The evolution of smart living is to extend this to all, while finding innovative ways to reduce consumption and increase efficiency. Digital avatars will be constructed that contains all one’s medical history and health requirements, which will intuitively maximize health and informed choice for the citizen user. While smart living centers on human needs, there is a new trend that is moving away from human centric to inclusive models connecting and incorporating all life forms. This trend reinforces the inevitable state of convergence of human, natural and technological states and supports the main proposition of this book that a new union is inevitable given the evolution and acceleration of technology and the need for a massive change in the humaneenvironment relationship. Based on this position, smart living is allencompassing as a holistic understanding of the balance of human, nature,

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and technology and the potential to create a new collective intelligence or higher consciousness to direct the next stage of human social evolution.

7.8.1.2 Challenges Our evolution is both enabled and hampered by technology, but only truly realizable through a holistic collective intelligence. Smart living is, in part, dependent on smart people and an educated collective intelligence. It also requires some regulation of food and drugs, so that people can effectively selfregulate their health. In the West, America especially, citizens have supplanted quality of life for quantity of life, driven by a “bigger is better” ethos. This leads to overconsumption and health concerns and epidemics such as obesity and heart disease. Smart living can provide health activities that create positive social interactions and better physiological homeostasis. 7.8.1.3 Direction Public health metrics collected globally show where the longest life spans and lowest mortality rates are (typically the Scandinavian countries), which can integrate with smart city collective intelligences to optimize well-being for all citizens. Living labs are experimental hubs not just for innovating city functions but maximizing user well-being through real-time data collection and feedback. Western medicine can also be complemented by Eastern medicine, philosophy and culture. Preventative and regenerative forms of health and lifestyle will be emphasized. Users as patients have access to their healthcare information to make responsible choices with real-time self-evaluation. 7.8.2 Objecteactioneoutcome Object Health The foundation for smart living is public health. Thus, the smart cities which meet all these “smart” criteria have a healthy populace as a strong foundation, not built by technology, but enabled by it. The better people can take care of themselves through the lower healthcare costs are and the less of a burden it is on the system. Technology can

Action Well-being Well-being goes beyond basic health and security to suggest restorative generative states and practices. Well-being is best achieved by grounding it in Max Neef’s (9) Fundamental Human Needs: subsistence, protection, affection, understanding, participation, idleness, creation, identity and freedom. AI can be applied to algorithmically innovating on the provision of well-being.

Outcome Actualization Self-actualization and cultural actualization means that people have meaningful purpose. It is the outcome desired from building a society of healthy fulfilled beings. Everything must have a role in generating high living standards and sustainability. The challenge is there to be realized on a daily basis, by working toward

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enable better monitoring, illness detection and treatment, but it must be preceded by smart policies that provide a baseline of health. Education about health can guide citizens to make better choices about diet, exercise and healthcare strategies and remedies that can now be hyperpersonalized.

Psychological well-being and happiness can only be layered on a foundation of physical health and security. The requirement for well-being is not just universal basic healthcare and legal and physical protections, but access to education, mental healthcare, counseling, various therapies, community activities and meaningful work.

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building smart cities for the 21st century. With strong foundations of health and well-being, the vision is for all people to be actualized, enabled by smart cities that provide the means to do so.

7.9 Conclusion We examined six major smart city functions, defined as mobility, environment, people, government, economy and living, innovated upon from the Smart City Mandala. These six functions represent the predominant organizational dimensions understood today as the basis of smart cities as operating systems that over time will develop new typologies and subfunctions. We introduced the MLP as a way to map and execute sociotechnical transitions across all levels through a four-phase process and timeline. The convergence methodology was reviewed and applied to this chapter’s context. Each function was reviewed in terms of its past (evolution), present (challenges) and future (directions). Each city will have a different past, present and future depending on its unique city DNA profile. This trajectory demonstrated the convergence of the realms of human, technology and nature. In addition, each city function defined along the dimensions of object, action and outcome as priorities for the collective intelligence to converge on, articulating the key variable to effect, the means to change it, and the desired result.

References Bratton, B.H., 2019. The Terraforming: Urban Planetarity. Tongji University. D3(5th floor), CAUP. Forbes, G.J., 2019. Solving The Carbon Problem One Blockchain At A Time. Forbes Article. https://www.forbes.com/sites/jemmagreen/2018/09/19/solving-the-carbon-problem-one-block chain-at-a-time/#1b4498f05f5e. (Accessed 3 December 2019). Geels, F.W., 2019. Socio-technical transitions to sustainability: a review of criticisms and elaborations of the multi-level perspective. Curr Opin Environ Sustain 39, 187e201. Go¨pel, M., 2016. The Great Mindshift: How a New Economic Paradigm and Sustainability Transformations go Hand in Hand. Springer. Stein, Z., 2019. Education in a Time Between Worlds: Essays on the Future of Schools, Technology. and Society, Bright Alliance.

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Further reading Bjo¨rkman, T., 2019. The World We Create: From God to Market. Whitefox Publishing Limited. Chermayeff, S., Alexander, C., 1963. Community and Privacy: Toward a New Architecture of Humanism. Doubleday. Dijk, M.. Towards a typology of urban transition and non-transition pathways. https://jpiurbaneurope.eu/app/uploads/2017/04/Dijk_Towards_a_typology_of_urban_transition_nontransition_pathways.pdf. (Accessed 19 December 2019). Geldenhuys, H.J., Brent, A.C., de Kock, I.H., 2018. Literature review for infrastructure transition management towards Smart Sustainable Cities. IEEE International Systems Engineering Symposium (ISSE), Rome, pp. 1e7, 2018. Knox, P., 2014. Atlas of Cities. Princeton University Press. Mattern, S., 2017. Code and Clay, Data and Dirt: Five Thousand Years of Urban Media. University of Minnesota Press. Mattoni, B., Gugliermetti, F., Bisegna, F., 2015. A multilevel method to assess and design the renovation and integration of smart cities. Sustain. Cities Soc. 15, 105e119. Noveck, B., 2015. Smart Citizens, Smarter State: The Technologies of Expertise and the Future of Governing. Harvard University Press. Ratti, C., Claudel, M., 2016. The City of Tomorrow. Yale University Press. Seto, K., Reba, M., 2018. City Unseen. Yale University Press. Silva-Morales, M.-J., 2016. Understanding the transformation of urban public services systems in a smart city initiative: multilevel perspective, drivers and barriers. In: 26th Annual RESER Conference. Naples, Italy. Winkless, L., 2016. Science and the city. Bloomsbury.

Chapter 8

Smart city functions Chapter outline 8.1 Introduction 8.2 Smart city enablers (hardware infrastructure) 8.2.1 Collection: IoT and low energy consuming sensors 8.2.2 Processing: scalable computing power and storage through edge and cloud computing 8.2.3 Transmission: network infrastructured5G 8.2.4 OS: AI smart city operating systems 8.3 Introduction to AI, AI applications and capabilities (software infrastructure) 8.3.1 Capabilities-based AI 8.3.2 Functionality-based AI 8.3.2.1 Critical AI capabilities needed to power smart city functions 8.3.3 Computer Vision 8.3.4 Natural language processing 8.3.5 Machine learning 8.3.6 Predictive analytics 8.3.7 Robotics 8.4 The convergence of AI applications within smart cities 8.4.1 Convergent applications 8.4.2 Hierarchy framework for scale and scope of smart city functions 8.5 Smart city functions 8.5.1 Smart environment

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8.5.1.1 Macro scale/ context 8.5.1.2 MESO scale/content 8.5.1.3 Micro scale/ component 8.5.1.4 Strategic functional objectives Smart government 8.5.2.1 Macro scale/ context 8.5.2.2 MESO scale/content 8.5.2.3 Micro scale/ component 8.5.2.4 Strategic functional objectives Smart mobility 8.5.3.1 Macro scale/ context 8.5.3.2 MESO scale/content 8.5.3.3 Micro scale/ component 8.5.3.4 Strategic functional objectives Smart economy 8.5.4.1 Macro scale/ context 8.5.4.2 MESO scale/content 8.5.4.3 Micro scale/ component 8.5.4.4 Functional strategic objectives Smart people

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8.1 Introduction Building on the underlying theories, models and scenarios presented in the previous chapters, here we explore the role of artificial intelligence (AI) in each of the six smart city functions, namely smart environment, governance, economy, mobility, people and living. While each function can be explored independently, it is through the interrelationship and codependencies of these systems and subsystems that we can start to visualize the impact technologies and AI will have on the design, operations and DNA of smart cities. The evolution of systems and the convergence of humans, technology and nature allowed us to identify six key functional states necessary to support the highest level of convergence. There are countless applications and they are quickly converging into combined forms of new technologies. The clarity of this picture is being enhanced in real time as we approach the possibility of creating simulated city environments and alternate digital realities. While computing power, sensor technology and smart connected objects are becoming ubiquitous, the vast amounts of data made available require new forms of collective intelligence, analytical capabilities and operating systems. The increasing resolution and mapping of reality is being demonstrated in the mainstream through the converging intelligence of machines and humans across various fields and scenarios. When one imagines all these sensor technology and algorithmic analysis at once, it is pervasive. Advances in different applications are happening logarithmically and also intersecting. Within each function, AI is accelerating convergence and each function plays a fundamental role in supporting the highest level of convergence outcome of well-being, balance and optimizationda combination representing the holistic union of humans, machines and nature through their contexts, interdependencies and behaviors. As this socio-technological convergence accelerates smart city functions toward a singularity, it is paramount to ensure that AI also converges all members of society toward a sustainable, circular and inclusive smart city system. This chapter presents the key technologies that enable the development and application of AI across the six smart city functions and explores how they manifest themselves in scale and scope at

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structural, operational and strategic levels to achieve the highest functional states. The applications level of convergence theory and methodology is the manifestation of the smart city functions and how those serve a greater purpose within the city as a gestalt formation, reminiscent of a living organism. The aim of this chapter is to describe how AI-powered technological capabilities will catalyze the convergence across each smart city function, allowing each functional system to achieve its highest level state and accomplish the objectives of smart city planning design and operations. The question is how to bring about the changes while mitigating and negating the human impact, existential risk and negative externalities. Automation is also a major background theme here, as AI alters the function of human labor and makes repetitive tasks increasingly obsolete in the fourth Industrial Revolution, as the focus of human labor shifts up the value chain. The positive implication is that this creates a strong demand for healthy, educated, active citizens within a sustainable environment, rather than simple industrial workers. Meeting this demand requires deep investment in people and the transformation of systems across city functions. The demand that it imposes on us is to act on smart city development in a coherent, collective, conscientious, sustainable and convergent manner (rather than merely a marketing strategy to stimulate economic growth).

8.2 Smart city enablers (hardware infrastructure) The smart city is first defined by its basic conditions based on its city DNA: its unique combination of geography, history, culture, information flows, physical environment, cultural elements, financial dimensions, challenges and opportunities presented. Next comes the specifics of the hardware and infrastructure, the actual practical technology that enables the smart city. Enabling technologies include both hardware and software at a system level. These include low energy consumption sensors for data collection and edge computing for realtime calculations, close to the source of data. Cloud computing enables storage and processing of big data and smart city OS can act as a brain. Finally, the software infrastructure enables collecting and analyzing data to continuously improve and optimize the city function for the benefits of its citizens, as well as connecting users through an interface. By improving the operations of the city and simulating the real conditions of the city within a virtual environment (effectively by digitalizing the blueprint of the city, with all its challenges, characteristics, dimensions and layers), we are able to identify opportunity areas and apply advanced algorithms to offer both granular and large-scale solutions for the development, improvement, etc. of the urban environment. Through contemporary methodologies such as codesign, generative design, rapid-prototyping solutions, cities as living innovation labs, etc., human and machine intelligence are able to converge, in order to prevent unwanted outcomes and solve issues closer to

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Level 1:

Level 2:

Level 3:

Level 4:

Collection

Processing

Transmission

Operating System

IoT & Low Energy Consuming Sensors

Scalable Computing

Network infrastructure

Control Center

FIGURE 8.1 Smart city enablers.

real time than ever before. The bigger the quantity of data we are able to collect, the more accurate the virtual representation becomes, allowing us to offer effective solutions in real time. We are early in the evolution of true machine intelligence and are still heavily dependent on human intelligence for creativity, problem-solving and abstract-thinking. Fig. 8.1 shows the most important enabling hardware infrastructure behind the smart city. Broadly speaking, smart cities need four core components d Collection, Processing, Transmission, Operating System d to enable industry 4.0 and the next generation of smart city functions. The development of this supportive technological infrastructure is critical for the deployment, continuous development and evolution of AI algorithms that will power the convergence at functional and city-wide levels.

8.2.1 Collection: IoT and low energy consuming sensors Sensors are used to collect data from the environment surrounding it. The type of data sensors measure includes temperature, humidity, pressure, velocity, current, or light, to name a few. The miniaturization and increasingly low power consumption of such devices is making their large-scale deployment possible across various types of technologies, systems and industries/sectors. The widespread use and adoption of such devices allows multidimensional data gathering, necessary to power the AI algorithms needed for the convergence of people with technology and their environment. As we increase the scale and scope of data collection, we will be able to generate increasingly accurate virtual simulations of cities and the environment.

8.2.2 Processing: scalable computing power and storage through edge and cloud computing Edge computing refers to real-time data processing closer to the sources of the data, or where the data are being collected by the sensors. Edge computing

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meets the demands for lower network latency and higher reliability that the multitude of connected devices need to function properly. Edge computing allows increasingly larger and more complex systems to collect and process smaller masses of data, before sending them up a centralized cloud. Broadly speaking, cloud computing refers to on-demand computing resources. These can take various forms, including SaaS, PaaS, IaaS, private clouds, public clouds and hybrid clouds. Cloud computing solves multiple legacy technological issues and limitations associated with computing, namely scalability, know-how, security, upfront costs (capex), maintenance, data loss prevention and more.

8.2.3 Transmission: network infrastructured5G The growing gathering, storage and transfer of information from connected devices, IoT sensors, etc. requires increasingly high-speed bandwidth to support such data traffic. Not only are the data increasing exponentially, but the average size of each data transfer is also constantly growing, as the complexity and quality grows. 5G refers to the fifth generation of internet infrastructure, often referred to as the new electricity, due to the level of disruption and new opportunity its deployment is expected to bring. According to Cisco (Robinson, 2019), 5G networks will increase data transfer speeds 10-fold to reach 20 Gbps in comparison to 4G, unlocking vast commercial and social opportunities across various industries, such as healthcare, IoT, manufacturing and financial industries.

8.2.4 OS: AI smart city operating systems Through all these convergent applications, we will arrive at a singularity where the human realm, the physical domain and the technological sphere converge on the smart city OS operations platform. The large-scale collection of realtime data resulting from digitalization and widespread use of sensors will require a comprehensive operating system to allow us to monitor each smart city function. Centralizing data management will be critical for smart city governments to make fast and well-informed decisions. The rules and protocols of the smart city operating system will need to be adapted to the unique needs, challenges and DNA of the cities where they will be applied. These combined enable cloud-based, real-time smart city operations and geospatial systems monitoring. Ubiquitous sensors and real-time simulation go hand in hand. Urban sensing, interaction design, simulation, gamification and other topics were discussed at length in Chapter 6. As applications evolve and converge, sensors and simulations combine in novel capabilities, to create ultrareal environments. The relationship between sensors and simulation is key, as the simulation relies on data transmitted from the sensors.

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8.3 Introduction to AI, AI applications and capabilities (software infrastructure) The path to complete convergence of humans, machines and the environment must go through a virtual simulation, or digital dimension, where we can leverage AI in making sense of the vast complexity of natural systems, their behaviors and codependencies. As we discuss the evolutionary convergence of people, machines and the natural environment, we must also explore the nature of AI, its functional characteristics and the core capabilities that make it so integral to the central theme of this book. Artificial intelligence is what gives machines, computer programs and systems the power to learn, adapt to new informational inputs, solve problems and make complex decisions independently of people. AI utilizes techniques and methods developed in several fundamental disciplines, including Mathematics, Statistics, Engineering, Natural Science, Computer Science, Linguistics and Neuroscience. These allow us to build machine learning algorithms that underpin a number of key technological capabilities that power the practical applications of AI we all experience daily when we search online, take a photo on our smartphone, or shop online. Fig. 8.2 represents a convergence funnel of AI, from the physical technological enablement (described in the previous section) to the scientific methods that enable machines to reason and learn, through to the core human capabilities that machines are able to mimic through the deployment of AI. All across the process, intelligence increases. From the initial stages of enabling technology, an AI baseline is established that can then be trained and aided by humans and machine learning algorithms. This converges

TECHNOLOGY ENABLERS

MACHINE LEARNING Ability to Learn

AI Ability to Sense, Reason, Engage & Learn

Supervised Learning OS

Regression

Networks Cloud Computing Edge Computing

Unsupervised Learning

Methodologies Ability to Reason

AI

CONVERGENCE / SINGULARITY

Sensors UX

Reinforcement Learning

Decision Trees

NARROW AI Dedicated to a Singular Task

Computer Vision

STRONG AI Exponential growth in intelligence and computational abilities, Exceeds human capability

GENERAL AI Performs like Humans

Natural Language Processing Voice Recognition Robotics & Motion

LEVEL OF INTELLIGENCE

FIGURE 8.2 AI convergence funnel. Based on diagram by Stefan Dobrev.

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through more advanced reasoning and sense-making methodologies. At this point we start to master Narrow AI for a variety of special purposes and eventually it converges into a singularity of General AI to an event horizon that we cannot see beyond.

8.3.1 Capabilities-based AI 1) Narrow AIdThis is the most widespread form of AI currently in use and it is trained to perform one specific task with intelligence. Beyond its specific task, this form of AI becomes unreliable and can fail unpredictably, as it cannot apply its experience beyond its confined area. The increasing deployment of narrow AI and the exponential advancement of research and capability in the field are making the next level of AI increasingly likely. Examples of narrow AI can be found in image recognition, online games such as chess, recommendations on e-commerce website, voice assistants such as Siri and Alexa, as well autonomous vehicles. 2) Artificial general intelligence (AGI)dThis type of AI is able to perform intellectual tasks on par, or better than humans and is not limited to just one task. The goal for training behind this development is for machines to apply learning from one field, with ambiguous information and little experience, to completely different fields. Such systems are being designed with the human brain as a point of reference and that has inherent limitations resulting from our limited grasp of how the human brain behaves. Although general AI does not exist yet, the rapid development of narrow AI across various applications and continuous scientific research in the field of AI are closing the gap between human and machine capabilities. 3) Super AIdThis is still in concept stage that goes beyond the AI singularity, where AI capabilities have surpassed those of humans. At this hypothetical stage, AI machines will be able to consciously think and reason independently, as well as acknowledging their own existence.

8.3.2 Functionality-based AI 1) Reactive machinesdThese are the most basic types of AI. They have no memory and cannot utilize past experiences to influence decision-making. They are only able to react to current existing scenarios and situations. An example of this is chess-playing bots. 2) Limited memorydLimited memory AI uses machine learning to obtain knowledge from stored data, past experiences, events and learnings. This is effectively a combination of preprogrammed knowledge and observational data used to make necessary adjustments. Autonomous vehicles use this type of AI to improve the time it takes for the vehicle to react to externalities found in various road conditions.

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3) Theory of minddTheory of mind AI is characterized by decision-making capabilities similar to those of the human mind. This class of AI will be able to understand the complexities of human emotional and behavioral patterns and how they affect discourse, decision-making and social interaction. This is the stage where AI begins to understand the depth and multifaceted nature of human minds, on path to AI singularity. 4) Self-awarenessdThis is the final stage of AI development, where artificial intelligence will achieve human-level consciousness, have desires, have emotions and make entirely independent decisions based on its own philosophical system and moral compass. Self-awareness starts occurring after AI achieves singularity.

8.3.2.1 Critical AI capabilities needed to power smart city functions This section introduces critical AI capabilities used in every smart city function to power the convergence of people, machines and their environment. These capabilities will mimic certain abilities of humans, such as vision, hearing, analytical thinking and precise physical movement. 8.3.3 Computer Vision Broadly speaking, computer vision is the process by which machines derive meaning from visual data or pixels. This is the subarea of AI that deals with how machines see. Computer vision works in three steps, namely acquiring, processing, and understanding the image. To achieve this, ML, NN, and DL algorithms work in the background, training AI to accurately discover common patterns across a multitude of examples fed to the system, by representing the commonalities mathematically. Computer vision is a critical AI capability for various applications in future smart cities including face recognition, object detection, augmented and mixed reality, image classification, security and surveillance, robotics, and autonomous vehicles.

8.3.4 Natural language processing Natural language processing (NLP) is the field of AI concerned with how computers analyze, understand and interpret human language. NLP allows humans to talk to machines in human language. Computer languages are inherently strict in their syntax and would not work unless they are correctly spelled. On the contrary, natural languages have more flexibility to adapt and interpret the flaws coming from mispronunciation, accents, word play, dialects, context, etc. To teach computers how to understand human languages, scientists have adopted concepts and models from linguistic fields. These include morphology (what is the function of words used), syntax (sentence structure/

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parse trees), semantics (meaning derived from context), pragmatics (social language, body language, implied meaning) and phonology (how language is pronounced). NLP is widely used in power search, chat bots, mobile and web applications, translation and voice-assisted devices. According to Tom Mitchell at Carnegie Mellon University, we are entering a 10-year window where computers make a leap in evolution, from not understanding language very well to understanding it quite reliably and in different contexts and conditions (Brynjolfsson and McAfee, 2014). Given that we are past the middle of that window and voice command is available from many services, this is coming true.

8.3.5 Machine learning Machine learning is a subset of AI that uses algorithms to analyze large amounts of data (often real-time) and make intelligent decisions based on the logic it has “learned.” Deep learning is a sophisticated ML technique that combines, layers and connects various machine learning computer algorithms to form an artificial neural network (mimicking the structure found in the interconnections of the human brain). This allows the system to continuously learn on the job, determining whether decisions are correct to constantly improve the quality and accuracy of results. This enables such complex systems to learn from unstructured data inputs (photos, voice recordings, videos, etc.), making them by broad definition intelligent. Machine learning has three broad classifications: 1) Supervised learningdAlgorithms that develop predictive models based on input and output data, to drive classification and regression. 2) Unsupervised learningdAlgorithms that organize raw and unstructured input data into clusters (clustering). 3) Reinforcement learningdThese algorithms seek to find the best path, or behavior in order to maximize reward.

8.3.6 Predictive analytics Predictive analytics refers to the process of analyzing large sets of historical data to uncover patterns that can help predict future outcomes. Predictive analytics is widely used in business applications (retail, financial modeling, insurance, energy production, demand forecasting), weather, sports and health.

8.3.7 Robotics In robotics, the symbiotic relationship between AI and machine learning allows robots and autonomous systems to manipulate physical objects far more efficiently than humans, while continuously improving and optimizing their performance over time. Robots have been used in manufacturing for decades,

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but as technology progresses we are seeing increasing autonomy and augmentation of processes in both production and services. Robots are widely used in industry 4.0 to either fully automate production, or augment and assist the human workforce, making the production process safer, more efficient and cost-effective. These critical AI capabilities enable machines to mimic humans in certain aspects such as NLP and image recognition. In other cases AI algorithms are able to demonstrate far superior capabilities, such as in robotics and predictive analytics.

8.4 The convergence of AI applications within smart cities To understand the role of AI in the formation, development and future of the smart cities, it is important to analyze the six smart city functions through the theory of convergence, the MLP (see Chapter 7) and the scale and scope of AI introduced earlier in this chapter. We use the term Applied Convergence to describe this more complex and multifaceted process of convergence. Rapid technological advancements and AI combine into more powerful recursive forms, gaining prominence for their highly convergent nature, acting as a catalyst for individual and collective convergence at a smart city level. Smart city functions are all vectoring toward the same singularity, self-organizing by their own logic, but also needing human guidance. This section is about how a snapshot of applications is illustrative of convergence in our everyday lives and how those applications are rapidly bringing the convergence of humane machine-nature into being.

8.4.1 Convergent applications Our intention is to build a total system that simulates nature, humans and the technology itself, so that each of these dimensions can be self-organized and sustainable in perpetuity within a holistic smart city operating system. Thus, we have constructed an iconographic information architecture throughout this book to visualize all the parts and interconnections between objects, layers, users and each other. The cluster exists hierarchically but also in a circular fashion, as described in other chapters, such that the six layers connecting the physical domain, the human realm and the technology sphere are integrated through a natureetechnology fusion that reaffirms the human dimension. In the broad sense, these layers are locally integrated and nonlocally connected. The convergence of AI applications around creating this cluster simulation is a necessary and desirable goal for the more rapid emergence of true smart cities.

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8.4.2 Hierarchy framework for scale and scope of smart city functions This chapter considers each of the six smart city mandala functionsd environment, governance, mobility, economy, people, livingdsorted into dominant scales of macroemesoemicro levels and described in terms of the useful scope of contextecontentecomponent. This is because scales and scopes enable us to consider the intersections between them, what applications are deployed in what industries and markets and what the impact is at the more granular levels of technologies, individuals, companies and organizations. This will provide a simple common structure to each subsection, allowing us to uncover important interrelations and codependencies across various subjects. To contextualize the application and impact of AI and related technologies we apply a contextecontentecomponent hierarchy (Table 8.1) that will provide a simple common structure to describe each of the smart city functions, allowing us to uncover important interdependencies across the various subjects and most importantly to trace the scale and scope of convergence resulting from the deployment of AI and ML across each of these systems.

8.5 Smart city functions Urbanization is a process that started in the 1700s and is showing no signs of decelerating in the increasingly connected, globalized and digitalized world. We live in the midst of the fourth Industrial Revolution that will redefine our relationship with our planet, ecosystems, governments, technologies, machines, data and each other. This stage of humanity’s socio-economic-technical development and convergence is entirely characterized, driven and defined by data, intelligence and hyperconnectivity. This section aims to explain how AI will support, drive and expedite convergence in each smart city function to achieve the outcomes postulated in Chapter 7. These outcomes are necessary preconditions and pillars that will support the vision for future smart cities. Table 8.2 provides a summary of the various capabilities made possible through artificial intelligence and related technologies in the context of smart city convergence toward higher level outcomes.

TABLE 8.1 Scales and scopes. Macro scale/context at the system level

Meso scale/content at the functionality level

Micro scale/component at the object level

City Industry Ecosystem

Strategy and purpose Interdependencies Behaviors

Technology solutions Features and characteristics Applications

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TABLE 8.2 AI-driven convergence in smart city functions and resulting system outcomes (singularities). Function

Themes

Technologies/ capabilities

Environment

Optimizing resource allocation, renewable energy, biomimicry, carbon neutrality, permaculture

Predictive analytics, machine learning, deep learning, cognitive computing, big data

Sustainability

Government

Participatory society, transparency, open data, enabler government

Blockchain, data mining, machine learning, digital identity, e-voting

Inclusiveness

Economy

Entrepreneurship, innovation, circular economy, planetary accounting, universal basic income

Blockchain, holochain, robotics, industry 4.0, cryptocurrency

Commonwealth

Mobility

Multimodality, customization, invehicle experience, on-demand, sharedownership, CaaS

Autonomous vehicles, warehouse automation, smart public transit

Freedom

Living

Individualized health, hypercustomization of living

Smart buildings, natural language processing, voice recognition, UX

Selfactualization

People

Harnessing collective intelligence, high levels of human development, live-long learning

Virtual reality, augmented reality, humanecomputer interaction, 3D virtual object manipulation

Enlightenment

Outcome

8.5.1 Smart environment To achieve a state of environmental sustainability, we must tackle the existential challenges faced by humanity. These include climate change, global warming, rising sea levels, excessive pollution, hunger for energy, scarcity of natural resources such as food, water and others. The environment is a highly complex nonlinear system in which the change in output is not proportional to the change in input; hence controlling or predicting such systems is currently impossible. A popular metaphor introduced by Edward Lorenz commonly known as the “butterfly effect” summarizes the unpredictable nature of such

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systems, whereby a small change in one state can have a disproportionately large impact in a later. In mathematics and biology, this is also known as chaos theory. Within such unpredictable systems, however, there are many natural objects that exhibit fractal properties, i.e., exhibit never-ending and infinitely complex patterns. Fractals can be found in the shapes of river beds, clouds, trees, weather patterns and more. As more sensors and devices become connected in our natural environment, increasingly complex and accurate data become available to train AI algorithms to uncover patterns much faster, allowing us to run complex virtual simulations on weather patterns, vastly improving the accuracy of forecasting the state of the environment. In energy, the shift to renewable energy is bringing new challenges to grid operators, as the electricity produced by wind, solar and tidal is intermittent in nature. These unplanned variations make the system unstable and electricity supply less reliable as demand fluctuations occur. A popular solution to lower the energy that is wasted and improve reliability is energy storage (batteries). Within these decentralized renewable energy systems, each wind turbine, solar panel, or battery will have sensors measuring performance and feeding this information back to the cloud. In agriculture, the challenges posed by increasingly unpredictable weather patterns, climate change, labor shortages in the sector, coupled with a rising demand from a high consumption global middle-class and population, means that we must find new solutions. AI will allow unprecedented automation, monitoring capability and optimization of natural land, greenhouses and indoor vertical farming. The smart city urban environment is modeled on convergent human, environmental and technological ecosystems and visualized as a complex blueprint of interconnected and self-regulated systems including all functions of the emerging smart city operating system (Fig. 8.3).

8.5.1.1 Macro scale/context According to the UN, as of 2018, 55% of the global population lives in urban areas. It is estimated that this figure will rise to 70% by 2050, as a result of population growth, migration resulting from economic and political events, as well as continued urbanization in developing areas of the world, such as Africa and South East Asia. This unprecedented migration will put tremendous pressure on urban environments for housing, infrastructure, public services (transport, hospitals, schools, commercial, security organization, etc.), green spaces, commerce, jobs, etc. Consequently, this increase in population will also strain the natural environment in the form of environmental pollution, resources (food, water, energy) and land use. As billions of people migrate to cities, increasingly complex challenges will arise that will require innovative and systematic solutions across smart city functions. These solutions will require not only technological transformation and convergence, but also a change in individual and collective consumption choices, patterns and behaviors.

Bike Path

Smart Energy

Smart Parking

Smart Roads

Smart Buildings

Vertical Farming

Embedded Sensor Network

Rapid Mass Transit

Smart Waste Recycling

Ambient Connectivity

Smart Public Transit

Grey Water Management

Smart Hot Spots

Smart Street Lights WiFi, Sensors, Surveillance

Smart Government Hub

Artificial Carbon Capture Trees

Smart Street Lights

FIGURE 8.3 Smart city urban environment.

Underground Logistics

Autonomous Vehicles

Info Kiosk

Biomimetic Architecture

Green Roofs

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8.5.1.2 MESO scale/content The planetary scale restructuring of earth both through accidental and purposive megastructures of human design is both a crisis and an opportunity for intervention. Smart city and AI technologies and applications are capable of the full spectrum of resource reorganization. Smart urban design needs to consider the aesthetics and implications of terraforming, including efficient high density vertical urban development, the principles of permaculture and the capability of AI to predict, analyze and optimize consumption patterns for social and environmental sustainability. 8.5.1.3 Micro scale/component Sustainable urban agriculture (rooftop gardens and vertical farming), renewable energy (solar roofs, wind turbines, daylight harvesting), permaculture-centered urban design (waste minimization and egalitarian access to resources) and innovative applications and solutions Investment into AI applications is critical. International relations and policy formulation will need to reflect the security challenges posed by climate change and global warming. 8.5.1.4 Strategic functional objectives 1) Carbon-neutral, renewable and sustainable models and strategies adopting a cradle-to-cradle approach to planning, design and implementation of cities and districts will become the norm. 2) Waste reduction of key life-sustaining resources, such as water, food, energy and materials will be at the core of smart city circular economies. 3) Optimizing energy production, transportation, storage and consumption through a combination of renewable energy and smart grids. 4) Efficient high density vertical urban development, including urban vertical farms and food production systems, coupled with principles of permaculture will transform current supply chains, ensuring a fairer and more egalitarian access to resources.

8.5.2 Smart government The public sector in many countries around the world is seeing unprecedented modernization resulting from the development of IT technologies, connectivity and changing public/business demands for easier, on-demand, personalized and less bureaucratic services. In the last few decades, many governments have been engaged in rather slow-paced digital transformation projects aimed at reducing costs and optimizing processes. Digital reinvention is the next stage of this process, whereby governments are replacing legacy systems by implementing cloud computing to harness all types of data, increase interconnectivity and optimize digital infrastructure to drive

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new classes of AI-driven applications and solutions. Governments will be able to deliver improved public services, using less resources, while also drastically improving the experience for its citizens. Cloud computing enables governments to reinvent their relationship with citizens, by improving access, quality and speed of public services. AI can help governments automate repetitive tasks, predict demand for public services, uncover societal trends, optimize spending and allocation of public funds, improve security, education and health, to name a few. Government institutions will play a crucial role in implementing the regulatory frameworks that encourage organizations, businesses and citizens to adopt sustainable and smart practices. Publiceprivate cooperation will need to intensify, to allow institutions access to the latest technologies and capabilities needed to power e-governments.

8.5.2.1 Macro scale/context For many years the processes of globalization, industrialization and freemarket economics have been dictating and transforming the relations between nation states. The rise of digital technologies and internet connectivity has further intensified the proximity of nations and individuals, altering societal norms, behaviors and expectations of their citizens. Politics, however, is in crisis, as the world zeitgeist moves into the 21st century threatened by climate change and the extractive nature of political corruption. The emerging metamodern spirit of reconstruction is finding expressions across the world and organizing collectively to affect systemic transformation. Although capitalism has led to unprecedented wealth creation, it has also led to vast socioeconomic inequality and excessive concentration of power in the hands of a few. The need to transition to a new system through meta-governance and new policy regimes is imminent, as governments respond to the uncontrollable social disruption that the internet, social media and other technologies are disrupting the status quo in many states in the world. 8.5.2.2 MESO scale/content To respond to the many challenges faced from a societal, environmental, security and technological perspective at the city level, governments will need to align to the new technological context, in order to avoid becoming obsolete, or losing credibility as a provider and guarantee for social peace, security and the rule of law. The governmental eco-system is moving from the physical and bureaucratic systems of the past, to an increasingly digitized system that is more closely aligned to how the majority of citizens are consuming content, shopping, communicating and more. The application of AI will unlock many positive benefits to the scale, scope and access of public services.

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8.5.2.3 Micro scale/component The micro level impacts and innovations are being made in all sectors of private and public enterprises. Through Geels concept of the multilevel perspective and socio-technical transitions and other similar systems change proposals and theories, we can understand how the niche innovations, scientific advancements and social movement motivations taking place can effectively work together on a global scale to enable a new stage of civilizational evolution. Cloud-based applications are virtually limitless and the computing power, not to mention the quantum computing revolution underway, allows for distributed information storage and processing on a global scale. As more online city services are available, bureaucratic operating modes can be bypassed more rapidly, providing streamlined on-demand services to citizens. 8.5.2.4 Strategic functional objectives 1) Optimizing public sector functions and processes to deliver critical support and high-quality services to every citizen. 2) Improving trust in government institutions through transparency, access to information, inclusiveness of government institutions and increased participation in decision-making of the civil society. 3) Smart resource allocation, budgets and controls will optimize the use of public funds. 4) Removing critical bureaucratic, legal and technological bottlenecks to encourage entrepreneurship, innovation and cross-border investment.

8.5.3 Smart mobility Convergence in mobility is driven by the challenges posed by rising populations, diminishing space in expanding urban centers, depletion of fossil fuels and an increased awareness of climate change. These disruptions are changing consumers’ demands, perceptions and expectations of transportation and logistics in profound ways. Tesla is credited with building the mainstream market for electric vehicles (EVs), Uber created the ride-hailing concept, while Amazon automated a large amount of their supply chain in order to start offering millions of products with next-day delivery. Smart mobility is about multimodal freedom of choice, on-demand delivery and exciting new passenger experiences. For decades, the development of urban areas has been predominantly focused on accommodating rising numbers of motor vehicles. This has had a major negative impact on the urban landscape, characterized by traffic, noise levels, pollution, health risks, unpleasant and stressful conditions. Increased government scrutiny and vehicle ownership regulations have dramatically altered the urban environment of cities such as Amsterdam, Singapore and Copenhagen over the last decade. Furthermore,

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Bus

TIME

COST

Subway

Walk

AI

UX

Bicycle

Car

Provide Journey Data & Route

User

Transportation Mode

Destination

FIGURE 8.4 Multimodal transport networks.

the current revolution in mobility is leading an increasing number of cities to rethink and redesign their urban spaces. As alternative mobility solutions are finding widespread acceptance, support and application in modern urban centers, an increasing number of people choose public transportation over personal vehicles and governments continue to encourage policies for EVs and smart mobility. The system is visually simplified in Fig. 8.4, putting AI at the centre of mobility connecting users with route data and transport infrastructure, getting the user to their destination most efficiently. Time and cost are both reduced, while UX is improved, when travelling by multimodal means.

8.5.3.1 Macro scale/context Transportation, whether personal, parcel, or freight, will go through various modes of transport such as air, sea and various ground routes and methods. The fourth Industrial Revolution entails deep supply chain integration and capacity

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for the multimodal mobility of all people and goods. The macro-scope context is that 21st-century mobility systems and transportation networks encircle the globe like a complex circulatory system, with cities and transportation clusters, i.e., aerotropolis providing hubs for regional distribution and exchange. Within a city confinement, transportation and logistics act as the life blood of the system. Citizens are increasingly demanding that urban environments become more people friendly, rather than continue to be vehicle-centric. The vast amount of pollution in urban centers is caused by cars, which directly impacts people’s well-being and quality of life. As consumption patterns become increasingly digitized and on-demand, there will be increased reliance not only on vehicles for transportation, but also to deliver individuals food and goods where and when they want them. The challenge is to streamline and integrate supply chains, disrupting markets and industries, but forming the backbone of a new global public infrastructure that serves humankind. Logistics is anything to do with organizing complex systems’ operations, inputs/outputs, shipping/receiving, movement, equipment, etc. and is the bridge between the multimodal context and the component level automation.

8.5.3.2 MESO scale/content The meso scale considerations concern the transition from personalized to shared vehicles as well as affordability and public access. Transportation and travel are intertwined through the logistics of moving things, whether it be objects, people, resources, or information. People have to consider and anticipate changes in their movement behavior and the optimal paths for mobility. Industrial transportation is one of the largest contributors to greenhouse gases (GHGs) that cause anthropogenic climate change. Companies such as Amazon and DPD are reinventing logistics and supply chains by implementing various technologies that drive higher levels of automation, autonomy and AI with a potential to reduce their carbon footprint through more efficient operations. 8.5.3.3 Micro scale/component Convenient navigation at smaller scales, such as by walking or bike, makes for a healthier and more efficient operational mobility. Precise GPS tracking allows for on-demand guided navigation within a very tight margin of error, almost anywhere in the world. The technologies for this are convergent in smart devices and biometric trackers. Autonomous vehicles will revolutionize public transit such that people’s geographical freedom will be optimized. The impact on people’s lives will be profound as the transition to smart, electric and autonomous transportation unlocks vast economic potential, as people spend less time driving, parking, or sitting in traffic and cities start to favor people over cars. Regulators will need to

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establish new safety standards that support the coexistence of people and AVs in ever-growing and evolving urban centers. The development of transportation in cities will be aimed at improving people’s quality of life by setting them free from cars.

8.5.3.4 Strategic functional objectives 1) Mobility (transportation) and capacity (logistics) as a service. 2) Customization of the rider experience and mode of transportation. 3) AIdPredictive routing based on big data (coordinates, weather, traffic, personal preferences such as health, speed, cost etc.). 4) Multimodal options (air, scooters, bikes, cars, boats); public transit to grow, while personal ownership to shrink (optimizing resources). 5) Enhanced in-vehicle experiences and safety. 6) Autonomous vehicles for transportation, logistics and fulfillment. 7) Electrification of personal vehicles, public transport, logistics and warehousing.

8.5.4 Smart economy Since the Industrial Revolution in the 18th century, businesses, entrepreneurs and inventors/innovators have been the key drivers of economic and societal progress. At no other time in our history has this been more prominent than it is today. The processes of globalization, democratization and free market reforms that we have seen since WWII have allowed private businesses to influence various aspects of our lives, such as the way we work, eat, or dress. The digital revolution that began with the commercialization of the personal computer in the late 1980s, continued with the internet in the late 1990s, to reach the mobile era we are currently in the midst of. Consumers’ hunger for faster and slicker devices, on-demand access to applications, tools, information and content, led to the invention of cloud computing and the introduction of 5G networks infrastructure to support their use. The miniaturization and development of key networking and communication technologies has been a key driver in the convergence of people with their environment. These devices have changed the way we communicate, collaborate, shop, create, consume and share information. It is within this technological timeline that we can trace the rise and development of AI capabilities, so critical to our ability to develop the smart cities of the future.

8.5.4.1 Macro scale/context The global economic landscape is characterized by record inequality, with extreme concentrations of wealth by a handful of individuals. Real wages have stagnated or declined in developed nations for decades, such that purchasing power is much lower for basic needs like food, housing and

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education. Meanwhile, electronics have become more accessible than ever, giving people a new medium for commerce and collaboration. There is ongoing discussion around the moral crisis in modern economic systems more investment needs to be directed towards sustainable and socially responsible business models and companies that help to rectified the inequalities in labor markets and through a better distribution of resources. The macro context of the smart economy involves a shift to commons-based sectors and abstract labor that is paired with universal basic income and decoupled from market fluctuations.

8.5.4.2 MESO scale/content The function of the smart economy at the city level will be to ensure prosperity, innovation, access to capital and continuous opportunities for its citizens to part-take in the value-creation process. Within a smart economy, this implies that interconnected, internet-based and highly data-driven infrastructures will create vast opportunities, but also interdependencies between all stakeholders and participants in the economic system. As described in Chapter 7, a new circular economy will leverage data, automation, robotics and decentralized distributed ledgers to track geosystems, resource extractions, manufacturing, economic flows, transactions and legal agreements to optimize resource allocation, speed up execution and ensure quality. New models for capital allocation, trading, ownership, investment, construction and consumptions are emerging to satisfy consumer demands and allow businesses to compete across industries and sectors. 8.5.4.3 Micro scale/component At the microlevel, technologies are reshaping the competitive forces regulating industries and their product/service offering across the economy: new business models based on peer-to-peer exchange, cloud-based business services, payments through crypto currencies, financing through crowdfunding and automation through robotics. Humans get better at their niche innovation tasks, as labor across multiple sectors becomes automated under machine intelligence. As we have created AI and ML, it is now catching up to us and will surpass us. Our role is to facilitate the automation of economic systems and processes while also protecting worker’s rights and making sure opportunities exist to thrive in such a system. The social component is labor organizing around new forms of work and solidarity and policy issues like universal basic income. The interface of this new digital economy will be the simplest part, where buyers and sellers can enter and exit the marketplace with ease and security through smart devices representing a more localized, money-free, verified and recorded transaction (Fig. 8.5).

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DAPP

TOKENPAY

PRODUCTS

SERVICES

BLOCKCHAIN

Kiosk

Desktop

Mobile

Kiosk

Desktop

Mobile

CRYPTOCURRENCY Interface

Interface

Payments

Stablecoins

Privacy

Buyer

Seller

MARKETPLACE

FIGURE 8.5 Digital transaction platform.

8.5.4.4 Functional strategic objectives 1) Cloud computing and AI are improving efficiency and increasing economic output by lowering cost. 2) Fintech and cryptocurrencies are removing legal, financial and physical barriers to global trade. 3) Blockchain-based systems will allow us to transition to new centralized and decentralized monetary systems. 4) Innovation, entrepreneurship and disruption are key drivers in smart economies. 5) 5G networks will exponentially speed up the deployment of complex AI to power the smart cities of the future. 6) Industry 4.0 (including IoT, 3D printing, robotics and AI) will allow us to automate manufacturing and augment people’s roles in the economy.

8.5.5 Smart people As a global society, we are now closer to each other, more informed, educated, tech savvy, connected, responsive, demanding and more empowered than at

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any other time in our evolution as a dominant species on Earth. Within the smart people context, we need to explore the fundamental drivers behind the convergence of humansemachines and environment and how the application of AI and technologies can propel us to a new level of consciousness. Education is the self-fulfilling prophecy that fuels the social development, technological innovation and environmental consideration that is essential to our survival as a species facing increasingly volatile prospects, complexities and uncertainties. It is through altering the role, perception, design and application of education that we would be able to unlock more of the vast potential of human ability, creativity, and intelligence. This is a key first step to leveraging collective intelligence for the benefit of our natural environment, society and urban landscape. In the previous chapter, we identify the desired outcome of human development as enlightenment achieved through socio-technical and environmental convergence. This state (singularity) of elevated perception and awareness of ourselves, our society and our environment is critical to supporting the mission and vision of smart cities. Maslow defines this highest value within the “hierarchy of needs,” as “self-actualization.” This occurs when we reach our full potential as individuals and begin to contribute to the continuous improvement of our surrounding environment and the society we are part of.

8.5.5.1 Macro scale/context Humans collectively have and are creating an unfathomable amount of knowledge in the 21st century. Our ability to generate, access and share data has been further reinforced by mobile devices and networks. The scale and scope of this data is fueling increasingly faster convergence across systems, such as mobility, education, work, commerce, etc. The internet provides ubiquitous, omniscient access to various types of data, information, knowledge and wisdom that were never before available. The knowledge/intelligence economy we live in today will require highly dynamic, flexible, adaptable and “on-demand” educational practices that will stimulate citizens to seek selfactualization in contributing to an enlightened society. 8.5.5.2 MESO scale/content A 21st-century enlightenment is underway and via AI and ML we are innovating knowledge and thought systems. The rapidly increasing pace of technological development and convergence is making traditional educational practices obsolete. The function of education as we know it was developed to prepare the masses for an industrial production workforce. Today, these needs are becoming increasingly obsolete as manufacturing becomes highly robotized. The function of education for the digital economy is to provide people skills to work with the technologies powering the digital world, such as data analytics, robotics, programming, etc.

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8.5.5.3 Micro scale/component Access to universal education needs to be fulfilled globally through smart city interfaces. The city itself is a library and learning lab of history, experiences, scientific research and innovation. The micro scale includes all the components and technologies related to education and learning, such as books, tablets, modules, lessons, UX, etc. As we break through new levels of AI and ML, we have to relearn about human learning itselfdwhat are the conditions and prerequisites for learningdand need to generate a basic human operating system for “smart people.” We can develop an ecology of practices such as workshops and focus groups to hone smartness in people and cities that AI and ML can facilitate as an iterative and progressive process. Most important is not merely the technical education aspects but the social sciences, including the arts, history, humanism and bildung (lifelong self-development, cultural maturation), that create a well-rounded citizen and active participant in society. 8.5.5.4 Functional strategic objectives 1) Access to information and technology to drive collective societal intelligence, entrepreneurship, innovation, creativity. 2) Human interaction with technology to augment and enhance our abilities, our experiments, our problem-solving and our experiences. 3) Hyperpersonalization/customization of teaching and learning based on individual abilities, strengths and limitations (memory, focus, abstract, analytical, verbal, reasoning, creativity, coordination, logic, etc.). 4) Interconnected through the internet, social media, mobile technology, content and shared experiences, encouraging the development and flourishing of a (glocal) culture and common set of values. 5) Unlocking massive untapped potential for individual and collective innovation, creativity. 6) Analyzing, forecasting and closing the talent gap that exists in many economies, while also enhancing and augmenting human performance and abilities.

8.5.6 Smart living Smart living is about improving the quality of people’s lives through interconnected technology that make living more efficient, automated, productive, sustainable and controllable. The widespread adoption of smart devices such as mobile phones, fitness trackers, smart watches, voice-enabled speakers, etc. has given rise to new industries such as Health Tech. Nowhere is the convergence of human, machine and environment more evident than in the integration of AI-enabled technologies in our homes, workplaces,

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and with our personal devices. Internet-enabled smart devices collecting and exchanging real-time information is also known as the Internet of Things. Smart connected objects will give us unprecedented control and customization of our daily lives. In Chapter 7 we state that the function of smart people in future smart cities will be to achieve a state of enlightenment through the convergence of elements discussed throughout this book. This state (singularity) of elevated perception and awareness of ourselves, our society and our environment is critical to supporting a system that is self-governing, self-regulating and selfperpetuating. Maslow defines this state as “self-actualization,” in reference to people’s pursuit and realization of their full potential, the pinnacle of individual human development. Maslow’s concept is strikingly similar to philosophical concepts of enlightenment found in many ancient teachings. This is a key first step to leveraging collective intelligence for the benefit of our society and its symbiosis with our urban and natural environments.

8.5.6.1 Macro scale/context As we place sensors everywhere, develop our own sense-making, achieve better collective intelligence and design toward artificial general intelligence, our interface becomes sentient itself. The context of smart living is not just about the maintenance of health and the positive experience of all living entities, including plant and animal tracking, but the awareness to sense it. This relies on systems of real-time feedback, pattern recognition, data mining and visualization. 8.5.6.2 MESO scale/content This is the practical level where smart living is scaffolded. Healthcare is directly connected with education, because education is often also a means to health knowledge, self-awareness and self-preservation. Here some of the biggest changes need to take place, to make healthcare more distributed and accessible. The various apps, practices and protocols for maintaining health converge through AI (Fig. 8.6). The idea is a holistic health architecture that streamlines access, prevention, diagnoses, research, data and more. 8.5.6.3 Micro scale/component The quintessential application concerned with smart living is the user experience. Whether a patient or a consumer, or a tourist or on a work trip, the modalities and aesthetics of smart city platforms need to be interactive and customizable, yet universal. The UX is the interface to which the user/citizen accesses the smart city as a holistic platform. In the human rights sense, improving citizen UX means ensuring the sovereignty and well-being of every individual as a holon of a living and biomimetic digital city simulacrum.

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Early Detection Remote Services

Recovery Life Care

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HOSPITAL INTERFACE Treatment

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Big Data

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FIGURE 8.6 Personalized health management.

8.5.6.4 Functional strategic objectives 1) Access to treatment and medical services will be vastly expanded via AI enabled technologies. 2) Prevention of diseases and anti-aging will become commonplace as health tech becomes increasingly mainstream. 3) Our homes will become more customized and efficient, enhancing our well-being and elevating our in-home experience. 4) Comfort, safety, physical and mental well-being will become key attributes to smart living. 5) Optimizing our resource production, use and reuse will ensure sustainability without compromising standards.

8.5.7 Convergence of smart city functions Environmental convergence: Themes such as renewable energy, biomimicry, permaculture and carbon neutrality are all complementary and

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serve the same outcome: sustainability. Technological capabilities including big data, deep learning and predictive analytics can contribute to the convergence of this outcome. The deployment of AI-driven solutions will vastly optimize supply and demand across sectors of the economy, such as food, water, energy, materials use, capital allocation and more. In energy, AI will facilitate the convergence of production, distribution, storage, maintenance and optimal energy consumption. By collecting data from each household and business and analyzing consumption patterns, AI algorithms will allow us to achieve equilibrium across various layers of the system. In agriculture, AI will enable cities to optimize supply chains and minimize waste through better understanding and management of consumption patterns and redistribution of food resources, bringing food production to densely populated urban areas through large-scale automated vertical farm systems, lab-produced meat alternatives and other sustainable practices. Governmental convergence: Themes of transparency, open data and participatory society are key to achieving the desired outcome of inclusiveness. The rise of corporations and growth of population put pressure on governments to converge on common laws and universal principles, that ICT facilitates through e-voting and open data platforms. Technological advancements such as cloud computing, smartphones and applications running on blockchain are enabling new forms of digital civil society and increasing bottom-up governance on both local and national levels. Many cities in the world including Rotterdam, Singapore, Tallinn, Shanghai and Shenzhen are making strides in the convergence of people and technology to improve governance. Civil society is an increasingly important factor in global affairs as citizens feel increasingly empowered as a result of higher levels of education, liberal economic and political policies, as well as global movements for environmental protection, human rights and social justice made more accessible via and global telecommunications and social media. Civil society must not only participate and assist, but must help drive the systemic transformation needed to enable smart governments to deliver inclusiveness to its citizens. Irrelevant of the political system in place, wider citizens participation enabled through technology will be a crucial aspect of restoring, or improving trust in government, as well as a means of rewriting the social contract between institutions and its citizens for the hyperdynamic, hyperconnected and technologically driven 21st century. Mobility convergence: Transportation has a lot of converging to do, but the road is clear. Driverless shared vehicles and multiple modes of transport converge on the idea of freedom. Individuals can not only go anywhere they wish, the potential of freedom to make other choices is also possible. At the city level, AI will enable the automation and autonomy of multimodal forms of transportation, freeing people from the burden of ownership and

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the stress caused by congestion, pollution and inefficient and unpredictable public transit systems. The deployment of AI to power smart mobility applications such as car sharing, bike sharing, food delivery, same-day delivery for groceries and shopping is leading to vastly converging patterns of consumption in cities. Logistics is becoming integrated across all scales and AI and ML can be applied across the board to improving mobility applications. The change in the way we consume transportation services will liberate smart city citizens, positively transforming their experience. Economic convergence: The themes of entrepreneurship and innovation are already popular and established, while ideas about a circular economy, planetary accounting and universal basic income are converging with new geopolitical and ecological realities. Technologies like blockchain, holochain, robotics, cryptocurrencies and other industry 4.0 innovations are converging to enable the desired economic outcome which is a global commonwealth. According to the World Bank, over 80% of global GDP is generated in cities (World Bank, 2019) and as AI continuously helps to increase the productivity of the economic system of cities, further innovation and entrepreneurship will emerge. As functions within the smart city system converge, they will enable the economic system to converge with citizens, powering prosperity for more stakeholders within the system. Economic development and growth are very often correlated with improved education as more funds are available in the system and citizens have a higher standard of living. Education and access to capital will spur further entrepreneurship in technology. People convergence: New universal living standards are emerging to confront the stark inequality across the global stage. Smart people need to be generated through codevelopmental processes and naturally converge toward attractors to promote functional enlightenment and a collective intelligence. VR and AR technologies enable closer engagement with digital learning resources that will help people and technology converge at the highest potential. At the societal level, convergence will move humanity closer to a new collective enlightened form of existence. Tech in education will more accurately allow society to uncover individual talents and develop these from a young age, further fueling the collective intelligence system that will power the smart cities of the future. As the internet expands and connects us, the world is shrinking and time and space is compressing. In the short term, nationalistic tendencies may continue to prevail due to regionalism, but this will evolve into a new form of cosmopolitanism that emphasizes a common humanity as technology unites us. Living convergence: The United States will be one of the last developed nations to adopt universal health care, greatly aiding the process of convergence toward holistic health for all beings. Individualized medicine matched with universal standards and preventive strategies will ensure smart living in

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every city, aided by technologies that support smart buildings and environments. At the human-level, AI will improve individual well-being, health and security. Leveraging consumer electronics, wearable technology and data gathering devices such as health trackers and mobile apps will allow real-time convergence of people with technologies. Sentience and healthcare are converging as more sensors get added to the process of diagnosis and maintenance of health. This will have a positive collateral effect on smart city environments, whereby people’s understanding of themselves, their society and their surroundings will profoundly impact their ability to positively influence outcomes.

8.6 Conclusion The combination and convergence of technology in the IoT, AI and 5G internet world is establishing the building blocks of the posthuman smart city. We have attempted to map the applied needs of the smart city functions and match them with the technological landscape across macro, meso and micro levels. After identifying the hardware infrastructure that enables the smart city, we introduced various forms of AI and their capabilities and applications. This chapter then took the six Smart City Functions addressed in the previous chapter (7) and overlaid a new framework to consider the Scale and Scope together. Applied convergence is described as the gestalt of the various applications, scales and scopes of converging smart city functions. Sensors and simulations play a key role of reflexivity and abstraction of the smart city digital interface and operating system. We analyzed each smart city function through terms of the intersections of macro, meso and micro, with notions of context, content and component to illustrate a form of scope, not scale. This also highlights a macro-context, meso-content and micro-components hierarchy, which are also the structural, operational and strategic levels to achieve necessary systems change and a new frontier of sustainable smart city urban design and AI applications toward a singularity. After an overview of the whole set of the six functions, a section on convergence covered each function, explaining how the facilitated convergence process contributes to the desired outcome. We have introduced an understanding of the multiple dimensionality and impact of applications within systems change. This is a step in the new direction of determining the many applications of AI for a new standard of smart city. Much of the development and deployment of new applications will hinge on recursive innovation within new sustainable models and principles. The true smart city is not just about technology, but convergence on a new holistic biomimetic architecture that optimizes the health and well-being of all people and the system itself.

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Further reading Amazon Web Services, 2020. Yes, Technology Can Build More Inclusive Societies, Amazon Web Services. https://aws.amazon.com/blogs/publicsector/yes-technology-can-build-moreinclusive-societies/. (Accessed 30 January 2020). Berger, A., Kotkin, J., Balderas-Guzma´n, C., 2017. Infinite Suburbia. Princeton Architectural Press. Boomen, T., Frijters, E., Van Assen, S., Broekman, M., 2017. Urban Challenges, Resilient Solutions. Haarlem, Trancity. Brynjolfsson, E., McAfee, A., 2014. The Second Machine Age. W.W. Norton & Company, Inc. Ford, M., 2018. Architects of Intelligence: The truth about AI from the people building it. Packt Publishing. Gift, N., 2019. Pragmatic AI: An Introduction to Cloud-Based Machine Learning. AddisonWesley. KPMG, 2019. Mobility 2030: Future of mobility. https://home.kpmg/uk/en/home/campaigns/2019/ 09/mobility-2030-future-of-mobility.html. (Accessed 30 January 2020). Kurzwell, R., 2005. The Singularity Is Near: When Humans Transcend Biology. Penguin Publishing Group. Mostafavi, M., Doherty, G., 2016. Ecological urbanism. Lars Mu¨ller Publishers. Nuncio, R., 2019. Strong Artificial Intelligence: Understanding the AI Revolution. Independently Published. Offenhuber, D., Ratti, C., 2017. Waste is information. The MIT Press. UN DESA - United Nations Department of Economic and Social Affairs, 2018. 68% of the world population projected to live in urban areas by 2050, says UN j UN DESA - United Nations Department of Economic and Social Affairs. https://www.un.org/development/desa/en/news/ population/2018-revision-of-world-urbanization-prospects.html. (Accessed 30 January 2020). UNEP - UN Environment Programme, 2019. Renewable energy investment in 2018 hit USD 288.9 billion, far exceeding fossil fuel investment. https://www.unenvironment.org/news-and-stories/ press-release/renewable-energy-investment-2018-hit-usd-2889-billion-far-exceeding. (Accessed 30 January 2020). Reynoso, R., 2019. 4 Main Types of Artificial Intelligence. https://learn.g2.com/types-of-artificialintelligence. (Accessed 30 January 2020). Steenson, M., 2017. Architectural intelligence. The MIT Press. White, R., Engelen, G., Uljee, I., 2015. Modeling Cities and Regions as Complex Systems. The MIT Press. World Bank, 2019. Urban Development Overview. https://www.worldbank.org/en/topic/ urbandevelopment/overview. (Accessed 30 January 2020). Yee, A., 2018. In Sweden, Trash Heats Homes, Powers Buses and Fuels Taxi Fleets. https://www. nytimes.com/2018/09/21/climate/sweden-garbage-used-for-fuel.html. (Accessed 30 January 2020).

Chapter 9

Smart city business models Chapter outline 9.1 Introduction 9.2 The smart city/Artificial Intelligence market 9.2.1 Business models and risk mitigation 9.2.2 A Marxist analysis of smart cities 9.2.3 Smart city movement marketing 9.3 Innovation-led economics 9.3.1 Innovation as the driver 9.3.2 Intellectual property as the new asset 9.3.3 ChinaeUSA race, India rising 9.3.3.1 Chinadeducation 9.3.3.2 USAdrenewable energy 9.3.3.3 Indiad biomimicry

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9.3.4 Cities as living labs 9.4 The new economy 9.4.1 Planetary accounting 9.4.2 Strategy shift 9.4.3 New forms of digital currency 9.4.4 Blockchain 9.4.5 Holochain 9.5 New forms of business exchange 9.5.1 Flow 9.5.2 Channeling on demand 9.6 Bringing it together 9.6.1 Convergent economies 9.6.2 Collaboration 9.6.3 Self-regulating systems 9.7 Conclusion References Further reading

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9.1 Introduction There are numerous ways to approach business models of smart cities and the broadest ways of understanding should be explored. What are the business opportunities? What new novel and ethical forms of business should be practiced in smart economies? These questions answer themselves when one understands the convergence bringing everything together. Digital technology is a double-edged sword. Automation potentially threatens not just jobs, but small business themselves. Thus, any serious discussion of business in smart cities and Artificial Intelligence (AI) needs to be foregrounded by the critique of capitalism. While technology continues to develop exponentially, “latestage capitalism” has shown itself to be inefficient and inhumane at times.

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This is rooted in fundamentally unjust levels of inequality economically and epistemically. The smart city that invests to close those gaps and generator functions will be a self-optimized steady state. This chapter explores business solutions to these problems, through understanding of new economics and social movements emerging that will change the sociotechnical economic landscape.

9.2 The smart city/Artificial Intelligence market A 2014 report for Gartner Research (Koslowski, 2014) states that “industry convergence represents the most fundamental growth opportunity for organizations.” It will redefine the way business is done by focusing on value experiences mediated through digital technology rather than individual products. The success will depend on the right businesses partnering. The convergence of business represents not only growth opportunities but also threats of acquisitions, absorbing other businesses functions and eventually monopoly. According to a Frost & Sullivan report from 2018, by 2025, the market value of smart cities will be over $2 trillion USD, with the Asia-Pacific region expected to grow the fastest. The Neom project in Saudi Arabia alone is estimated at $500 billion. China will have more than half of the smart cities in Asia, thereby leading the field with a market share of about $320 billion by 2025. Smart city initiatives are dominating urban planning in North America, South America and Europe, producing relatively competitive numbers for economic growth. One problem with such large numbers is they do not convey any sense of what the individual business or consumer opportunities are. HSBC produced a report (Kawaguchi, 2018) titled “Smart Cities: Convergence and collaboration to build communities of the future.” It conveys optimism about the long-term path for smart city development that is still in the early stages and will evolve rapidly. Understanding the role banking plays in developing the global smart city architecture, HSBC is investing in the tech automation boom, while offering insight and partnership with the public sector to put the “ smart citizen” in control at the center of smart city life. The report cites automotive, telecommunications, infrastructure and innovation as the major sectors of opportunity. To paraphrase, firms can participate in new smart city developments in five ways: (1) collaborate in different industries (such as Uber and NASA), (2) think holistically, (3) anticipate new risks, (4) be futureoriented and (5) bank wisely (with HSBC, presumably).

9.2.1 Business models and risk mitigation A study was conducted by Anthopoulos and Fitsilis (2015) that found as many as 26 different business models for smart cities, the details of which are not fully expounded. They acknowledge that their research on the topic is a

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work in progress and express reservations about the path of business ahead. The need and want and subsequent market for smart cities is clear, but due to the scale of investment and reliance on healthy privatee public relationships, the unknown complexity weakens confidence in potential businesses. In addition, the public side is especially skeptical of the various profit-driven motivations and intentions of the private (capitalist) interests. The bulk of the business models found prioritize the smart city owner’s perspective, while other stakeholders are neglected. The relationship between money and values remains unresolved. Having a business management strategy mitigates the investment risk on smart cities that governments are pursuing. Between the market opportunities, the need for new forms of partnership and the potential entropy of capitalist systems, alternative perspectives and business models are required to help direct sustainable smart city economic development.

9.2.2 A Marxist analysis of smart cities Kevin Rogan (2019) ascertains the dark side of smart cities through a Marxist critique. He mocks the mainstreaming of the mythical narratives that ubiquitous and automated technology culture markets to us. While the smart city is the future state, Rogan suggests that the deep history of capitalism matters just as much for understanding where the story is headed. Market forecasts also admit the “smart city” is no real place (a true “utopia”), but rather the synergy of “smartness” applied to smart health, mobility, economy, governance, etc. Rogan understands the smart city as a relational and ideological tendency rather than an object. According to Michel Foucault, a French philosopher, social theorist and literary critic, knowledge and power are intertwined in the application and deployment of technology, so that the spread of capital also implies the hegemony of knowledge and control of information. Here the smart city poses a threat and an anti-intellectual one in Rogan’s terms. As knowledge becomes a commodity and resource itself, profit-seeking manifests incentives to secrecy and instrumental forms. Certain types of knowledge flow to people as is required by the capitalist system. On one hand, in general, knowledge becomes more freely available through the Internet, but on the other hand, the critical thinking to utilize that knowledge cannot easily be monetized. The tension between knowledge and power remains the means of reproducing economic consumption and class stratification, which stagnates as it is arbitrarily maintained. This means that hypercapitalist smart cities could undermine their own aspirations to honor the user/citizen at the heart of a living system. Businesses are likely to benefit from the short-term innovation of technology despite any adverse

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consequences of economic inequality. This has implications for epistemic/ informational equality and the knowledge worker. The ordinary consumer is beholden to the curated worldviews presented to them by businesses, while the knowledge worker must struggle in the context of information warfare. An atmosphere of knowledge as a public good and knowledge workers as civil servants need to prevail to achieve a common interest in the smart city platform. Rogan calls Sidewalk Labs in Toronto “a fever dream in the long night of infinite control” by corporate interests. All of it is underpinned by an ideology of technochauvinism, basically techno-optimism and objectivism with a more invasive twist. Rogan argues it is intertwined with “smartness.” All this is to say that the pure capitalist pursuit of smart cities often purposely leaves out the political and social dimensions that actually matter the most. Investors and developers have a financial incentive to neglect the negative impact that their technology poses for multiplying social injustice and/or worker exploitation in the global sense. Rogan invokes Lukacs and Badiou as sources of critical inspiration on technology to reject partisan claims that fetishize the smart city, for it is part of a system of global control and in some cases strangulation, of various regions. The strategy to avoid such techno-driven decay is to invest in people and to employ more civil servants and social scientists not beholden to any material interest. Christopher Chen (2019), an Associate Research Fellow with the Humanitarian Assistance and Disaster Relief (HADR) Program, writes that the international community understands the risky role of technology in effective humanitarianism. A balance must be struck over what actions and humanitarian technology (HUMTECH) achieve what humanitarian principles. Is the technology used to collect data from people or empower them with information? A case study on Typhoon Haiyan is used, in which disaster-affected people responding to aid feedback by text message gave unduly praise, for fear of not appearing grateful. In truth, the humanitarian aid was insufficient, especially in developing countries, where power imbalances sew distrust between victims and aid agencies. The application of HUMTECH must be reexamined so that it does not exacerbate or perpetuate dangerous inequalities, but rather solves them. Chen suggests that the introduction of new technologies should be guided by a broad web of regulations, consultations, best practices and engagement with local stakeholders.

9.2.3 Smart city movement marketing Much of the smart city hype is its own commercial movement of sorts, heavily marketed to citizens and developers alike. Whether from science fiction or business visions, futurism has an enduring appeal and it is rapidly converging on us. In terms of aligning with the transitions posed in Chapters 7 and 8, this

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discourse around marketing and business prospects must also change. As technology enables new forms of currency and economics altogether, our conversation must also evolve and the sentiments of Silicon Valley must shift to more socially oriented movements. Social movements and business do not typically go together at first, as many protests are against corporate interests and the exploitations of capitalism, from the WTO protests at the turn of the century to Extinction Rebellion today. Businesses also invest in marketing that tracks and manipulates public opinion. However, in time, they tend to converge as new norms become acceptable. Thus, environmental, feminist, or other causes can be aligned with business interests to be exploited for advertising gains. The terms greenwashing and pinkwashing are used to critique such corporate practices, which appropriate the activists’ message for commercial purposes. However, many companies capitalize on more general esthetic and material trends in culture. Examples of companies that have employed “movement marketing” include Apple, Smart Car, or IKEA, which promoted a certain lifestyle and esthetic with their brands. Campaigns like these emphasize sharing ideas and experiences over selling and talking around the product itself. Scott Goodson, a marketer behind the idea of movement marketing, wrote about it in an article for Forbes titled “Convergence Is The Future Of Marketing” (2012). Similar to this book, he points to the trend of technological convergence of media and platforms. As technology has converged, so has the advertising world, as its techniques and tools are blending and merging with cultural developments. Goodson argues that this movement potential is a logical extension of the technological convergence. Also, it would seem that convergence itself can be its own business model. Goodson writes that “Businesses throughout the world are discovering that ‘convergence’ is fastbecoming a key business model - one that will help them to stay lean, focused and as profitable as possible without compromising on quality.” Goodson’s company, StrawberryFrog, advises businesses doing movement marketing to sustain movements 80% of the time and sell their product 20% of the time. Ideally we want businesses to support progressive movements, ones that positively bring society together. However, it is easy for cynical or greedy agents to hijack regressive trends as well. Smart city businesses and movements can converge successfully by adopting a higher set of values informed by the Marxist critique and mutually grounded “movement marketing” principles. Rather than just supporting progressive causes, the deeper imperative is for businesses to support systems change. This requires not just investment in sustainable development but divestment from destructive or extractive enterprises. It requires investment on constant recursive innovation in niches, nested in the next generation of economics, but not at the expense of funding the

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social and ecological foundation. The Green New Deal proposed in the United States as well as by the United Nations is just such a platform and movement marketing campaign to enable a successful systems transition.

9.3 Innovation-led economics The business sector is driving the smart city movement because it stands to gain the most from it. As discussed throughout this book, innovation is the major commercial locus of smart city development. That innovation is not exclusive to business as much of it comes from academia. The entrepreneurial spirit is at the heart of both and its intention is to create environments that are conducive to business and human flourishing. Angelidou states that information technology is the first major characteristic of smart cities and the second is the people who use it, along with the advancement of human capital in knowledge production and distribution.

9.3.1 Innovation as the driver A recent study by Schiavone et al. (2019) reviewed strategic management literature to determine how cities reinvent their business models to become smart cities. Because the concept of smart city emerges with the development of the Fourth Industrial Revolution, cities must revise their business models. This is also part of the evolutionary process, just as the self-organization of cities is. The authors note the lack of studies applying business models to novel situations, usually focusing on the traditional firm instead and that smart cities are a promising research setting. Their paper introduces and describes a process of urban “smartization” for business model innovation, “which refers to the planned and organized process by which private and public players adopt and implement smart technologies in one metropolitan area.” In their research they discuss a way to measure the degree of urban smartization through linking the four main featuresdacademia, industry, government and civil societydwithin the six dimensions or functions of the smart city model presented in Chapters 7 and 8. Part of this smartization is grounded in the formation of innovation zones and university-industry-community clusters exemplified in cities with a high concentration of higher educational institutions as illustrated in Fig. 9.1. This concentration emphasizes the importance of intellectual property (IP) and the development of new knowledge industries as an integral part of social, scientific and technological advancements within the business ecosystem driving smart city economics. Start-ups, governments, academics, producers, entrepreneurs and local citizens and stakeholders all contribute to the innovation process. In the context of the multi-level perspective and sociotechnical systems transition discussed in Chapter 7, Geels writes that there are four types of

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Community

Local Community

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Ac

ad

em

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ia

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FIGURE 9.1 Smart city innovation platform.

innovation of which business models is one: radical technical innovation, grassroots and social innovation, business model innovation and infrastructural innovation. Using mobility as an example, the technical focus is on electric vehicles and biofuels, while grassroots innovation focuses on car sharing, multimodel transit and teleconferencing. Business model innovation would be represented by shared services for vehicles and bikes that address consumer needs. Infrastructural innovation involves urban planning and mass transit systems. Transitioning through all the phases, from experimentation to anchoring, requires many niche actors breaking through windows of opportunity with convergent applications to transform the sociotechnical system.

9.3.2 Intellectual property as the new asset In the knowledge economy with the move away from industrial manufacturing and production, IP in the form of R&D and the output of patented technologies have become the next major generator of wealth creation. Data are the new gold or oil and IP is the new platinum. It is also a form of commodity leveraged through advanced financial instruments including IPOs and other financial mediums to establish competitive market advancement and realization of technology as the new industrial value. This shift from the Old World industrialization into the New World of the digital economy has evolved over

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the last several decades and not without a massive fight between the old guard and the new. The 1999 crash of the tech sector was a last stand of the brick-andmortar economy to attempt to crush the momentum of the new digital economy with the potential to exponentially take over the collective value of the old world economic framework. This final defensive led to a 5-year setback to the momentum of the new world order but was shortly reversed by the end of the 2000s yet further complicated with the collapse of the New York Stock Exchange, which consequently revealed the unsustainability of the banking and financial institutions that were struggling to maintain dominance in the face of the technological revolution. Ironically the banking and financial sector has been late to the table of the digital revolution and slow with the process of integrating new digitally driven business operating systems. In the new economy, IP in the form of technological research, development and applications is the new driving force of economic growth. It is typically clustered and packaged as an integral part of the ecosystem of smart city strategies and business models where academic institutions are is imbedded within the urban fabric as drivers of IP development. Cities without the valuable asset of academic research and institutions will be challenged to remain competitive to attract skilled workforce and to create a critical mass of the digital economy culture. Cities such as Beijing, Boston, Seattle, Amsterdam, London and others have a double competitive advantage of both attracting talent and creating centers of new IP development. On the other hand, oil wealthy nations including Saudi Arabia, Abu Dhabi and Norway are accelerating the development of new technologies by reinvesting massively in tech and new energy solutions to potentially dominate in the future. In the knowledge economy, urban planning represents its own kind of IP and it is under constant pressure to keep up with today’s cultural and political demands. Networking and thinking critically about smart cities fosters collective image making, sharing best practices and product and service diversification. As Angelidou writes, cities pursuing collaboration and co-creation, while emphasizing their unique character (city DNA, in our terms), will perform better. The creative class constitutes a new breed of knowledge workers that is informed by a deeper critique, greater skill sets and can envision more ambitious alternatives for the future. These trends in knowledge production and business innovation continue to unfold and evolve, dovetailing with movement marketing, as discussed above. Edvinsson writes about “Aspects on the city as a knowledge tool.” It emphasizes the relational aspects of the city as mediated through the knowledge economy and its human capital constrained or enabled by

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structures. The city itself is a good space to generate support for knowledge workers. At the time, in 2006, he speaks about the concept of a multidisciplinary “knowledge harbor” being prototyped in Sweden and Denmark, countries that are now leading in some measures of innovation. This is exemplary of codesign principles and publiceprivate relationships to make smart cities flourish. Like the flow of goods, the flow of knowledge and its workers has become the strategic focus of business, as Angelidou predicted. The creative class sits on the precipice of designing smart cities from the ground up, the top down and the inside out. Following this trend, every smart city can become a knowledge harbor and hub. Instead of the old value chains driven by capital, these will be driven by social and human capital. New types of knowledge workers must emerge to meet the demands of rapidly shifting intellectual labor environments. Kenneth Mikkelsen and Richard Martin (2019) coined the term “neo-generalist” to define the new type of thinker able to resist the trend toward fragmentation by trying to learn everything and becoming a jack of all trades. In some sense, this is the logical trajectory within some forms of work and that a hypercomplex world will require those who specialize in the general. Workers who can more ably move between different job roles provide added value and reduce costs. Access to life-long learning within smart cities supports the retraining of workers to adjust and adapt to new requirements in advanced technological landscapes.

9.3.3 ChinaeUSA race, India rising China and the United States are locked in various forms of competition in the race for technological superiority, with India rising. Collectively these three countries are accelerating the digital convergence of smart cities at different scales and with diverse approaches. China has declared a 2050 target to be the global leader in technology and by some indicators is ahead of the United States in the smart city race. China is making major investments into smart city infrastructure, but their funding of the hardware exceeds their progress in developing human resources and software to manage the massive urban constructs. But this issue in China is quickly changing with more and more investment in innovation hubs and startups after the Alibaba success story ignited the imagination of Chinese investors. Competition globally serves to fuel innovation, economic growth and new business frameworks. As city populations are set to grow at predictable rates, development must anticipate the demand and smart city innovation must provide sustainable solutions for much of the world’s concentration of population. Within the global palette of smart development and technological innovation, there are diverse solutions meeting the demands of each region, individual country and city. We live in a multipolar world now, with other

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countries (such as the BRIC nations) matching or exceeding the economic clout of the United States, so holistic and experimental approaches are required to draw together massive investment and commitment to co-creation and collaborative solutions. In Europe, and particularly in Scandinavia, the trend is toward devolution with scaling back and moving toward holistic methods to manage and optimize resources. In much of Asia, the trend is the opposite with heavy industrialization and reliance on fossil fuel at least for the next several decades to underpin the massive growth that has pulled the rest of the world along through recent economic turbulence. The United States is somewhere in the middle with the opportunity to shift toward clean energy to offset imbedded patterns of major overconsumption that set the bar high as an unsustainable global benchmark. In all of the noise, what is clear is that the combined global condition represented by the developed and developing economies and societies necessitates an urgent accelerated process of the convergence of humans, technology and nature to resist collective global entropy. In this process, each country and city must contribute in its own way to facilitating the convergence process and offering solutions that are appropriately designed to accelerate change and disrupt the status quo. Each of the three major tech economies and societies, China, India and the USA, offer their unique perspective and modus operandi in the race for smart city development and technological convergence.

9.3.3.1 Chinadeducation China offers a glimpse into the future of smart cities through their mass application of AI within the education system. With over 200 million students of all ages, this presents the single most significant collection of data to train AI algorithms that could and most likely will one day power education across the globe. China is running the single largest data collection in the application of AI in education. Students are given headbands that collect data on concentration levels and feed these data into AI algorithms, aimed at monitoring and improving the quality of the lessons. The data are also presented to parents in real time, effectively allowing parents to monitor their children and how they perform against others in their class. No other country on the planet can currently manage this large-scale experiment with the potential to influence future academic performance and standards. 9.3.3.2 USAdrenewable energy The USA’s movement toward renewable energy is perhaps the singular most significant change that the country has to offer beyond the continual technology innovation from Silicon Valley and the economic clout that the Big Five tech giants or “FAAMG”dFacebook, Amazon, Apple, Microsoft and

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Google (alphabet) represent. This direction appears to be in contrast with Washington policies including severing from the Paris Climate Accord. But the market has a different voice and America’s vast and abundant renewable clean energy reserves can provide power to run all US cities and communities and drive the country’s emerging smart-tech industries and transportation systems with the potential to transition to virtually 100% clean renewable energy. This move toward renewables is not without acknowledging other countries such as Germany, Japan and the United Kingdom that have more systematically addressed these issues and have developed policies and technology solutions that have been pioneering. Yet the issue is that the United States is still the largest consumer on the planet and must be the most aggressive to reverse this negative trend and global carbon footprint by turning this into a positive economic growth engine.

9.3.3.3 Indiadbiomimicry India offers a different perspective in this race for convergence. Prashant Dhawan (2016), biomimicry architect, offers comment on India’s 100 Smart Cities Mission, insisting they must be truly livable for people and nature, not just the current problems layered with technology. He advocates better holistic coordination, such as the conductor of a symphony. The very idea of smartness is nested in life itself and that is the place to look for intellectual exploration. Dhawan directs us to biomimicry to understand how life support systems work and to build these principles into urban settings. This approach draws on systems thinking as well as ancient Indian philosophy, inspired by mandalas and mantras. He provides a humane tech-centered approach to the definition of the smart city as “. one that provides a healthy, nourishing, harmonious, selfmaintaining (adaptive and evolving) environment where all life thrives and citizens enjoy sustainable happiness, while enabling each to pursue a way of life and work of choice.” What Dhawan calls the ‘genius of place’ refers to the space at the nexus of networks, flows and co-evolution. This means that we are all part of a network constantly exchanging information and are interdependent, between each other with natural systems. The genius of place will be unique to each city, just like our concept of city DNA. The true genius of it would be designing the smart city to perfectly match the needs and opportunities of the bioregion, including the culture and people that make up its citizens and users. This is in line with our metamodern sensibility toward humane technology. 9.3.4 Cities as living labs The living lab model is a space to experiment with flexible and agile forms of work and computing that transcend the current structural limitations of city life.

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The living lab is in constant transformation, modeled after nature’s living systems. A singular format or approach will not keep pace with societal change and planetary requirements, so living labs must themselves be as alive as the surrounding culture, market and technological ecosystem. Building on the idea of cities as living labs from previous chapters, this can present novel business opportunities. Chrone´er et al. (2019) conducted a literature review and produced many different approaches to the urban living lab concept. They identify seven main aspects of an urban living lab: (1) governance, (2) financing and business models, (3) physical space, (4) innovation, (5) partners and users, (6) stakeholders and data collection and (7) ICT and IoT infrastructure. They consider financing and business to be “crucial to make it happen and thus is key to its sustainability.” Because it is necessarily a “long-term program,” living labs require commitment and investment in financing and maintenance. Thus, the core parts of this business model include building relationships with financiers, citizens and stakeholders alike to make sure the living lab is sustained and successfully scaled. To overcome divergent ideological approaches and operating methodologies, the living lab model can allow diverse systems to converge rather than conflict, out of shared necessity. In this way, China and the United States can be partners, rather than competitors. Dualities must embrace convergent and collaborative solutions; east versus west, left versus right, public versus private and capitalism versus commons-based economics can engage each other cooperatively and dynamically drive innovation to achieve commonwealth prosperity and sustainability. Living labs are designed to nurture the convergence of society, knowledge, technology, science and nature. As we stand on the threshold between the old and new worlds, we must consciously experiment with our socioeconomic constructs within experimental yet real applied urban situations and markets (that the living lab provides) to facilitate a collective intelligence that solves the paradoxes of our progressive technological advancement and the regressive externalities produced. There is a need and opportunity to rehabilitate thoughtful spaces and knowledge production to understand the ubiquitous role of information technology in our lives, as austerity economics has devalued the education sector. This makes it more expensive and less efficient in the end. The living lab should be applied in all possible contexts: universities, public spaces, corporations, governance, ecosystems, etc. The humanities and social theories have been diminished by the rise of scientism, hence the technochauvanist impulses that dominate Silicon Valley. As Peters notes in Digital Socialism or Knowledge Capitalism? (2019), “Neoliberal policies stripped out the non-productive subjects, especially in the humanities, developed instrumental strategies to lift reputation and digitalized the

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university administration . Increasingly, the emphasis has fallen on knowledge exploitation with an accent on encouraging growth in high-tech investments and industries.” As part of the rise of knowledge workers and IP as the new asset and the potential for poorly deployed technology exacerbating social or ecological issues, we argue that humane tech, the humanities and human centered are the heart and mind of the living lab. These values are applied in the living lab to find ways to scale up solutions to the global level while anticipating unexpected negative consequences.

9.4 The new economy Recalling the essential functions of the smart city from Chapters 7 and 8, a smart economy is critical to the successful establishment of sustainable smart cities. Economics at its core definition is about the distribution of scarce resources. Technology is able to increase efficiency and reduce scarcity of resources, but the hoarding and price-fixing of resources and products creates artificial scarcities that exploit profit opportunities. Alternative forms of economics emerging and converging go beyond standard capitalist frameworks to new hybrid and postcapitalist forms that better enable the fruition of sustainable smart cities.

9.4.1 Planetary accounting The P2P Foundation published a report titled “Accounting for Planetary Survival,” (2019) in which a person-to-person infrastructure for a just society, using perma-circular supply chains, postblockchain distributed ledgers and more have been outlined. The report describes the next major economic models by three new types of postcapitalist accounting on distributed ledgers, via (1) contributive accounting, (2) values in the commons economy (beyond wage labor) and (3) REA (resources, events and agents) accounting or ,“flow accounting.” The report identifies a central weakness in the current political economy, which is negative externalities, unaccounted social and ecological costs due to doing business as usual. It also leads to artificial scarcity and pseudo-abundance rather than moving us to a postscarcity society. Overcoming this is the driving principle in the development of smart economics. Kate Raworth, known for “Doughnut Economics,” writes the foreword that rightly posits a basic economic model with a social floor and ecological ceiling. All these ideas combined allow for the accounting of everything, as well as the preemptive containment of negative externalities. They consider economic productivity as “thermodynamic flows” that need to be accounted

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for in terms of matter and energy cycles within planetary limits. This dovetails with John Bellamy Foster’s (2012) notion of metabolic rift, based on Marx’s idea of ruptures in the social and ecological metabolism, acknowledging the notion of cities as living organisms. The solution is to nurture “commons-based peer production” as the postcapitalist seed form. The authors write that activist researchers have a double duty not only to track emerging trends and seed forms that pursue this alternative but also to strengthen such communities. Their notion of change is based on investing in the seeds of the new system within the current system, so that it grows from within and overtakes the means of the previous system. This is consistent with the Marxist analysis and “movement marketing” above and with the multi-level perspective of systems transition from earlier chapters.

9.4.2 Strategy shift Business models deployed by the AI sector are currently similar to the biopharma industry, large R&D investment, long cycles, phase funding and high-risk high returns. One major difference is that incumbent AI businesses like Google are swallowing up the emerging startup space with what Corea (2018) calls the “DeepMind strategy.” The other difference is that in AI opensource models are emerging to challenge the traditional Software-as-a-Service model. These developments give way to a new business archetype described as “Paradigm 37e78,” named after a Human-versus-AI tournament in the game of Go, in which AlphaGo defeated a human (Lee Sedol) after 37 moves in one game and in another game the human beat AlphaGo in 78 moves. The common theme that the moves share is that they were both entirely unexpected by each opponent. Sedol learned a new way of seeing after studying the AI move and subsequently won. Paradigm 37e78 teaches us about the feedback relationship between users and machines: “we make the machine better and they make us better off in turn.” This becomes a mantra for how AI is trained and how it approaches data and learns itself. In AI, data are described as “the perfect good” because it is not perishable and is multipurpose and duplicable. Given that the data are good in the first place, usage of it can improve recursively through AI and ML. Data are thus also the competitive advantage multiplier for firms and are at risk of converging into monopolies. Fortunately, academics and stealth mode companies are leading the focus on the opposite trend to teach AI without it being dependent on large datasets, to have machines learn more like humans do. While the conventional business model is to feed the AI more data, the academic model is to make algorithms better. We can consider this to dovetail with a metamodern shift, in which our collective sovereignty and survival is empowered through AI and ML oscillating between human and algorithmic innovation and insight.

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9.4.3 New forms of digital currency As the world advances, new forms of trade and communication overlap with digital currencies and the old world economy is being forced to develop new monetary systems and transaction methods. China is leading the way in the transformation of digital currency building a cashless economy. Ali Baba’s Ali Pay and Tencent’s WeChat are mobile applications that are used by over 1 billion people in China, allowing for seamless trade and exchange 24/7. All transactions would theoretically be recorded and traceable information for data mining and pattern recognition on macro scales of transactional behavior. In this space, cryptocurrencies function as tokens of blockchain architecture, underpinning the transactional activities of smart cities. According to Forbes, Dubai is set to be the first blockchain powered smart city in 2021, while Estonia has been experimenting with it since 2012. Transparency and security are the two main benefits promised by blockchain technology, enabling the citywide integration of transactional systems.

9.4.4 Blockchain A key factor in the success of the future of the convergence of human, machine and natural systems is the advancements and implementation of blockchain on a global scale. With the abilities to record all transactions in every sector of commerce and public sector management, blockchain functions as the DNA that records the evolution of the global composite and can be data mined to understand systemic patterns and anomalies. In addition, the ability to record all transactions will force systems to become more transparent and therefore underpinning the ability to resist the entropy of planet earth and the tendency for human and machine systems to move toward corruption as the natural state of things. There are seven tendencies of blockchain, according to Sarah Manski (in Bauwens): verifiability, globality, liquidity, permanence, ethereality, decentralization and future focus. These assure encrypted transactions around the world. The liquidity is not mediated by any central bank or private company and will always be there. All transactions are digital and distributed. Blockchain still has some things to prove, but on the whole has generated momentum and will be a key factor in establishing a baseline for smart cities and smart economies as digital mediums overtake other forms of transactions.

9.4.5 Holochain Blockchain is akin to libertarian economics, while holochain is an example of commons-based ledger systems. Competition is a key principle of blockchain, whereas holochain plays cooperative games. Blockchain consumes vast

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resource to operate, therefore it is not sustainable in the long run. Holochain is a distributed ledger based on biomimicry and has a much lighter architecture and energy load. Blockchain is more about profit and extractive ecosystems, whereas holochain is more purpose-driven and generative with nature. The latter is based on a web of trust and can potentially support commons-based economic models and peer-to-peer networks promoting grassroots businesses and offsetting industry-wide monopolies.

9.5 New forms of business exchange As we transition to new commons-based architectures with the political will to challenge capitalist overreach, new types of businesses and markets can emerge. Following on the crypto discussion above, this is primarily considered through the method of exchange. The metaphor of flow and the notion of motivationcollaboration helps illustrate ways of conducting business exchange from traditional (business-to-consumer) to new forms (peer-to-peer) and supports the basic tenets outlined in the report “Accounting for Planetary Survival.”

9.5.1 Flow The flow state is a mental experience of hyperfocus and coordination in which an activity can be performed with higher efficiency and satisfaction. Flow theorist Mihaly Csikszentmihalyi (1997) notes that people are happier when in flow states, absorbed in the task at hand. Another way of describing the flow state includes “being in the zone.” The experience of time and meaning is also distorted, such that one can exclude distractions or get lost in the moment. Three conditions define a flow state: (1) engaged with a clear goal, (2) in a task with real-time feedback and (3) congruence of skill level with the task. The reason flow is important in psychology is the same reason it can be applied at the macro level, to create a happier more efficient society. If people were automatically matched with jobs at their skill level and their work was meaningfully integrated within a larger whole, social systems could enter flow states. For example, people are naturally inclined to work together when there are mutually beneficial opportunities to advance ideas and projects in a context that supports individual and collective flow. This requires establishing a delicate balance of personal and collective motivations that can be sustained over the life cycle of a task or project. The objective of flow applied to cities is to harness the creative energy and technical skills of many talented individuals and groups seeking alternative/collective opportunities for involvement and advancement. Smart cities will require more effective use of human capital and the ability of AI to assist in getting people in the flow as we transition away from traditional jobs and shape new forms of value-added business exchange.

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Supporting the flow are new opportunities including coworking and remote access that are transforming workforces, companies and individuals to operate in more flexible ways and on demand. Coworking emphasizes not only shared workspace but also the potential for businesses to exchange services and to expand and contract in more organic ways. Another emerging trend transforming traditional top-down Taylorist modes of production (that served the purpose of perfecting late stage industrial production) is codesign discussed in Chapter 3, an umbrella term to cover participatory and open-source design processes. Codesign enables diverse perspectives and feedback to be incorporated in the design process to ensure maximum creativity, functionality and problem solving. The ability to incorporate codesign culture in the planning and design of smart cities will be extremely useful in supporting the flow of people, organizational systems, physical spaces and technology applications. In the formation of a more enlightened global society, creativity will play an important role and sharing diverse approaches, methodologies and solutions will be necessary in the administration of the shared global resourcedPlanet Earth. To facilitate codesign on a global scale, cross-cultural collaboration will be necessary. Interacting with other cultures is not necessarily intuitive and the ability to work effectively with others who may not necessarily share common methods for communicating, evaluating, persuading and leading requires a collective platform. Cross-cultural collaboration can be enabled by real-time translation technologies and AR/VR technologies allowing people to collaborate in the flow on a global scale. To break the constraints of century-old labor constructs that have exacerbated the balance of human equity, new forms of the exchange of human capital, labor, products and services can be enabled through new technologies that allow systems to flow in their natural state rather than being utilized against their optimal state of higher efficiency and satisfaction.

9.5.2 Channeling on demand Traditional business channels now have the opportunity to be empowered by the possibility of real-time market behavior and feedback. In the AI-driven world of business operations, multiple business channels can be simultaneously routed within the business ecosystem allowing companies to no longer be monodirectional. Business-to-many (B2M) is businesses that sell to both other businesses and consumers. Unlike traditional B2B or B2C, B2M firms can engage anyone. The expansion of business channels enabled by AI will predict consumer patterns and route information to the appropriate channel on demand. Companies will be able to simultaneously engage in B2C and B2B channels. Individuals will also have the potential to link to other individual buyers and sellers in P2P channeling.

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At a broader level of business connectivity, governments will be able to expand their function as a central provider of business services. Part of managing the city as a living system, city governments will manage contractual arrangements through blockchain-enabled concessions, tender processing and project tracking. This will allow the management and operations of diverse business combinations from BOO to MOD where publiceprivate sector hybrid enterprises can foster economic balance within city operating systems. There is a clear requirement for publiceprivate partnerships to serve as a necessary vehicle and a critical dimension in the viability of smart cities economic growth in order to achieve sustainability by providing equitable and transparent business solutions using digital ledgerbased accounting and project tracking. As Fig. 9.2 visualizes, a pattern of public, private, businesses and consumers connect and cross-sect across diverse business channels and formats as an expanding web of business combinations. Private-Public-Partnerships (PPP) connect the most business models, but private and public have their roles as well.

9.6 Bringing it together There are still many unknowns in the future business landscape, but what is clear is the evolutionary convergence of common goals and guidelines for a new sustainable economy and the need to support and participate in that convergence. Business has always been about making money, typically by fulfilling some market need. Business in the 21st century has to create value first and foremost, rather than extract profit as the only objective. This will ensure the longevity and sustainability of future societies that will have the benefit of new digital tools and a knowledge economy to liberate us from BUSINESS MODELS BOO

STAKEHOLDERS

P2P

BOT

OBM

PRIVATE

BOM

BOC

PPP

B2C

B2B

MOD

PUBLIC

B2G

G2C

BUSINESS CHANNELS

FIGURE 9.2 Business channels and formats.

G2G

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centuries-old class divisions and limitations on our human potential to achieve a collective intelligence. Below we attempt to tie everything together by discussing economic convergence, the role of collaboration and the dynamic of self-regulation.

9.6.1 Convergent economies In economics, convergence often refers to closing the gap between different countries per capita income. Poorer countries “catch up” to richer countries and therefore eventually all economies are destined to converge toward income parity. This can be broken down further into two types: sigma- and betaconvergence. Sigma-convergence refers to reducing income disparity across economies, while beta-convergence refers to poor economies catching up to rich ones. Developing countries have the opportunity to grow faster by receiving technological innovation (techniques, methods) passed down from other countries. They can pursue a variety of strategies, such as leapfrog as discussed in Chapter 1. This notion of economic convergence is not a hard science and has slowed considerably since the recession after 2008, but it gives us insight into the trends and different growth rates of economic actors and their eventual equalization. Meanwhile, technological convergence will continue unabated. Kemal Dervis¸ (2018) for the Brookings Institute wonders “Are we at the end of economic convergence?” and observes that the world is going through a “synchronized” growth surge, but it will not be sustained unless developing countries deploy technological upgrades efficiently. For large-scale projects like smart cities, this means they may only get one shot to do it right. It’s important to plan the automation of jobs along with the training of new ones; and because AI and robotics largely automate menial jobs while producing high value, there is ample opportunity to provide people with education and flexible skills such as service and knowledge workers. The easy innovation leaps in manufacturing have largely all been made, leaving the next race to be in the fields of AI and robotics, which are more difficult to copy. Much of the upgrade process will depend on the investment of global firms, so the regulatory environment of each country matters in how they attract partners and protect their public interest.

9.6.2 Collaboration Collaboration is the new competition. Atomization has been a trend within the nature of work because of increasing flexibility and autonomy. To counter this, independent workers are drawn to coworking spaces, where they can not only work alone but also have a community. Ethnographies on coworking spaces show that communities are built through espousing, learning and enacting.

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These three pathways are different ways members can pursue their community needs. This mutual exposure often leads to innovation and collaboration. Another issue is not just collaborating with each other, but with technology and AI itself, as described in Paradigm 37e78 above. As human, tech and nature converge, we must entertain new forms of collaboration between all.

9.6.3 Self-regulating systems Based on the entropy of the planet ecosystem, collaboration may not be a choice but will be a requirement. It will be motivated and compelled through the convergence of humans, technology and nature, among themselves and with each other. Chairman of the World Economic Forum, Klaus Schwab, argues we must seize the narrow window of opportunity to repair the world’s institutions, our societies and the environment. In the Global Risks Report 2018, he says we need “to find the will and momentum to work together for a shared future.” This requires collaboration in self-organizing and selfregulating systems.

9.7 Conclusion The smart city presents an obvious business opportunity but the status of business through the next systems change is not certain. The proliferation of business models makes it more confusing than clear. A Marxist analysis showed the tendencies of capitalist-driven smart cities, masking externalities and restricting public sector opportunities and collective control. Technology firms are already largely capitalizing on movement marketing, but to be sincere they must not just align brands, but actual corporate culture with social and political movements that are on the cusp of achieving transformation of the system. Movements like Extinction Rebellion mobilize intellectual and social forces to put immediate pressure on the public, the media and decision-makers alike. They call out in unison that we cannot have a smart city if society collapses into warring city-states because the world has been destroyed. This brings us full circle to the constant need for innovation, at the niche, regime and landscape levels, to drive humanitarian and knowledge-focused productivity to achieve sociotechnical regime change to arrive at new shared sustainable economic model. Through the convergence process, many opposites are drawn together and combined into higher forms; Top Down authority is contrasted with Bottom-Up power, but through convergence, their values, goals and methods can find common ground. Likewise, private and public needs balance. Market capitalism is inefficient without a basic social safety net, including housing, healthcare and education. And finally, to stem the hegemony of corporations, cottage industries can thrive. (See Fig. 9.3).

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Top Down

Open Source

Capitalism

Cottage Industry

Private

Public

Corporations IP Ownership

213

Socialism

CONVERGENT

Bottom Up

INTEGRATION

FIGURE 9.3 Converging opposites.

The new IP race among states and firms incentivizes collaboration and commons-based forms of accounting and ownership. New economic models are emerging to meet these demands and to keep economic growth within planetary boundaries of a social floor and ecological ceiling. The concept of convergence explains the rapid bringing together of research, economics and business models with the human, technological and natural flowing systems. Through continued algorithmic innovation, our cities as living systems and the living labs within them, can oscillate between binaries and polarities such as east and west, capitalist and commons-based, to create a synthesis of the dynamic principles of yin and yang in flowing equilibrium. We can overcome cynical and exploitative tendencies to business challenges and direct humane tech to convergent solutions in city life to benefit all stakeholders.

References Anthopoulos, L.G., Fitsilis, P., 2015. Understanding Smart City Business Models: A Comparison. In: 24th International World Wide Web Conference (WWW’15). Bauwens, M., Pazaitis, A., 2019. Accounting for Planetary Survival. P2P Foundation. Chen, C., 2019. Humanitarian Technology: Taking the ‘Human’ out of Humanitarianism? S. Rajaratnam School of International Studies. http://hdl.handle.net/11540/10913. (Accessed 20 January 2020). Chrone´er, et al., 2019. Urban Living Labs: Towards an Integrated Understanding of their Key Components. Technology Innovation Management Review 9, 50e62. Corea, F., 2018. An Introduction to Data, Studies in Big Data 50. Chapter 6: AI Business Models, pp. 41e46. Csikszentmihalyi, M., 1997. Finding Flow. Basic Books. Dervis¸, K., 2018. The Future of Economic Convergence. Brookings Institute. https://www. brookings.edu/opinions/the-future-of-economic-convergence/. (Accessed 3 December 2019). Dhawan, P., 2016. Bio-mimicry & the ‘Smart’ Indian City. The future of design (TFOD). https:// www.tfod.in/art-design-articles/3376/bio-mimicry-and-the-%E2%80%98smart%E2%80%99indian-city. (Accessed 3 December 2019). Foster, J.B., 2012. Marx’s Ecology. Braille Jymico Inc.

214 SECTION | III Applications (Operations and Management) Frost & Sullivan Report, 2018. https://go.frost.com/VIG_SmartCities https://www.marginalia.online/ smart-cities-are-anticipated-to-create-huge-business-opportunities/. (Accessed 3 December 2019). Goodson, S., 2012. Convergence Is The Future Of Marketing. Forbes Article. https://www.forbes. com/sites/marketshare/2012/03/01/convergence-is-the-future-of-marketing/#750c788a5401. (Accessed 3 December 2019). Kawaguchi, L., 2018. Smart Cities: Convergence and collaboration to build communities of the future. https://www.gbm.hsbc.com/-/media/gbm/insights/attachments/smart-cities-convergenceand-collaboration-to-build-communities-of-the-future.pdf. (Accessed 30 January 2020). Koslowski, T., 2014. Industry Convergence d The Digital Industrial Revolution. Gartner Research. https://www.gartner.com/en/documents/2684516. (Accessed 3 December 2019). Mikkelsen, K., Martin, R., 2019. The Neo-Generalist. LID Publishing. Peters, M., 2019. Digital Socialism or Knowledge Capitalism? Educational Philosophy and Theory 52 (1), 1e10. Rogan, K., 2019. Anti-Intelligence: A Marxist critique of the smart city. https://www.acade mia.edu/39125907/Anti-intelligence_A_Marxist_critique_of_the_smart_city. (Accessed 30 January 2020). Schiavone, F., Paolone, F., Mancini, D., 2019. Business model innovation for urban smartization. Technological Forecasting and Social Change 142, 210e219. The Global Risks Report 2018 (Online). World Economic Forum. http://www3.weforum.org/docs/ WEF_GRR18_Report.pdf. (Accessed 30 January 2020).

Further reading Angelidou, M., 2016. Four european smart city strategies. International Journal of Social Science Studies 4 (4). ARUP, 2013. Report reveals $400 billion smart cities opportunity globally. https://www.arup. com/news-and-events/news/report-reveals-%24400-billion-smart-cities-opportunity-globally. (Accessed 19 December 2019) BC campus, 2019. Chapter 20. Economic Growth. https://opentextbc.ca/principlesofeconomics/ chapter/20-4-economic-convergence/. (Accessed 19 December 2019). Belknap, C., Design Earth Synergy. https://designearthsynergy.com/. (Accessed 3 December 2019). Forbes, 2017. Dan Pontefract. Don’t be afraid to call yourself a neo-generalist. Forbes Article;. https://www.forbes.com/sites/danpontefract/2017/02/15/dont-be-afraid-to-call-yourself-a-neogeneralist/#e18b40f3b085. (Accessed 3 December 2019). Garrett, L., Spreitzer, G., Bacevice, P., 2014. Co-constructing a sense of community at work: the emergence of community in coworking spaces. Academy of Management Proceedings 2014 (1), 14004-14004. Outlier ventures, 2019. Convergence in Smart Cities. https://outlierventures.io/wp-content/uploads/2019/05/OV-SMART-CITIES-FINAL.pdf. (Accessed 3 December 2019). Outlier ventures, 2019. Dubai Could Be Producing Over 620M Gigabytes By 2020, Which Will Now Have The Potential To Be Connected To Blockchain. https://outlierventures.io/re search/dubai-is-piloting-a-decentralised-data-programme-with-outlier-ventures/. (Accessed 3 December 2019). Forbes, 2019. McFarlane, Chrissa. Are Smart Cities The Pathway To Blockchain And Cryptocurrency Adoption? Forbes Article. https://www.forbes.com/sites/chrissamcfarlane/2019/10/ 18/are-smart-cities-the-pathway-to-blockchain-and-cryptocurrency-adoption/#4ca6d05f4609. (Accessed 3 December 2019).

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Johnson, P., 2016. The Digital Transformation of london.gov.uk, Drupal. https://www.drupal.org/ case-study/the-digital-transformation-of-londongovuk. (Accessed 19 December 2019). Tech Republic, Maddox, Teena, 2018. Inside London’s brilliant plan to update its smart city technology. https://www.techrepublic.com/article/inside-londons-brilliant-plan-to-update-itssmart-city-technology/. (Accessed 19 December 2019). Medium, Theo Blackwell, 2017. Looking ahead for digital transformation in local government. https://medium.com/@camdentheo/2017-looking-ahead-for-digital-transformation-in-localgovernment-d72394026480>. (Accessed 19 December 2019). Computer Weekly, Evenstad, Lis, 2018. CIO interview: Theo Blackwell, London chief digital officer, Mayor of London’s Office. https://www.computerweekly.com/news/252435313/CIOinterview-Theo-Blackwell-London-chief-digital-officer-Mayor-of-Londons-Office. (Accessed 19 December 2019). IoT Agenda, Rosencrance, Linda, 2017. San Francisco smart city pilots aim to make streets safer. https://internetofthingsagenda. (Accessed 19 December 2019).

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Conclusions Chapter outline 10.1 10.2 10.3 10.4

From theory to practice East-West Collaboration The human factor Wide-spread automation

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10.1 From theory to practice This book attempts to cover a wide range of concepts related to smart cities and artificial intelligence within a convergent frame. It is a snapshot of a rapidly changing discourse we are deeply immersed within, both in academic research and professional practice, about the relationship of technology, design, and the innovation of smart cities with a broader agenda of transforming global society into a sustainable permaculture. The basic goal is to establish cities as self-regulating living systems with a higher mission to transform political and technological cultures that put people and the environment at risk. It is about defining a meta-architecture guiding the convergence of humans, technology, and nature. Each of the three sections of the book cover distinct perspectives and thematic concepts, starting with our Approach section and design principles, to the biomimetic and virtual Architecture section, to the concrete Application section on pragmatic strategies and solutions to converge towards artificial general intelligence, sustainable urban infrastructure and architecture simultaneously so that the convergence of humans, technology, and nature is harmonious. Approach leads to Architecture that leads to Application. The structure of the book provides the opportunity for us to explore the features we consider most relevant to the development of smart cities and AI. The first three chapters describe our approach, the research, theory, strategies and planning. The next three chapters define the system architecture, the structure, typologies, connectivity and interface of cities. And the final three

Smart Cities and Artificial Intelligence. https://doi.org/10.1016/B978-0-12-817024-3.00010-6 Copyright © 2020 Elsevier Inc. All rights reserved.

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chapters introduce functional, operational and business model considerations, the so-called application, including everything from apps to opportunities that AI enables within smart cities and potential new humane socio-cultural landscapes. We open the Introduction to book with two major premises: a metamodern motion and a convergence theory proposition. The idea of a metamodernism of humane technology and communitarian values underpinning our smart cities approach is not a far-fetched provocation but a simple moral imperative. Technology is a double-edged sword, and when it is on an exponential curve, that sword can incidentally facilitate the destruction of nature and ultimately civilization. When technology undermines human sovereignty or ecological integrity, there is no real economic gain, only an illusion of such. The convergence proposition is about the common emergence of factors and constraints that guide biological, social, and technological evolution in similar ways, toward convergent and sustainable ends, from various initial states of potential. Various concepts are emphasized in different chapters, but these themes of evolution, convergence, and metamodernism go somewhat beyond the scope of the book. Merely recounting the contents of the book would not do the conclusion justice. Thus, the Introduction and Conclusion of the book are about the bigger picture. This book is not a formal metamodern theory of the smart city, but it makes strides in that direction. Albert Borgmann’s idea of metamodernism, as revived and elucidated by theorist Brent Cooper, proposes a humane philosophy of technology which has been incorporated here in the context of smart cities and AI. There is much inspiration in the visions of futurism and humanism that is beginning to be made real. The diverse theories of convergence are themselves converging and thus validate each other by forming an overlapping consensus. Our point with convergence is not to prove the technological singularity is happening with a particular point or moment, but rather to acknowledge it as an attractor and possible event horizon for technological evolution in the near future. By some standards, the singularity has already happened, and we are still driven and compelled by it. The smart city is described here as a “convergent socio-cyberphysical complex”dan optimally flexible, adaptive space where the social, virtual, and physical realms are converging as interactive, reflexive and hybrid systems. Technology is bringing together the different realms by embedding itself between the layers, based on the deep interdependence of natural systems in reality. The idea of simulation is the means to connect the abstract and experiment with the real-world challenges and opportunities. Our idea of metaconvergence combines all the theories of convergence and all the six dimensions of our smart infostructure into a comprehensive conceptual system where everything converges into a continuum. Starting with the physical dimension, rooted in nature, theories of evolutionary convergence

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show distant species evolving in convergent ways, and co-discoveries being made, such as that of evolution itself. This leads into scientific convergence and the city infrastructure dimension. Through the base layers in nature, technology connects up through to the human dimension and cultural level, where knowledge is also converging in the social and technological discourses. In the pure technology dimension, nanotech and biotech are reinserting themselves into the nature layer, as well as integrating and being embedded in one another, such that it forms a continuum. At our phenomenological and experiential level, nature, human, and technology have all converged. What is clear is that smart cities and AI are inexorably on a convergent path, and we must form smart coalitions toward civil collective intelligence to operate it and nurture the environment. Design must adhere to the core values to serve the user and citizen as participants of the living city. This is fostered and maintained by principles of co-design, co-development, citizen participation and UX feedback loops. Living labs and innovation hubs provide opportunities and spaces to prototype such projects. It is upheld at a policy level by publiceprivateepeople partnerships and global civil society organizations. Transdisciplinary approaches are needed more than ever to expand our scope of inclusion to all life forms and the rights of animals and nature as stakeholders. The 4th Industrial revolution, the technological singularity, and perhaps a metamodern turn in globalization, are all convergent attractors in the 21st century and beyond. These attractors can be grounded in smart cities, which are expected to grow in population over the century as migration from rural areas to cities continues. This presents all sorts of challenges and opportunities that require deep foresight and humane planning to adequately address the existential risks presented by human exploitation of Earth’s resources and social inequality imposed on ourselves. We attempt to be realistic about the logistical scale and scope of the changes implied and the means to get there, which requires simulating the entire planetary system down to the nano-level. Granted there are economic opportunities in smart cities with much of the investment provided from venture capital and speculative investments, however, parts of the economy must be pluralized and commons-based to ensure proper management and equitable outcomes. Business thinking plays a role, but smart city and AI development cannot be exclusively a commercial enterprise that considers citizens as customers. The underlying logic of convergence is challenging the conventional relationships between business and civil society to remake the economy.

10.2 East-West Collaboration Our conclusion to this book is an attempt to put it all together, knowing that there is still much work to be done. The book proposes that everything is

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converging, including the contents of the book, toward a singularity. Integrating and distilling it all is also a work in progress. Each chapter has its own contained topic filled with diverse references, but is connected to the overall arc of the book. The goal is to acheive a gestalt impression of the next-generation smart city, and we have achieved that, no doubt with some gaps or missed opportunities. A big takeaway is that getting real about smart cities means being wise about systems change, not being opportunistic about development and transition or protecting self-interests. Authors Kirwan and Fu have worked together for more than a decade between Beijing, New York and London to shape and experiment with new forms of communications/cross-cultural connectivity and interface in the context of smart cities. Through the coauthorship we hope to continue to build positive relations between the East and the West, as well as further build bridges between the USA and China, two of the most powerful countries leading the smart city race. This is especially necessary when the erosion of trust and cooperation is an easy scapegoat in these challenging times of rapid globalization, economic co-dependency, and unprecedented opportunities for technological transformation. Richard Baldwin in The Great Convergence describes the process of global inequality beginning to be resolved from around 1990 when formerly underdeveloped countries transformed into new industrial centers. The G7 countries refocused economic growth to knowledge and service-based economies with an increasing share of manufacturing going to the Industrializing Six (“I6”), led by China. The next stage of transformation is a rebalancing of a combination of knowledge industries and new forms of AI-driven manufacturing. In this way, the global north and south will be inextricably linked as integrated global supply-chain ecosystems, allowing rich and poor nations to equalize through industry, technology, knowledge and financial flows. This presents new opportunities for collaboration, co-design, co-development and global competition to accelerate convergence. In the Michael Porter model of competitive advantages, each city will require the development of its unique subsystems and capabilities as the basis of city operations and economic activity, while incorporating global standards to reinforce connectivity and interoperability. This relates to the city DNA profile based on its unique characteristics and strengths. The convergent evolution of the city depends on the inherent characteristics of each city to adopt and integrate technology within the operations and management of the city. It is determined by many factors including population, levels of education, available resources, geography, social capital, etc. It is also dependent on the system of governance and the cultural orientation of the city and its citizens. We propose universal systems architecture that can be adapted to local needs and specific user demands. The ability for cities to adopt technology and a pathway of convergence depends on the initial state (convergence for initial value problems) of the city

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based on the city DNA model. Each city must fully understand and leverage its unique resources and characteristics to develop the appropriate solution for how technology will enable the city to achieve an optimal state. Each function serves its own purpose within the urban framework while simultaneously supporting the collective operation of the city. The urban operating systems (OS) must connect these diverse functions to allow an integrated management. The role of AI is now more critical than ever to align functions and to optimize individual and collective systems. This new alignment will allow cities to function much better, reducing noise within system functionality while creating efficiencies. Edge computing is an example of such reduction of energy consumption and optimization in action, as a term for distributed cloud computing that makes data processing more efficient, overcoming latency and bandwidth issues. Like the human body or other organisms, the city is comprised of multiple parts, organs, cells, and functions, each autonomously working with its own requirements while simultaneously collaborating within the broader framework of the system. The city is a complex set of systems like the body, but it is more holographic. There is a symbiotic relationship between the organism (the city) and bacteria (the people) for lack of a better image. As new technologies provide potential augmented functionality, the city is becoming more and more complex and able to function in real time on its own as a self-regulating entity. In the same way, just as a person develops healthy and stable regimes of diet and exercise, the body learns to adapt and regulate better. The concept of flow in the context of city design is the ultimate harnessing of collective intelligence, resource optimization and attentional focus, bringing technology to the highest state of realization. People’s navigation and enjoyment of city functions becomes a part of the user flow. The idea of flow in the individual is that all systems are engaged toward a unified purpose. In a social context, flow would suggest the fluid orchestration of daily life, the symbiotic activity of people coming together and interacting with the city. This addresses the issue of open versus closed systems and the requirements for international guidelines and ISO standards to allow flow to occur beyond the individual organism. Many of these transformations of science and society may seem to be simply happening around us and require very little planning or preparation by any individual. To a large extent, everything is set in motion by people who came before us and their predetermined rules and structures of human activity. Smart cities seem to just be emerging through the force of history, but too often we use this as an excuse to compromise. As our solutions come with new problems, we need to be able to anticipate the next outcomes and innovate upon the potential itself using AI and machine learning (ML). Through the merging of man-made and natural systems following biomimetic models and combining design thinking and ML to drive generative design solutions, we can design and facilitate the emergence of the best possible smart city that can

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continually adapt and improve. In this way, cities become self-regulating, incorporating strategy, planning, design, and operations in a seamless continuum. Customized algorithms can introduce novelty and imperfection into designs, just as nature would, to give a more dynamic feel, but not at the expense of integrity.

10.3 The human factor Throughout the book, design thinking and industrial applications have converged in examples and proposals for ways to evolve the smart city as a living system and users as citizens. Concerns were addressed over the potential for human rights abuses and neglect, particularly how technology sometimes displaces the sovereignty of the human and capital covertly exploits labor around the world. Thus, a commons-based economy is a key part of the next wave of smart city development. This is also why our advocacy for citizenfocused and participatory forms of governance is based on metamodern values and ideals, which signal a new stage of development with humane technology (HumTech), to avoid being co-opted by neoliberal market forces. If we are not careful, there is a risk of the smart city slipping into oppression or dysfunction. As Cardullo and Kitchen warn, “Despite attempts to recast the smart city as ‘citizen-focused’, smart urbanism remains rooted in pragmatic, instrumental and paternalistic discourses and practices rather than those of social rights, political citizenship, and the common good. In our view, if smart cities are to become truly ‘citizenfocused’, an alternative conception of smart citizenship needs to be deployed, one that enables an effective shift of power and is rooted in the right to the city, entitlements, community, participation, commons, and ideals beyond the market.” Cardullo and Kitchen. Big Data engineer Phil James describes the status of the smart city in Newcastle, UK (a leading city experimenting with IoT, tech hubs, public services, etc.), noting it is quite advanced and at the forefront technologically. Much investment and effort goes into planning projects, with little regard for the ultimate impact. However, Big Data analytics and sensors everywhere do not make a smart city. It requires smart people, continually being educated and informed, reflexively adapting to new insights, and converging on important decisions that will impact the future. He says we need to “democratize data.” James captures the spirit of the human-centered nature-convergent tech-enabled smart city that we are striving for. In a TEDx talk, he explains what a big task it is to interpret data or make even small changes in the city, and we need to coordinate and solve problem better: “What that demonstrates is that smart cities aren’t a technical problem. That little decision is sitting on top of an iceberg of social economic political and process challenges that we have to overcome . smart cities are all about people and processes and making the right and hard decisions.” Phil James.

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Within years, the potential of augmented reality (AR) will make breakthroughs that enable new forms of infostructure and higher resolution UX. AR glasses will finally be commercially available and commonplace platforms for experimentation. Wearable technology is rapidly converging on being inconspicuous and ubiquitous. Every development ripples out social and environmental consequences, so by being woven into the social fabric, AI and AR can have not only sentience but also conscience. With the ubiquity of sensors as prying eyes, the public and smart city stakeholders should be wary of and divest from “surveillance capitalism” because it undermines, and to some extent violates, the principles of life and sovereignty. It is an example of market failure, where no value is created but capital rushes to exploit a vulnerability, in this case a culture of fear, for profit. We have to imagine the positive potential of these transformations on the horizon. Real-time biometric sensors and data analysis is not just about enriching your life, it’s about enriching everyone’s. The convergent technologies and knowledge to be showcased in smart city life are as much about rebuilding the foundations of society for a 21st and 22nd century civilization. This long-term thinking is to ensure resilience and sustainability amidst climate change upheaval, logistical and scaling challenges and political turbulence. Everyone must be provided for in terms of housing, food, and basic resources to participate in society. Inclusion is a critical value and component of achieving a total solution. Energy and transportation systems are already intertwined, but are converging more, as roadways become smart grids and electricity networks become information highways. From the point of view of the smart city AI and OS, it’s all just flows as part of a living system. The city and the neighborhood become more deeply enmeshed so that no community is left behind or deprived of resources. Unlike space settlement, smart city development is more on our doorstep. We must completely close the gap between increasing city population and affordable housing. There is an untapped surplus of human capital with infrastructure needs. In the meantime hope for the future is held together through a patchwork of innovation communities, such as Barcelona’s 1 sq km self-sufficient zone.

10.4 Wide-spread automation The automation revolution unfolding will have various time scales and regional effects, but the process as a whole is convergent on applications that will have universal impacts, including writing itself. It won’t be long before books such as this one will be written by ML algorithms using neural networks for text generation, with novel forms of human guidance of the research process. The role of the human as knowledge worker is also a point of convergence, as automation eliminates traditional jobs and increases the abstractness of labor. The task at hand is actually to educate the world ahead of the technological singularity.

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Part of convergence is the disruptive technologies accelerating toward us, which have large scale effects extending to everyone. From ubiquitous sensing to geospatial mapping to wearable technology, from the nano to the global, the convergence is rapidly reshaping culture. The hypermodern tendencies of the system alienate humans and destroy the environment but people are rightly rising up to confront the contradictions. System transformation depends on this civic organization and mobilization of political and commercial will to converge on the most ethical and sustainable path forward. Philosopher Hanzi Freinacht describes a deeper democracy through codevelopment processes to foster a principled collective intelligence. He writes eloquently of decentralized “circles of solidarity” between networkers, philosophers, and entrepreneurs to cocreate the next stage of social design and justice to reconstruct a metamodern society. The Convergent Urban Interface, a concept we have introduced, is the next major technological milestone for the universal smart city. The convergence of data visualization, machine learning, and design thinking are fast making this a reality. Speech processing and ML algorithms will be able to overlay AI interfaces to produce compelling virtual teachers and assistants. Art and architecture will spring forth through AR displays. But before we get carried away with utopian thinking, we must upgrade the paradigm to metadesigndone of the generative engagements with the social and technological singularities forthcoming.

10.5 Consequences of embracing convergence The way it all fits together starts with a theory of convergence. More specifically seven theories of convergence that complement each other and give way to the idea of a meta-convergence. These theories become methodologies for our approach and understanding of urban scenarios. AI augments design process addressing Smart City Functions in their wholeness. Building on this, we can then establish a collective intelligence that leads towards the most equitable, efficient and sustainable outcomes. We have illustrated this progression informing the book in Fig. 10.1. Our notion of metaconvergence is the singularity of this book, the endpoint or attractor of its convergent research at which point the technology has runaway effects, with the commingling and integration of nature, technology, and human layers of reality. It is perhaps beyond the book itself, but we have attempted to capture and communicate the essence. From the evolution of cities to social organisms to autopoietic networks entailing OS to the digital revolution converging everything into new interfaces, we must understand the smart city functions and applications to transition to new economic systems and enterprise (business) models. Through this methodology and vision, we can facilitate the convergence of smart cities and AI to achieve the ultimate goals of human well-being, technological optimization, and balance in nature.

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Convergence hierarchy (Fig. 10.1) Wellbeing

Convergent Outcomes

Balance

Optimization

Outcomes Sustainability

Enlightenment

Collective Intelligence

Freedom

Convergent Applications

Actualization

Commonwealth

Environment

People

Smart City Functions

Mobility

Convergent Scenarios

Inclusiveness

Living Biomimicry

Governance

Economy Self Regulating Systems

Convergent Methods / AI Processes Design

Smart Objects

Operations

Algorithms

Computing Ability

Strategy

Living Labs

Planning

Machine Learning

Flow

Data

Convergence Methodologies

City DNA

Generative Design

Co-design

Convergent Objects

Convergent Approaches

Convergence Theories

Convergence Theories

Technology Nature

Society Knowledge

Evolution Media

Economy

FIGURE 10.1 Convergence Hierarchy explains the approach, architecture and application of AI and smart cities in the form of a multi-tiered axial hierarchy representing the convergence of Theory and Practice. Radiating from the diverse foundational Convergence theories at the base of the conditions are in-turn analysed using Multi-level Perspective modelling and reconfigured as smart city functions at macrocontext, meso-content, and micro-component scales and scope to determine and describe collective intelligence outcomes shaping the evolution and trdiagram, emerging tools and methods convert these theories into practical applications based on contemporary urban conditions termed scenarios. These ansformation of each city’s unique DNA.

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Further reading Baldwin, R., 2016. The Great Convergence: Information Technology and the New Globalization. Belknap Press. Borgmann, Albert, 1992. In: Cutcliffe, Stephen H. (Ed.), “The postmodern economy.” New Worlds, New Technologies, New Issues. Lehigh University Press. Cardullo, P., Kitchin, R., 2018. Smart urbanism and smart citizenship: the neoliberal logic of ‘citizen-focused’ smart cities in Europe. Environment and Planning C: Politics and Space 37 (5). Cooper, B., 2019. Borgmannian Metamodernism. The Abs-Tract Organization. https://medium. com/the-abs-tract-organization/borgmannian-metamodernism-8ed5e275f8ae. (Accessed 20 January 2020). Freinacht, H., 2019. Nordic Ideology: A Metamodern Guide to Politics, Book Two. Metamoderna ApS. Go¨pel, M., 2016. The Great Mindshift: How a New Economic Paradigm and Sustainability Transformations go Hand in Hand. Springer. Kirwan, C., 2010. Cybernetics Revisited: Toward a Collective Intelligence. Published in: Visual Complexity: Mapping Patterns of Information. Princeton Architectural Press. Porter, M.E., 1985. The Competitive Advantage: Creating and Sustaining Superior Performance. Free Press. James, P., 2019. Where Are All The Smart Cities? TEDxNewcastle. https://www.tedxnewcastle. com/speakers/phil-james/. (Accessed 19 December 2019). Volkov, A., 2018. Smart city: convergent socio-cyber-physical complex. MATEC Web of Conferences 251, 03065. https://doi.org/10.1051/matecconf/201825103065 (Accessed 19 December 2019). Zuboff, S., 2018. The Age of Surveillance Capitalism: The Fight for the Future at the New Frontier of Power. Profile Books.

Appendix

Explanations of convergence theories Convergent evolution The theory of convergent evolution explains how species of different lineages can evolve similar traits. It refers to a converging of the range of variation due to common external conditions and environmental pressures. For example, all birds, bats, and some insects have common features (wings) for flight, and all fish have similar hydrodynamic body shapes for swimming. Different primates can evolve similar eye color variety, but for different reasons and without a common ancestor. The way various fruits all bare seeds (eaten by animals) to be dispersed as a method of propagating is also convergent evolution. It occurs when a species develops analogous structures with similar form or function to another species, but those structures were not passed down from the ancestor. The opposite is divergent evolution, when species split and develop different traits in similar conditions, such as the finches of the Galapagos being in different colors. Coincidentally, we can find an example of convergent evolution in the study of evolution itself. The concept of “multiple discovery” is famously expressed by the fact that Charles Darwin and Alfred Russell Wallace both independently came to very similar theories of natural selection (though they had been in contact earlier). Independent co-discovery has also occurred historically for various inventions from around the world. Thus, (good and true) ideas, theories, and products express convergent evolution, not just biology. In this way, convergent evolution is playing a role in our everyday lives, in the construction of society and the technology that accelerates it. As a result, individuals, groups, and institutions around the world are evolving forms and traits that are convergent in a harmonious way. However, there are also pathological forms of divergent evolution that we must be wary of; the point is to be mutually adaptive as a civilization. The concept of convergent evolution is the scaffolding to help us understand the evolution of society and the imminent transformation. Smart cities are a key attractor point for the current paradigm shift as they will 227

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come to express global collective intelligence, ecological resiliency, and optimizing human well-being. By observing convergent evolution in nature and in ourselves, it allows us to reestablish our connection to nature through reflection. And by anticipating common threats, such as climate change, we can converge on solutions while working together in decentralized ways.

Convergence theory of society The idea of the convergence of society goes back to 18th- and 19th-century social philosophers including Tocqueville, Toennies, Maine, Marx, Spencer, Weber, and Durkheim. The notion that societies move toward a condition of similarity, that they converge in one or more respect, is a common feature of their various theories of social change. The sociological discourse from the 1960s onward, modernization theories largely connected economic development with the convergence of sociopolitical forms. A most stark example might be how birthrates predictably decline and stabilize as an economy becomes enriched and its citizens more affluent. The idea of the harmonization of living standards in post-war Europe has been explored in Convergence in Social Welfare Systems (Bouget, 2006) to great effect. More generally, advanced economies have transitioned from agriculture and manufacturing to service and knowledgebased industries. Convergence theories also explain how patterns of industrial organization and socioeconomic stratification are similar across capitalist and communist societies. Some avant-garde theories postulate how all societies go through various development stages, such as from premodern, to modern, to postmodern, to metamodern (see Hanzi Freinacht, 2017). According to this principle of convergence, societies will eventually develop similar forms, whether independently or cooperatively. These ideas are part and parcel of the current paradigm shift we are going through as a civilization. Through the convergence of societies and ideas about them, East and West worldviews are being synthesized into a global “collective intelligence.” The word converge means to “incline together,” and that is what many cities are doing by adopting global standards and protocols. Going beyond conventional agreements and treaties, ISO for smart cities is attempting to develop standards for cities to adopt similar benchmarks for efficiency, sustainability, and resiliency. The City Protocol is another example of the attempt to develop an international method. It leverages new ICTs and new modes of leadership and engagement to improve performance across environmental, economic, infrastructural, and quality-of-life measures. It is driven by the reality that cities are changing complex systems with their own metabolism, and the idea that antifragility and self-repair functions need to be built into the design.

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The convergence of science, technology, and nature Science and technology are evolving (along with nature), but also convergentedivergent (spin-offs). This sense of convergence refers to the combination and intersection of scientific subjects and tools, and their increasing overlap with nature. Past research of convergence has focused on Nanotech, Biotech, ICT, and Cognitive science (NBIC). However, the ongoing convergence of science, technology, and nature are of a greater scale, impact, and difficulty. Anthropogenic climate change is accelerating under our current way of life, while efforts to thwart it are hampered by policies that are ideological, co-opted, or just plain ineffectual. Much of “science” is doing the bidding of the corporate sector rather than addressing public needs or environmental concerns. This leads to the dominance and exploitation of nature, rather than symbiosis with it. For better or worse, the globalization of science and technology is further mapping, quantifying, integrating “nature” into its valuations. In climate change debates, science is given a status secondary to power interests, and thus it fails to provide the objective certainty is intended for. Our inseparability from nature evolved in our deep past is being thrust back upon us by climate change. Our deep interdependence with the web of nature becomes more exposed and apparent the more we grow and abuse the relationship. Following the body analogy, as our global society evolves we start to realize the body parts have always been connected, but not in healthy homeostasis. Rather, resources and wealth have been extracted largely from the “Global South” and thus asymmetrical power relations have dominated the world order. The vital organs have been at war with each other, within and across societies. Meanwhile, the cancer of war has been posing as an indispensable organ of its own kind; one that perhaps was once necessary but is now obsolescent, and must be made vestigial. Smart cities are where science, technology, and nature converge in exciting ways, necessarily converging on climate change resiliency. The ideas of permaculture and the genre of solarpunk converge on the optimization of form and function for a sustainable future. As technology evolves, it potentially brings us closer to nature, but only if we harness that ability. The convergence of science, technology, and nature represents an opportunity to minimize human fallibility in the equation. The convergence theory proposition will optimize tech adoption, city-planning, and proper determination of “initial values.”

Convergence in knowledge, technology, and society The convergence of knowledge, technology, and society (CKTS) refers to the integration and holism of these forces. For knowledge specifically it is akin to the concept of “consilience” (aka “convergence of evidence”) which means “jumping together.” Consilience is also the title of E.O. Wilson’s (2008) book, which is both

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a descriptive and normative call for the “unity of knowledge.” We have discussed the convergences of technology and society above, but by including knowledge in this particular arrangement it takes on greater significance. In the comprehensive book Convergence of Knowledge, Technology and Society (Roco et al, 2014), the authors contend that convergence will have such a profound impact on our future knowledge, similar to the engine in the Industrial Revolution. Three stages of convergence are highlighted. The first stage was the general convergence of scientific and engineering fields brought together by research in nanotechnology; they include “biology, chemistry, condensed matter physics, materials science, electrical engineering, medicine, and others.” The second stage is the NBIC fields, which are convergent through the use of “shared abstractions from information science and system theory” such as atoms, bits, synapses, and DNA (p. xv, Roco et al). CKTS is the third stage of convergence, building on the second (NBIC). As convergence accelerates through the third stage and applied across three scalesdthe human, societal, and globaldit increases the potential to solve metaproblems that isolated capabilities cannot. The four essential platforms for CKTS convergence are “(1) ‘NBIC’ foundational tools, (2) human-scale activities, (3) Earth-scale environmental systems, and (4) societal-scale platform.” Knowledge is the linchpin of it all in this delicate transition phase. Where would we be without knowledge? AI does not yet have the collective intelligence we want it to, and nor do people have the knowledge to use it wisely. The convergence of knowledge is an imperative that touches on education, media, politics, and information systems. This book is in part of expanding specialized knowledge on the topic, as well as highlighting how knowledge is mediated through technology and society. The question of knowledge is one of veracity and epistemic access (what is true, and how can we know?), which is enabled (or disabled) through technology. With augmented reality (AR) and VR, AI-driven smart cities can and should be educational and enlightening experiences.

Digital convergence Digital convergence is the integration of services, content, and distribution under one technology or business eco-system. Platforms like Netflix, Google Play, and iTunes are prime examples. Digital convergence also includes the congruence of regulations, technology, culture, business, etc. In general, digital convergence is most evident by the internet as the ever-widening platform for media, text, video, and communication. Media convergence is a subset of the digital that redefines our information economies and intellectual appetites. Through new technologies and mediums, the media landscape is constantly shifting and being rewritten by both top-down (producer) and bottom-up (consumer) processes. As a result, our patterns of learning, creation, consumption, and interaction are all being reshaped. Through AI assistants like Siri, Cortana, Google Assistant, and Alexa, digital convergence reaches

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into our homes and transforms our lives. Likewise, we are able to reach back into the digital world and influence it. The key is optimizing the signal-to-noise ratio of information. Currently, it is difficult for an idea as bold and lucid as OS Planet Earth to even emerge in the public or policy discourse. But the digital revolution makes us all global citizens, with rights and responsibilities as such. Social media and frictionless information sharing has both polarizing and positive integrating effects. Understanding the theory of convergence allows us to reduce the former and enhance the latter. Digital convergence is the process by which more platforms for global news, knowledge dissemination, and systems modeling will come online, but it requires our committed and cautious guidance.

Organizational convergence Traditional organizations are based on the division of labor and specialization, so large corporations cannot operate like a holistic organism. Notwithstanding the scourge of monopoly, traditional “[i]ndustries were broken into fairly neat sets of competitors” so companies could specialize and businesses could be more manageable (Wind et al, 2002). Because ICT drives convergence and goes against the grain of traditional companies, adapt or die is the mantra for the 21st century. As such, many firms are practicing organizational convergence (Lee et al, 2010). One way is how organizations seek increasingly abstract platforms such as blockchain, which can be a new foundational protocol to do (trustworthy) business on. Businesses now strive for symphonic integration, rather than horizontal or vertical. The lines between industriesd for example, between finance and supply chainsdare being blurred. In a nutshell, organizational convergence is the universal pursuit of best practices and efficient operations. It is a process of innovation enabled through ICT, including the techniques of open sourcing, outsourcing, new products and services, and alliances. Convergence revolution is at the center of the whirlwind of global change. Lee et al, The Impact of Convergence on Organizational Innovation, 2010 The source of competitive advantage has progressed from economies of scale, to economies of scale and scope, to economies of scale-scope-expertise, to new economies of scale-scope-expertise-convergence. The primary innovative strategy has gradually gravitated from exploitation of current competencies to exploration of new competencies. Lee et al, The Impact of Convergence on Organizational Innovation, 2010

Organizational convergence can help nonprofits, businesses, governments, and institutions alike to innovate in evolutionary congruent ways. The great realization of organizations will be the holism and convergence between the reality on the ground and the abstract systems that manage it. The dark side of

232 Appendix

organizational convergence is the conglomeration of corporate entities. Fewer and fewer individuals and companies own an ever greater share of resources and value in the world. This denies convergence at the micro-level of society. Proper understanding of convergence brings ourselves and organizations back into harmony and symbiosis with nature and the living systems with which we are inextricably connected.

Further Reading Baimbridge, M., Litsios, I., Jackson, K., Lee, U., 2017. The Segmentation of Europe: Convergence or Divergence between Core and Periphery? Springer. Bainbridge, W., 2016. Virtual Sociocultural Convergence. Springer. Baldwin, R., 2019. The Great Convergence: Information Technology and the New Globalization. The Belknap Press of Harvard University Press. Bieber, T., 2016. Soft Governance, International Organizations and Education Policy Convergence. Palgrave Macmillan. Boni, M., 2017. World Building: Transmedia, Fans, Industries. Amsterdam University Press. Bunge, M., 2003. Emergence and Convergence: Qualitative Novelty and the Unity of Knowledge (Toronto Studies in Philosophy). University of Toronto Press. Freinacht, H., 2019. Nordic Ideology: A Metamodern Guide to Politics, Book Two. Metamoderna ApS. Grinin, L., Korotayev, A., 2015. Great Divergence and Great Convergence. Springer International Publishing, Cham. Hassler-Forest, D., Nicklas, P., 2015. The Politics of Adaptation: Media Convergence and Ideology. Springer. Hell, J., 2009. ‘Katechon: Carl Schmitt’s imperial theology and the ruins of the future’. The Germanic Review 84 (4), 283e326. Hendricks, V., 2011. The Convergence of Scientific Knowledge. Springer. Karthik, S., Paul, A., Karthikeyan, N., 2018. Deep Learning Innovations and Their Convergence with Big Data. Jenkins, H., 2016. Convergence Culture: Where Old and New Media Collide. New York University Press. Katsikides, S., Hanappi, G., 2016. Society and Economics in Europe: Disparity versus Convergence? Springer. Lee, S., Olson, D., Trimi, S., 2010. The impact of convergence on organizational innovation. Organizational Dynamics 39 (3), 218e225. Madni, A., 2018. Transdisciplinary Systems Engineering: Exploiting Convergence in a HyperConnected World. Springer. Rougier, E., Combarnous, F., 2017. The Diversity of Emerging Capitalisms in Developing Countries: Globalization, Institutional Convergence and Experimentation. Springer. Sparviero, S., Peil, C., Balbi, G., 2017. Media Convergence and Deconvergence. Springer. ¨ nver, H., 2018. Global Networking, Communication and Culture: Conflict or Convergence? U Springer. Volkov, A., 2018. Smart City: Convergent Socio-Cyber-Physical Complex. MATEC Web of Conferences 251, 03065. https://doi.org/10.1051/matecconf/201825103065. Woodley, D., 2015. Globalization and Capitalist Geopolitics. Taylor and Francis. Wilson, E., 1998. Consilience. Alfred Knopf.

Glossary of Terms

Abstraction The principle of design efficiency, reducing redundancy, distilling information, and conceptual organization. Abstraction is the central concept in computer science and Artificial Intelligence, as well as the core cognitive process in human thinking and experience, thereby necessitating a bridge between the different expressions and related forms as it correlates to social-material processes, representation, and more. All cognitive and material processes are mediated through abstraction, as is design, which is convenient. Accelerationism The cultural and philosophical movement associated with high technology and late capitalism, with the belief that the only way out is through acceleration and that convergence on a technological singularity is one of the main organizing principles. Artificial Intelligence Artificial Intelligence is the human created technological simulation of human intelligence, based on learning algorithms and computation. The idea of Artificial Intelligence evolves with technology and culture, and there is convergence towards a new synthesis for technological integration, ubiquitous knowledge, automating production, and collective intelligence. Ambient connectivity The ubiquitous access to the Internet enabled by expanding communication infrastructure. New ICT is convergent on higher bandwidth, faster speeds, wireless connection, and uninterrupted service resulting in ambient connectivity. Autopoiesis The process of self-generation innate to living systems, most demonstrated in the process of cell division (mitosis). At larger scales, it refers to networks of processes that create and sustain the dynamic autonomy of the living system. At the city scale and beyond, the entire system is alive and self-generating, revealed in the constant flows and metabolic rhythms of people, energy, and resources. Biomimicry The imitation and replication of nature’s elegant living systems and particular tools or techniques built into the life form, such as photosynthesis. Biomimicry is inspiring biological solutions to technical problems. Citizen participation The user as citizen has input and influence into the city as a living lab, providing feedback and innovation for the evolving city. Direct human contributions to the operating system are more qualitative than the collective intelligence, so citizen participation and user feedback are essential to propel better machine learning solutions. City DNA The complex composition of each city, the unique history, geography, demographics, and different opportunities and challenges posed due to technological and economic levels. Collective intelligence Group wisdom that emerges from collaboration, competition, coordination, and consensus, synergized through knowledge workers and the technical and information architecture. Mass peer review and crowdsourcing are techniques that utilize collective intelligence. In the context of smart cities, collective intelligence is the citywide interface that adapts to the behaviors of the city. Convergence Generally when two or more things come together. Convergent evolution is when the same features or traits evolve in different species under similar conditions. Through accelerating globalization, we are witnessing ubiquitous forms of convergence in technology, knowledge, society, organizations, and theory itself.

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234 Glossary of Terms Convergent Urban Interface The interoperable streamlined platform that fully integrates the city simulation and services with the actual urban environment in a ubiquitous sensory environment and a seamless interface provided through devices and the physical architecture as well. Data visualization The graphical representation of datasets, as static or dynamic media sometimes in the form of infographics, involving communicating the meaning and relevance of such data to the reader or viewer. Deep learning The processes and ability to imitate and emulate a human brain in data processing and pattern recognition, as a deeper manifestation of machine learning and Artificial Intelligence, using networks that learn unsupervised and from unstructured data. Deep learning has evolved with the availability of Big Data. Design thinking The broad interdisciplinary methodology of creative processes involved in design and the particular open-ended mindset of creation and process innovation of products, services, or systems. Determinism (technological/biological) The theory that attributes technology to be a main causal factor driving society’s evolution and that our biological and technological evolution are linked through such relationships. Divergence The opposite of convergence, divergent evolution is when different features or traits evolve under different conditions. Meaning things coming apart, divergence can manifest in negative ways such as political polarization and extreme (and varied) levels of inequality. Emergence An evolutionary process where complexity has reached a level where interaction effects produce something new and unexplainable by merely the sum of parts. It speaks to an understanding of deep ecology, self-organizing autopoietic systems, personal development, and social movements. Generative design An iterative process of design, where variations are simulated to fine-tune the ideal values and relations in a design artifact. This way it is possible to enter design constraints that automatically generate certain outcomes within those parameters. Globalization The multifaceted macro-evolutionary process of global integration that is being accelerated by expanding populations and technological breakthroughs in connectivity, energy, logistics, trade, travel, etc. Hypermodernism The hypertrophied version of postmodernism, where hyperreality enabled by technology becomes dominant. The hyperintelligence of the system is on a destructive path, and the sense of alienation and nihilism is heightened. Privileges the “rugged individual” to pathological point. Interface The medium or dashboard of a machine that interacts with the user. In computing it is typically a screen presenting an operating system, which allows interfacing between the user and the Internet or a program, for example. In the context of the city, it enables access to services. Living lab An innovation ecosystem within a city, typically in a university or business district, that puts user experience into a feedback loop as an interactive innovation complex. The living lab model is a space to experiment with flexible and agile forms of work and computing that transcend the current structural limitations of city life. The living lab is in constant transformation, modeled after nature’s living systems. A singular format or approach will not keep pace with societal change and planetary requirements, so living labs must themselves be as alive as the surrounding culture, market, and technological ecosystem they serve. Machine learning The ability of computers to learn a task without explicit instructions by using algorithms, statistical models, inference, and training data. It is a subset of Artificial Intelligence. Metamodernism The alternative to the hypermodern black hole, seeking to amend the blindspots of postmodernism and definitively solve problems of ideology, ecology, and

Glossary of Terms

235

economy. Advocates for “commodious individualism” and the “dividual,” a more socially constituted sense of self through transpersonal psychology. Affirms technology and architecture that gives back to its environment and communities. Metadesign The emerging paradigm of design thinking that broadens the scope to create social, economic, and technical infrastructure for new modalities of co-design to emerge and further recursively innovate. Operating system The software platform that manages the hardware, system resources, and interfaces with devices, services, settings, and programs. The operating system comprises programming languages and aesthetics to form the basic template for the system to load other programs and navigate different levels of abstraction. Postmodernism The historical era, intellectual paradigm, and architectural aesthetic dominant in the second half of the 20th century, whereas postmodern economics denotes the “postindustrial society,” specialization, and information processing. Postmodernism bifurcates into hypermodernism and metamodernism in the late 20th century. Sentience The capacity to experience subjectivity, feelings, and perceptions. The notion is invoked as part of expanding our circle of value to include more life forms. As we recognize the sentience of the environment and animals, our technology is also evolving ambient sentience. Self-regulation The ability for an organism or community to self-organize and autopoietically realize its functions, based on the principle of the living planet and synergy between life forms and environments. Singularity The speculated moment when artificial superintelligence (ASI) is achieved and at which trends seem to be converging rapidly. This concept can be considered loosely as the potentials of many technological and social singularities which have already begun. With a projected date around 2040, the next 20 years are crucial because it makes a difference if we are on a hypermodern or metamodern path. Urban sensing A function of the new intelligent urban interface that collects data and informs the systems of behaviors that can assist in regulating patterns, such as traffic and pedestrian flow. Urban interaction A new emerging hybrid practice that attempts to facilitate the process of developing and managing a collective intelligent, self-regulating city, through a ubiquitous smart city interface. Urban media Represents the multiple media communication types and content formats that stream the city as a living organism by revealing the inherent behaviors and shape the culture of the system as a dynamic flow and landscape. User experience (UX) A person’s subjective emotions, attitudes, and interactions in relation to the product or service in question. This varies dynamically between people and over time. Urban user experience (UUX) The application of UX on the urban experience scale to provide a collective experience within universal guidelines and customizable options. As urban sentience evolves, the UUX can be more nuanced and precise with higher definition and context awareness of the city interface and user experience.

Index ‘Note: Page numbers followed by “f ” indicate figures and “t” indicates tables.’

A

AI. See Artificial intelligence (AI) “Architectonics of simulation”, 73 Artificial Intelligence (AI), 25e26, 141e142, 217 capabilities, 169 computer vision, 170 convergent applications, 172 critical AI capabilities, 170 functional hierarchy, 168f functionality, 169e170 hierarchy framework, 173 machine learning, 171 natural language processing, 170e171 predictive analytics, 171 robotics, 171e172 Automation revolution unfolding, 223

B Bandwidth, 99e101, 100f Beijing, 10e11 Biomimicry, 29 human anatomy, 30e31 Blockchain, 206e207 Boundaries bridging global ecosystem, 42, 49, 54, 75, 120e121, 143, 151, 179, 182, 206e207, 219e220 Brain sensorial representations, 32, 32f Business exchange, 207e209 channeling on demand, 209 flow, 207e208 Business models, smart city artificial intelligence market, 194e197 business exchange, 207e209 channeling on demand, 209 flow, 207e208 collaboration, 211 convergent economies, 210 innovation-led economics, 197e204 biomimicry, India, 202e203 ChinaeUSA race, 201e203 driver, 198e199

education, China, 202 India, 201e203 intellectual property, 199e201 renewable energy, USA, 202 living labs model, 203e204 Marxist analysis, 195e196 new economy, 204e207 blockchain, 206e207 digital currency, 206 holochain, 207 planetary accounting, 205 strategy shift, 205e206 risk mitigation, 194e195 self-regulating systems, 211 smart city movement marketing, 196e197

C Cities/technologies city DNA narratives, 9e17 Beijing, 10e11 Dubai, 13e14 London, 11e12 Masdar, 15e16 NEOM, 16e17 New York, 12e13 Songdo, 14e15 convergence theory, 21e22 applies to smart cities, 21e22 evolution and integration of technology, AI and cities, 4e9 and potential for convergence, 17e21 smart city concept and context, 2e4 Citizen-centered approach, 56e57 Citizen centric cities, 56e59 Citizen engagement, 128f City anatomy, 20 City DNA narratives, 9e17, 74 Beijing, 10e11 data collection and mapping, 39e43 Dubai, 13e14 global brands/destinations, 37e39 London, 11e12

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238 Index City DNA narratives (Continued ) mapping as basis of smart cities, 41e42 Masdar, 15e16 NEOM, 16e17 New York, 12e13 real-time behavioral data, 42e43 Songdo, 14e15 City ecosystem, 78e80, 79f City operating systems, 69e70, 85e87 convergence-based, 87e92 design considerations, 76 information architecture and technical architecture, 77e78 language and representation of systems architecture, 71e78 metaarchitecture, 74 planning considerations, 74e76 representational hierarchy of, 78e84 smart city, 73e74 City systems, infrastructure dimension, 19 Citywide interface boundaries bridging global ecosystem, 120e121 citizen engagement, 128f city OS extension, 118e119 design practices, 128e132 ecosystem, 120e121, 120f functions, 121e128, 122f hyperlocal ecosystem, 120e121 infrastructure, 121 method, 129 scale, 120e121 theory, 129 urban interaction, 126e128 urban interaction design, 131 urban media, 124e125 urban navigation, 122e124, 123f urban sensing, 125e126 urban simulation and gaming, 131e132 urban user experience, 129e130 Cloud computing, 166e167 Co-design, 56 Co-development/open source/open data, 90e91 Collaboration, 211 Collective intelligence interface, 70 definition, 133 dynamic frames of reference, 134 human to human, 134e135 human to machine, 134e135 machine to machine, 134e135 machine to nature, 134e135

participation/interaction, 133e134 user typologies, 135f Connectivity anatomy, 103e108 backbone, 106e107 bandwidth, 99e101, 100f brain, 104e105, 104f characteristics, 98 definition, 96 electromagnetic patterns, 99e100 electromagnetic spectrum, 99e101, 100f evolution, 97e99, 97t evolutionary algorithms, 102e103 frequencies, 99e101, 100f human body, 103e104 human energy fields, 99e100 integrated networks/services, 108e112 living organisms, 97 mobile connectivity, 108 neural networks, 103e104 next-generation networks (NGNs), 99 organic models, 105e106, 105f radio frequency machine learning systems, 101e102, 102f sensorial layer, 107e108 telecommunication networks, 106e107, 106f Convergence, 144f, 145e146 application method, 65e66, 66f design method, 65, 65f development method, 64, 64f hierarchy, 225f methodologies, 60e66 generative design and metadesign, 63e64 human machine collaboration, 60e61 information architecture and philosophy of information, 62 real-time visualization, 61e62 real world/virtual simulation, 62e63 operating systems, 87e92 Convergence theory, 21e22 applies to smart cities, 21e22 consequences, 224e225 Convergence urban interface (CUI), 135e137, 135f, 224 AI, 137 big data, 137 pattern recognition, 137 sensors, 137 total interface solution, 137 Convergent economies, 210

Index CUI. See Convergence urban interface (CUI) Customized algorithms, 221e222

D Data collection and mapping, 39e43 Design thinking, 59e60 machine learning, 65, 65f Digital currency, 206 Diverse theories of convergence, 218 Double-edged sword technology, 218

E East-west Collaboration, 219e222 Economic convergence, 190 Ecosystem, 120e121, 120f Edge computing, 166e167, 220e221 Electromagnetic spectrum, 99e101, 100f Enablers (hardware infrastructure), 165e167, 166f Environmental convergence, 188e189 European UnioneChina collaboration, 7 Evolutionary algorithms (EAs), 102e103

239

ChinaeUSA race, 201e203 driver, 198e199 education, China, 202 India, 201e203 intellectual property, 199e201 renewable energy, USA, 202 Integrated networks/services, 108e112 connectivity singularity, 111 convergence connectivity, 109e110, 110f Industry 4.0, 108e109 intelligent connectivity, 110e111 smart objects, 111e112, 112f Intellectual property, 199e201 Interface. See Citywide interface

L

Generative design and metadesign, 63e64 Global brands/destinations, 37e39 Governmental convergence, 189

Living convergence, 190e191 Living labs, cities as, 53e54 Living labs model, 203e204 Living organisms, 25e33, 97 city DNA, 36e39 data collection and mapping, 39e43 global brands/destinations, 37e39 mapping as basis of smart cities, 41e42 real-time behavioral data, 42e43 concepts of space and representation, 26e27 principles of collective intelligence, 33e36

H

M

G

Holochain, 207 Human anatomy, biomimicry, 30e31 Human civilization, 19e20 Human factor, 222e223 Human functions to smart cities, 33, 35t Human machine collaboration, 60e61 Human patterns and constructs, 19 Human/technology systems, 33, 34t Hyper-accelerated cities, 8 Hyperlocal ecosystem, 120e121

Machine learning (ML), 91, 221e222 Macro-scale feedback system, 34e36 Marxist analysis, 195e196 Meta-architecture, 73e74 Meta-convergence, 218e219, 224 Mobile Ad Hoc Networks (MANET), 108 Mobile connectivity, 108 Mobility convergence, 189e190 Multilevel perspective (MLP) modeling, 142e145, 142f

I

N

Industrializing Six (“I6”), 220 4th Industrial revolution, 219 Information architecture and philosophy of information, 62 Initial value problem, 5 Innovation/innovation-driven cities, 54e56 Innovation-led economics, 197e204 biomimicry, India, 202e203

Neural networks, 103e104 Next-generation networks (NGNs), 99 Nordic model, 57

O Open-innovation ecosystem, 53 Operating systems (OS), 83e84, 220e221 Organic models, 105e106, 105f

240 Index OS behavioral typologies, 81e82 Outcome-based modeling, 51, 65e66

P People convergence, 190 Physical/environment dimension, 18e19 Planetary accounting, 205 Publiceprivate partnership, 75

R Radio frequency machine learning systems, 101e102, 102f Ray Kurzweil’s Law of Accelerating Returns, 9 Real-time behavioral data, 42e43 Real-time biometric sensors, 223 Real-time visualization, 61e62 Real world/virtual simulation, 62e63 “Ring roads”, 10 Risk mitigation, 194e195

S Self-regulating systems, 28e29, 91e92, 211 Sensorial layer, 107e108 Smart cities, 2e4, 73e74 AI smart city operating systems, 167 artificial intelligence. See Artificial intelligence artificial intelligence (AI), 141e142 characteristics, 76, 77t convergence, 144f, 145e146 convergence methodologies, 60e66 convergence application method, 65e66, 66f convergence design method, 65, 65f convergence development method, 64, 64f generative design and metadesign, 63e64 human machine collaboration, 60e61 information architecture and philosophy of information, 62 real-time visualization, 61e62 real world/virtual simulation, 62e63 economic convergence, 190 edge and cloud computing, 166e167 enablers (hardware infrastructure), 165e167, 166f environmental convergence, 188e189 framework, 80e81

functional strategic objectives, 184, 186, 188 macro scale/context, 185 MESO scale/content, 185 micro scale/component, 186 smart people, 184e186 functions, 173e191, 174t 5G network infrastructure, 167 governmental convergence, 189 human functions to, 33, 35t IoT, 166 living convergence, 190e191 mapping as basis of, 41e42 mobility convergence, 189e190 movement marketing, 196e197 multilevel perspective (MLP) modeling, 142e145, 142f operating system flow, 84 people convergence, 190 planning and design of, 47e48 approaches to innovation for, 52e60 outcome-based modeling, 51 principle objectives of, 49, 49t strategic goals, 48e51 representations, 78, 78t scalable computing power, 166e167 smart economy, 157e159, 182e184 challenges, 157e158 direction, 158 evolution, 157 macro scale/context, 182e183 MESO scale/content, 183 micro scale/component, 183 objecteactioneoutcome, 158e159 smart environment, 174e177 challenges, 150e151 directions, 151 evolution, 150 macro scale/context, 175 MESO scale/content, 177 micro scale/component, 177 objecteactioneoutcome, 151e152 strategic functional objectives, 177 smart governance challenges, 155e156 direction, 156 evolution, 155 objecteactioneoutcome, 156e157 smart government macro scale/context, 178 MESO scale/content, 178 micro scale/component, 179

Index strategic functional objectives, 179 smart living challenges, 160 direction, 160 evolution, 159e160 objecteactioneoutcome, 160e161 smart mobility, 146e149, 179e182 challenges, 147 directions, 149 evolution, 146e147 macro scale/context, 180e181 MESO scale/content, 181 micro scale/component, 181e182 objecteactioneoutcome, 149 pastepresentefuture, 146e149 strategic functional objectives, 182 smart people, 152e155 challenges, 152e153 direction, 153e154 evolution, 152 objecteactioneoutcome, 154e155 systems changes, 143e146 Strategy shift, 205e206 Superintelligence, 60e61

241

T Technological infrastructure dimension, 20 Telecommunication networks, 106e107, 106f

U Ubiquitous dimension, 20 Urban interaction, 126e128 design, 131 media, 124e125 navigation, 122e124, 123f sensing, 125e126 simulation and gaming, 131e132 user experience, 129e130 User typologies, 135f Urban user experience (UUX), 149e150, 152, 154, 157 Urban user interface (UUI), 141, 142f User experience design (UX), 139, 148e150, 201, 207, 209, 241, 245 User interface (UI), 141

W Wide-spread automation, 223e224