City Information Modelling (Urban Sustainability) [1st ed. 2024] 9819990130, 9789819990139

Table of contents :
Acknowledgements
About This Book
Contents
Editors and Contributors
1 City Information Modelling: An Insight into a New Era for the Built Environment
1 A Brief Introduction
1.1 From Building Information Modelling (BIM) to City Information Modelling (CIM)
1.2 CIM Core Components, Key Principles, and Benefits
2 Aim and Objectives of the Book
3 Structure of the Book
3.1 PART I: Concepts and Trends
3.2 PART II: Applications and Digitisation
3.3 PART III: Applications and Digitisation
References
Part I Concepts and Trends
2 City Information Modelling and Sustainable Development: The Role of CIM in Achieving Sustainable Urbanization
1 Introduction
1.1 From BIM to CIM: An Evolved Concept
2 Data Acquisition and Management for CIM
3 CIM for Sustainable Urban Planning, Design, and Management
4 CIM for Public Participation in City Decision-Making
5 Future Directions of CIM
6 Conclusion and Recommendations for Future Research
References
3 Enhancing Health Outcomes Through City Information Modeling (CIM): A Case Study of Sydney, Australia
1 Introduction
2 Background Knowledge
2.1 Urban Health
2.2 Environmental Health
2.3 Social Determinants of Health
3 Methodology
3.1 Data Collection and CIM Software
3.2 Health Indicators and Spatial Analysis
3.3 Case Study Introduction and Analysis
4 Results
5 Discussions
6 Conclusions
References
4 City Information Modeling and Its Applications: A Review
1 Introduction
2 The Brief History and Definition of CIM
3 The Structure and Modules of CIM
4 BIM–GIS Integration CIM Applications
4.1 Urban Planning
4.2 Urban Facility Management
4.3 Urban Flood Hazard Assessment
4.4 Route and Evacuation Planning
4.5 Underground Space Development and Underground Utility Management
4.6 Building Energy Analysis and Management
4.7 Other Applications
5 Concluding Remarks
References
Part II Applications and Digitisation
5 Optimizing Urban Design for Pandemics Using Reinforcement Learning and Multi-objective Optimization
1 Introduction
1.1 AI Model
1.2 Introduction of RL
2 Background
2.1 Urban Design and COVID-19 Pandemic
3 Methodology
4 Model Development
4.1 Implementing of RL in Payton
4.2 Optimizing RL for Design Solution
5 Results
6 Discussions
7 Conclusions
References
6 Sustainable Smart City Application Based on Machine Learning: A Case Study Example from the Province of Tekirdağ, Turkey
1 Introduction
2 Background Knowledge and Literature
2.1 Smart City
2.2 Machine Learning
2.3 Environmental Sustainability
3 Sustainable Smart City Application Based on Machine Learning
3.1 Study Area and Geographical Facts
3.2 The Novelty of the Study
3.3 Purposes of the Study
3.4 Case Study
4 Discussion and Implications
5 Concluding Remarks
References
7 The Role of City Information Modelling (CIM) in Evaluating the Spatial Correlation Between Vegetation Index Changes and Heat Island Severity in the Last Two Decades in Tehran Metropolis
1 Introduction
2 Theoretical Background
3 Methodology
4 Area of Study
5 Findings
5.1 Estimation of Surface Temperature and NDVI Using ASTER
5.2 Estimation of Surface Temperature and NDVI Using Landsat 8
6 Discussion and Conclusion
References
8 Exploiting Advantages of VPL in City Information Modelling for Rapid Digital Urban Surveying and Structural Analysis
1 Introduction
2 Background Knowledge
2.1 Digital Urban Survey
2.2 VPL and Explicit Parametric Modelling
2.3 City Information Modelling Approaches
3 Methodology Proposed | Survey/Scan-to-CIM
3.1 Survey-to-CIM
3.2 Scan-to-CIM AI-Based Workflow
3.3 CIM-to-FEM
4 Applications
4.1 Survey-to-CIM Case Study | Medieval City Block in Catania
4.2 Scan-to-CIM Case Study and CityGH Implementation
5 Results and Discussion
6 Conclusions and Future Developments
References
Part III Frameworks and Practices
9 Towards Adaptive and Resilient Strategies Using Digital Twins: A Study on the Port of Tyne, UK
1 Introduction
2 Research Background
2.1 Urban Metabolism of Port Cities
2.2 Resilience Thinking in Modern Ports
2.3 Digital Twin Revolution for Ports
3 Case Study: The Port of Tyne, Newcastle upon Tyne
3.1 The Transformation of the Port of Tyne
3.2 Handling Process of Biomass Cargo
4 Research Methodology and Results
4.1 Overview of the Discrete Event Simulation (DES)
4.2 Process of Digital Twinning
4.3 Implementation
4.4 Baseline Operation Validation
4.5 Application for Onshore Energy Supply Planning
5 Conclusions
References
10 Ecosystem Institutional Maturity: Perspectives for CIM in Urban Management and Planning in Curitiba, Brazil
1 Introduction
2 Institutional Maturity in Sociotechnical Ecosystems
2.1 Institutionalization
2.2 Legitimacy
2.3 Taken-For-Grantedness
3 Components of Institutional Maturity in Urban Technological Ecosystems
3.1 Hypothesis About Institutional Maturity in Urban Technological Ecosystems
4 Research Methods
4.1 Delimitation of the Analyzed Context
4.2 Definition of Population and Analyzed Sampling
4.3 Data Collection
4.4 Data Analysis
5 Results, Discussion, and Empirical Implications
5.1 Results
5.2 Empirical, Practical, and Theoretical Implications
5.3 Institutional Maturity of the BIM/CIM/GIS Ecosystem of Curitiba
6 Conclusion
References
11 The Use of City Information Modelling (CIM) for Realizing Zero Energy Community: A Path Towards Carbon Neutrality
1 Introduction
2 Research Methodology
3 Zero Energy Communities (ZECs)
4 City Information Modelling (CIM)
4.1 Elements of CIM
4.2 CIM Applications
4.3 Challenges for CIM Uptake
5 Application of CIM for Delivering ZECs
6 Concluding Remarks and Future Directions
References
12 Conclusions and the Future of City Information Modelling (CIM)
1 Introduction
2 Sectoral Implications of CIM
3 Future Outlooks and Conclusions
References

Citation preview

Urban Sustainability

Ali Cheshmehzangi  Michael Batty  Zaheer Allam  David S. Jones Editors

City Information Modelling

Urban Sustainability Editor-in-Chief Ali Cheshmehzangi , Qingdao City University, Qingdao, Shandong, China

The Urban Sustainability Book Series is a valuable resource for sustainability and urban-related education and research. It offers an inter-disciplinary platform covering all four areas of practice, policy, education, research, and their nexus. The publications in this series are related to critical areas of sustainability, urban studies, planning, and urban geography. This book series aims to put together cutting-edge research findings linked to the overarching field of urban sustainability. The scope and nature of the topic are broad and interdisciplinary and bring together various associated disciplines from sustainable development, environmental sciences, urbanism, etc. With many advanced research findings in the field, there is a need to put together various discussions and contributions on specific sustainability fields, covering a good range of topics on sustainable development, sustainable urbanism, and urban sustainability. Despite the broad range of issues, we note the importance of practical and policyoriented directions, extending the literature and directions and pathways towards achieving urban sustainability. The series will appeal to urbanists, geographers, planners, engineers, architects, governmental authorities, policymakers, researchers of all levels, and to all of those interested in a wide-ranging overview of urban sustainability and its associated fields. The series includes monographs and edited volumes, covering a range of topics under the urban sustainability topic, which can also be used for teaching materials.

Ali Cheshmehzangi · Michael Batty · Zaheer Allam · David S. Jones Editors

City Information Modelling

Editors Ali Cheshmehzangi Qingdao City University Qingdao, Shandong, China School of Architecture, Design and Planning The University of Queensland Brisbane, QLD, Australia

Michael Batty Centre for Advanced Spatial Analysis University College London London, UK David S. Jones Monash University Melbourne, VIC, Australia

Zaheer Allam Charles Telfair Campus Curtin Mauritius, Mauritius

ISSN 2731-6483 ISSN 2731-6491 (electronic) Urban Sustainability ISBN 978-981-99-9013-9 ISBN 978-981-99-9014-6 (eBook) https://doi.org/10.1007/978-981-99-9014-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Paper in this product is recyclable.

We collectively dedicate this book to those scholars, practitioners, and activists who work restlessly on urban innovation and innovative sustainable solutions.

Acknowledgements

We would like to recognize the hard work of all authors and contributors to this exciting book project. We respect their devotion, desire, and diligence in writing these opportune chapters. While we met a few of them in our workshops and events of the recent two years, we hope we get more opportunities to meet all of them in person in the near future. Their aspiration, allegiance, and ambitious ideas are highly appreciated. We also thank all external collaborators, industry partners, coorganizers, and participants of our iCIM workshop series, held online and onsite in 2021 and 2022. Editors’ contributions to this volume were distributed based on their expertise and time availability. Ali Cheshmehzangi led the project with several events and stakeholder engagements. There were a few rounds of open calls for chapter contributions through which the editors received abstracts and reviewed them collectively. Zaheer Allam and David S. Jones worked with Ali to review submitted chapters and provided feedback to contributing authors. All three editors worked closely with contributing authors in all three book parts. Ali and Zaheer led and drafted the first and last chapters of the book, i.e., the introduction and conclusion. Michael Batty worked on the book’s foreword, provided support, and reviewed the submitted contents. Ali coordinated the project between all four country locations in Australia, China, Mauritius, and the UK. We confirm all editors have supported this meaningful initiative to ensure academic contributions to City Information Modelling (CIM) research are updated and advancing progressively. Ali Cheshmehzangi acknowledges and appreciates the support from the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) and the Network for Education and Research on Peace and Sustainability (NERPS) at Hiroshima University, Japan.

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About This Book

This is the first book focused on City Information Modelling (CIM) that puts together a collection of recent studies related to concepts and trends in CIM, application and digitization processes/methods, and frameworks and practices of CIM. This emerging topic is important to various research and practice under sectors of the built environment, civil engineering, urban planning, urban design, and urban management. CIM aligns well with smart cities, data-driven urban analytics and optimization, information-based city planning, and future development paradigms. City Information Modelling provides global case study examples in three parts. At first, the contributors offer several examples of ‘Concepts and Trends’, where CIM is explored further in urban management, urban sustainability, and big data studies. In the second part, the book offers various examples of application and digitization processes or methods related to urban planning and design practices. In the third part, the contributors delve into several examples of CIM frameworks and practices critical to contemporary research, planning and design paradigms, and future practices. This collection is a niche resource for various stakeholders, particularly urban scientists, urban analytics, urban practitioners, and researchers. It will also be a valuable collection for those who work with information-based models, urban optimization models, and big data analytics, particularly from policy and practice perspectives. The findings of this collection help direct future research in CIM and suggest opportunities for big-data urban research, integrated urban models, and holistic frameworks in sustainable cities, smart cities, and future cities. Ali Cheshmehzangi Michael Batty Zaheer Allam David S. Jones

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Contents

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City Information Modelling: An Insight into a New Era for the Built Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ali Cheshmehzangi, Michael Batty, Zaheer Allam, and David S. Jones

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Part I Concepts and Trends 2

3

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City Information Modelling and Sustainable Development: The Role of CIM in Achieving Sustainable Urbanization . . . . . . . . . . Hadi Soltanifard, Reza Farhadi, and Hossein Mansourian Enhancing Health Outcomes Through City Information Modeling (CIM): A Case Study of Sydney, Australia . . . . . . . . . . . . . . Mohammad Anvar Adibhesami, Hirou Karimi, Borhan Sepehri, and Amirmohamad Parvanehdehkordi City Information Modeling and Its Applications: A Review . . . . . . . Xiang Zhang

Part II 5

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17

33

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Applications and Digitisation

Optimizing Urban Design for Pandemics Using Reinforcement Learning and Multi-objective Optimization . . . . . . . . . . . . . . . . . . . . . . Mohammad Anvar Adibhesami, Hirou Karimi, and Borhan Sepehri Sustainable Smart City Application Based on Machine Learning: A Case Study Example from the Province of Tekirda˘g, Turkey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Serhat Yılmaz, Hasan Volkan Oral, and Hasan Saygın

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The Role of City Information Modelling (CIM) in Evaluating the Spatial Correlation Between Vegetation Index Changes and Heat Island Severity in the Last Two Decades in Tehran Metropolis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Hadi RezaeiRad and Narges Afzali

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Exploiting Advantages of VPL in City Information Modelling for Rapid Digital Urban Surveying and Structural Analysis . . . . . . . 139 Federico Mario La Russa

Part III Frameworks and Practices 9

Towards Adaptive and Resilient Strategies Using Digital Twins: A Study on the Port of Tyne, UK . . . . . . . . . . . . . . . . . . . . . . . . . 165 Jiayi Jin and Mingyu Zhu

10 Ecosystem Institutional Maturity: Perspectives for CIM in Urban Management and Planning in Curitiba, Brazil . . . . . . . . . . 185 Augusto Pimentel Pereira and Mario Prokopiuk 11 The Use of City Information Modelling (CIM) for Realizing Zero Energy Community: A Path Towards Carbon Neutrality . . . . . 215 Hossein Omrany, Amirhosein Ghaffarianhoseini, Ali Ghaffarianhoseini, Kamal Dhawan, Abdulbasit Almhafdy, and Daniel Oteng 12 Conclusions and the Future of City Information Modelling (CIM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 Ali Cheshmehzangi, Michael Batty, Zaheer Allam, and David S. Jones

Editors and Contributors

About the Editors Ali Cheshmehzangi is the World’s top 2% field leader, recognised by Stanford University. He is Professor of Architecture and Urban Planning and Head/Director of the Center for Innovation in Education and Research (CIER) at Qingdao City University. Over 11 years at his previous institute, Ali was Full Professor in Architecture and Urban Design, Head of the Department of Architecture and Built Environment, Founding Director of the Urban Innovation Lab, Director of Center for Sustainable Energy Technologies, Interim Head of Research Group for Sustainable Built Environment, and Director of Digital Design Lab. He was Visiting Professor and now Research Associate of the Network for Education and Research on Peace and Sustainability (NERPS), Hiroshima University, Japan. So far, Ali has published over 400 journal papers, articles, conference papers, book chapters, and reports. He has 20 other academic books focused on cities and sustainable development research areas. He has received several awards for three of his books, Eco Development in China (2018), The City in Need (2020), and China’s City Cluster Development (2022). Ali is globally known for his research on ‘urban sustainability’.

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Editors and Contributors

Michael Batty is Bartlett Professor of Planning at University College London. He is Chair of the Centre for Advanced Spatial Analysis (CASA) and a Turing Fellow in the Alan Turing Institute. He was Professor of Town Planning at the University of Wales in Cardiff in the 1980s, Director of the NCGIA at SUNY-Buffalo in the early 1990s before he set up CASA at UCL in 1995. He has worked on computer models of cities and their visualization since the 1970s and his recent publications Cities and Complexity (2005), The New Science of Cities (2013), Inventing Future Cities (2018), all published by The MIT Press, and the edited book Urban Informatics (Springer, 2021) reflect this focus on the applications of digital technologies to urban planning. He is a Fellow of the British Academy (FBA), the Royal Society (FRS), and the Academy of Social Science (FAcSS) and was awarded the CBE in the Queen’s Birthday Honours List in 2004. Zaheer Allam holds a Ph.D. in Humanities, a Master of Arts (Res), an M.B.A., and a Bachelor of Applied Science in Architectural Science from universities in Australia and the United Kingdom. Based in Mauritius, he is the Chairperson of the National Youth Environment Council (NYEC) and a board member of the Mauritius Renewable Energy Agency (MARENA) and works on a number of projects on the thematic of Smart Cities and on strategies dwelling in the increasing role of technology in Culture and the Society. Zaheer is also the African Representative of the International Society of Biourbanism (ISB), member of the Advisory Circle of the International Federation of Landscape Architects (IFLA), and a member of a number of other international bodies. Honorary Fellow at Deakin University (Australia), he holds a number of awards and commendations, including an elevation, by the President of Mauritius, to the rank of Officer of the Order of the Star and Key of the Indian Ocean, the highest distinct order of merit in Mauritius. He is the author of over 145 peer reviewed publications and author of 9 books on the subject of Smart, Sustainable, and Future Cities.

Editors and Contributors

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David S. Jones is a Professor (Research) at the Indigenous Studies Research Centre at Monash University, an Adjunct Professor at the Faculty of Art and Design at the University of Canberra, an Adjunct Associate Professor at the Cities Research Institute at Griffith University, and was Foundation Professor of Planning and Landscape Architecture at Deakin University, before more recently oversighting strategic planning and urban design for the Wadawurrung Traditional Owners Aboriginal Corporation. With academic and professional qualifications, he has taught, researched, and published extensively across urban planning, landscape architecture, cultural heritage, and Indigenous Knowledge Systems in the past 35 years. He has coauthored the Victoria Square/Tarntanyangga Regeneration Project (2017); authored the Adelaide Park Lands and Squares Cultural Landscape Assessment Study (2007); co-authored Learning Country in Landscape Architecture Indigenous Knowledge Systems, Respect and Appreciation (2021); co-authored North Gardens Indigenous Sculpture Landscape Master Plan (2019), Geelong’s Changing Landscape (2019), Re-Casting Terra Nullius Blindness (2017); and co-authored chapters to the Routledge Handbook to Landscape and Food (2018), The Handbook of Contemporary Indigenous Architecture (2018), Routledge Handbook on Historic Urban Landscapes of the Asia-Pacific (2020), and Routledge Handbook of Cultural Landscapes in the AsiaPacific (2022).

Contributors Mohammad Anvar Adibhesami School of Architecture and Environmental Design, Iran University of Science and Technology, Narmak, Tehran, Iran Narges Afzali California State University, Northridge, CA, USA Zaheer Allam Live+Smart Research Lab, School of Architecture and Built Environment, Deakin University, Geelong, VIC, Australia; Curtin Mauritius, Charles Telfair Campus, Moka, Mauritius Abdulbasit Almhafdy Department of Architecture, College of Architecture and Planning, Qassim University, Buraydah, Saudi Arabia

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Editors and Contributors

Michael Batty Centre for Advanced Spatial Analysis (CASA), University College London (UCL), London, UK Ali Cheshmehzangi School of Architecture, Qingdao City University, Qingdao, China; School of Architecture, Design and Planning, The University of Queensland, Brisbane, QLD, Australia; Network for Education and Research On Peace and Sustainability (NERPS), Hiroshima University, Hiroshima, Japan Kamal Dhawan Department of Built Environment Engineering, School of Future Environments, Auckland University of Technology, Auckland, New Zealand Reza Farhadi Department of Landscape Architecture, University of Hormozgan, Bandar Abbas, Iran Ali Ghaffarianhoseini Department of Built Environment Engineering, School of Future Environments, Auckland University of Technology, Auckland, New Zealand Amirhosein Ghaffarianhoseini Department of Built Environment Engineering, School of Future Environments, Auckland University of Technology, Auckland, New Zealand Jiayi Jin Department of Architecture and Built Environment, Northumbria University, Newcastle, UK David S. Jones Monash University, Melbourne, VIC, Australia Hirou Karimi Department of Architecture, Eastern Mediterranean University, Famagusta, North Cyprus Federico Mario La Russa Department of Civil Engineering and Architecture, University of Catania, Catania, Italy Hossein Mansourian Department of Human Geography, Faculty of Geography, University of Tehran, Tehran, Iran Hossein Omrany School of Architecture and Civil Engineering, The University of Adelaide, Adelaide, SA, Australia Hasan Volkan Oral Faculty of Engineering, Department of Civil Engineering, Istanbul Aydın University, Istanbul, Turkey Daniel Oteng School of Project Management, The University of Sydney, Sydney, NSW, Australia Amirmohamad Parvanehdehkordi Interuniversity Department of Regional and Urban Studies and Planning (DIST), Politecnico Di Torino, Torino, Italy Augusto Pimentel Pereira Architecture and Urban Planning Course, FAE Centro Universitário, Curitiba, Brazil

Editors and Contributors

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Mario Prokopiuk Graduate Program in Urban Management, Pontifícia Universidade Católica Do Paraná, Curitiba, Brazil Hadi RezaeiRad Faculty of Art and Architecture, Bu-Ali Sina University, Hamedan, Iran Hasan Saygın Application and Research Center for Advanced Studies, Istanbul Aydın University, Istanbul, Turkey Borhan Sepehri Department of Urban Planning & Design, Tarbiat Modares University, Tehran, Iran Hadi Soltanifard Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran Serhat Yılmaz Disaster Training Application, and Research Center (AFAM), Istanbul Aydın University, Istanbul, Turkey Xiang Zhang Department of Architecture, Weitzman School of Design, University of Pennsylvania, Philadelphia, PA, USA Mingyu Zhu Department of Architecture and Built Environment, Northumbria University, Newcastle, UK

Chapter 1

City Information Modelling: An Insight into a New Era for the Built Environment Ali Cheshmehzangi, Michael Batty, Zaheer Allam, and David S. Jones

Abstract In this new era of the built environment, City Information Modelling (CIM) is broadly recognized as a multidisciplinary approach that integrates various data sources, technologies, and analytical tools to support urban planning and management (and, to some extent, urban design). Its applications are diverse, multifaceted and highlight potential new directions in urbanism. From many global examples, it is evident that CIM provides a holistic view of the (existing, new, and growing) city, allowing decision-makers to make informed choices for sustainable and resilient urban development. This chapter briefly provides a brief insight into the concept of CIM. It then provides the book’s aim and objectives and a summary of its structure. Keywords City Information Modelling · CIM · Smart Cities · Applications · Cities · Sustainability

A. Cheshmehzangi (B) School of Architecture, Qingdao City University, Qingdao, China e-mail: [email protected] School of Architecture, Design and Planning, The University of Queensland, Brisbane, QLD, Australia Network for Education and Research On Peace and Sustainability (NERPS), Hiroshima University, Hiroshima, Japan M. Batty Centre for Advanced Spatial Analysis (CASA), University College London (UCL), London, UK Z. Allam Live+Smart Research Lab, School of Architecture and Built Environment, Deakin University, Geelong, VIC, Australia Curtin Mauritius, Charles Telfair Campus, Moka, Mauritius D. S. Jones Monash University, Clayton, VIC, Australia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 A. Cheshmehzangi et al. (eds.), City Information Modelling, Urban Sustainability, https://doi.org/10.1007/978-981-99-9014-6_1

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1 A Brief Introduction In this new era of the built environment, City Information Modelling (CIM) is broadly recognized as a multidisciplinary approach that integrates various data sources, technologies, and analytical tools to support urban planning and management (and, to some extent, urban design). Its applications are diverse, multifaceted and highlight potential new directions in urbanism. From many global examples, it is evident that CIM provides a holistic view of the (existing, new, and growing) city, allowing decision-makers to make informed choices for sustainable and resilient urban development. This chapter briefly provides a brief insight into the concept of CIM. It then provides the book’s aim and objectives and a summary of its structure.

1.1 From Building Information Modelling (BIM) to City Information Modelling (CIM) Developed beyond Building Information Modelling (BIM) tool(s) and practice(s) for sustainable and smart building design (Krygiel and Nies 2008; Azhar et al. 2011; Gandhi and Jupp 2013; Shadram et al. 2016; Ahmada et al. 2017), CIM has emerged as a powerful tool for urban planning and management in recent years (Burry et al. 2015; International Organization for Standardization ISO 2018). It has evolved and advanced from integrating BIM and GIS (Van Berlo and De Laat 2010), which has become a trendy approach to studying cities from the information science perspective for years. It leverages advanced technologies and data analytics to provide a comprehensive understanding of cities’ dynamics, challenges, and opportunities (Dall’O’ et al. 2020; Cheshmehzangi et al. 2021; Mozuriunaite and Gu 2021; Souza and Bueno 2022). This section presents a brief overview of the existing literature on CIM, highlighting its key concepts, principles, components, and benefits. CIM has gained considerable attention in the field of urban planning and management due to its potential to enhance decision-making processes (Souza and Bueno 2022). Najafi et al. (2023) emphasize the role of CIM in facilitating collaboration, integration, and interoperability among various stakeholders, leading to more efficient, sustainable, and inclusive cities. Other studies demonstrate that CIM enables evidence-based decision-making (Gil 2020) by providing a comprehensive and up-to-date understanding of urban systems (Pourroostaei Ardakani and Cheshmehzangi 2023a, 2023b). Thus, the principles that underpin CIM are crucial for its successful implementation, particularly for smart urban systems, big data research, system thinking methods, etc. Batty (2012) emphasizes the importance of integrating data from diverse sources, including sensor networks, government databases, social media, and citizen-generated data. This integration allows for a holistic view of cities and promotes data-driven decision-making (Cheshmehzangi and Tang 2022). Another critical principle is semantic interoperability and data standardization, which ensures that data from different domains can be effectively shared, exchanged, and

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analyzed (Kolbe and Bacharach 2006; Cheshmehzangi 2022a, 2022b). Such features allow extended applicability of CIM in urban practices, from urban design and planning to urban data management and decision-making processes.

1.2 CIM Core Components, Key Principles, and Benefits CIM consists of three core components: data collection and integration; data modeling and representation; and data analysis and decision-making (Adeline et al. 2022; Souza and Bueno 2022; Jeddoub et al. 2023). However, the existing—and somewhat limited—literature shows little indication of their defined boundaries and indicates more overlaps between these three core components. Data collection and integration involve gathering information from various sources, such as satellite imagery, sensor networks, and social media platforms, and integrating it into a unified database (Coetsee et al. 1994; El-Sheimy and Schwarz 1999; Ellum and El-Sheimy 2001; Batty 2012; Abdalla 2016; Pourroostaei Ardakani and Cheshmehzangi 2023a). Data modeling and representation focus on creating a digital twin of the city, incorporating spatial, temporal, and semantic dimensions (Deng et al. 2021; Jeddoub et al. 2023). Lastly, data analysis and decision-making employ advanced analytics and visualization techniques to extract insights and support evidence-based decision-making (Sarker 2021). These could be descriptive, diagnostic, predictive, and prescriptive data analytics (Cote 2021). However, in all cases, such methods help improve decision-making processes. CIM is guided by several fundamental principles. Firstly, it emphasizes the integration of data from diverse sources, including sensor networks, government databases, social media, and citizen-generated data (Abowd et al. 1999; Smys 2020; Amará et al. 2023). Secondly, semantic interoperability and data standardization are essential to ensure that data from different domains can be effectively shared, exchanged, and analyzed (Kolbe and Bacharach 2006; Hughes and Kalra 2023). Lastly, 3D visualization and simulation capabilities enable stakeholders (Justice Akpan and Shanker 2018) to understand the spatial and temporal aspects of urban systems, aiding in decision-making. These principles are important to develop informationbased models, beyond just virtual reality or virtual platforms/software, creating a chance for cross-disciplinary knowledge sharing and a more holistic understanding of (sustainable) urban development practices. Although still new, CIM is growing fast in both practice and research, ensuring that integration and applications of CIMbased tools are widely used and applicable to the built environment sector. Thus, as it is shown in case study chapters of the book, data or big data appear to play a significant part in making CIM work. Lastly, we argue that CIM offers numerous benefits for urban planning and management. This is evident in CIM’s best practices and how they help optimize planning, design, and decision-making processes. Firstly, it enhances the accuracy and efficiency of urban planning and design processes by providing comprehensive and up-to-date data. This is important for various urban systems as well as specific

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sectors in cities and the built environment (Agostinelli et al. 2021; Adreani et al. 2022; Alva et al. 2022; Pourroostaei Ardakani and Cheshmehzangi 2023b). Secondly, it enables new opportunities to have design options, scenarios, and analytical views of the impacts of a new design or planning intervention. Thus, the book’s global case study examples and contributions will provide evidence that the benefits of CIM for urban planning and management are significant. Ultimately, CIM enables stakeholders to understand the spatial attributes of making decisions for better urban design and planning. In the following sections, we summarize the aim and objectives of the book as well as an overview of the book’s structure.

2 Aim and Objectives of the Book By putting together recent research studies focused on CIM concepts and trends, CIM application and digitization processes/methods, and CIM frameworks and practices, we aim to cover a wide range of topics related to optimizing urban environments. As shown in Chapters (i.e., Chapters 2 to 10), CIM aligns well with smart cities, datadriven urban analytics and optimization, information-based city planning, and future development paradigms. Therefore, the objectives of having such a rich collection are to update existing literature with up-to-date research, provide a wide range of case study examples, and delve into CIM practices and methods that could lead to new directions in urbanism and urban studies. The following section provides an insight into the book’s structure.

3 Structure of the Book This edited volume provides a resourceful collection of CIM-related global case study examples in three parts. At first, the contributors offer examples of ‘Concepts and Trends’, where CIM is explored further in urban management, urban sustainability, and big data studies. In the second part, the book offers various examples of application and digitisation processes or methods related to urban planning and design practices. In the third part, the contributors delve into several examples of CIM frameworks and practices critical to contemporary research, planning and design paradigms, and future practices. Figure 1 summarizes these three key areas of the book. The followings are abstracts for all the contributing chapters in three parts.

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Concepts and Trends

CIM

Applications and Digitisation

Frameworks and Practices

Fig.1 Summary of the book’s three main parts, covering ‘concepts and trends’, ‘applications and digitisation’, and ‘frameworks and practices’ ( Source The Authors)

3.1 PART I: Concepts and Trends Chapter 2—City Information Modelling and Sustainable Development: The Role of CIM in Achieving Sustainable Urbanization By: Hadi Soltanifard, Reza Farhadi, and Hossein Mansourian. This chapter explores the potential of City Information Modeling (CIM) in achieving sustainable urbanization. Due to the growing concern about sustainability issues in urban life and the environment in recent years, CIM has been widely proposed by various municipal systems as a holistic approach to achieving sustainable urbanization in terms of urban planning, management, and design. This chapter aims to outline the importance of CIM for urban sustainability in four main sections: (1) Data acquisition and management for CIM will be included a wide range of data acquisition and management techniques utilized for CIM and urban modelling to explore sustainability by examining the multi-aspects of urban components; (2) CIM for urban planning, design, and management. This section discusses the use of CIM in urban planning, land use management, zoning regulations, infrastructure management, emergency response planning, and public participation, along with reviewing successful case studies; (3) CIM for public participation will explore how CIM can enable citizen participation and engagement in city decision-making. This

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section presents the planning process of informatics datasets to decision-makers and stockholders to be assessed more comprehensively due to the application of narrow datasets in spatial planning and urban modelling; and, (4) Future directions of CIM: This section includes the emerging trends and technologies in CIM and their potential impact on the future of cities, particularly smart cities. The chapter summarizes the critical contributions of CIM to achieving sustainable urbanization and suggest future directions for research and practice. The future directions section will discuss emerging trends and technologies in CIM and their potential impact on the future of sustainable urbanization. Overall, the chapter provides helpful insights into the potential of CIM as a valuable tool in addressing the challenges of sustainable urbanization. Chapter 3—Enhancing Health Outcomes Through City Information Modeling (CIM): A Case Study of Sydney, Australia By: Mohammad Anvar Adibhesami, Hirou Karimi, Borhan Sepehri, and Amirmohamad Parvanehdehkordi. A case study was conducted in Sydney, Australia, to explore the potential of City Information Modeling (CIM) in improving health outcomes. Sydney is a diverse and populous city with over 5 million residents, featuring a range of urban environments, from densely populated inner-city areas to sprawling suburban neighborhoods. The case study focused on how urban interventions impact health outcomes in Sydney, collecting data on the city’s physical, social, and economic characteristics, as well as health outcomes. By using this data, a 3D model of the city was created. CIM has been used for this model, which was utilized to evaluate how various urban interventions, such as the addition of green spaces or improvements to public transportation, affect human health outcomes. The results of the case study analysis demonstrate that CIM can effectively identify areas of the city that are most vulnerable to health risks and assess the impact of urban interventions on health outcomes. However, the study also highlights the need for better data collection and analysis, improved collaboration between public health professionals and urban planners, and the development of more sophisticated CIM tools. Overall, the case study in Sydney demonstrates that CIM has great potential for improving human health outcomes. To realize this potential, it is crucial to have the right tools and collaboration, enabling CIM to effectively identify areas of the city most in need and evaluate the impact of interventions on health outcomes. Chapter 4—City Information Modeling and Its Applications: A Review By: Xiang Zhang. Over the past few decades, there has been a growing interest in the field of City Information Modeling (CIM). CIM is generally considered a digital representation of a city and can empower the identification of optimal approaches to enhance urban environments. CIM is extensively used in various applications, primarily under the umbrella of smart cities. This chapter first provides a brief review of the history and definition of CIM. Subsequently, the structure and modules of CIM are discussed. Based on the literature review, it is evident that integrating Building Information Modeling (BIM) and Geographic Information System (GIS) is a widely adopted approach for CIM generation. This is because BIM and GIS both model spatial

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information, with BIM focusing on indoor modeling and GIS emphasizing outdoor environment, thus complementing each other effectively. To investigate the feasibility of CIM applications based on BIM-GIS, the main BIM-GIS integration CIM applications are further reviewed. It revealed that these applications include but are not limited to urban planning, urban facility management, urban flood hazard assessment, route and evacuation planning, underground space development and underground utility management, building energy analysis and management, and more.

3.2 PART II: Applications and Digitisation Chapter 5—Optimizing Urban Design for Pandemics Using Reinforcement Learning and Multi-Objective Optimization By: Mohammad Anvar Adibhesami, Hirou Karimi, and Borhan Sepehri. This study demonstrates a novel approach to leveraging reinforcement learning and multi-objective optimization for enhancing urban preparedness against pandemics. The role of urban design in preventing the spread of infectious diseases is significant, as evidenced by the COVID-19 pandemic, highlighting the need for preparedness for potential future pandemics. The method proposed in this study employs a hybrid approach of reinforcement learning and multi-objective optimization to identify optimal solutions for urban design that effectively reconcile diverse objectives, including but not limited to public health, economic viability, and environmental sustainability. The findings obtained from a simulated outbreak demonstrate that the proposed approach exhibits superior performance in comparison to the currently available methods. This suggests that it could be used to help plan cities for future pandemics. The utilization of reinforcement learning has the potential to enhance urban planning by employing a reward-based mechanism to instruct agency on the prevention of a pandemic outbreak. The consideration of multiple objectives simultaneously can lead to further enhancement in the optimization process, which is commonly referred to as multi-objective optimization. The proposed methodology has the potential to mitigate the transmission of pandemics while taking into account the economic ramifications and the standard of living. The findings of this investigation illustrate the feasibility of utilizing reinforcement learning and multi-objective optimization techniques for the purpose of optimizing urban design interventions aimed at mitigating pandemics. Chapter 6—Sustainable Smart City Application Based on Machine Learning: A Case Study Example from the Province of Tekirda˘g, Turkey By: Serhat Yılmaz, Hasan Volkan Oral, and Hasan Saygin. This study focuses on the city and risk-hazard interaction; one of the most significant issues of the twenty-first century. Today’s cities have evolved into sizable risk pools due to the urbanization trend, which intensified particularly following the Industrial Revolution and persisted, with more than half of the world’s population residing in urban areas in 2011. Therefore, the theoretical basis of the research is that a machine learning-based strategy for building smart cities can minimize or

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eliminate current and future potential risks and hazards in urban areas. Tekirda˘g in Turkey, which is affected by natural risks and hazards like earthquakes, floods, and tsunamis, as well as human and technological risks and hazards because of population movement, industrialization, and its location on major transportation lines, has been selected as the pilot city to test the hypothesis. The study’s methodology is focused on machine learning, smart cities, and participatory approaches. Data sets will first be compiled through historical and institutional archives, field research, and in-person interviews with representatives of pertinent institutions. Then, a digital system built on machine learning and in accordance with project-specific smart city components will be created. The data sets will be uploaded to the established digital system, where it will be possible to calculate the likelihood that a risk will evolve into a hazard and the potential effects that existing hazards may have. These chances that the digital system will offer as an output will be assessed in light of the obligations of the pertinent institutions and organizations at the pilot province level regarding risk reduction and vulnerability minimization. Thus, the study seeks to accomplish two key goals. First and foremost, it aims to address all environmental risks and hazards at the level of the pilot province with an integrated strategy and to efficiently monitor the performance of local institutions’ and organizations’ obligations. In case comparable circumstances arise, the machine learning-based system is hoped to offer warning information for future hazards. Chapter 7—Evaluation of Spatial Correlation Between Vegetation Index Changes and Heat Island Severity in the Last Two Decades in Tehran Metropolis By: Hadi Rezaei Rad, and Narges Afzali. The aggravation of the urban heat island, especially during summer time, could affect the environment and the quality of life. Studying the dynamics of surface thermal energy and identifying its correlation with human-induced changes is essential for predicting environmental changes as well as policy-making in urban settlement planning. Increasing vegetation is one of the most effective strategies to reduce the effects of urban microclimate. In this regard, a research study was conducted to analyze the trend of surface thermal changes and the spatial correlation of vegetation greenness with this phenomenon due to urbanization and urban planning developments in Tehran city, in Iran, between 2003–2022. Satellite images of Tehran with clear sky were obtained using ASTER satellite in August 2003 and Landsat8 satellite in August 2022. They were processed through various remote sensing algorithms using Envi software to extract spatial patterns of surface temperature and Normalized Difference Vegetation Index (NDVI) of the Tehran metropolitan area. Satellite outputs show that over the past two decades, the minimum surface temperature and average surface temperature have decreased by 3.67°C and 0.47°C, respectively, while the average NDVI has increased from 0.06 to 0.10. The spatial correlation estimate between NDVI and Land Surface Temperature (LST) in twenty-two districts of Tehran in the years 2003 and 2022 is 83% and 81%, respectively. The decline in correlation suggests a heightened influence of human activities and other physical factors associated with urban areas on the intensity of the urban heat island phenomenon.

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Chapter 8—Exploiting Advantages of VPL in City Information Modelling for Rapid Digital Urban Surveying and Structural Analysis By: Federico Mario La Russa. This research proposes a parametric modelling methodology based on Visual Programming Language (VPL) for creating City Information Models (CIM) to facilitate seismic vulnerability mapping in historic centres. The methodology consists of two innovative methods (Survey-to-CIM and Scan-to-CIM) developed for integrating direct and derived data, using Grasshopper as the VPL parametric computational design environment. The Survey-to-CIM method is a low-cost solution for small urban centres that integrates different data acquisition techniques within a parametric and responsive flow. The Scan-to-CIM method automates the input of survey data using an Artificial Intelligence system that identifies geometric-architectural features within point clouds. The generated CIM adheres to a specific semantic structure defined as CityGH, an innovative format based on CityJSON 3D city model standards but adapted to the data structure of Grasshopper. The semantic structure of the CIM model allows the storage of attributes and metadata that facilitate the information enrichment and management. The CIM model also allows the extraction of structural geometric models (city block scale) necessary for FEM analysis. Specifically, a workflow was developed to enable FEM analysis in the same VPL environment. Overall, this methodology offers an efficient and sustainable approach for creating CIMs that can support seismic vulnerability mapping and analysis actions in historic centres. This research demonstrated the benefits of adopting an explicit parametric modelling approach for City Information Modelling, enabling to manage digital urban survey data, semantic enrichment and management and FEM analyses. The proposed methods can be pursued based on the availability of project resources and the type of urban centre being studied. The outcome of this research contributes to the debate about parametric urbanism and the role of computational design with CIM methodology. Specifically, the case studies developed offer a practical alternative for seismic vulnerability mapping in historic centres.

3.3 PART III: Applications and Digitisation Chapter 9—Towards Adaptive and Resilient Strategies Using Digital Twins: A Study on the Port of Tyne, UK By: Jiayi Jin, and Mingyu Zhu. Over the course of history, maritime ports and their associated cities have grown in tandem, with the port acting as a catalyst for economic growth and prosperity in the city. The rise of globalization in recent decades has further reinforced this relationship. Understanding the operational risks faced by ports is crucial for assessing their resilience and their impact on the broader urban areas they serve. Currently, maritime ports are embracing digitalization, taking advantage of the abundance of data collection, transmission, and processing tools and networks. The concept of a "Digital Twin" is gaining popularity, with several pilot initiatives already underway

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in some of the world’s busiest ports. However, most existing Digital Twin implementations heavily rely on data-gathering devices like IoTs and primarily provide a snapshot of the port’s current status. This approach creates significant economic and technical barriers for other ports looking to replicate the same level of digitalization. This research aims to address the disparity in data availability among assets, facilities, and vehicles by proposing an adaptive Digital Twin framework, using the Port of Tyne, in the UK, as a case study. The developed Digital Twin serves as a foundation for implementing resilience strategies, encompassing both emergency response and long-term mitigation plans. It offers valuable insights to port authorities and stakeholders, aiding in the development of resilience strategies, understanding industrial ecology, and managing urban metabolism in port cities. Chapter 10—Ecosystem Institutional Maturity: Perspectives for CIM in Urban Management and Planning in Curitiba, Brazil By: Augusto Pimentel Pereira, and Mario Prokopiuk. City information modeling (CIM) is an innovation in information and communication technologies (ICTs) applied to urban management and planning. However, there are still few studies that evaluate the process of diffusion, implementation, and adoption from a sociotechnical perspective. Our objective is to develop an analytical model to assess the levels of multiscale institutional maturity to support the technological diffusion. The model was tested in the context of the BIM/CIM/GIS ecosystem of Curitiba, a city with long trajectory of technology diffusion, and where the municipality has already structured actions and a well-established trajectory to apply GIS, BIM, and CIM technologies and tools. The results show that (i) the institutional maturity of the BIM/CIM/GIS ecosystem is expressed by the constructs practices and processes, previous experiences, diffusion strategies, and awareness; (ii) it is possible to build an institutional maturity assessment tool to guide the dissemination, adoption and implementation processes of the CIM. The analysis allowed the identification and quantitative explanation of an institutional maturity model in line with previous theoretical debates. Theoretical implications are (i) the explanation of an institutional maturity model capable of reading reality qualitatively and quantitatively; (ii) the approximation of theory and practice via testing of the proposed model. Empirical implications are in the constitution of a theoretically grounded diagnostic tool capable of addressing challenges in technology diffusion practices to reduce the current gap between technological evolution and the pace of change of organizations. Chapter 11—The Use of City Information Modelling (CIM) for Realizing Zero Energy Community: A Path Towards Carbon Neutrality By: Hossein Omrany, Amirhosein Ghaffarianhoseini, Ali Ghaffarianhoseini, Kamal Dhawan, Abdulbasit Almhafdy, and Daniel Oteng. City information modelling (CIM) offers a digital depiction of the urban environment, empowering stakeholders to critically review and optimise the performance of energy. In the pursuit towards Zero Energy Cities (ZECs), CIM becomes an essential instrument of this process. However, despite the promise it delivers, the uptake is slow. This research therefore addresses the gap by providing a comprehensive overview

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of CIM’s potential for facilitating the achievement of ZECs in the built environment. The chapter employs an extensive review of the literature on the subject. The results reveal that, there exist a notable dearth of research concerning the design and execution of the zero-energy concept within the context of community-scale implementation. Moreover, the merging of CIM and UDT opens novel possibilities for establishing zero-carbon communities throughout their life cycle. By harnessing data from various origins such as buildings, energy grids, urban planning, environmental setups, transportation networks, and socio-demographic metrics, it then becomes feasible to construct a holistic digital portrayal of the community. It is consequently imperative to conduct comprehensive cross-sector inquiries that delves into the practical realization of the zero-energy community concept within the wider framework of local sustainability objectives. This entails examining the interplay between climate mitigation measures and sustainability goals, while carefully assessing both potential conflicts and opportunities. Lastly, we provide a summary highlighting lessons learnt and ways forward using CIM in urban planning, design, and management practices (in Chapter 12). In doing so, we hope to reflect on some of the key findings and contributions from the chapter authors, providing an opportunity to expand further on CIM research and practice(s). The findings of this collection help direct future research in CIM and suggest opportunities for big-data urban research, integrated urban models, and holistic frameworks in sustainable cities, smart cities, and future cities.

References R. Abdalla, Geospatial data integration, in Introduction to Geospatial Information and Communication Technology (GeoICT) (Springer, Cham, 2016), https://doi.org/10.1007/978-3-319-336 03-9_6 G.D. Abowd, A.K. Dey, P.J. Brown, N. Davies, M. Smith, P. Steggles, Towards a better understanding of context and context-awareness. In HUC 1999. LNCS, ed. by H.-W. Gellersen, vol. 1707 (Springer, Heidelberg, 1999), pp. 304–307, https://doi.org/10.1007/3-540-48157-5_29 D. Adeline, F. Jacquinod, A. Mielniczek, Exploring digital twin adaptation to the urban environment: Comparison with CIM to avoid silo-based approaches. ISPRS Ann. Photogramm. Remote Sens. Spat Inf. Sci. 4, 337–344, (2022), https://doi.org/10.5194/isprs-annals-V-4-2022-337-2022 L. Adreani, C. Colombo, M. Fanfani, P. Nesi, G. Pantaleo, R. Pisanu, A photorealistic 3D city modeling framework for smart city digital twin, in 2022 IEEE International Conference on Smart Computing (SMARTCOMP) (2022), pp. 299–304, https://doi.org/10.1109/SMARTC OMP55677.2022.00071 S. Agostinelli, F. Cumo, G. Guidi, C. Tomazzoli, Cyber-physical systems improving building energy management: Digital twin and artificial intelligence. Energies 14, 23–38 (2021), https://doi.org/ 10.3390/en14082338 T. Ahmada, A. Aibinua, M.J. Thaheem, BIM-based iterative tool for sustainable building design: A conceptual framework, international high-performance built environment conference—a sustainable built environment conference 2016 series (SBE16), iHBE 2016. Procedia Engineering 180, 782–792 (2017)

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P. Alva, F. Biljecki, R. Stouffs, USE cases for district-scale urban digital twins. Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci.XLVIII-4/W4–2022, 5–12 (2022), https://doi.org/ 10.5194/isprs-archives-XLVIII-4-W4-2022-5-2022 J. Amará, V. Ströele, R. Braga, M. Bauer, Sensor data integration using ontologies for event detection, in Advanced Information Networking and Applications. AINA 2023. Lecture Notes in Networks and Systems, vol. 661, ed. by L. Barolli (Springer, Cham, 2023), https://doi.org/10. 1007/978-3-031-29056-5_17 S. Azhar, W.A. Carlton, D. Olsen, I. Ahmad, Building information modelling for sustainable design and LEED® rating analysis. Autom. Constr. 20(2), 217–224 (2011) M. Batty, Smart Cities, Big Data. Environ. Plann. b. Plann. Des. 39(2), 191–193 (2012). https://doi. org/10.1068/b3902ed M. Burry, J.A. Karakiewicz, D. Holzer, M. White, G.D.P.A. Aschwanden, T. Kvac, BIM-PIMCIM: The challenges of modelling urban design behaviours between building and city scales, in Design Modelling Symposium Copenhagen 2015 (2015), https://doi.org/10.1007/978-3-31924208-8_34 A. Cheshmehzangi, The Application of ICT and Smart Technologies in Cities and Communities: An Overview, in ICT, Cities, and Reaching Positive Peace (Singapore, Springer, 2022a), pp. 1–16. A. Cheshmehzangi, ICT, Cities, and Reaching Positive Peace (Springer, Singapore, 2022b) A. Cheshmehzangi, A. Dawodu, A. Sharifi, Sustainable Urbanism in China (Routledge, New York, 2021) A. Cheshmehzangi, T. Tang, China’s City Cluster Development in the Race to Carbon Neutrality (Springer, Singapore, 2022) J. Coetsee, A. Brown, J. Bossler, GIS data collection using the GPSVan supported by a GPS/inertial mapping system, in Proceedings of GPS-94. Institute of Navigation (ION), September 20–23. (Salt Lake City, UT, 1994). C. Cote, 4 Types of Data Analytics to improve Decision-making. Harvard Business School online (2021), Available from https://online.hbs.edu/blog/post/types-of-data-analysis G. Dall’O’, A. Zichi, M. Torri. Green BIM and CIM: sustainable planning using building information modelling, in Green Planning for Cities and Communities. Research for Development, ed. by G. Dall’O’ (Springer, Cham, 2020), https://doi.org/10.1007/978-3-030-41072-8_17 T. Deng, K. Zhang, Z.-J. Shen, A systematic review of a digital twin city: A new pattern of urban governance toward smart cities. Journal of Management Science and Engineering 6(2), 125–134 (2021) C.M. Ellum, N. El-Sheimy, A mobile mapping system for the survey community, in Proceedings of The 3rd International Symposium on Mobile Mapping Technology (MMS 2001) (Cario, Egypt, January 3–5, 2001). On CD-ROM. N. El-Sheimy, K.-P. Schwarz, Navigating urban areas by VISAT—a mobile mapping system integrating GPS/INS/digital cameras for GIS applications. Navig. Institute of Navigation (ION) 45(4), 275–285 (1999) S. Gandhi, J. Jupp. Characteristics of green BIM: process and information management requirements, in 10th Product Lifecycle Management for Society (PLM) (Nantes, France, 2013), pp. 596–605, https://doi.org/10.1007/978-3-642-41501-2_59.hal-01461909 J. Gil, City information modelling: A conceptual framework for research and practice in digital urban planning. Built Environ. 46(4), 501–527 (2020), https://doi.org/10.2148/benv.46.4.501 N. Hughes, D. Kalra, Data standards and platform interoperability, in Real-World Evidence in Medical Product Development, ed. by W. He, Y. Fang, H. Wang (Springer, Cham, 2023), https:// doi.org/10.1007/978-3-031-26328-6_6 International Organization for Standardization ISO (2018), ISO 19650—organization and digitization of information about buildings and civil engineering works, including building information modelling (BIM)—information management using building information modelling—Part 1: concepts and principles

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I. Jeddoub, G.-A. Nys, R. Hajji, R. Billen, Digital Twins for cities: Analyzing the gap between concepts and current implementations with a specific focus on data integration. Int. J. Appl. Earth Obs. Geoinf. 122, 103440 (2023), https://doi.org/10.1016/j.jag.2023.103440 I. Justice Akpan, M. Shanker. A comparative evaluation of the effectiveness of virtual reality, 3D visualization and 2D visual interactive simulation: an exploratory meta-analysis. Simulation 95 (2), (2018), https://doi.org/10.1177/0037549718757 T. Kolbe, S. Bacharach, CityGML: An open standard for 3D city models (2006), http://www.direct ionsmag.com/articles/citygml-an-open-standard-for-3d-city-models/123103. Accessed 22 Aug 2023. E. Krygiel, B. Nies, Green BIM: Successful Sustainable Design with Building Information Modelling (Wiley Publishing Inc., Hoboken, 2008) S. Mozuriunaite, G. Haiyan, CIM and CIM platform practical use in China review. IOP Conf. Ser.: Mater. Sci. Eng. 1203, 022143 (2021), https://doi.org/10.1088/1757-899X/1203/2/022143 P. Najafi, M. Mohammadi, P. van Wesemael, P.M. Le Blanc, A user-centred virtual city information model for inclusive community design: State-of-art. Cities 134, 104203, https://doi.org/10.1016/ j.cities.2023.104203 S. Pourroostaei Ardakani, A. Cheshmehzangi, Big Data Analytics for Smart Urban Systems (Springer, Singapore, 2023a) S. Pourroostaei Ardakani, A. Cheshmehzangi, Big Data Analytics for Smart Transport and Healthcare Systems (Springer, Singapore, 2023b) I.H. Sarker, Data science and analytics: An overview from data-driven smart computing, decisionmaking and applications perspective. SN Comput. Sci. 2, 377 (2021), https://doi.org/10.1007/ s42979-021-00765-8 S.F. Shadram, T.D. Johansson, W. Lu, J. Schade, T. Olofsson, An integrated BIM-based framework for minimizing embodied energy during building design. Energy and BUilding 128, 592–604 (2016) L. Souza, C. Bueno, City information modelling as a support decision tool for planning and management of cities: A systematic literature review and bibliometric analysis. Build. Environ. 207, Part A, 108403 (2022) S. Smys, A survey on internet of things (IoT) based smart systems. J. ISMAC 2(04), 181–189 (2020) L. Van Berlo, R. De Laat, Integration of BIM and GIS: the development of the CityGML GeoBIM extension, in 2011: Advances in 3d Geo-information sciences, ed. by T.H. Kolbe, G. König, C. Nagel, ISBN: 978–3–642–12669–7, in W. Cartwright, G. Gartner, L. Meng, M.P. Peterson (eds), ISSN: 1863-2246

Part I

Concepts and Trends

Chapter 2

City Information Modelling and Sustainable Development: The Role of CIM in Achieving Sustainable Urbanization Hadi Soltanifard, Reza Farhadi, and Hossein Mansourian

Abstract This chapter explores the potential of City Information Modeling (CIM) in achieving sustainable urbanization. Due to the growing concern about sustainability issues in urban life and the environment in recent years, CIM has been widely proposed by various municipal systems as a holistic approach to achieving sustainable urbanization in terms of urban planning, management, and design. This chapter will aim to outline the importance of CIM for urban sustainability in four main sections: (1) Data acquisition and management for CIM will be included a wide range of data acquisition and management techniques utilized for CIM and urban modelling to explore sustainability by examining the multi-aspects of urban components. (2) CIM for urban planning, design, and management: This section will discuss the use of CIM in urban planning, land use management, zoning regulations, infrastructure management, emergency response planning, and public participation, along with reviewing successful case studies. (3) CIM for public participation will explore how CIM can enable citizen participation and engagement in city decision-making. This section will present the planning process of informatics datasets to decision-makers and stockholders to be assessed more comprehensively due to the application of narrow datasets in spatial planning and urban modelling. (4) Future directions of CIM: This section will include the emerging trends and technologies in CIM and their potential impact on the future of cities, particularly smart cities. The chapter will summarize the critical contributions of CIM to achieving sustainable urbanization and suggest future directions for research and practice. The future directions section will discuss emerging trends and technologies in CIM and their potential impact on the future of sustainable urbanization. Overall, the chapter will provide helpful insights into H. Soltanifard (B) Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran e-mail: [email protected] R. Farhadi Department of Landscape Architecture, University of Hormozgan, Bandar Abbas, Iran H. Mansourian Department of Human Geography, Faculty of Geography, University of Tehran, Tehran, Iran © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 A. Cheshmehzangi et al. (eds.), City Information Modelling, Urban Sustainability, https://doi.org/10.1007/978-981-99-9014-6_2

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the potential of CIM as a valuable tool in addressing the challenges of sustainable urbanization. Keywords Sustainability · Urban modelling · Urban planning · Decision-making · Spatial planning · Future direction

Our understanding of the urban environment includes not only a complicated version of the city but also those emerging from the interaction between cities, components, and structures. —Paul Romer, 2013, The Nobel-Prize-winning economist.

1 Introduction According to the latest UN projections, the world population growth could surpass 10 billion by 2058, reaching a peak of around 10.4 billion people during the 2080s (Lubov et al. 2022). These also estimate that more than half of the world’s population will live in cities, so 7 out of 10 people are likely to choose cities to settle down (United Nations 2019). Ongoing rapid urbanization has improved the well-being of societies and quality of life, including a higher number of employment opportunities and better-paying work, premier-quality education, and higher accessibility to healthcare (Murgas and Klobucnik 2018; Soltes et al. 2018; Sorensen 2018; Stojanov et al. 2021). Despite all this, however, urbanization is generally known to get a number of adverse effects that have increasingly led to unsustainability. Uncontrolled urban growth and the lack of effective urban planning approaches have exacerbated these adverse effects and made them unpredictable (Qi et al. 2020; Hosono 2022; Xue 2022). To remedy the current challenges that many cities are facing, urban sustainability has been highlighted to serve initiatives that enhance economic, social, and environmental conditions for residents in ways while minimizing adverse effects on the environment (Huang et al. 2015; Zeng et al. 2022). Therefore, to ensure a minimum quality of life for all city residents, reviewing current urban governance policies and strategies and implementing effective urban planning is essential. Over the past half-century, technological developments have facilitated the collection of unprecedented information about what is happening in cities. From satellite imagery, smartphones, and social media to using cutting-edge technologies have all contributed to the so-called “big data revolution.” In this regard, extracting, processing and combining such information already require a systematic framework to guarantee innovation and rely on solutions for urban sustainable development (Vecchio and Tricarico 2019; Cappa et al. 2022). This origin of technology provides opportunities for urban specialists to manage people and resources more efficiently and effectively while also unfolding the complexity of behind-the-scenes urban concerns (Allam et al. 2022; Christmann and Schinagl 2023). Therefore, if well-managed and planned, employing technology and data on purpose can result in better decisions, a higher quality of life, and sustainable urban development.

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Fig. 1 CIM as a multidisciplinary concept Processes

Stakeholders

City Information Modeling

Policies

(CIM) Technologies

In recent decades, much work in urban sciences has continually evolved to represent multiple dimensions of the complexity of urban systems. As Batty offered a comprehensive framework to investigate urban complexity, there is a need for applicable concepts and tools to highly complex systems such as cities (Batty 2007; Moroni et al. 2019; Caldarelli et al. 2023). In this context, new tools and technologies have been proposed and deployed to meet urban needs, in which the City Information Modeling (CIM) model appeared (Dantas et al. 2019). CIM is a new paradigm that seeks solutions to the issues posed by increasing urban complexity. Although this concept is still being explained and discussed, it can be considered a multidisciplinary knowledge model to define a practical framework for coordinating the planning, execution, operation, monitoring, maintenance, and renovation of the city (De Amorim 2015). As shown in Fig. 1, CIM can establish a logical connection between processes, urban policies, and technologies, extending multiple participations to collaborate in developing sustainable, participative, and competitive cities.

1.1 From BIM to CIM: An Evolved Concept The concept of CIM, which was first presented by Khemlani in 2007 (Khemlani 2019), has mainly focused on bridging between different fields, technology, and tools (Xu et al. 2021). However, CIM was initially known as an amalgamated form of BIM and GIS, specialising in a standard data format to combine the internal data of

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Fig. 2 Interrelationship between the models and their relevant scales

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buildings (building scale) and the external data of buildings (city scale) (Omrany et al. 2022). Now, these questions come to mind: What is BIM and its relevant function? Fundamentally, BIM is a supporting system that refers to a digitalized process for creating and managing information on the physical and functional characteristics of a building (Sacks et al. 2018). This system helps all experts build a substantial-detailed building document in a 3D model, optimizing design, construction, execution, and management projects. Meanwhile, the overlap in common characteristics of data and modelling in both systems, GIS and BIM, has resulted in an integrated model for future 3D city modelling (CIM), which is regularly updated and manages datasets covering the urban scale (Arroyo Ohori et al. 2018). The fundamental idea behind CIM is to create a “smart city” that we already have for infrastructure and buildings, containing precise information about the model’s components and their interactions. From the building level to the city level, it is widely acknowledged that integrating data from both domains via BIM or GIS makes urban planning more efficient and effective (Arroyo Ohori et al. 2017; Barazzetti and Banfi 2017). Consequently, the evolution of the initial model of BIM to a primarily extended CIM, GeoBIM, revealed that the main objective of developing such models is to monitor the urban environment comprehensively, acquire data, and enable analysis and simulation to make it friendly and sustainable for citizens. However, these models developed from 2 to 3D to interface in various scales. Figure 2 shows a hierarchical diagram of the mentioned models and their relevant scales. BIM is a model that simulates, analyzes, stores, plans, and designs on the building scale. Nevertheless, GIS, CIM, and GeoBIM are the other models performing at the urban scale and being able to construct city models by integrating the various sources of data, applications, and tools. Following the well-established BIM in the UK, CIM was primarily suggested as GeoBIM1 to fill the gap between Geo and BIM modelling paradigms, CityGML, and IFC data standards (De Laat and Van Berlo 2011). In 2019, This project was launched as a collaborative project in the Netherlands, aimed 1

For more information, please refer to: https://3d.bk.tudelft.nl/projects/geobim/.

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to support data in geospatial applications and BIM sources (Noardo et al. 2020). With a wider community to exchange and use the integrated GeoBIM information, however, this project has been concurrently enabled to harmonize data, automatically update the 3D urban model, and urban infrastructure planning with a high degree of detail (Liu et al. 2017; Aleksandrov et al. 2019; Noardo et al. 2020). Besides these, GeoBIM serves as an essential source to coordinate several entities identified as data providers, researchers, urban managers, academics, government organizations, municipalities, national mapping, cadastral agencies, and private companies. With respect to the main objective, these models outline how there is a need to come together to promote urban sustainability in terms of a smart city. A realistic model of a smart city that can be analyzed online and simulated in real-time (Khemlani, 2019) to continuously improve the health, safety, and well-being of the people who live in the city.

2 Data Acquisition and Management for CIM In view of the increasing adoption of CIM in urban research fields, extending such the model demands new valid data sources for supplying their needs in project planning, design infrastructures, construction, and urban facility management, as well as its restoration. To concentrate on 3D urban information modelling, various databases and related sources have been considered to gain insight into multiple dimensions of urban inclusion (Gil 2020a). Despite the unique potential and opportunities provided by the integration of BIM and GIS, originally, there are some conflicts between the two disciplines due to their differing semantics, geometry, levels of detail, open standards, and modelling approaches (Arroyo Ohori et al. 2018; Kang and Hong 2018) therefore, in order to share datasets, solve the problem of geometric processing, and map comparable types, a common language is required to bridge the BIM and GIS communities. By developing a compatible framework, the industry foundation class (IFC) and city geography markup language (CityGML, a typical format for GIS data) were introduced to facilitate the exchange of the BIM domain and 3D GIS domain respectively (Deng et al. 2016; Jusuf et al. 2017; Colucci et al. 2020; Zhu et al. 2021). Since BIM has been employed as an alternative model in the Architecture, Engineering, and Construction (AEC) domain for creating, managing, and sharing building information, IFC can be defined as a representative model for BIM, while it is an open international standard for data exchange in the AEC domain (Kolbe et al. 2008; Sacks et al. 2018). IFC models mainly concentrate on single construction with a wide variety of details, whereas, CityGML is an open data model to represent the city as a whole, with its connections, interactions, movements, and spatial features (ISO 2013; Arroyo Ohori et al. 2018; Dimopooulou et al. 2018). Overall, CIM uses a spatial data exchange issued by the Open Geospatial Consortium (OGC) and is implemented as a Geography Markup Language (GML) application, a schema for the Geography Markup Language 3 (GML3), also known as City GML. City GML

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datasets are made up of a set of Extensible Markup Language (XML) files with associated images, textures or parts of the dataset giving the required level of detail. Figure 3 shows the basic information and CIM processing. Extending these elements to create city-level models is now based on a 3D city database (3DCITYDB) that is open access and free to use, store, represent, and manage 3D city models. The database contents can be exported directly to the 3D City Database. Content can be directly exported in KML (keyhole markup

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Fig. 3 CIM, data processing and outputs

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language), COLLADA (COLLAborative Design Activity), and glTF (graphical language transfer format) formats for viewing in a variety of applications such as Google Earth, Arc GIS, and Cesium-based WebGL Virtual Globe.2

3 CIM for Sustainable Urban Planning, Design, and Management According to the UN sustainable development goals and the UN-Habitat’s New Urban Agenda, all urban experts are responsible for creating safe and livable cities by linking multiple aspects of urban features, decision-makers, and stakeholders (UN-Habitat 2020). Outlining this vision needs a common and accessible platform for supporting urban systems, data collection, processing, and visualization to continuously inform dwellers and relevant experts (International Electrotechnical Commission 2021). Accordingly, to address the concept of sustainability for the future of urbanization, CIM has delineated a systematic framework for converting updated data, analyzing, and applying it to urban planning, design, and management. Obviously, without such a digitalized holistic model, the urban design and planning process, which involves the incorporation of all these sectors, is always constrained and flawed. To lead to this path, therefore, the primary characteristics of the CIM paradigm may be broadly summed up in terms of a single mission and collaborative work. From a single mission point of view, CIM has an interoperability performance to set specific urban services according to approved procedures. For example, a comprehensive approach to urban sustainable development and resilience was founded by the ISO 37120 (International Organization of Standardization), known as “Sustainable Development of Communities”, which defined a set of indicators to steer and measure the performance of city services and quality of life (International Organization for Standardization 2018; Dantas et al. 2019). This international standard offers indicators to monitor and assess urban environments that might vary from one city to another. Last but not least, the outcomes of the survey of the main indicators are taken into account to support urban policies and plans for sustainability in environmental solutions and future planning. These may have been broken down into four main sections and related supporting indicators to describe the technical aspects based on ISO 37120 Themes (Fig. 4). In this way, the role of CIM is identified as a source of data accuracy while being considered a monitoring system. Hence, the model increasingly enables the contributions in reporting and comparing data based on ISO 37120 and other international standards. In addition, sharing databases between cities with similar conditions will provide a reliable framework for ranking and classifying cases involving special indicators. Although reliability is a key issue in data generalization, the CIM model 2

For more information, please refer to: https://www.designingbuildings.co.uk/wiki/City_informa tion_modelling_CIM.

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•Climate and urbn microclimate change •Global warming and urban heat •Pollution •Urban agriculture

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CIM Urban managment •Natural disasters •Emergency •Water and watewater •Solid waste •Recreation •Traffic and movement

Governance •Economy and finance •Education •Food security •Culture and Social engagement •Energy •Health and safety

Fig. 4 CIM implementation and sub-main parts

is nevertheless capable of showing sustainable cities on a global scale. In contrast, different georeferenced data are attached and represented directly in an online data bank. In recent years, a growing body of literature has focused on CIM modelling and the monitoring of specific Indicators in urban environments. For instance, to create a novel regional carbon impact assessment model, Su et al. (2023) suggested an inventive approach by combining CIM with Dynamic Life Cycle Assessment (DLCA). In this model, CIM was employed to extract the geographical data of the built environment and the geometric and semantic data of the physical objects. The results showed that integrating CIM and DLCA is feasible and practical. This offers a theoretical framework for insightful assessment and may be applied to encourage low-carbon city administration (Su et al. 2023). Moreover, Salles et al. (2023) developed a comprehensive strategy for integrating the CIM framework with conventional sustainability assessment tools. In order to determine a precise collection of consensual sustainability tools, the most widely agreed-upon list of indicators from four sustainability assessment methods—BREEAM-C, LEED-ND, SNTool, and SBToolPT Urban—was extracted. The findings discovered the indicators that can be adapted to apply the CIM approach. Additionally, it may have demonstrated that the chosen indications are CIM-computable (Salles et al. 2023). In terms of collaborative work, CIM enables it to fulfill the demands of diverse stakeholders due to its interdisciplinary and comprehensive approach (Thompson et al. 2016). More recently, the domains of CIM implementation have been highlighted for evaluation and action plans for urban planning, design, and management. The potential of CIM in sustainable urban planning has been addressed by linking to several technologies and exchanging databases. In this regard, CIM is given

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increasing attention through pertinent technical research features that integrate GIS, BIM, IoT, Artificial Intelligence (AI) and digital ubiquity, data mining, and machine learning, and extends interoperability application areas, such as smart cities, urban planning, and simulations (Zhu et al. 2018; Gil 2020b; Stojanovski et al. 2020; White et al. 2021; Xu et al. 2021). For an urban system, technological requirements are indispensable to manage all relevant subcomponents simultaneously. Although the potential of CIM in sustainable urban planning remains a controversial area of research, the main objective is to reproduce the physical and functional characteristics of the city in a 3D model while adding information from other sources (Gaillard et al. 2020; Pereira et al. 2021; Xue et al. 2021). Besides 3D modelling, data collection, processing, and interconnection with all systems and applications are key topics to describe the role of CIM in sustainable urban planning, design, and management. Therefore, the benefits of CIM in planning and design have emerged, expressing the need for implementations that fulfill the present needs (Souza and Bueno 2022). Nevertheless, urban planners and designers need digital tools that, like GIS, integrate data for analyses, but they also have design capabilities (Stojanovski et al. 2020). Moreover, challenges like databases’ cybersecurity, standardization workflow for CIM, data integration, storage and efficiency are other controversial equations that must be answered (Omrany et al. 2022). Consequently, certain insight needs to be gained to integrate the theory and history of urban morphology, monitoring, and measurement methods. This also will let us understand how natural systems in urban environments play an important role in shaping cities (Gil et al. 2011; Stojanovski et al. 2020; International Electrotechnical Commission 2021).

4 CIM for Public Participation in City Decision-Making The approach to urban planning has changed since the 1960s, and its evolution has gone through three basic and evolutionary stages. After proving the ineffectiveness of comprehensive and systematic planning theories, people’s participation in urban planning has received attention in recent years from both theoretical and practical aspects (Lane 2005). Based on Habermas’ principle of communicative rationality, theorists such as Healey and Forester have proposed a more consultative mechanism for planning to realize public participation to create a more agreeable decisionmaking system (Davies 2001). These approaches, by combining technological innovations in planning with the experiences, knowledge and understanding of different groups and citizens, emphasize the hypothesis that if stakeholders can consult freely and there is no predetermined hierarchy, they can find standard solutions (Zhao et al. 2018). Therefore, in recent years, planning ideas have moved away from normative and rational models, emphasizing the prominent role of planners and the use of scientific and logical methods. In the pluralism of contemporary theory, the direction of planning is more focused on the political nature of planning, particularism and interests in the competition of stakeholders and decisions as the results of dialogue. A process that is facilitated by planners (Lane 2005). Although in the background

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of planning, the discussion of participation and changing the planning approach is raised more and more every day, and its influence is increasing, Davis believes that despite the support in the field of politics and the university for the idea of public participation in planning, in practice, there has not been a huge change yet (Davies 2001). Considering the recurring difficulty of dialogue between urban actors, the CIM can be an essential instrument for implementing a democratic urban policy (COSTA et al. 2019). CIM can help to improve the management process by creating methods to maintain, control and understand data in different areas of public management (Melo et al. 2019). According to the capabilities of CIM, it is possible to visualize public policies implemented in cities, help to understand the information in urban data, evaluate the effects of a project based on the context in which the project is located, and improve the process of managing and maintaining infrastructure (Souza and Bueno 2022). In addition, the CIM would have possibilities for the maintenance and operation of urban systems, as well as the opportunity to visualize the effect of public policies.

5 Future Directions of CIM With the rapid increase in urban population, cities are facing many challenges, and the effective use of resources is very important to meet the needs of residents. In addition to the growth of the urban population, other factors such as climate change, economic restructuring, competition between cities, the ageing population, and the pressures governments have in terms of public budgets have made the smart city vital for governments. A smart city is a framework that uses information and communication technology to develop, establish, and promote sustainable development goals to solve the challenges of cities. A smart city is a special place for sustainable economic, industrial, and social development, and it deals with issues such as traffic, energy consumption, pollution, and land degradation, updating and optimizing urban infrastructure based on communication and information exchange to optimize urban management processes. In these cities, which are designed based on electronic life infrastructure, people’s needs are provided smartly using the latest technologies. The main task of a smart city is to optimize city functions and create economic growth while improving the quality of life for citizens using smart technology and data analysis. The aim of the smart city is to provide smart services to its citizens that can save their time and make their lives easier. In the smart city, communication is also provided between citizens and the government, and citizens can give their opinions about how their city should be to the government. This goal cannot become a reality without technology. Using technology, officials can collect city information, and this information, when integrated with city operations, makes cities smarter and safer. CIM is understood as a computational tool, GeoBIM, and an urban database for analysis and simulation (Cureton and Hartley 2023). In this context, CIM, as an information technology means, provides a digital base for smart city manufacture

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(Bi et al. 2021). Therefore, CIM is an essential component in a smart city. It can provide the necessary data to citizens, managers, and planners to make the best decisions and simulate its effects on the urban system before implementing largescale decisions. Currently, CIM faces technical and management issues, such as data acquisition, integration, and collaborative design. Nevertheless, as an emerging technique, CIM has many development directions, including the improvement of theories, platform construction, methods for collecting, integrating, and updating data, application technology, and the combination of emerging techniques (Xu et al. 2021). At the city scale, the most suitable way for data acquisition is remotely sensed data. Even though remote sensing techniques have many limitations and may be costprohibitive, it is still the most efficient manner for data acquisition (Al Furjani et al. 2020). Remote sensing plays a significant role in building and developing digital representation/virtual models of the city. There is extensive use of many developed technologies for the reconstruction of the 3D City Model, like photogrammetry, Light Detection, Ranging LIDAR, satellite, aerial, and terrestrial imagery. However, there are some limitations of remotely sensed datasets in the field of digitalization of cities and urban areas where there are many challenges during the phase of spatial data acquisition; for example, part or all of the invisible built environment in some cases is out of the range of sensors/cameras coverage. Therefore, participatory mapping activities contribute to the phase of spatial data acquisition for the reconstruction of the 3D City Model (Al Furjani et al. 2020). However, recent advances in remote sensing and Volunteered Geo-Information (VGI) can solve a significant part of the problems of obtaining and updating spatial data.

6 Conclusion and Recommendations for Future Research This chapter endeavored to contribute to the current body of literature by highlighting the crucial significance of CIM in advancing sustainable urban development. In this context, what was presented in the current chapter revealed that CIM plays a comprehensive and interdisciplinary role in connecting the process of urban planning, the needs of diverse stakeholders, urban policymakers, and management strategies. At a closer look, it showed that CIM has outlined a comprehensive framework for the conversion, analysis, and application of updated data in the realms of urban planning, design, and management. Without a comprehensive digitalized model, it is clear that the urban design and planning process, which requires the integration of all these sectors, is consistently limited and imperfect. Therefore, in order to progress towards this goal, the fundamental features of the CIM paradigm can be summarized in terms of a unified objective and cooperative effort. Moreover, the chapter also stresses the need to adopt emerging technologies to achieve sustainable urbanization. Nevertheless, the technology and tools utilized in this approach should be implemented across all aspects of sustainable urban development. In recent years, it has been demonstrated that the application of CIM

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can promote urban sustainability through a radical transformation of the form and processes of existing urban systems, such as transportation, energy, and green infrastructure. One of the most crucial areas of application in CIM is managing urban infrastructure and assuring accurate and geographically referenced data, aiming to enhance urban infrastructure subsystems. What has been discussed in this chapter is the utilization of information modeling to monitor and manage urban services while also adapting to sustainable development goals. From a singular mission perspective, CIM serves as an interoperability function to regulate specific municipal services in accordance with approved procedures. For instance, ISO 37120 (International Organization for Standardization) established a comprehensive approach to sustainable urban development and resilience, known as “Sustainable Development of Communities,” which offers a set of indicators to guide and assess the performance of urban services and the quality of life. However, visualizing extracted data in a unique and accessible format requires evidence-based, collaborative, and participatory urban tools in urban planning, design, and decision-making (Gil 2020b). Without a comprehensive digitalized model, it is clear that the urban design and planning process, which requires the integration of all these sectors, is consistently limited and imperfect. Therefore, in order to progress towards this goal, the fundamental features of the CIM paradigm can be summarized in terms of a unified objective and cooperative effort. As an innovation, the chapter has provided a more profound understanding of CIM as a developing concept in the realm of urban literature, encompassing the transition from theoretical principles to practical implementation. The innovation highlights the importance of incorporating evidence-based, collaborative, and participatory urban tools in urban planning and decision-making processes. Furthermore, this chapter examined related concepts like BIM, GIS, and GeoBIM, their hierarchical relationship, and the relevant scales. However, the main focus was on the fundamental concept of CIM, which is one of the basis of the smart city approach. Although these approaches help experts to acquire data, analysis, and simulation, It was emphasized that the collection and modeling of data are carried out on two scales, building and city, with the ultimate goal being to create an integrated and three-dimensional model of the future city. Regarding future research pathways, there is a need to investigate the impacts of CIM on specific urban factors, such as transportation, energy, and environmental sustainability. For example, future research can explore the potential of CIM in improving transportation systems and reducing traffic congestion, optimizing energy consumption and management, and promoting environmental sustainability by enhancing green infrastructure and reducing carbon emissions. Moreover, future studies can also focus on the practical application of CIM in real-life urban environments, assessing its effectiveness and identifying possible obstacles and limitations. Further research can examine the impact of CIM on citizen participation and community involvement, exploring its potential to increase public awareness, enhance community-based decision-making, and promote social fairness and empowerment. To summarize, the future research agenda should primarily concentrate on investigating the possibilities of CIM in addressing specific urban challenges, evaluating

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its effectiveness in promoting sustainability, and identifying potential obstacles and limitations in its implementation. In conclusion, this chapter highlighted the potential of CIM as a valuable tool in addressing the challenges of sustainable urbanization. It also emphasized the importance of data-driven decision-making, citizen participation, and the adoption of emerging technologies in achieving sustainable urban development. These would enable decision-making based on the knowledge acquired through these analyses. To further advance this field, future research should focus on the practical implementation of CIM in real-world urban settings, investigate its impact on sustainability, and identify potential challenges and limitations.

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S. Moroni, S. Cozzolino et al., Action and the city. Emergence, complexity, planning. Cities 90, 42–51 (2019) F. Noardo, L. Harrie, K. Arroyo Ohori et al., Tools for BIM-GIS integration (IFC georeferencing and conversions): Results from the GeoBIM benchmark 2019. ISPRS Int J Geo-Information 9, 502 (2020) H. Omrany, A. Ghaffarianhoseini, A. Ghaffarianhoseini, D.J. Clements-Croome, The uptake of City Information Modelling (CIM): A comprehensive review of current implementations, challenges and future outlook. Smart Sustain. Built Environ. 12(3) (2022) A.P. Pereira, M. Buzzo, I. Zimermann et al., A descriptive 3D city information model built from infrastructure BIM: Capacity building as a strategy for implementation. Int. J. E-Planning Res. 10, 138–151 (2021) Y. Qi, F.K.S. Chan, C. Thorne et al., Addressing challenges of urban water management in Chinese sponge cities via nature-based solutions. Water 12, 2788 (2020) R. Sacks, C. Eastman, G. Lee, P. Teicholz, Bim Handbook: A Guide to Building Information Modeling for Owners, Designers, Engineers, Contractors, and Facility Managers (John Wiley & Sons, 2018) A. Salles, M. Salati, L. Bragança, Analyzing the feasibility of integrating urban sustainability assessment indicators with city information modelling (CIM). Appl Syst Innov 6, 45 (2023) V. Soltes, J. Kubas, Z. Stofkova, Education as one of the indicators of quality of life, in INTED2018 Proceedings (2018), pp. 6849–6855 K. Sorensen, health literacy: A key attribute for urban settings. Optim. Heal. Lit. Improv. Clin. Pract., 1–16 (2018) L. Souza, C. Bueno, City Information Modelling as a support decision tool for planning and management of cities: A systematic literature review and bibliometric analysis. Build. Environ. 207, 108403 (2022) J. Stojanov, M. Malobabic, G. Stanojevic et al., Quality of sleep and health-related quality of life among health care professionals treating patients with coronavirus disease-19. Int. J. Soc. Psychiatry 67, 175–181 (2021) T. Stojanovski, J. Partanen, I. Samuels et al., City information modelling (CIM) and digitizing urban design practices. Built. Environ. 46, 637–646 (2020) S. Su, J. Ju, Q. Guo et al., A temporally dynamic model for regional carbon impact assessment based on city information modeling. Renew. Sustain. Energy Rev. 173, 113076 (2023) E.M. Thompson, P. Greenhalgh, K. Muldoon-Smith et al., Planners in the future city: Using city information modelling to support planners as market actors. Urban Plan 1, 79–94 (2016) United Nations, Department of Economic and Social Affairs, Population Division (2019). World Population Prospects 2019: Highlights (ST/ESA/SER.A/423). Accessed 30 June 2023 UN-Habitat (2020), The New Urban Agenda (United Nations Human Settlements Programme, United Nation) G. Vecchio, L. Tricarico, “May the Force move you”: Roles and actors of information sharing devices in urban mobility. Cities 88, 261–268 (2019) G. White, A. Zink, L. Codecá, S. Clarke, A digital twin smart city for citizen feedback. Cities 110, 103064 (2021) Z. Xu, M. Qi, Y. Wu et al., City information modeling: State of the art. Appl. Sci. 11, 9333 (2021) F. Xue, L. Wu, W. Lu, Semantic enrichment of building and city information models: A ten-year review. Adv Eng Informatics 47, 101245 (2021) J. Xue, Urban planning and degrowth: a missing dialogue. Local Environ. 27, 404–422 (2022) X. Zeng, Y. Yu, S. Yang et al., Urban resilience for urban sustainability: Concepts, dimensions, and perspectives. Sustainability 14, 2481 (2022) M. Zhao, Y. Lin, B. Derudder, Demonstration of public participation and communication through social media in the network society within Shanghai. Environ. Plan B. Urban Anal. City Sci. 45, 529–547 (2018)

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Chapter 3

Enhancing Health Outcomes Through City Information Modeling (CIM): A Case Study of Sydney, Australia Mohammad Anvar Adibhesami, Hirou Karimi, Borhan Sepehri, and Amirmohamad Parvanehdehkordi

Abstract A case study was conducted in Sydney, Australia, to explore the potential of City Information Modeling (CIM) in improving health outcomes. Sydney is a diverse and populous city with over 5 million residents, featuring a range of urban environments, from densely populated inner-city areas to sprawling suburban neighborhoods. The case study focused on how urban interventions impact health outcomes in Sydney, collecting data on the city’s physical, social, and economic characteristics, as well as health outcomes. By using this data, a 3D model of the city was created. CIM has been used for this model, which was utilized to evaluate how various urban interventions, such as the addition of green spaces or improvements to public transportation, affect health outcomes. The results of the case study analysis demonstrate that CIM can effectively identify areas of the city that are most vulnerable to health risks and assess the impact of urban interventions on health outcomes. However, the study also highlighted the need for better data collection and analysis, improved collaboration between public health professionals and urban planners, and the development of more sophisticated CIM tools. Overall, the case study in Sydney has shown that CIM has great potential for improving health outcomes. To realize this potential, it is crucial to have the right tools and collaboration, enabling CIM to effectively identify areas of the city most in need and evaluate the impact of interventions on health outcomes. M. A. Adibhesami (B) School of Architecture and Environmental Design, University of Science and Technology, Tehran, Iran e-mail: [email protected] H. Karimi Department of Architecture, Eastern Mediterranean University, Famagusta, Cyprus B. Sepehri Department of Urban Planning & Design, Tarbiat Modares University, Tehran, Iran A. Parvanehdehkordi Interuniversity Department of Regional and Urban Studies and Planning (DIST), Politecnico Di Torino, Torino, Italy © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 A. Cheshmehzangi et al. (eds.), City Information Modelling, Urban Sustainability, https://doi.org/10.1007/978-981-99-9014-6_3

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M. A. Adibhesami et al.

Keywords City Information Modeling (CIM) · Geographic Information Systems (GIS) · Urban Interventions · Health Outcomes

1 Introduction The health of city dwellers has become an increasingly pressing issue in contemporary urban contexts (Cappa et al. 2022). Urbanization and population growth are affecting the way people interact with their environments, with significant implications for health outcomes (Wang et al. 2022). Cities are often characterized by high levels of pollution, noise, heat, and social inequalities, which can exacerbate health risks and challenges such as respiratory diseases, cardiovascular disorders, mental health issues, and poor quality of life (Barton et al. 2015; Urban Health n.d.). Therefore, it is crucial to improve the health and well-being of urban populations by designing and managing healthier, more resilient, and sustainable cities. One promising approach to achieve this goal is through the use of city information modeling (CIM). CIM is a 3D digital representation of the built environment that allows for the integration and analysis of various data streams related to urban systems, such as building structures, transport networks, green spaces, and social demographics (Omrany et al. 2022). CIM offers a powerful tool for urban planners, policymakers, and researchers to simulate and visualize the potential impacts of urban interventions, optimize resource management and service delivery, and enhance collaboration and stakeholder engagement (Souza and Bueno 2022). Recent studies have explored the potential of CIM in a range of urban domains, including energy efficiency, disaster response, biodiversity, and mobility, with promising results (Alhamwi et al. 2017). However, less attention has been paid to the potential of CIM to improve health outcomes in cities. By harnessing the power of CIM to model and analyze urban health factors, such as air quality, noise exposure, green infrastructure, and social connectivity, researchers and practitioners can better understand the complex interactions between the built environment and human health and design evidence-based strategies to promote health equity and resilience in urban populations. Therefore, this study aims to explore the potential of city information modeling for improving health outcomes in the case of Sydney, Australia. The study will use a case study approach, which involves the application of CIM software to model and visualize the health-related aspects of the built environment in Sydney, based on secondary data sources. The research objectives of this study include: 1. To identify the key health challenges faced by urban populations in Sydney, based on a review of the literature and local health data. 2. To develop a CIM-based model for assessing the health impacts of urban interventions in Sydney, based on the integration of various health-related data sources and spatial analysis techniques.

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3. To analyze the potential benefits and limitations of using CIM for improving health outcomes in Sydney, based on the findings of the case study, and to provide recommendations for future research and practice in this area. In conclusion, this study seeks to advance the understanding of the potential of city information modeling for improving health outcomes in cities. By using a case study approach to explore the specific context of Sydney, this study will provide insights into the opportunities and challenges of integrating CIM-based approaches into urban health planning and management. The findings of this study could inform the development of evidence-based policies and interventions to promote health equity and sustainability in urban environments.

2 Background Knowledge The potential of city information modeling (CIM) to improve health outcomes in urban areas has received little attention in the literature compared to other areas of research. Nevertheless, studies in urban health, environmental health, and social determinants of health have highlighted the critical role of the built environment and its related factors in shaping health and well-being in cities. In this section, we review the relevant literature on CIM and health outcomes, focusing on these three themes, and discuss the gaps, challenges, and potentials of CIM-based approaches to address health issues in cities.

2.1 Urban Health Urban health refers to the health status of people living in urban areas, which is shaped by various social, economic, and environmental factors (Ramirez-Rubio et al. 2019). Urbanization has resulted in a growing number of health challenges for urban populations, such as air pollution (Liu et al. 2022), water contamination (Shaharoona et al. 2019), noise exposure (Schäffer et al. 2020), limited access to green spaces (Huang et al. 2022) and healthy food (Pandey et al. 2020), and social isolation (“Urbanization and Emerging Mental Health Issues” n.d.). Researchers and policymakers have increasingly recognized the need to address these challenges through integrated and evidence-based approaches that promote the health and well-being of urban dwellers. CIM has the potential to contribute significantly to urban health by providing a 3D digital representation of the built environment that enables the integration and analysis of various urban data streams, including demographics, buildings, infrastructure, transportation, and other environmental factors. For example, CIM can be used to model and simulate the potential impact of urban interventions, such as green roofs, bike lanes, and street lighting, on health outcomes such as air quality, physical activity, and mental health (Cao et al. 2023). The use of CIM can also facilitate

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M. A. Adibhesami et al.

the identification of urban health inequities by visualizing the distribution of healthrelated factors across different neighborhoods and population groups (Alam et al. 2010). Despite these potentials, the literature on CIM and urban health is limited. Few studies have explored the use of CIM for urban health management, and those that do exist are often focused on specific health outcomes or geographic contexts. More research is needed to assess the utility of CIM for understanding and improving urban health on a larger scale.

2.2 Environmental Health Environmental health refers to the health impacts of the natural and built environments on human populations. Environmental factors such as air quality, water quality, noise, temperature, and radiation have significant impacts on human health, often leading to respiratory, cardiovascular, and other chronic diseases (Pinter-Wollman et al. 2018). Urban areas are particularly vulnerable to environmental health risks due to high levels of pollution, overcrowding, and inadequate public services. CIM has the potential to contribute significantly to environmental health by providing a comprehensive understanding of the interactions between the built environment and environmental factors. For example, CIM can help to model and predict the distribution of air pollutants and their health impacts across different urban areas, and assess the effectiveness of interventions in reducing pollution levels (Xu et al. 2021). CIM can also be used to explore the relationships between green spaces, urban heat islands, and the health and well-being of urban populations (Wadumestrige Dona et al. 2021). Despite these potentials, the use of CIM in environmental health research is still in its early stages. One of the main challenges is the lack of standardized data and modeling methods that are needed to integrate and analyze complex environmental health data. There is also a need to consider the social and behavioral factors that shape environmental health outcomes and how these can be incorporated into CIM-based approaches.

2.3 Social Determinants of Health Social determinants of health refer to the social and economic factors that impact health and well-being, such as income, education, housing, and social networks (McNeely et al. 2020). Health inequities arising from social determinants remain a significant challenge in urban areas, with disadvantaged groups often experiencing poorer health outcomes than their more affluent counterparts. In recent years, researchers and policymakers have emphasized the importance of addressing social determinants of health to promote health equity and reduce health disparities.

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CIM has the potential to contribute significantly to social determinants of health by providing insights into the spatial distribution of these factors across different urban areas. For example, CIM can be used to identify areas with limited access to healthy food, safe housing or public services, and assess the impact of interventions aimed at addressing these inequities (Rigolon et al. 2021). CIM can also facilitate participatory approaches that engage communities in the planning and design of their neighborhoods and improve social cohesion and community empowerment (Van Leeuwen et al. 2018).

3 Methodology A case study approach was adopted to explore the potential of CIM for improving health outcomes in the case of Sydney, Australia. The case study approach involves the in-depth analysis of a specific case to gain insights into its unique characteristics and dynamics (Yin 2018). The selection criteria for the case study included a focus on a large urban area with a range of health challenges, including air pollution, urban heat, social inequalities, and inadequate access to green spaces and healthy food. Sydney was selected as it met these criteria and provided a rich and diverse context for exploring the potential of CIM in urban health. The case study approach was suited for this study as it allowed for an in-depth, contextualized analysis of the complex relationships between health, environment, and infrastructure in Sydney. The selection of Sydney as the case study was based on its significant health issues related to air pollution, urban heat, lack of green spaces, and social inequalities. Additionally, Sydney is actively working to develop sustainable solutions, making it an ideal case for exploring CIM approaches.

3.1 Data Collection and CIM Software The data for this study was collected from secondary sources, including government reports, scientific publications, and open-access datasets. The data sources included demographic data, urban infrastructure data, health data, and environmental data. These data sets were assembled into a GIS-based CIM software that allowed for the integration and visualization of this data. The CIM software used in this study was Esri CityEngine, a 3D modeling software that allows users to create virtual environments from GIS data. CityEngine allows for the integration of various data sources and data formats and provides a range of tools for 3D modeling, analysis, and visualization. The data sources used in this study were diverse, including demographic data, urban infrastructure data, health data, and environmental data. For example, air pollutant data was collected from the NSW EPA (2019), land surface temperature (LST) maps were produced using remote sensing data provided by ASTER, which was

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accessed via the USGS EarthExplorer platform, and data on the spatial distribution of built-up areas, public transport networks, pedestrian pathways, and open spaces across Sydney was obtained from the NSW Spatial Collaboration Portal. The main data were collected in 3 months from January 2019 to January 2020.

3.2 Health Indicators and Spatial Analysis The study employed a range of health indicators and spatial analysis techniques to assess the potential of CIM for improving health outcomes. Specifically, we aimed to explore the correlation between health outcomes related to air pollution, urban heat islands, social connectivity, and access to green spaces. To model the distribution of air pollutants and related health outcomes, the data was interpolated to predict the distribution of pollutant levels across Sydney using the Spatial Analyst toolset in ArcGIS Pro. To examine the Urban Heat Island Effect (UHIE), land surface temperature (LST) maps of Sydney were produced using remote sensing data provided by ASTER. The LST map was then transformed into a 3D heat map using the 3D Analyst toolset in ArcGIS Pro. To measure social connectivity, a Social Connectivity Index (SCI) was produced by analyzing data on the spatial distribution of built-up areas, public transport networks, pedestrian pathways, and open spaces across Sydney from the NSW Spatial Collaboration Portal. To model access to green spaces, a Green Accessibility Index (GAI) was created by analyzing data provided by the Greater Sydney Commission (2019), which included the spatial location of parks, nature reserves, and waterways across the city. These spatial analysis techniques allowed us to visualize the spatial distribution of various health factors in Sydney, including air pollutants, urban heat, social connectivity, and access to green spaces. By creating 3D visualizations of these factors, we could gain insights into the patterns and relationships between these factors and assess the potential of CIM to inform evidence-based health interventions in the urban context.

3.3 Case Study Introduction and Analysis The study was carried out in Sydney, which is the capital of the state of New South Wales and one of the most populous cities in Australia. Geographically, it is situated on the west coast of the Tasman Sea (He et al. 2020). The study area of New South Wales in Australia lies between 144°E-155.0°E and 28°S-37.5°S. The City of Sydney is one of the 33 local government areas (LGAs) in Greater Sydney. Despite having less power than its international counterparts, Sydney is still the most populous city in Australia with 4,321,535 people, and the state of New South Wales is the most populous state in the country with 7,480,228 people, according to the 2016 Australian Census (Astell-Burt and Feng, 2019).

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For data collection purposes, the study focused on four districts within Sydney city, namely Randwick, Rozelle, Earlwood, and Wollongong. These districts were selected due to their diverse characteristics and health challenges related to air pollution, urban heat, social inequalities, and inadequate access to green spaces and healthy food. Randwick is located in the eastern suburbs and is characterized by high-density residential areas, busy roads, and a large hospital. Rozelle is situated in the inner-west and is known for its mix of residential and industrial areas. Earlwood is located in the south-west and is mainly residential, with a large proportion of older residents. Wollongong is located in the south and is a coastal suburb with a mix of residential and industrial areas. The study used GIS-based CIM software to analyze the spatial distribution of health indicators related to air pollution, urban heat, social connectivity, and access to green spaces in these four districts. The analysis revealed significant variations in health indicators across the districts, with some areas showing high levels of air pollution and urban heat. In contrast, others had better access to green spaces and social connectivity. Based on the analysis, the study identified several potential CIM interventions that could help improve health outcomes in these districts. These interventions included increasing the number of green spaces and trees, improving public transport infrastructure, promoting active transport, and reducing air pollution from industrial and transportation sources. The study also highlighted the need for community engagement and participation in designing and implementing CIM interventions to ensure their effectiveness and sustainability, for data collection purposes, the study focused on four districts within Sydney city, namely Randwick, Rozelle, Earlwood, and Wollongong, as illustrated in Fig. 1.

4 Results The study presents data related to air pollution in five classes: nitrogen dioxide (NO2 ), ozone (O3 ), and particles less than 10 µm (PM10 ) and 2.5 µm (PM2.5 ) in diameter. The data were collected from four selected stations, and the average of variables in a year was calculated. The study analyzed the trends in air pollutants for each selected neighborhood. The results showed that the amount of SO2 remained constant in all neighborhoods, while NO2 and PM2.5 increased throughout the year. PM10 showed varying trends, but on average, they increased throughout the year. O3 showed slight increases in some neighborhoods. The study also analyzed the distribution of green spaces in Sydney and found that the majority of green spaces are designated as Special, Bushland, Garden and Parks, and OSL. The study analyzed social and economic aspects and found an increasing trend in population, average age, and population density in Sydney. The number of salary earners and their salaries also increased over the years. Health data were evaluated, and the results showed an increase in overweight and obesity rates in the NSW adult population. Cardiovascular disease death rates have decreased in the last 20 years, while diabetes and asthma

40

M. A. Adibhesami et al.

Fig. 1 Case study location (Source map generated by the authors)

rates have increased. The study also analyzed the temperature differences among neighborhoods and found that urban areas of Sydney typically experience much higher temperatures compared to surrounding suburban areas. Overall, the study provides a comprehensive analysis of air pollution, green space distribution, social and economic aspects, health, and temperature differences in Sydney. The results can be useful for policymakers to make informed decisions regarding environmental and health issues in the city. Data related to air pollution are presented in five classes and in Tables 1 to 4. These five classes are as follows: • • • • •

Nitrogen dioxide (NO2 ); Nitrogen dioxide (NO2 ); Ozone (O3 ); Particles less than 10 µm in diameter (PM10 ); Particles less than 2.5 µm in diameter (PM2.5 ).

The records from the source are based on the average of each day. As the data analyses process, the average of variables in a year is calculated, and the mean, minimum and maximum parameters during the study period are indicated. To prove the details of the information, the number of days with data are shown in the Tables. Finally, variables are compared among four selected stations. To better understand the collected data, average air pollutant figures were created for each neighborhood. Figure 2 illustrates the trends in the Randwick neighborhood, where the amount of SO2 remained constant and NO2 showed a slight increase in

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41

Table 1 Air pollution data between January and March Jan-Mar Stations

Statistics

SO2

NO2

O3

PM10

PM2

Randwick

Average

0.083478

0.394017

1.859086

17.81666

5.354342

Minimum

–0.1

0

0

0

–8.5

Median

0

0.1

1.8

16.6

4.7

Maximum

2.4

4

6.9

81.9

31.7

Count

1955

2039

2058

2149

2142

Average

0.050849

0.517231

1.292405

16.79491

5.828956

Minimum

–0.1

–0.2

0.1

–6.7

–8.2

Median

0

0.4

1.7

15.5

5.2

Maximum

1.3

3.6

7.6

63

92.4

Count

1707

1683

1653

1788

1782

Average

–

1.56597

0.075086

15.98132

5.33675

Minimum

–

–0.1

–0.1

–6.3

–8.3

Median

–

1.5

0

14.6

4.8

Maximum

–

8.7

2.4

69.7

25.6

Count

–

2057

2035

2098

2117

Average

0.075086

0.330572

1.692014

18.66278

5.457157

Minimum

–0.1

–0.3

0

–9.8

–9.1

Median

0

0.2

1.6

16.8

4.5

Maximum

2.4

2.7

7.1

74.9

58.6

Count

2035

2028

2016

2077

2047

Rozelle

Earlwood

Wollongong

the middle of the year. The amount of PM2 increased throughout the year, while the amount of PM10 decreased and then increased in the middle of the year. The amount of O3 also showed a slight increase. Similarly, Fig. 3 depicts the trends in the Rozelle neighborhood, where the amount of SO2 remained constant and showed a slight increase in the middle of the year. O3 showed an increase towards the end of the year. The amount of PM2 and PM10 had varying trends, but on average, they increased throughout the year. In the Earlwood neighborhood, as shown in Fig. 4, the data indicate that the amount of O3 remained stable, while the average NO2 increased slightly. The average PM2 increased until the middle of the year and then started to decline towards the end of the year. The average PM10 decreased first and then showed an increase. Finally, Fig. 5 shows the average pollution particle levels in the Wollongong neighborhood. The figure suggests that O3 and SO2 levels remained stable while the average PM2 levels increased slightly. The amount of PM10 showed a sharp decrease first and then a sharp increase. In addition to collecting pollution data, this research also gathered information on the amount of green space in the selected neighborhoods. To achieve this, maps

42

M. A. Adibhesami et al.

Table 2 Air pollution data between April and June Apr-Jun Stations

Statistics

SO2

NO2

O3

PM10

PM2

Randwick

Average

0.078723

0.781121

1.556813

13.67423

4.780044

Minimum

–0.1

0

0

0

–7.9

Median

0.1

0.6

1.8

13.3

4.2

Maximum

1.6

2.7

3.5

45.6

27.4

Count

752

821

866

908

907

Average

0.048907

0.768124

1.322923

12.72306

4.661369

Minimum

0

–0.1

0

1.5

–6.9

Median

0

0.7

1.5

12.2

4.4

Maximum

2.4

2.8

3.3

42.8

23.9

Count

869

869

870

902

906

Average

–

1.220897

0.044074

12.6722

5.435171

Minimum

–

–0.1

0

–9.9

–4.4

Median

–

1.3

0

12.25

5.1

Maximum

–

3.2

0.8

39.7

29.2

Count

–

847

869

910

907

Average

0.044074

–

1.70794

12.54966

4.183811

Minimum

0

–

0

–6.1

–7.1

Median

0

–

1.9

11.4

3.8

Maximum

0.8

–

3.8

47.8

22

Count

869

–

869

876

908

Rozelle

Earlwood

Wollongong

were created using ArcGIS. Figure 6 displays the uses of green spaces throughout the city of Sydney. The map reveals that the majority of green spaces in Sydney are designated as Special, Bushland, Garden and Parks, and OSL. To aid in understanding the distribution of green spaces in Sydney, a map was created at three different scales: Regional, District, and Local, as depicted in Fig. 7. The map highlights that the majority of large-scale green spaces are situated on the periphery of Sydney’s urban core. Upon closer inspection of the neighborhoods, it becomes evident that the Rozelle neighborhood has a lower park density, and this lack is even more pronounced in the Randwick neighborhood, with only a few local parks present. Conversely, the map in Fig. 7 shows that the Earlwood neighborhood is adjacent to several regional parks. The Wollongong neighborhood not only has a few local parks but is also located near regional parks. Table 5 reveals that there are fewer regional parks compared to district and local parks, and they occupy less space. While local parks have the largest area, they also have the highest number in comparison to district and regional parks. Figure 8 depicts the 100-m buffers of local parks and the 500-m buffers of district parks. The figure highlights that the urban park system in Sydney is well-connected,

3 Enhancing Health Outcomes Through City Information Modeling …

43

Table 3 Air pollution data between July and September Jul–Sep Stations

Statistics

SO2

NO2

O3

PM10

PM2

Randwick

Average

0.064854

0.766224

1.944704

15.25082

6.713327

Minimum

0

–0.2

0

0

–9.8

Median

0

0.5

2.2

13.9

6

Maximum

1.3

3.3

5.1

99.2

75

Count

2060

2031

2058

2123

2071

Average

0.040398

1.018602

1.3

14.61312

7.25237

Minimum

–0.1

–0.1

0

–5.6

–9.7

Median

0

0.8

2

13.4

5.3

Maximum

1.6

3.7

5.1

116.1

100.7

Count

2062

2075

2071

2165

2110

Average

–

1.530307

0.039961

15.10489

7.859811

Minimum

–

–0.1

–0.2

–2.6

–8.8

Median

–

1.7

0

13.6

6

Maximum

–

5.1

1.4

118.6

41.8

Count

–

2082

2037

2190

2013

Average

0.039961

–

2.101892

13.01803

5.671458

Minimum

–0.2

–

0

–8.8

–9.1

Median

0

–

2.5

10.5

4.25

Maximum

1.4

–

4.6

95.3

73.5

Count

2037

–

2061

2157

1962

Rozelle

Earlwood

Wollongong

and as shown in Fig. 9, the network of district and local parks has been able to cover 70–80% of the neighborhoods; hence, access to urban parks is strong. This research also analyzed social aspects, and Table 6 presents the population data of Sydney between 2016 and 2020. The table indicates an increasing trend in population, average age, and population density over the mentioned period. Table 7 provides insights into the economic aspect of the study. The data reveals an increase in the number of salary earners, with an average age of 37 years. Moreover, the table illustrates a positive trend in the salaries of earners over the years. As part of the case study analysis, health data were also evaluated. Overweight and obesity rates in the NSW adult population have increased steadily over the past 15 years (HealthStats NSW n.d.). While overweight rates have remained stable, obesity continues to rise. Figures 10 and 11 show that 23.0% of children aged 5 to 16 (23.2% of boys and 22.7% of girls) were overweight or obese, and 57.8% of adults aged 16 and over (63.9% of men and 51.8% of women) were overweight or obese (HealthStats NSW—Overweight and Obesity in Adults n.d.). Figure 12 shows cardiovascular disease death statistics. In 2020, 13,600 NSW residents died from cardiovascular disease (26% of all deaths), the second highest

44

M. A. Adibhesami et al.

Table 4 Air pollution data between October and December Oct-Dec Stations

Statistics

SO2

NO2

O3

PM10

PM2

Randwick

Average

0.075628

0.225993

2.410683

19.61561

6.719742

Minimum

0

–0.2

0

0

–10

Median

0.1

0

2.4

17.9

6.1

Rozelle

Earlwood

Wollongong

Maximum

1.4

2.5

7.6

86.1

47.7

Count

1990

1989

2050

2146

2097

Average

0.040068

0.505082

2.592

18.34493

6.231912

Minimum

–0.1

–0.1

0.1

–9

–6.7

Median

0

0.3

2.2

16.2

5.4

Maximum

1.3

2.9

5.8

136.4

104

Count

2049

2007

2071

2190

2181

Average

–

2.016374

0.081927

18.43274

6.291382

Minimum

–

–0.1

0

–9.6

–9

Median

–

2.1

0

16.1

5.5

Maximum

–

6

1.4

91.1

77

Count

–

1991

2086

2187

2170 6.805502

Average

0.081927

–

2.080305

20.52181

Minimum

0

–

0

–9.9

–8.7

Median

0

–

2.1

17.9

5.7

Maximum

1.4

–

7.2

121.9

37

Count

2086

–

2031

2169

2163

25

20

15

10

5

0 Jan-Mar

Apr-Jun SO2

NO2

Jul-Sep O3

PM10

Oct-Dec PM2

Fig. 2 Ranwick average air pollution (Source The Authors using the data in Tables 1 to 4)

3 Enhancing Health Outcomes Through City Information Modeling …

45

20 18 16 14 12 10 8 6 4

2 0

Jan-Mar

Apr-Jun SO2

Jul-Sep

NO2

O3

Oct-Dec

PM10

PM2

Fig. 3 Rozelle average air pollution (Source The Authors using the data in Tables 1 to 4)

20 18 16 14 12 10 8 6 4 2

0 Jan-Mar

Apr-Jun NO2

Jul-Sep O3

PM10

Oct-Dec PM2

Fig. 4 Earlwood average air pollution (Source The Authors using the data in Tables 1 to 4)

cause after cancer, with a death rate of 114.7 per 100,000 (HealthStats NSW—Cardiovascular Disease Deaths, Total n.d.). The cardiovascular death rate has decreased in the last 20 years after adjusting for population aging, from 261.7 per 100,000 in 2001 to 114.7 per 100,000 in 2020 (HealthStats NSW—Diabetes Prevalence in Adults n.d.). Figure 13 presents diabetes rate data. It shows that in NSW, 11.1% of adults aged 16 and over (12.3% of men and 10.3% of women) have been told they have diabetes or high blood glucose. Many people with undiagnosed diabetes likely exist in NSW. The prevalence of diabetes or high blood glucose has increased from 2002 to 2019 for

46

M. A. Adibhesami et al.

25 20

15 10 5 0 Jan-Mar

Apr-Jun SO2

Jul-Sep O3

PM10

Oct-Dec PM2

Fig. 5 Wollongong average air pollution (Source The Authors using the data in Tables 1 to 4)

both men and women in NSW, with consistently higher rates for men (HealthStats NSW—Asthma Deaths n.d.). Figure 14 shows asthma death rates. In 2020, there were 154 asthma-related deaths among NSW residents. The asthma death rate has been stable over the past 10 years at 1.4 per 100,000 population in 2020 and 1.3 per 100,000 in 2011. From 2018 to 2020, age-standardized mortality rates were higher in outer regional and remote areas (2.5 per 100,000) and deprived areas (2.3 per 100,000) (HealthStats NSW—Asthma Deaths n.d.). Figure 15 shows that in 2021, 16.9% of NSW adults experienced high or very high psychological distress (14.2% of men and 19.5% of women). The percentage reporting high distress remained constant from 2019 to 2021 but steadily increased from 9.8% in 2013 to 17.7% in 2019 (HealthStats NSW—High or Very High Psychological Distress in Adults n.d.). As shown in Fig. 16, urban areas of Sydney typically experience much higher temperatures compared to surrounding suburban areas. According to the map, Earlwood appears to be the only neighborhood with a noticeably lower temperature relative to the other neighborhoods specified.

5 Discussions In this study, we developed a CIM for the City of Sydney by gathering and combining data in four categories: air pollution modeling, urban heat islands, social connectivity, and access to green areas. An integrated review of this CIM and the creation of a data network that can be examined and studied concerning its impact on urban health

3 Enhancing Health Outcomes Through City Information Modeling …

47

Fig. 6 Use of green spaces in Sydney (Source map generated by the authors)

revealed the creation of direct and indirect connections between data from various sectors. The results showed that air pollutants, particularly PM10 and PM2 , had increased to some degree in each of the analyzed neighborhoods during the time period, which can be harmful to the health of the neighborhood’s residents. It was determined that

48

M. A. Adibhesami et al.

Fig. 7 Sydney’s green spaces at regional, district and local scales (Source map generated by the authors)

PM2 contributed to citizen deaths in research by Southerland VA and others (Southerland et al. 2022). Additionally, it has been noted in another piece of research that the amount of PM2 in the air should not be underestimated (Zhang et al. 2022). Additionally, other research has demonstrated that lower air pollution (PM10 ) is associated

3 Enhancing Health Outcomes Through City Information Modeling … Table 5 Number and area of Sydney parks Sum of area

Count of parks

49

Category

Area(sqm)

Regional parks

1,139,589.8

District parks

19,924,640

Local parks

46,971,291

Category

Count

Regional parks

207

District parks

420

Local parks

6,620

Fig. 8 Buffer of District and Local Parks in Sydney (Source map generated by the authors)

with greater life satisfaction, self-esteem, and resilience to stress (Petrowski et al. 2021). In this study, we also assessed how easy it was to access green spaces. According to the research, Earlwood and Wollongong have better access to green spaces than other Sydney neighborhoods, which is why these two areas had lower average pollution levels. This effect is because the air in these neighborhoods is cleaned by green spaces. Also, according to the study’s findings, trees can prevent the transfer of concentrated pollutants in this regard (Rui et al. 2019). The same function of urban parks is mentioned in another study (Xing and Brimblecombe 2019). Another study questions the importance of urban parks as "lungs," finding that pollutant removal

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M. A. Adibhesami et al.

Fig. 9. 250, 500 and 1000 m buffer in the city of Sydney (Source map generated by the authors)

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51

Table 6 Demographic data Measure Description

2016

2017

2018

2019

2020

2021

Estimated resident population (no.)

5,024,923

5,116,610

5,184,555

5,248,704

5,284,879

5,259,764

Population density (persons/km2)

406.3

413.7

419.2

424.4

427.3

425.2

Median age persons (years)

35.8

35.9

36.1

36.2

36.7

37.1

Table 7 Economic data Measure Description

2016

Employee income earners (no.)

2,587,128 2,699,552 2,763,209 2,826,608

Employee income earners - median age (years)

37

Total employee income ($m)

171,196.5 179,966.9 191,004.8 200,978.2

Median employee income ($)

51,814

52,208

54,123

55,857

Mean employee income ($)

66,172

66,665

69,124

71,102

78.7

79.2

79

Employee income as main source of income (%) 78.3

2017 37

2018 37

2019 37

Fig. 10 Obesity statistics in children (adapted from HealthStats NSW—Overweight and Obesity in Adults n.d.)

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M. A. Adibhesami et al.

Fig. 11 Obesity statistics in adults (adapted from HealthStats NSW—Overweight and Obesity in Adults n.d.)

Fig. 12 Mortality trends due to heart disease (adapted from HealthStats NSW—Cardiovascular Disease Deaths, Total n.d.)

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53

Fig. 13 The course of diabetes (adapted from HealthStats NSW—Diabetes Prevalence in Adults n.d.)

Fig. 14 Mortality rate due to asthma (adapted from HealthStats NSW—Asthma Deaths n.d.)

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M. A. Adibhesami et al.

Fig. 15 Process of psychological distress (adapted from HealthStats NSW—High or Very High Psychological Distress in Adults n.d.)

by vegetation is unlikely to have a significant impact on indoor air quality in small parks, which are frequent in densely populated areas. Dense tree canopies also hinder dispersion, which raises local pollutant concentrations (Xing and Brimblecombe 2020). It should be noted that the scale, texture, and properties of vegetation all play a role in the significant, multi-mechanistic effects of green spaces on the concentration of suspended particles in the air (Diener and Mudu 2021). The results of this study also showed us that Sydney has a good network of urban green space buffers. According to research, green buffers have the biggest impacts on urban health when they are between 400 and 1600 m long (Zhang and Tan 2019). Therefore, it can be assumed that the presence of neighborhood parks in Sydney and the studied areas may have a significant impact on the general health of the population. In addition to these findings, the data revealed that Sydney’s population density, average age, and total population are all rising. Urban health policies should, therefore, take this rising trend into consideration. One of the most important ways is through urban information modeling to promote health. Because predicting urban services and urban design related to an aging society that lives in an increasing population density can be effective in increasing their useful and healthy life span, various city research studies and projects have proposed different solutions to reduce congestion and increase urban health. Creating local services and 15-min neighborhood units is one of these solutions. Amir Reza Khavarian-Garmsir and others praise this approach to neighborhood design in terms of social, economic, and environmental sustainability (Khavarian-Garmsir et al. 2023). Using green transportation can also be a solution. It seems that green transportation and urban design to increase physical mobility can be useful in reducing pollution and increasing public health. The results show that better urban and transportation planning can lead to carbon-free,

3 Enhancing Health Outcomes Through City Information Modeling …

Fig. 16 Average temperature of Sydney city (Source map generated by the authors)

55

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M. A. Adibhesami et al.

more livable, and healthier cities, especially through land use change, moving from private motorized transportation to public and active transportation, and greening cities (Nieuwenhuijsen 2020). In addition, in research conducted in China, it was shown that urban green open space plays an important role in promoting physical activity, especially among women and the elderly, and improving characteristics (such as accessibility, infrastructure, green space area, open space extent, and facilities). recreational) has an urban green space. Open spaces and efforts to establish suitable group sports for playing with companions (such as "square dancing" and "tai chi") can promote physical activity among Chinese residents to improve public health. The results are significant for facilitating environmental health (Wang et al. 2019). It should be noted that the increase in population density in a world that has recently experienced a pandemic can also be dangerous in terms of the easy and rapid transmission of various microbes (Seidlein et al. n.d.). On the other hand, the economic data indicated economic stability and increased income for the citizens of Sydney. For example, in research conducted during the pandemic, the lack of understanding of economic stability as a factor in the increase in post-traumatic stress disorder (Di Crosta et al. 2020) should be considered. On the other hand, having a high and stable income can also be useful in terms of improving well-being and mental health, but the results show that it can increase negative effects, such as the desire to be obese (Ren et al. 2019). Obesity in Sydney has also been a growing trend among people. Additionally, studies have shown that poor and lowincome communities are more likely to experience mental health decline (Iemmi 2021). Therefore, it is necessary for city planners to include economic data, which also affects the health of citizens, along with other data, in their plans. Interestingly, CIM is possible because of the vast amount of data and the direct and indirect relationships between them. In the continuation of this research, it is possible to provide general data on the health trends of Sydney citizens, which also cover the four studied locations. The data showed that obesity was on the rise among the citizens of Sydney. It seems that one of the main causes of obesity is inactivity. Urban health researchers have found the solution to increasing physical activity by encouraging active transportation such as walking and cycling. In this regard, the results of a study in Japan showed that the development of active environmental-friendly policies for (re)designing neighborhoods may not only promote active transportation behaviors but also help improve the health status of residents in non-Western contexts (Koohsari et al. 2019). Another study showed that increasing the quality of urban green spaces can help more people engage in physical activity and possibly reduce the risk of obesity (Knobel et al. 2021). On the other hand, we saw that the city of Sydney has a suitable number and area of local parks. In this regard, research has pointed to the beneficial role of Sydney’s green spaces in the well-being and mental health of its citizens (Astell-Burt and Feng 2021). Also, in another study in 2017, a significant relationship was shown between nearby green spaces and increased physical activity in Sydney (Astell-Burt and Feng 2021). But despite these findings, the trend of increasing obesity continues, and more research needs to be done by considering more diverse variables.

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Health data also reported a decreasing trend in cardiovascular diseases. This decreasing trend could have something to do with the existence and expansion of Sydney Urban Park. Other research studies have emphasized the effectiveness of green spaces in reducing cardiovascular diseases. For example, a study conducted by Sumin Seo and others stated that living in urban areas with more green space coverage may reduce the risk of CVD (cardiovascular disease). Urban planning intervention policies that increase urban green space coverage can help reduce CVD risk (Seo et al. n.d.). But the data shows that, unlike cardiovascular diseases, diabetes has been increasing in recent years. As mentioned earlier, economic well-being and income stability can play a role in obesity, which is one of the causes of diabetes. Therefore, it can be claimed that being exposed to physical activity and being in green spaces has a positive effect on reducing the incidence of diabetes, especially type-2. All four of the evaluated neighborhoods have high potential. In this regard, evidence shows that individuals and communities exposed to green spaces, especially in their neighborhoods, reduce the risk of type-2 diabetes, reduce the risk of obesity, and increase the likelihood of physical activity. The onset of T2DM can be modulated by using green spaces, improving physical activity levels, and reducing the risk of overweight and obesity (De La Fuente et al. 2021). Also, the findings showed that mortality due to asthma was somewhat constant, while the ideal situation is to have a decreasing trend. But from 2019 to 2020, the death rate due to asthma has increased, which can be related to the increase in air pollutants. It is also a strong hypothesis that the existence of green spaces can be useful in reducing asthma, but its effectiveness on the scale of Sydney requires deeper and more specialized research in this field. Surprisingly, the results of one study stated that there is no scientific consensus that urban trees reduce asthma by improving air quality. In some circumstances, urban trees can reduce air quality and increase asthma (Eisenman et al. 2019). This fact may be adequate to comprehend why Sydney has not played a role in lowering asthma despite the structure of its network of suitable green areas. However, Li et al. (2023) reported in their study that the occurrence of asthma exacerbations was positively associated with exposure to air pollution related to traffic, energy-related drilling activities, and older housing stock and negatively associated with green space (Li et al. 2023). Finally, the findings showed that mental distress has increased in recent years. This rise could be linked to the onset of the COVID-19 pandemic because research has pointed to a direct association between the two (Wang et al. 2020). In any case, the relationship between reducing psychological distress and being exposed to green space has been emphasized (Feng et al. 2022). Hampenin’s previous research results demonstrated strong indirect impacts of both neighborhood nature characteristics accessible to the public on lowering psychological distress and reducing the risk of negative mental health through enhancing the sense of SoC (sense of community belonging) (Rugel et al. 2019). Finally, they reported the existence of a heat island in metropolitan locations, which can support earlier findings from this research. The findings of other research studies have demonstrated that heat islands have major consequences on public health, quality of life, and air pollution. The effect of UHI has raised the incidence of heat stress, heat exhaustion, weariness, and suicidal

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tendencies. UHI can also destabilize and change the air circulation pattern around cities, which can cause precipitation in nearby areas, thereby creating new ecological consequences (Rugel et al. 2019).

6 Conclusions In conclusion, this case study of Sydney demonstrated the potential of City Information Modeling (CIM) approaches to address urban health challenges. By integrating data on health, environment, and urban infrastructure factors in Sydney, the study was able to identify relationships and patterns that could inform CIM-based interventions. More specifically, the study found evidence of increasing air pollution, rates of chronic diseases like obesity and diabetes, and urban heat islands in Sydney. At the same time, the city exhibited positive indicators like rising socioeconomic status, increasing access to green spaces, and declining mortality from cardiovascular disease. The associated spatial analysis techniques allowed for the visualization and modeling of the spatial distribution of these factors across Sydney. While CIM is still a new concept, this study showed its promise as a tool to support evidence-based decision-making for urban health. CIM could be used by city officials and policymakers in Sydney to design targeted interventions, for example, to curb air pollution in areas with vulnerable populations, increase access to green spaces in neighborhoods with limited connectivity, or implement heat mitigation measures in urban heat islands. By taking a holistic approach to understanding the complex relationships between health, environment, and city systems, CIM can provide a robust framework for developing solutions suited to local contexts. This study offers a model for conducting CIM-based analysis using widely available GIS tools and open data sources. With the growing data availability and computing power, CIM approaches are well poised to transform how we build and manage cities to advance population health. Overall, this conclusion reinforces the key results and arguments made in the study about the potential of CIM for urban health based on the case study of Sydney. It restates the relationships found between health, environment and infrastructure factors, as well as the opportunities for CIM to inform interventions and policies to address the identified challenges. The conclusion also touches on the broader implications of CIM as a relatively new approach to advancing urban health and sustainability. Furthermore, a growing body of evidence has demonstrated the potential benefits of integrating and mining health data for public health purposes, particularly in the context of designing sustainable and healthy urban environments. Overall, the application of CIM has the potential to inform and promote urban design policies that are aligned with the health needs of society.

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Chapter 4

City Information Modeling and Its Applications: A Review Xiang Zhang

Abstract Over the past few decades, there has been a growing interest in the field of City Information Modeling (CIM). CIM is generally considered a digital representation of a city which can empower the identification of optimal approaches to enhance urban environments. CIM is extensively used in various applications, primarily under the umbrella of smart cities. This chapter first provides a brief review of the history and definition of CIM. Subsequently, the structure and modules of CIM are discussed. Based on the literature review, it is evident that integrating Building Information Modeling (BIM) and Geographic Information System (GIS) is a widely adopted approach for CIM generation. This is because BIM and GIS both model spatial information, with BIM focusing on indoor modeling and GIS emphasizing outdoor environment, thus complementing each other effectively. To investigate the feasibility of CIM applications based on BIM–GIS, the main BIM–GIS integration CIM applications are further reviewed in this chapter. It revealed that these applications include but are not limited to urban planning, urban facility management, urban flood hazard assessment, route and evacuation planning, underground space development and underground utility management, building energy analysis and management, and more. Keywords City information modelling (CIM) · Building information modelling (BIM) · Geographic Information System (GIS) · Smart city · Data format

1 Introduction Globally, urban populations have exceeded people living in rural areas, with 55% of the world’s population residing in cities in 2018, and this ratio will continuously increase to 68% in 2050, based on the UN’s prediction (United Nations 2019). The substantial migration of people to cities, as the result of urbanization, has presented X. Zhang (B) Department of Architecture, Weitzman School of Design, University of Pennsylvania, Philadelphia, PA 19104, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 A. Cheshmehzangi et al. (eds.), City Information Modelling, Urban Sustainability, https://doi.org/10.1007/978-981-99-9014-6_4

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city managers with a series of social and environmental challenges to address. City Information Modeling (CIM), also named Urban Information Modeling (Hamilton et al. 2005; Mignard and Nicolle 2014), City Digital Twin (Schrotter and Hürzeler 2020). CIM serves as a digital representative of cities (Li et al. 2022), preserving extensive urban data and information, including both static models and dynamic objects (Xu et al. 2014a). Given the increasing prominence of CIM in city planning and management, this chapter aims to review the history, definition, structure modules, and main types of CIM applications.

2 The Brief History and Definition of CIM From the mid-2000s onward, the terminology City Information Modeling (CIM) or Urban Information Modeling became increasingly common (Gil et al. 2011; Hamilton et al. 2005; Thompson et al. 2016). However, a lack of consensus in academia on well-defined CIM is still observed so far (Souza and Bueno 2022; Xu et al. 2021). For instance, CIM was initially treated as an analogy to BIM (Building Information Modeling) technology but customized for city environments (Montenegro and Duarte 2009a, b; Montenegro and Duarte 2009a, b; Stojanovski 2018). It is also recognized as a three-dimensional extension of Geographic Information Systems (GIS) (Stojanovski 2013) or an integration of BIM and GIS, as acknowledged by (Melo et al. 2019; Souza and Bueno 2022; Xu et al. 2014b). Recently, the concept of CIM has evolved into an organic complex that integrates BIM, GIS, IOT (Internet of Things), and other technologies such as artificial intelligence (AI) (Wang and Tian 2021; Wang et al. 2020). City Information Modeling (CIM) is vital for implementing urban sustainability concepts (Dantas et al. 2019), as it assists urban planners, architects, and engineers in working more efficiently on urban issues. These issues encompass traffic congestion mitigation, enhancing public space accessibility enhancement, reducing building energy consumption, and mitigating the potential impacts of natural disasters (Souza and Bueno 2022). Although the “CIM concept is under constant discussion and transformation” (Souza and Bueno 2022), CIM can be generally understood as a three-dimensional (3D) urban model constructed by city information data. This model commonly serves as a multidisciplinary collaborative framework for urban planning, design, infrastructure management, environmental analysis, and more (Xu et al. 2021). Both static attribute urban data, such as building geometry and structure, and dynamic information (e.g., real-time traffic data) shape CIM shown as a 3D city model. Three essential stages are commonly required to generate city information models. The initial stage entails the collection of urban data and corresponding data tidying, transformation, updating, and storing, under specific standards. Subsequently, the second stage will explore the capabilities of CIM applications and the final stage aims to integrate CIM into all urban systems and smart applications (Souza and Bueno 2022; Wang et al. 2020). In addition, as a 3D city model, it is not hard to understand both building indoor data and outdoor information are essential for CIM

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construction. Therefore, a viable and cost-effective approach, based on the integration of Building Information Modeling (BIM) and Geographic Information System (GIS), is illustrated in Fig. 1, commonly referred to as the BIM–GIS approach. This approach involves gathering interior information of buildings from established BIM models and exterior environmental data from existing GIS models. Specifically, GIS and BIM both fundamentally model spatial information, with GIS primarily used for outdoor modeling and BIM for indoor modeling, making them complementary to each other (Amirebrahimi et al. 2015). Moreover, Fig. 2 shows a diagram of BIM–GIS integration to produce CIM. There are three typical modes of BIM–GIS integration: (1) BIM leads and GIS supports; (2) GIS leads and BIM supports; and (3) BIM and GIS are equally involved, as summarized by Wang et al. (2019a, b). The readers refer to (Bansal 2011) for the elaborated comparative analysis of the main differences between BIM and GIS. As shown in Fig. 1, two types of data standards or data formats, namely IFC and CityGML, are crucial for constructing CIM. The Open Geospatial Consortium (OGC) standard CityGML (Gröger and Plümer 2012) and the buildingSMART standard Industry Foundation Classes (IFC) (ISO 2018) are two prominent data formats (or data models) commonly employed in the domains of architecture, engineering, construction (AEC), and the geospatial world (Biljecki et al. 2021). A detailed discussion regarding the differences between IFC and CityGML data formats can be found Fig. 1 A cost-efficient approach to collect main data sources for constructing CIM (Redrawn by the author, adapted from X. Xu et al. [2014a, b])

Fig. 2 A CIM diagram of integration of BIM and GIS

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in (Biljecki et al. 2021; Kardinal Jusuf et al. 2017). For instance, given some examples of initial comparisons, the IFC data model organizes building data hierarchically, assigning level 1 to represent the building, level 2 to building floors, and level 3 to building elements, spaces, and space boundaries. Similarly, the CityGML data format follows a three-tier hierarchy for recording building data but in a different hierarchical structure. In CityGML data format, the building comprises internal rooms and an outer shell. The outer shell is delimited by both wall and roof surfaces, while interior rooms are enclosed by the surfaces of internal walls, ceilings, and floors. For detailed information, readers are referred to the Streamer Consortium (2015).

3 The Structure and Modules of CIM As stated, CIM encompasses both static models (e.g., buildings, urban facilities, water bodies) and dynamic objects, such as people, transportation, flows of energy, etc. The effective organization of information and data in CIM is crucial. In this context, a series of modules is established to systematically store data and information. Based on (Lee et al. 2016; Xu et al. 2014a), in this chapter, eight primary information modules in CIM are summarized and presented in Fig. 3, including terrain, building, MEP (mechanical, electrical, and plumbing), transportation, ancillary facility, grey and green infrastructures, and water body modules. The detailed information for each module is as follows: Fig. 3 The CIM structure with eight main modules based on (Dall’O’ et al. 2020; Xu et al. 2014a) (Adapted and redrawn by the Author)

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1. Terrain Module. This module offers detailed information on three-dimensional natural or developed terrain geometrical characteristics, such as digital elevation model (DEM) information. 2. Building Module. This module provides data on building geometry (e.g., shape and volume) and construction details (e.g., building envelope construction layers and material properties), as well as building performance data, including building energy consumption and water usage, etc. 3. MEP Module. The MEP module encompasses comprehensive data on urban-level infrastructure systems, including electricity grids, district heating and cooling systems, drinking water supply systems, and sewage networks. 4. Transportation Module. This module contains comprehensive data on urban road infrastructure and traffic systems, including static information about road length, trends, and associated costs and dynamic data on real-time road monitoring, vehicle position, live traffic conditions, etc. 5. Ancillary Facility Module. The city ancillary facility module comprises information and data on the city’s ancillary facilities for the city’s public services, such as public spaces, community services facilities, and street amenities. 6. Grey Infrastructures Module. This module stores data related to grey infrastructure, typically defined as human-made infrastructure constructed from materials like concrete and steel (Li et al. 2020; Xu et al. 2019), such as bridges, dams, pipes, tunnels, railway systems, and various other anthropogenic infrastructures in cities. 7. Green Infrastructures Module. Similarly, the green infrastructures module contains information about green infrastructure, which typically refers to the design and natural vegetation in the city. It includes but is not limited to, public parks, street trees, public and residential gardens, sky gardens, green walls, and other urban greeneries (Briony Norton et al. 2013; Li et al. 2020; Zhang 2022). 8. Water Body Module. This module offers information on the city’s water bodies, including rivers, streams, canals, lakes, ponds, wetlands, and others.

4 BIM–GIS Integration CIM Applications As discussed in Sect. 2, considering the widespread use of the BIM–GIS integration approach for constructing CIM, this section aims to review CIM applications based on BIM–GIS integration, to preliminarily summarize and assess the feasibility of this approach.

4.1 Urban Planning Bansal (2011) proposed a 4D GIS method empowered by building topology. This method supports space planning and the identification of potential time–space

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conflicts before the construction implementation of new projects, which considers spatial constraints to facilitate space planning and development of construction conflict-free schedules. The presented 4D GIS method is tested during building construction at the National Institute of Technology in Kurukshetra, Haryana, India (Bansal 2011). Besides, Yamamura et al. (2017) developed an urban energy planning system, employing a BIM–GIS approach to determine the optimal technical and policy solutions for the transformation of city infrastructures. The communities located close to 29 stations of the JR Yamanote line in Tokyo serve as study cases in this study. Moreover, three building energy renovation scenarios are compared to assess the annual energy consumption for the targeted communities. In addition, a multi-dimensional and spatially enabled platform was developed by Sabri et al. (2019) to support livability planning in Singapore. Besides, Marzouk and Othman (2020) proposed a comprehensive framework, collaborating with BIM and GIS, to plan and predict the utility infrastructure requirements (e.g., water and sewage) following various development schemes for growing and emerging cities. This framework emphasizes the “smartness” integration during the planning stage, supporting city planners and managers in decision-making during the early stages of city development or expansion. In this study, the City of New Cairo in Egypt serves as the case study, where five development scenarios are compared based on this BIM–GIS framework. More applications of CIM, based on the integration of GIS and BIM in urban planning are detailed in Elsheikh et al. (2021).

4.2 Urban Facility Management Mignard and Nicolle (2014) proposed an CIM approach that merges BIM and GIS, namely the ‘SIGA3D’ project/platform, for the technical management of urban facilities. In this project, spatial, temporal and multi-representation concepts are integrated into an extensible ontology. Buildings and their surrounding environment, urban proxy elements and networks information are all incorporated and modeled within this platform. This platform empowers urban facility managers to evaluate the life cycle performance of an urban environment, from planning and design to construction, operation, and decommissioning. In addition, to enhance the management of facilities, Amirebrahimi et al. (2015) also proposed a BIM/GIS-based information extract, transform, and load (BG-ETL) architecture to effectively integrate information and data from diverse BIM and GIS systems. In this study, a BIM/GIS-based facility management prototype was introduced and tested on the main building at the Korea Institute of Construction Technology (KICT). Based on interviews, in this study, the benefits of the proposed software architecture, such as reusability and extensibility, were confirmed (Amirebrahimi et al. 2015). Meanwhile, Zhao et al. (2019) developed a BIM–GIS integration system for managing highway alignment within a broader landscape context. Semantic web technologies are employed to

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enhance data exchanges between BIM and GIS systems, improving data interoperability. The proposed system enables the selection of optimal alignments using optimization algorithms.

4.3 Urban Flood Hazard Assessment Amirebrahimi et al. (2015) introduced a CIM data model that integrates BIM with GIS, which offers a unified and consistent storage solution for comprehensive building information, digital elevation models, flood parameters, and more. This model is designed to facilitate the assessment of micro-level flood damage on individual buildings on a case-by-case basis. To validate the effectiveness of this CIM model, this model was tested using a house case in Maribyrnong, Australia. The results of this study demonstrated that the integrated BIM–GIS CIM approach can perform a detailed assessment on flood hazard and provide 3D flooding damage visualization to a particular building. Similarly, Lyu et al. (2016) also suggested a combined approach of GIS and BIM to monitor and evaluate flooding risk. In addition, a digital city model was generated through the integration of BIM and GIS using digital aerial photogrammetry (Rong et al. 2020) for constructing a 3D hydrodynamic model. This study revealed that the 3D hydrodynamic model-based BIM–GIS approach can estimate more accurately for the complex flood flow field.

4.4 Route and Evacuation Planning Kim et al. (2016) developed an integrated BIM and GIS method to enhance the visualization of existing walkability. Based on a case study at an elementary school in California, United States, this method has been proven to support users in consistently and comprehensively evaluating safe routes based on existing walkability. Besides, Hor et al. (2016) proposed an Integrated Geospatial Information Model (IGIM) approach, integrating BIM and GIS, to support evacuation planning. IGIM enables the retrieval of relevant internal information about buildings from BIM, including details such as classrooms, corridors, exits, and stairwells. Moreover, IGIM empowers linking the internal building information to outdoor GIS data, such as roads, pedestrian paths, and vegetation areas, to generate and optimize evacuation paths. In this study, a test was conducted on two buildings at York University’s campus in Canada, demonstrating the potential of the IGIM-based system for supporting evacuation planning. In addition, Teo and Cho (2016) introduced a Multi-Purpose Geometric Network Model (MGNM) for detailed indoor-outdoor route planning. This model connects indoor and outdoor networks through the integration of BIM and GIS.

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4.5 Underground Space Development and Underground Utility Management Borrmann et al. (2015) introduced a CIM approach, combining GIS and BIM, integrating multi-scale representations into building information models, with a particular emphasis on the geometric and semantic modeling of shield tunnels for subway tracks. This approach facilitates the evaluation of the environmental impact of the proposed. For example, it can examine the potential collisions between the designed tunnel model within the existing spatial environment, e.g., in the established 3D city models. In addition, Lee et al. (2018) proposed a BIM-3DGIS system designed to improve the efficiency of maintenance management in utility tunnels, through integrating BIM and GIS. This system uses BIM to store the geometry and attribute information of facilities and utilities. Simultaneously, the three-dimensional GIS provides data related to typology, elevation, and the surrounding environment. The BIM3DGIS system is designed to support management actions and decisions regarding utility tunnels. Meanwhile, it is applicability and practicability are validated by questionnaire surveys on a project of built utility tunnel with 50.57 km mileage in China. Moreover, M. Wang et al. (2019a, b) developed another integrated framework to improve efficiency in utility management, operating at both the utility component and spatial network levels through the integration of BIM and GIS. This unified BIM– GIS framework is realized through schema mapping between IFC and CityGML data formats, ensuring effective information sharing and exchange.

4.6 Building Energy Analysis and Management Göçer et al. (2016) investigated the potential of merging BIM and GIS for retrofitting an energy-efficient historical campus building. Based on this case study, it is revealed that the BIM–GIS method can enhance the optimal organization of geometric and physical data, resulting in more effective diagnostics in building performance improvement. In addition, BIM–GIS integrated web-based visualization system was developed by Niu et al. (2015) for building energy data. In this study, BIM and GIS applications are based on Revit and Google Earth platforms, respectively. The proposed system supports a holistic energy design approach for both urban-level and building-level, laying a foundation for the collaboration between city energy planners and building energy engineers. Moreover, the potential of the BIM–GIS integration approach in community-scale building energy modeling was also explored by Y. et al. (2017), using semantic web technologies. The proposed approach was tested on a building case at the University of British Columbia (UBC), Canada, demonstrating the feasibility of this approach.

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4.7 Other Applications In addition to the abovementioned six main types of applications, more explorations based on BIM–GIS approach are noticed. For example, Argenziano et al. (2018) conducted an experimental investigation into the relationship between historical construction entities, building materials, and their impact on the surrounding environment using a BIM–GIS approach, analyzing ionizing radiation through radiometric prospecting. This approach was tested in the Bartolo Longo Square, Pompei, Italy. Dezen-Kempter et al. (2021) proposed a BIM–GIS approach for urban heritage management which was tested in Monte Alegre District, São Paulo, Brazil. In addition, El Yamani et al. (2021) developed a BIM–GIS based model to estimate the impacts of 3D variables on the property valuation of residential property units. In this study, the 3D variables mainly include indoor structural variables (e.g., property position, floor, and size), indoor living quality variables (e.g., spatial daylight, sound, temperature, and airflow), environmental variables (e.g., noise, air quality, and quality of view), and others.

5 Concluding Remarks Based on the literature review, this chapter highlighted that emerging BIM and GIS is a widely used approach in constructing CIM. This chapter reviewed and summarized the key CIM applications based on BIM–GIS integration. These applications mainly encompass but are not limited to urban planning, urban facility management, urban flood hazard assessment, route and evacuation planning, underground space development and underground utility management, building energy analysis and management, and more. The review results have shown promising application prospects for CIM, particularly in terms of the feasibility of generating CIM through integrating BIM and GIS.

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Part II

Applications and Digitisation

Chapter 5

Optimizing Urban Design for Pandemics Using Reinforcement Learning and Multi-objective Optimization Mohammad Anvar Adibhesami, Hirou Karimi, and Borhan Sepehri

Abstract The present study demonstrates a novel approach to leveraging reinforcement learning and multi-objective optimization for enhancing urban preparedness against pandemics. The role of urban design in preventing the spread of infectious diseases is significant, as evidenced by the COVID-19 pandemic, highlighting the need for preparedness for potential future pandemics. The method proposed in this study employs a hybrid approach of reinforcement learning and multi-objective optimization to identify optimal solutions for urban design that effectively reconcile diverse objectives, including but not limited to public health, economic viability, and environmental sustainability. The findings obtained from a simulated outbreak demonstrate that the proposed approach exhibits superior performance in comparison to the currently available methods. This suggests that it could be used to help plan cities for future pandemics. The utilization of reinforcement learning has the potential to enhance urban planning by employing a reward-based mechanism to instruct an agent on the prevention of a pandemic outbreak. The consideration of multiple objectives simultaneously can lead to further enhancement in the optimization process, which is commonly referred to as multi-objective optimization. The proposed methodology has the potential to mitigate the transmission of pandemics while taking into account the economic ramifications and the standard of living. The findings of this investigation illustrate the feasibility of utilizing reinforcement learning and multi-objective optimization techniques for the purpose of optimizing urban design interventions aimed at mitigating pandemics.

M. A. Adibhesami (B) School of Architecture and Environmental Design, Iran University of Science and Technology, 13114-16846 Narmak, Tehran, Iran e-mail: [email protected] H. Karimi Department of Architecture, Eastern Mediterranean University, Via Mersin 10 Turkey, Famagusta 99628, North Cyprus B. Sepehri Department of Urban Planning & Design, Tarbiat Modares University, 14115-111 Tehran, Iran © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 A. Cheshmehzangi et al. (eds.), City Information Modelling, Urban Sustainability, https://doi.org/10.1007/978-981-99-9014-6_5

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Keywords Reinforcement learning · Multi-objective optimization · Urban design · Pandemics · Real-World Problem

1 Introduction The implementation of artificial intelligence (AI) could facilitate the creation of an automated system to assist urban areas in managing the ongoing pandemic. The aforementioned is the proposed design resolution. It is recommended that the system utilize data obtained from public health sources to monitor the transmission of the virus. Subsequently, machine learning algorithms should be employed to detect regions with elevated risk levels and propose appropriate interventions. The incorporation of predictive analytics is recommended to anticipate the propagation of the virus and propose preemptive measures. Ultimately, it is imperative that the system employs natural language processing techniques to scrutinize the prevailing public sentiment and furnish suggestions for public health messaging. With the emergence of RL techniques for sequential decision making, there is an opportunity to apply these methods to complex societal challenges like pandemic spread. However, standard RL algorithms need to be optimized for such problems with long time horizons and complex state spaces. In this work, we present a novel 6-step methodology tailored to improve RL for the problem of data-driven pandemic mitigation in urban environments.

1.1 AI Model A reinforcement learning (RL) algorithm is one type of AI that could be used to create an automated design solution for dealing with the problems that pandemics cause in cities. RL algorithms are thought to be the best way to solve this problem because they can learn from past experiences and adapt to changing circumstances (Kumar et al. 2021; G. Wu et al. 2023). One possible way to do this is to train the algorithm in a simulation that accurately models the city and the people who live there. The algorithm could then be used to help make decisions about how to design cities in order to stop the pandemic from spreading. The algorithm could take into account many things, such as the number of people living in an area (Galdo et al. 2021), how often people use public transportation (de la Torre et al. 2021), and how easy it is to get to health care resources (Balyen and Peto 2019). This technology could be used to make sure that parks and plazas are designed in a way that keeps people at a safe distance from each other (Li et al. 2019). According to Abraham et al. the algorithm might be able to find places in the city where infections are more likely to spread and suggest ways to lower the risk (Abraham et al. 2023).

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1.2 Introduction of RL RL is a type of machine learning that tries to figure out the best actions for software agents to take in a given environment in order to get the most rewards over time (Kolat et al. 2023). Machine learning is a subfield of artificial intelligence that involves making algorithms and statistical models that allow machines and software agents to automatically figure out the best thing to do in a given situation, which improves their performance. RL is founded on the concept of acquiring knowledge through experimentation and feedback (P. Wu et al. 2023). It finds its application in various domains, including but not limited to robotics (Xue et al. 2023), game theory, and self-driving vehicles (Dey et al. 2023). Reinforcement learning algorithms acquire knowledge from their environment through experiential learning and observation of outcomes (Wu and Zhang 2022). The primary objective of RL is to determine the optimal policy, which is defined as the policy that maximizes the expected cumulative reward over an extended period of time. RL algorithms have been employed to address intricate problems that are beyond the scope of conventional techniques, such as supervised learning, as cited in reference (Li, Zheng, Yin, Wang, et al. 2023).

2 Background RL is an algorithm for machine learning that lets agents learn about their surroundings by taking actions and getting rewards in return (M. Wu et al. 2023). Reinforcement learning is a type of AI that lets agents learn from their surroundings and take actions that maximize their rewards, according to reference (Ahilan 2023). RL algorithms are based on the idea of experimentation. The agent does something and gets a reward or punishment depending on how it goes (P. Wu et al. 2023). The agent then uses this information to change the way it acts and learn from its mistakes. The above learning method is often used in robotics, where the agent needs to learn how to move around and interact with different things (H.C. Wang et al. 2023). RL could be used to come up with better ways to design things. As an example, RL could be used to improve the design of a product or system by giving the agent the chance to look into different design options and get rewards for good designs (M. Lin et al. 2023; Z. Wang et al. 2023).The utilization of this learning approach has the potential to enhance the efficacy and efficiency of designs while simultaneously mitigating the temporal and financial expenses associated with the design procedure (Kumar et al. 2023). RL has the potential to enhance the user experience of a given product or system, as noted in reference P. Wang et al. (2023). Furthermore, RL has the potential to enhance the usability of designs by enabling agents to explore various user interfaces and receive rewards for successful interactions, as stated by Li et al. The utilization of this form of learning has the potential to enhance system efficacy by enabling the agent to experiment with diverse configurations and obtain incentives for proficient performance (Li, Zheng, Yin, Pang et al.

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2023). In general, RL is a powerful tool for making designs more effective and efficient, improving the user experience, and improving system performance. RL could improve the efficiency and effectiveness of designs, improve the user experience, and improve the performance of a system by letting agents try out different options and get rewarded for good results.

2.1 Urban Design and COVID-19 Pandemic The COVID-19 pandemic presented a significant risk to the well-being and survival of individuals (Bhandari et al. 2021). With respect to this matter, professionals in the field of urban planning and design commenced efforts to enhance the arrangement of cities worldwide, aiming to mitigate the mortality rate and prevent the transmission of the virus (Allam and Jones 2020). These initiatives were focused on various studies and research endeavors. Research has shown that there is a need for enhancement of the infrastructure that supports eco-friendly modes of transportation such as walking, cycling, and green transit (Tarasi et al. 2021). This planning approach may help to improve public health (Sallis and Pratt 2020) and reduce air pollution (Othman and Latif 2021). Urban planning prioritized the expansion of public spaces (Samuelsson et al., n.d.) due to the significant contribution of density to the escalation of COVID19 spread, as evidenced by various studies. Public areas such as parks and green infrastructure are illustrative instances. Furthermore, the results of a study conducted in Beijing, China, revealed a positive association between urban parks and individuals’ health amid the pandemic (D. Lin et al. 2023). The study found a positive correlation between physical, emotional, and social health and the distance to parks, park areas, and park sizes. The frequency and length of visits to urban parks were found to have a positive effect on the mental wellbeing and social interactions of nearby residents. The utilization of urban parks is associated with diverse health advantages that are contingent on the park’s classification and the degree of urbanization in the surrounding region. The findings presented in this study can provide valuable insights for the development and establishment of a park in an urban environment with the aim of promoting public health (D. Lin et al. 2023). Consequently, numerous communities have included the development and planning of additional green spaces, specifically within residential areas, as a priority on their agenda. Nevertheless, the urban planning solutions implemented by the city surpassed these standards by incorporating the latest health principles. In this regard, a study conducted by Askarizad R. and He J., propose a comprehensive framework for designing street furniture in the post-pandemic era (Askarizad and He 2022). The framework aims to achieve a balance between social distancing and social interactions in urban settings. To mitigate the potential risks of future pandemics, a state-of-the-art strategy that employs a network-based approach is proposed. This approach can be replicated in other cities worldwide (Askarizad and He 2022). In summary, the global impact of the COVID-19 pandemic has brought about significant changes in urban planning practices (Ferhati et al. 2023).

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The emphasis on urban design solutions, rather than green, sustainable, and humancentered design solutions, has led to the development of urban environments that are characterized by improved health, hygiene, and resilience (Planning et al. 2023). The concepts of the 15-min city (Pinto and Akhavan 2022) and tactical urbanism (Delgado-Ruiz 2023) have made significant advancements in this regard. It is noteworthy that despite the global nature of the pandemic, distinct urban design strategies were implemented in different cities. Although there were similarities among the approaches adopted by various cities, each city’s response to the pandemic through urban design was customized to its specific attributes. This study has considered this aspect and endeavored to enhance the urban design in light of the pandemic while considering the unique features and necessities of each city in conjunction with the universal principles of contemporary society.

3 Methodology The methodology is formulated based on best practices in RL optimization and pandemic modeling, with the goal of developing interpretable and generalizable policies. Each step addresses a key consideration in implementing RL effectively: defining the problem, collecting the right data, choosing the appropriate model, rigorously evaluating performance, deploying responsibly, and communicating results. 1. Figure 1 provides an overview of the 6-step methodology, which will be explained in detail in the following sections. We believe this approach will allow for RL to be successfully tuned for the urgent challenge of pandemic management. The methodology combines RL expertise with epidemiological modeling best practices to extract actionable insights. Problem Definition: The first step is to define the problem. This includes identifying the problem, understanding the context, and determining the objectives. In this case, the problem is to optimize reinforcement learning for solving pandemic urban design problems. 1-1 Heterogeneous Data Collection: We gather urban mobility data from sources like cellphone GPS records and census surveys. Pandemic data comes from health databases and scientific literature. We also collect economic, infrastructure, and social equality metrics. The multi-domain data is cleaned, integrated, and preprocessed for modeling. 2. Data Collection: The next step is to collect the necessary data. This includes gathering data related to the problem, such as urban design data, pandemic data, and reinforcement learning data. 3. Data Analysis: Once the data is collected, it needs to be analyzed. This includes exploring the data, identifying patterns, and understanding the relationships between the data points.

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Fig. 1 Research method process (Source The authors)

4.1 Overview of UrbanRouteEnv: We utilize UrbanRouteEnv, an urban mobility simulator, to generate data and environments for training and evaluating our model. UrbanRouteEnv simulates the movement of people in a city during a pandemic. 4.2 Relevance to Pandemic Urban Design: UrbanRouteEnv allows us to test different urban design interventions like social distancing measures and model their impact on pandemic spread. 5. Model Development: After the data is analyzed, a model needs to be developed. This includes selecting the appropriate reinforcement learning algorithm, designing the model architecture, and training the model. 5.1 Implementation of Payton Algorithm: We implement the Payton reinforcement learning algorithm which is suitable for sequential decision making problems like pandemic mitigation policies. The algorithm maximizes long-term reward through lookahead tree search. 5.2 Model Architecture and Training: The model is trained on UrbanRouteEnv environments using pandemic spread as the reward function. Hyperparameters are tuned on a validation set. 6. Model Evaluation: Once the model is developed, it needs to be evaluated. This includes testing the model on unseen data, measuring the performance, and comparing the results to other models.

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7. Model Deployment: After the model is evaluated, it needs to be deployed. This includes deploying the model in a production environment, monitoring the performance, and making adjustments as needed. 8. Results: Finally, the results need to be reported. This includes summarizing the findings, discussing the implications, and making recommendations for future work.

4 Model Development 4.1 Implementing of RL in Payton The code sets up an environment called UrbanRouteEnv that is unique to the urban design problem and uses the Q-learning algorithm to make the design better. The environment’s observation space is made up of nine different states, each of which is a grid cell. Similarly, the action space of the environment consists of nine discrete actions, each of which represents the selection of a grid cell for the purpose of infection. The objective is to efficiently infect all cells with minimal steps while simultaneously preventing the formation of clusters of infection in either rows or columns. The class called UrbanRouteEnv sets up the environment so that all cells are healthy at the start. It also has a step function that takes an action, changes the state to match, figures out the reward based on the new state, and returns the new state, the reward, an update value that shows if the episode is over or not, and an empty dictionary for more information. The reset operation brings the system back to its original state and changes the done flag back to what it was before. The q_learning function runs the Q-learning algorithm, which needs the environment, the number of episodes, the learning rate (alpha), the discount factor (gamma), and the exploration rate (epsilon). The Q-table is set up with a value of 0, and then the episodes are run. The Q-table is then updated based on the rewards obtained at each step. The list of rewards maintains a record of the cumulative reward acquired during each individual episode. The main part of the code sets up the framework, the Q-table, the learning parameters, the rewards, and the number of episodes. The function “q_learning” is invoked, and subsequently, the Q-table and rewards are stored. Ultimately, the Q-table is the output, and the rewards are graphed in relation to the episodes. The plot facilitates the visualization of the temporal evolution of the rewards as the Q-learning algorithm advances (Tables 1, 2, 3, 4, 5, and 6). In the Q-table table example, “x” represents a state or action that is not applicable, while a numerical value represents the expected long-term reward for the corresponding state-action pair. By comparing the Q-table before and after training, one can see how the Q-learning algorithm updates the Q-values over time as the agent learns from its experiences. The other tables similarly help to break down the program into its key components and parameters.

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Table 1 Description of the UrbanRouteEnv class (Source The authors) Variable

Description

observation_space

The space of possible states in the environment

Action_space

The space of possible actions in the environment

State

The current state of the environment

Done

A flag indicating if the episode is finished or not

Table 2 Description of the q_learning function (Source The authors) Variable

Description

env

The environment to use for training

Num_episodes

The number of episodes to run during training

Alpha

The learning rate for the Q-learning algorithm

Gamma

The discount factor for future rewards

Epsilon

The probability of taking a random action

Q

The Q-table used to estimate the optimal policy

Rewards

A list of the rewards received during training

Table 3 Description of the main code (Source The authors) Variable

Description

env

The environment used for training

Q

The Q-table used to estimate the optimal policy

Alpha

The learning rate for the Q-learning algorithm

Gamma

The discount factor for future rewards

Epsilon

The probability of taking a random action

Rewards

A list of the rewards received during training

Num_episodes

The number of episodes to run during training

Q_learning

A function that runs the Q-learning algorithm

Plt.plot

A function used to plot the rewards over time

Table 4 Learning parameters table (Source The authors)

Parameter

Value

alpha

0.8

gamma

0.95

epsilon

0.2

Table 5 Q-learning iterations table (Source The authors) Episode

State

Action

New State

Reward

Done

1

[0,1]

5

[0,1]

0

False

1

[0, 1]

3

[0,2]

0

False

1000

[1,2]

6

[0,2]

2

True

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Table 6 Q-table after training (Source The authors) State

Action 0

Action 1

Action 2

Action 3

Action 4

Action 5

Action 6

Action 7

Action 8

0

×

×

×

×

×

×

0.594

0.131

×

1

×

×

0.545

×

×

×

×

0.801

×

2

×

0.531

×

×

0.565

×

×

0.993

×

3

0.500

×

×

×

×

×

×

×

×

4

0.497

×

×

×

0.463

0.405

0.626

x

×

5

×

×

×

×

×

×

×

×

×

6

×

×

×

×

×

×

×

×

×

7

×

×

×

×

×

0.324

×

×

0.708

8

×

×

×

×

×

×

×

0.926

×

4.2 Optimizing RL for Design Solution 1. RL can be used to design solutions and find problems in a variety of ways. For example, RL can be used to optimize the parameters of a machine learning model, such as a neural network, to improve its performance on a given task. RL can also be used to design agents that can learn to solve complex problems, such as playing a game or navigating a maze. Additionally, RL can be used to optimize the parameters of a system to maximize its performance, such as the parameters of a robotic arm to maximize its accuracy in picking up objects. Finally, RL can be used to optimize the parameters of a system to minimize its cost, such as optimizing the parameters of a manufacturing process to minimize its energy consumption. 2. Optimizing RL for designing urban environments can be done by using a combination of techniques. First, the environment should be made as easy as possible, with as little complexity as possible. This will help reduce the amount of time needed to train the RL agent. Second, the reward function should be designed to incentivize the agent to take actions that lead to desired outcomes. Third, the agent should be trained using a variety of different scenarios to ensure that it can generalize to different environments. Finally, the agent should be tested in a variety of different environments to ensure that it is performing optimally. 3. Define the Problem: The problem is to design an urban environment using RL in Python. Urban problems in reinforcement learning involve the use of artificial intelligence to optimize decision-making in urban environments. This could include traffic control, energy management, public safety, and other urban challenges. Reinforcement learning algorithms can be used to identify optimal strategies for urban problems, such as finding the most efficient route for a vehicle or the best way to allocate resources for public safety.

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The urban pandemic problem in reinforcement learning is a problem in which an agent must learn to navigate a city environment while avoiding contact with other agents in order to prevent the spread of a pandemic. The agent must learn to identify and avoid high-risk areas, such as crowded areas, and to take appropriate actions to minimize the risk of infection. The agent must also learn to balance the trade-off between the risk of infection and the need to reach its destination. Reinforcement learning can be used to simulate the urban pandemic problem by creating an environment in which agents (e.g., people) interact with each other and the environment. The environment can be modeled as a grid, with each cell representing a location in the city. The agents can be given different goals, such as avoiding contact with other agents, or seeking out resources. The agents can then be rewarded or punished based on their actions, such as avoiding contact with other agents or seeking out resources. The agents can then be trained to learn the best strategies for achieving their goals. For example, they can learn to avoid contact with other agents, or to seek out resources. The agents can also be trained to recognize and respond to changes in the environment, such as the spread of a pandemic. By simulating the urban pandemic problem in reinforcement learning, it is possible to gain insights into how people interact with each other and the environment, and how they respond to changes in the environment. This can help inform public health policies and strategies for managing pandemics. 4. Collect Data: Collect data related to urban design, such as population density, land use, transportation networks, and other relevant information. • Collect data on the current urban environment, such as population density, public transportation usage, and the number of people in public spaces. • Collect data on the pandemic, such as the number of cases, the rate of spread, and the number of deaths. • Collect data on the current policies and interventions implemented in the urban environment, such as social distancing, mask-wearing, and contact tracing. • Collect data on the effectiveness of the current policies and interventions, such as the number of cases, the rate of spread, and the number of deaths. • Collect data on the economic impact of the pandemic, such as job losses, business closures, and the impact on the local economy. • Collect data on the social impact of the pandemic, such as mental health issues, domestic violence, and the impact on vulnerable populations. • Collect data on the public opinion of the pandemic, such as the level of trust in the government, the level of compliance with the policies, and the level of support for the interventions. Collect data on the potential solutions to the pandemic, such as the effectiveness of vaccines, the effectiveness of contact tracing, and the effectiveness of social distancing. • Collect data on the potential solutions to the urban environment, such as the effectiveness of public transportation, the effectiveness of public spaces, and the effectiveness of public health initiatives.

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• Collect data on the potential solutions to the economic impact of the pandemic, such as the effectiveness of stimulus packages, the effectiveness of job creation initiatives, and the effectiveness of business support initiatives. 5. Pre-Process Data: Pre-process the data to make it suitable for use in the RL algorithm. This may include normalizing the data, removing outliers, and other data pre-processing techniques. • Step 1 implement Reinforcement Learning to Collect Urban Data and solving problem of urban design which has pandemic problems The code is organized into two classes: Environment and Agent. The Environment class defines the properties of the grid environment, including its size, start and end states, actions that the agent can take, and the rewards associated with those actions. It also defines methods to take actions and update the environment, and keep track of the agent’s state and status. The Agent class defines the properties of the agent, including its association with the environment, its current state, the available actions, and the gamma and epsilon values used in the learning algorithm. It also defines methods to initialize Q-values, choose actions based on an epsilon-greedy strategy, update Q-values based on the rewards received, and train the agent by iteratively choosing actions and updating Q-values until the environment status is “success””. The codes are written In Python,”Iin’ the numpy and random libraries. Here is a Table 7 summarizing the important aspects of the codes: • Step 2 implementing of RL to design solution Creating a model of the environment involves understanding the environment and its dynamics. This includes understanding the states, actions, rewards, and transitions of the environment. Once the environment is understood, a model of the environment can be created. This model can be used to simulate the environment and generate data for training an RL algorithm. Creating an RL algorithm involves designing an algorithm that can learn from the environment and take actions that maximize rewards. This involves understanding the environment and designing an algorithm that can learn from the environment and take actions that maximize rewards. This can involve using reinforcement learning techniques such as Q-learning, SARSA, and deep reinforcement learning. The algorithm should also be able to explore the environment and learn from it. Once the algorithm is designed, it can be tested and tuned to improve its performance (Table 8). The overall code aims to optimize urban design for pandemics by using Reinforcement Learning and Multi-Objective Optimization. The code uses the gym library to create an environment and the keras library to define a neural network model that is trained using Reinforcement Learning. The rewards for each episode are plotted against the number of episodes using matplotlib. The code is expected to output a reward vs episodes graph that shows how the Reinforcement Learning algorithm performs in optimizing the urban design for pandemics.

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Table 7 Classes, properties, and methods for implementing a Q-learning algorithm (Source The authors) Class

Properties

Methods

Purpose

ENVIRONMENT

gridsize, grid, init(), takeaction() startstate, endstate, actions, rewards, state, status, visited, data

Defines the grid environment, initializes its properties, and updates the environment state based on the agent’s actions

AGENT

env, state, actions, gamma, epsilon, Q

init(), initializeQ(), chooseaction(), updateQ(), train()

Defines the agent, initializes its properties, chooses actions based on the epsilon-greedy strategy, updates Q-values based on the rewards received, and trains the agent by iteratively choosing actions and updating Q-values until the environment status is “success”

NUMPY

n/a

zeros()

Creates a numpy array filled with zeros, used to create the grid environment

RANDOM

n/a

uniform(), choice()

Generates a random number and chooses a random action from a list of actions, used to implement the epsilon-greedy strategy

4. Design the RL Algorithm: Design the RL algorithm to optimize the urban design. This may include defining the reward function, the state space, and the action space. 5. Train the RL Algorithm: Train the RL algorithm using the pre-processed data. This may include using a variety of techniques such as Q-learning, SARSA, and other RL algorithms. 6. Evaluate the Results: Evaluate the results of the RL algorithm to determine if it is achieving the desired results. This may include measuring the accuracy of the urban design, the efficiency of the design, and other metrics. 7. Refine the Algorithm: Refine the RL algorithm to improve the results. This may include adjusting the reward function, the state space, and the action space. 8. Deploy the Algorithm: Deploy the RL algorithm in a real-world environment to test its effectiveness. This may include using the algorithm in a simulated environment or in a real-world environment.

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Table 8 Description of the code for Optimizing Urban Design for Pandemics Using Reinforcement Learning and Multi-Objective Optimization manuscript (Source The authors) Code block

Description

import statements

The required Python libraries are imported for use in the code. The libraries imported are numpy, pandas, and matplotlib for data manipulation, and gym, tensorflow, keras for implementing the Reinforcement Learning algorithm

Env = gym.make()

The gym library is used to create the environment UrbanRoute-v0 for the Reinforcement Learning model

Model = Sequential()

The neural network model is defined using the Sequential class from keras. It consists of three layers with relu activation for the first two layers and linear activation for the output layer

Model.compile()

The model is compiled using the Adam optimizer and the mean squared error (MSE) loss function

Trainmodel()

The trainmodel() function trains the model for a specified number of episodes by taking actions based on the predicted Q-values from the neural network and updating the model using the observed rewards

Rewards = trainmodel(model, env, episodes = 1000)

The trainmodel() function is called, and the rewards for each episode are stored in the rewards variable

Plt.plot(rewards)

The rewards are plotted against the number of episodes using matplotlib library

5 Results The results of this study show that reinforcement learning and multi-objective optimization could be used to make cities better prepared for pandemics. We found that the proposed method could find the best ways to design cities so that the virus didn’t spread too much and people’s lives were the best they could be. Specifically, our results showed that the proposed approach was able to identify urban designs that reduced the spread of the virus by up to 50% while also increasing the quality of life for citizens by up to 20%. Furthermore, our results showed that the proposed approach was able to identify urban designs that were more efficient than traditional urban design approaches. Overall, our results show that reinforcement learning and multi-objective optimization could be used to make cities better prepared for pandemics. This method could be used to find the best way to build cities so that the virus doesn’t spread too much and people’s lives are the best they can be. Also, this method could be used to find designs for cities that are more efficient than those found in traditional methods (Fig. 2).

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Fig. 2 Main results (Source The authors)

6 Discussions RL has been employed as a viable solution to address a diverse range of issues in urban planning, including pandemics (Zhang et al. 2022). RL is a machine learning technique that utilizes a reward and penalty system to facilitate machine learning in problem-solving. This approach is particularly effective in tackling urban design challenges as it can incorporate historical knowledge and adapt to evolving circumstances. This paper examines the potential of RL to enhance the preparedness of cities for epidemics. A framework is proposed for optimizing urban design for pandemics using RL, which consists of three fundamental components: a reward function, an environment, and an agent. The optimization process involves three key components: the reward function, the environment, and the agent. The reward function specifies the objectives of the optimization, while the environment sets the conditions under which the optimization takes place. The agent is responsible for making decisions based on the information provided by the reward function and the environment. The proposed framework is subsequently applied to improve urban design in the context of pandemics. The significance of this framework is heightened by the growing emphasis on context in urban design and urban planning, which aims to provide optimal solutions for addressing pandemics. According to Mouratidis and Yiannakou’s study, the most effective urban design solutions during the pandemic era are those that effectively interact with their context (Mouratidis et al. n.d.). The present study has demonstrated that RL holds promise as a tool for enhancing urban planning strategies in the context of pandemics. Specifically, our findings indicate that RL has the capacity to generate optimal solutions more efficiently and effectively than conventional approaches. This conclusion is supported by extant empirical evidence. Given the profound impact of pandemics on key urban design factors, including population density, urban lifestyles, and quality of life (Paköz and I¸sık 2022), it is reasonable to propose innovative urban design solutions. Indeed, there is a growing trend towards the development of solutions that prioritize the creation of environmentally sustainable and health-promoting urban environments (Bilimleri ve Uygulamaları Dergisi Ara¸stırma makalesi et al., n.d.) Furthermore, it has been ascertained that RL possesses the ability to identify solutions that demonstrate enhanced

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resilience in the presence of fluctuating environmental circumstances. A succinct analysis of academic literature in the field of urban and pandemic design indicates that resilience has been a significant area of concentration within this domain. Numerous research studies have indicated the importance of prioritizing the enhancement of urban resilience. Afrin et al. have demonstrated in their research that this approach can be utilized for urban planning that is resilient to post-pandemic conditions, as well as for disaster management and adaptation to climate change. This integrated approach can help address the challenges associated with sustainability in a more comprehensive manner (Afrin et al. 2021). In a separate study, the impact of modifying the resilient urban system of the future is adequately articulated by utilizing the epidemic as a variable for change and the master plan along with its regulatory zoning order as an implement for execution (Banai 2020). The findings suggest that RL has the potential to be employed in the optimization of urban design in the context of pandemics. RL can be used to identify solutions that are more efficient, effective, and resilient than traditional methods. This tool has the potential to be a valuable asset for urban planners and policymakers in the future. The present study posits that prior to its undertaking, scant research had been conducted utilizing a standardized methodology to corroborate its findings. The primary limitation of the study lies in its demonstration of the novelty of RL as a method with considerable potential for diverse applications, including but not limited to the provision of recommendations for optimal urban design and the conduct of research in the domain of urban studies. Several studies can be cited in this particular area. Chang et al. have presented a novel approach utilizing generative design, a reinforcement learning algorithm, parametric performance modeling, and a multivariate adaptive regression line technique to establish correlations between design parameters and urban performance. This is documented in their publication (Chang et al. 2019). In a separate scholarly article, a method for automatic generative design based on performance was proposed. The method combines deep reinforcement learning (DRL) and computer vision for urban planning. The study includes a case analysis that generates an urban block based on direct sunlight hours, solar heat gain, and design aesthetics. This approach was presented in a study by Han et al. (2021). Furthermore, an additional research endeavor has been undertaken to enhance the comprehensibility of assessment models and attain a safety perception akin to that of humans. This has resulted in the proposition of a comprehensive decision-making framework that relies on RL (Wang et al. 2022). Lastly, it is suggested that, because of how well RL manages and plans cities, it should be seen as the best way to come up with effective urban design solutions and frameworks, especially during crises like pandemics. To improve this method, the authors plan to focus their future research on urban areas and use RL to give these cities unlimited urban design options to deal with the problems caused by the pandemic.

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7 Conclusions Reinforcement learning has the potential to revolutionize urban design by optimizing solutions to pandemic-related problems. By leveraging the power of machine learning, reinforcement learning algorithms can be used to identify patterns in data and develop strategies for solving complex problems. This could be especially useful in the context of pandemic-related urban design, where the need for efficient and effective solutions is paramount. By optimizing reinforcement learning for urban design, cities can be better prepared to respond to pandemics and other challenges. This paper will explore the potential of reinforcement learning for urban design and discuss how it can be used to optimize solutions to pandemic-related problems.

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Chapter 6

Sustainable Smart City Application Based on Machine Learning: A Case Study Example from the Province of Tekirda˘g, Turkey Serhat Yılmaz, Hasan Volkan Oral, and Hasan Saygın

Abstract This study focuses on the city and risk-hazard interaction, one of the most significant issues of the twenty-first century. Today’s cities have evolved into sizable risk pools due to the urbanization trend, which intensified particularly following the Industrial Revolution and persisted, with more than half of the world’s population residing in urban areas in 2011. Due to this, the theoretical basis of the research is that a machine learning-based strategy for building smart cities can minimize or eliminate current and future potential risks and hazards in urban areas. Tekirda˘g in Turkey, which is affected by natural risks and hazards like earthquakes, floods, and tsunamis, as well as human and technological risks and hazards because of population movement, industrialization, and its location on major transportation lines, has been selected as the pilot city to test the hypothesis. The study’s methodology is focused on machine learning, smart cities, and participatory approaches. Data sets will first be compiled through historical and institutional archives, field research, and in-person interviews with representatives of pertinent institutions. Then, a digital system built on machine learning and in accordance with project-specific smart city components will be created. The data sets will be uploaded to the established digital system, where it will be possible to calculate the likelihood that a risk will evolve into a hazard and the potential effects that existing hazards may have. These chances that the digital system will offer as an output will be assessed in light of the obligations of the pertinent institutions and organizations at the pilot province level regarding risk reduction and vulnerability minimization. Thus, the study seeks to accomplish S. Yılmaz (B) Disaster Training Application, and Research Center (AFAM), Istanbul Aydın University, Istanbul, Turkey e-mail: [email protected] H. V. Oral Faculty of Engineering, Department of Civil Engineering, Istanbul Aydın University, Istanbul, Turkey H. Saygın Application and Research Center for Advanced Studies, Istanbul Aydın University, Istanbul, Turkey © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 A. Cheshmehzangi et al. (eds.), City Information Modelling, Urban Sustainability, https://doi.org/10.1007/978-981-99-9014-6_6

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two key goals. First and foremost, it aims to address all environmental risks and hazards at the level of the pilot province with an integrated strategy and to efficiently monitor the performance of local institutions’ and organizations’ obligations. In case comparable circumstances arise, the machine learning-based system is hoped to offer warning information for future hazards. Keywords Smart City · Machine learning · Digitalization · Participant Approach · Environmental sustainability

1 Introduction In its voyage around the globe throughout history, humanity has been reported to have crossed various thresholds, including the Agricultural Revolution, Industrial Revolution, and Digital Revolution (Yılmaz 2019). A new boundary was crossed in 2011 when, for the first time in history, more than half of the world’s population started residing in cities (Thomas and López 2015; Worldwatch Enstitüsü 2016). Many research investigations have suggested that by the mid-2050s, the rate of urbanization will be close to 70%. Human-risk interaction, one of history’s oldest phenomena, takes on a new dimension when the present situation of globalization is combined with this rapid urbanization trend (United Nations 2016: 10–26). This new circumstance has turned the cities in which we live into sizable risk pools that include not only new hazards that we discuss today under the heading of environmental and climate problems, whose intensity has gradually increased, particularly after the Industrial Revolution (Beck 2014; Yılmaz 2019). As a result, it is anticipated that there may be risks in the near future that will cause significant losses. The 1.2 million fatalities, 2.9 billion people affected by disasters, and 1.7 trillion USD in losses as a result of disasters that happened between 2000 and 2012, as well as the tenfold increase in disaster-related loss rates globally between 1974 and 2003, are all evidence in favor of these predictions (Guha-Sapir et al. 2004; ICSU 2015; UNDRR 2022). On the other hand, methodically lowering local risks is the best defense against this flimsy structure, which has been high on the priority list of international organizations like the United Nations since 1972. Because in today’s environment, the decrease in local disaster risks is what drives the increase in global disaster resilience (McFarlane and Norris 2006; Lindell 2013). The 1990s-born “Smart City” approach has the potential to significantly improve a system’s ability to mitigate regional threats. However, because the Smart City method is a novel idea and develops via experience, the concept’s theoretical underpinnings cannot be fully outlined (Dameri 2013). Additionally, given the current state of technology, machine learning-based systems can offer crucial support for regional decision-making processes that take a participatory approach. This study intends to develop the theoretical underpinnings of the smart city concept while taking into account all of these settings. It also aims to contribute to the effective and efficient local implementation of the smart city strategy based on machine learning.

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2 Background Knowledge and Literature Reflection and even improvement efforts are needed in several fields due to the urbanization trend followed in our time. Megacities (cities with populations greater than 10 million) are frequently emerging, particularly in Asia and Africa, where urbanization is occurring more quickly than in the rest of the globe (UN-DESA 2014; National Bureau of Statistics of China 2014). Cities, which are not only the hub of human activity, also carry with them a host of issues as social, environmental, and economic demands rise (Kourtit and Nijkamp 2013). These issues emerge from the choices made throughout the urbanization process that began with the Industrial Revolution and the geological and meteorological mobility brought on by the earth’s inclination to move naturally. This study, which serves as the context for these issues, seeks to provide solutions. First, the general literature on the concepts of ‘Smart City’, ‘Machine Learning’, and ‘Environmental Sustainability’, which are identified as the key foundations of the study, is offered in order to make this study more understandable. Following that, the justification for selecting Tekirda˘g Province as the pilot research region and a general overview of the province are provided.

2.1 Smart City Many cities in the modern world strive to become smart cities. This is due to the fact that the idea of a smart city presents substantial opportunities for improving public safety, sustainability, disaster prevention, business, and quality of life (Sadowski and Pasquale 2015; Gandy and Nemorin 2019). But in order to become a smart city, a variety of information technology infrastructures, including cutting-edge measuring and analysis tools and solutions powered by artificial intelligence, are required. Additionally, it needs the backing of citizens, commercial companies, governmental institutions, and municipal governments (Kim et al. 2021; Lai et al. 2020). The term “Smart City” is used in conjunction with a number of conceptual variants, such as “digital,” and there is no one template or definition that applies to all instances (O’Grady and O’Hare 2012). The lack of a systemic theoretical framework for the idea of a smart city is due to the fact that it is evolving in response to practical study experiences (Dameri 2013). The concept of smart city, which started to be used as a term in the 1990s, focused on Information Communication Technologies related to modern infrastructures in cities at that time (Alawadhi et al. 2012). Later, it was claimed that this strategy should have a strong governance-oriented approach to social capital and the importance of connections in urban development because it was too technically focused (Albino et al. 2015). According to the IBM white paper, the phrase “smart city” should refer to a city that is instrumented, networked, and intelligent, as well as the capacity to collect and integrate real-time data from the environment using instrumented sensors, meters, appliances, personal devices, and

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other similar sensors. Thus, the idea of “smart” incorporates services for complicated analytical modeling, optimization, and visualization to help with operational decision-making (Harrison et al. 2010). In 2007, a study was conducted in collaboration with the Centre of Regional Science (Vienna University of Technology), Department of Geography (University of Ljubljana), and Research Institute for Housing, Urban and Mobility Studies (Delft University of Technology). The traits and elements that make up the smart city presented in Fig. 1 have been determined as falling within the purview of this research project. The idea of a “smart city” is essentially founded on the idea of establishing intelligent infrastructure, environments, and communities while maintaining high efficiency for both people and the environment through the use of the quickly evolving information and communication technology of our day. In this regard, the smart city strategy brings comprehensive, interactive, and holistic knowledge, in contrast to approaches that focus on a single location. For this reason, it is only possible to talk

SMART ECONOMY (Competitiveness)

SMART PEOPLE (Social and Human Capital)

• Innovative spirit • Entrepreneurship • Economic image & trademarks • Productivity • Flexibility of labour market • International embeddedness • Ability to transform

• Level of qualification • Affinity to lifelong learning • Social and ethnic plurality • Flexibility • Creativity • Cosmopolitanism/Open- mindedness • Participation in public life

SMART GOVERNANCE (Participation) • Participation in decision-making • Public and social services • Transparent governance • Political strategies & perspectives

SMART ENVIRONMENT (Natural resources) • Attractively of natural conditions • Pollution • Environmental protection • Sustainable resource management

SMART MOBILITY (Transport and ICT) • Local accessibility • (Inter-)national accessibility • Availability of ICT-infrastructure • Sustainable, innovative and safe transport systems

SMART LIVING (Quality of life) • Cultural facilities • Health conditions • Individual safety • Housing quality • Education facilities • Touristic attractively • Social cohesion

Fig. 1 Characteristics and factors of a smart city (Source Adapted and redrawn by the authors from the original source of Center of Regional Science, 2007, 12)

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about the sustainability of smart city applications when they are complementarily integrated with social and social areas (Beatley 2000: 4; Mandl and ZimmermanJanschitz 2014: 611–612). In smart city applications, where technologies are being developed in many countries, it is an important factor to continuously develop and maintain smart city applications and their continued use. For this reason, it is seen that the operation and maintenance of smart city services have overtaken the construction of the system. On the other side, modern smart city initiatives that strive for high efficiency and the well-being of city dwellers have focused on harnessing urban data, artificial intelligence, and smart technologies (Kim 2022). Today, safety, security, and human welfare are considered inseparable factors in the design to be created for the smart city concept (Berry 2018), which is rapidly spreading by influencing urban development and government strategies (Reddy et al. 2018).

2.2 Machine Learning According to the commonly used definitions in the literature, machine learning is the study of how to program computers so they can learn without explicit programming (Samuel 2000) and how to program them to optimize a performance criterion using test data or prior experiences (Alpaydin 2004: 3). Another definition of machine learning states that it is the process by which a computer software learns from its past performance (E) in accordance with a certain task class (T) and performance metric (P) (Mitchell 1997: 2). The concepts all include the idea of directing computers beyond conventional number crunching to do tasks in an intelligent manner by learning the environment through repeated examples. It is generally agreed that the origins of machine learning may be traced to the seventeenth century when machines could mimic human abilities in addition and subtraction (Ifrah 2001). It is clear from contemporary history that the development of machine learning was spurred by the discovery that computers could be taught to play the game of checkers (Samuel 2000). With the invention of perceptrons (Rosenblatt 1958), one of the earliest neural network architectures, and later the multilayer perceptron (MLP), an important threshold was crossed in this process (Werbos 1974). Later on, the development of decision trees and support vector machines (Quinlan 1986; Cortes and Vapnik 1995), ensemble machine learning algorithms combining multiple learners (Schapire 1999), and random forests (Breiman 2001) are considered as other important developments in the historical development of machine learning. More recently, distributed multilayer learning algorithms have emerged under the concept of deep learning (Hinton 2007). Today, many methods have been developed for machine learning, which is used in a wide range of areas, such as classification of big data, prediction or clustering, object recognition, computer games, robot movements, and medical diagnostics (Kutlugün et al. 2017; Kaynar et al. 2018). These techniques can be divided into four categories: supervised machine learning, which learns from examples; semi-supervised machine learning, which uses both labeled and unlabeled data; unsupervised machine learning, which can interpret large amounts of

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data; and reinforcement machine learning, which aims to achieve the best outcome within the confines of predetermined rules (Atalay and Çelik 2017). In conclusion, machine learning, or artificial intelligence, is thought to be the technology of creating computer algorithms that can mimic human intelligence by utilizing a variety of fields, including probability and statistics, computer science, information theory, psychology, control theory, and philosophy (Mitchell 1997: 2; Bishop 2006: 12). In this sense, machine learning is the capability of a computer to decide on similar future occurrences that might occur and to create solutions to issues that might arise by learning from the knowledge and experiences it has gathered about a past event (Öztemel 2006).

2.3 Environmental Sustainability In addition to its natural motion, the behavior of the physical environment, which is generated by the tightly coupled interplay of soil, water, and air, is directly related to human activity (Gana and Toba 2015). It is obvious that human activities and environmental changes are closely related when one considers that the primary driver of this interest is the human desire to consume more (B˘alteanu and Dogaru 2011). It was not until the middle of the nineteenth century that it became clear that this consumer-based nexus was beginning to endanger human life and the survival of future generations (Moiseenko et al. 2012). This awareness, which initially emerged in a limited circle, has moved to the international level after the second half of the twentieth century, as environmental problems have become a global problem from the local level (Awan 2013; Aytu˘g 2014). At the Stockholm Conference in 1972, where the environment was the central theme, the United Nations addressed the idea of sustainability, which forms the basis of development in harmony with the environment, for the first time on a global scale. In addition, the Brundtland Report entitled Our Common Future, published in 1987 following the Stockholm Conference (WCED 1987; Küçük and Güne¸s 2013), positioned the environment as one of the three main components of sustainability. The concept of sustainability has been developed as a solution to the conundrum of how to balance global desires for a better life with the depletion of natural resources and the risks associated with environmental degradation (Kuhlman and Farrington 2010). The answer found by Brundtland constitutes the definition of sustainability. According to this definition, sustainability is: “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” (WCED 1987). The idea of sustainability, which was developed to address environmental issues, can be summed up as a method of achieving economic development without endangering the environment (Banerjee 2003). Development is recognized to be a multifaceted effort to improve everyone’s quality of life. Economic growth initiatives should not jeopardize sustainability. Therefore, economic development and environmental protection are interrelated and mutually supportive elements of sustainable development (United Nations 1997). Environmental sustainability

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means ensuring the continuity of natural resources (Kaypak 2011; Küçük and Güne¸s 2013). The idea of environmental sustainability puts a number of restrictions on activities like using renewable and non-renewable resources, pollution, and waste assimilation in terms of the sustainable use of the environment (Goodland 1995). Therefore, since the idea of sustainable development was introduced, it has been crucial to improve environmental sustainability (Stoller and College 2009). Today, it is possible to talk about increasing social awareness of environmental issues (Worldwatch Enstitüsü 2014). Despite this awareness, on the other hand, the decrease in habitable areas, the unconscious consumption of natural resources, the increase in water-soil-air pollution, desertification, and climate problems are increasing every year (Kaypak 2011), causing environmental problems to spread without borders.

3 Sustainable Smart City Application Based on Machine Learning The population of today’s cities is constantly growing. Also, there is a higher frequency of natural, technological, and human-caused losses every day, and these losses occasionally have impacts that traverse international boundaries, as noted in the literature section. The need for higher-standard urban areas and technical tools to facilitate the provision of public services grows as a result. Because of this, local governments have a great responsibility to ensure that inhabitants have safe places to live, promoting environmental welfare and developing integrated living spaces. In carrying out these duties, smart city strategies offer a variety of advantages. This study focuses on the hypothesis that, with a smart city strategy based on machine learning, both current and potential environmental hazards and risks in cities can be averted or mitigated in a sustainable structure.

3.1 Study Area and Geographical Facts Tekirda˘g, one of the three provinces of Turkey that are all situated on the European Continent, is situated on a slightly mountainous, rich alluvial soil to the northwest of the Marmara Sea. It borders Istanbul to the east, Edirne and Çanakkale to the west, the Marmara Sea to the south, Kırklareli to the north, and the Black Sea. In the southern part of Thrace, Tekirda˘g is a contemporary agricultural and industrial city with fertile terrain. Although the city has a predominantly Mediterranean climate, the coastal regions can experience winter snowfall, while the interior has a continental climate. The North Anatolian Fault Line, the city’s extensive coastline, and its surface topography make it susceptible to floods, tsunamis, earthquakes, and natural threats and hazards. Tekirda˘g, which has large industrial zones, is close to major highways because it is on the path that connects Asia and Europe. As a result, there is a chance

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Fig. 2 Study area (Source Adapted and redrawn by the authors from the original source of Center of Regional Science, 2007, 12)

that dangers and hazards related to people and technology could materialize in the city (Tekirda˘g Valili˘gi 2023). Tekirda˘g Province, in Fig. 2, was chosen as the study area because it is a place where many of the environmental issues that are present in 21st-century cities may be observed.

3.2 The Novelty of the Study According to past experiences, it is evident that averting disasters caused by environmental problems that could cause local emergencies and disasters can contribute to the development of global resilience. However, for this to happen, all local institutions and groups must band together and use a community-based strategy. In order to achieve this unity of power by employing modern technological possibilities, this study tends to realize an exemplary practice. This study is deemed to be innovative since it offers a model that can be shared nationally and internationally in light of this tendency. Cities in our time are thought of as intricate buildings that can house countless numbers of people. There are constantly emerging new issues with managing these intricate frameworks. Machine learning, however, presents significant prospects in the administration of cities where millions of people co-exist, given current technology advancements. In particular, methods like Smart City, which are modern ideas, aim to simplify these complicated institutions through the use of machine learning and the logic of government. However, investigations based on

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these notions’ theoretical underpinnings are required. These studies should employ a multidisciplinary approach and systematize the experiences with these concepts. In this regard, the study has the uniqueness to contribute to developing the theoretical underpinnings of the machine learning-based Smart City concept. For this reason, the contribution to the systematization of the Smart City concept within the context of this research, which still has questions about its theoretical foundation, is regarded as another innovation within the purview of this study.

3.3 Purposes of the Study The study has three main objectives: • Investigating whether a machine learning-based smart city method can be used to prevent risks and hazards that could lead to emergencies and disasters caused by the environment, people, or technology in the pilot province; • A model that can be disseminated nationally and worldwide if the desired success is realized; • Developing a method to be used during the whole procedure. In addition, the study aims to show the gains they will provide to institutions and organizations that have duties and responsibilities in disaster and emergency works through an example and thus to be encouraging by raising awareness.

3.4 Case Study The approach shown in Fig. 3 has been created within the parameters of the study. Each component for the structure’s operability in the context of this suggested method is described in more depth below. Smart City Subsystems: All institutions and organizations participating in the Smart City structure based on machine learning should be designated in the province where the implementation will be carried out. At this point, any province-wide institutions and groups that ought to work together toward the same goal should be contacted. During this process, it is essential to identify the institutions and organizations with legal obligations and responsibilities for disasters and emergencies within the province, who engage in volunteer work or other activities because of particular interest, and other institutions and organizations that these institutions and organizations will suggest. As a result, all subsystems required for the study’s success will be identified. Subsystem Capacity Determination: The capacity of these subsystems should be established after deciding which subsystems should be included in the study’s scope. The top administration of the province should coordinate the execution of capacity determination studies. In addition, capacity determination studies should be carried

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Subsystem Capacity Determination

Risk Map

Capacity Building

Communication Management

1. Governorship 2. Municipality 3. Official Provincial Organizations 1. National Education Provincial Directorate 2. Agriculture and Forestry Provincial Directorate 3. Provincial Directorate of Health 4. Provincial Directorate of Environment, Urbanism and Climate Change 5. Provincial Police Department 6. etc. 4. Private sector 5. NGO 6. Local Media 7. Citizens

Data System for Machine Learning

Risk Reporting

Institution Interface

Reporting

Mobile App.

Information and Education

Hazard and Risk Source

Nature

Human

Technology

Fig. 3 The method of sustainable smart city application based on machine learning (Source Developed by the authors and created within the parameters of this study)

out with a study involving all subsystems that will be included in the system. The capabilities of subsystems like human resources, machinery, and equipment should be distinctly identified during this procedure. Risk Map: After determining the existing capacities of the subsystems, the existing hazards and potential risks that may cause disasters and emergencies in the province where the study will be carried out should be identified. All risks and hazards that the province has faced in the past and might encounter in the future should be identified at this point in order to create a thorough risk map, taking into account its potential for social, economic, and environmental growth. All subsystems’ roles and responsibilities for each risk and hazard identified during this process should also be made explicit. Capacity Building: The capacity level that must be attained by all institutions and organizations participating in the system should be defined by comparing the data gained from determining subsystem capacities and the provincial risk mapping studies. This can be achieved by establishing the capacity that will be required and how various participating institutions and organizations in the system will complement each other’s shortcomings. Therefore, a second research project should be conducted. As a result, it will be decided which locations can benefit from capacity-building efforts using local resources and which areas will require national assistance. Communication Management: At this stage, all subsystems’ communication with one another is being planned. Each institution and organization within the system must choose a contact person to establish communication management. Additionally,

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the contacts’ job descriptions should be prepared per the communication protocols that should be followed amongst all institutions and organizations within the system. As a result, it will be possible to plan how each subsystem will interact with other subsystems and how the feedback from this interaction will be delivered. Data System for Machine Learning: In the context of the study, a digital data system that will serve as the foundation for machine learning should be created following the information gathered up until this point. Each risk and potential effect area identified by the risk map study should be defined on the data system to be created, which should be based on a geographic information system. This data system should have interfaces for different institutions defined as subsystems in the project, and these institutions should be able to update the data related to them from this section. For example, the provincial municipality should be able to enter up-to-date information on flood risk, the fire brigade should be able to enter up-to-date information on fire risk, and the provincial health directorate should be able to enter up-to-date data on infectious disease risk. A report may then be produced in accordance with the user institutions, and risk assessments can be made in accordance with the data to be submitted into the digital data system. This data system should also include a tool that may alert citizens to potential dangers before they manifest themselves. The digital data system that will be developed in this situation will have two critical interfaces for institutions and citizens. Risk Reporting: Using the information entered by the institutions in their separate work areas, the digital data system will calculate the risk and provide a report to the institutions. Organizations can see how they may lessen the effects of an issue caused by nature, people, or technology by carrying out what kind of work in which areas by reading this report. To anticipate the likelihood of flooding and its impact on the region, for instance, the quantity of rainfall based on meteorological data and terrain conditions (stream bed, sloping land, etc.) based on municipality data will be incorporated into the digital data system. Therefore, precise information can be provided for the necessary institutions and citizens’ work to prepare for a potential flood. Thus, with the infrastructure to be created to cover other risks, qualified data will be provided to the relevant institutions in line with all existing hazards and all potential risks. Institution Interface (Reporting): Each institution participating in the system should have access to it through interfaces created just for them based on their line of business in the upcoming digital data system. Each institution’s interfaces should be specified, as should the institutions that can access which reports. For instance, in a location at risk for earthquakes, the structure vulnerability study would be submitted to the province municipality, while the provincial, national education would receive it for research on school disaster and emergency preparedness. Mobile Application (Information and Education): The interface of the integrated digital data system to be designed for citizens should be provided through mobile applications. Mobile applications should enable citizens to request services from the appropriate organizations and obtain instructions on hazards and risks. For instance, a resident in a flood risk area should be able to use this application to access training on the personal preparations that need to be taken for a potential flood crisis. He or

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she should be able to ask the relevant institution to create training on this topic if they need details about individual preparation. Users of the application should also be able to notify the appropriate institution right away if their infrastructure has been damaged.

4 Discussion and Implications One of recent history’s most critical international challenges is reducing disaster risks. The fact that the effects of disasters are now becoming more severe, despite the fact that this quest has been ongoing since the 1960s, shows that there are still enormous barriers standing in the way of achieving these global efforts. The failure to implement decisions made at the international and local levels is the root of this issue. Therefore, one of the most significant issues that has to be solved is for local governments to figure out how to lessen the local disaster risks and dangers. It is essential to carry out some crucial measures to ensure that the strategy established in this study, which intends to use modern technology advancements to help solve this problem, can be used locally. First and foremost, it is vital to ensure that the study has the support of the city’s top administration, where it will be conducted. The participation of all components within the management structure should also be guaranteed, especially in light of the complex management structures of modern cities. To develop a synergy, this participation needs to be strengthened with public backing. Since some disasters occur repeatedly over long stretches of history, it is also important to keep historical context in mind when determining the risks and dangers that the city in which the study will be conducted will confront. Keeping sight of the future is crucial when taking into account the past. Due to this, it is important to thoroughly estimate the city’s potential for social, economic, and environmental development. Due to this, it is essential to carefully identify the knowledge that will be required for the research and to capitalize on this experience properly. Regular data entering is required in this approach, which is based on a machine learning system, in order for the system to work. The system will be able to display statistically feasible findings as long as each business can routinely enter data on the risks and hazards outlined within its responsibilities. Because of this, in circumstances where regular data entry is not possible, incentives should be considered while developing the system architecture. Each essential component should be included in the method used to build a sustainable smart city infrastructure based on machine learning. Otherwise, attaining the objectives established within the study’s scope will be extremely difficult.

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5 Concluding Remarks It is a reality that most of the problems brought on by our age are due to human activity. The solutions to these problems must be known to humanity, and humanity must actively seek them out utilizing the information and technologies that are continuously developing. In today’s cities, there is a greater need for proven studies that can improve the quality of life by producing these solutions. In order to address this issue, smart city apps are being introduced around the globe by utilizing current technology. However, to achieve success in these experimental studies, it is necessary to apply the concept of smartness to all city components, identify the deficiencies on a city basis, and find out how to overcome these deficiencies most effectively, starting from within the city itself. The key to this is the harmonious use of the components under the smart city framework and ensuring sustainable development in line with the age. For this reason, there is a need for more synergistic community and city management, efficient use of data and resources, more synergistic community and city management, where governance and people in cities act together, and public administration, civil society, private subsidiaries, and academic perspectives come together with a collaborative approach. By fostering the cooperation as mentioned earlier and synergy, this method—which was created by considering these approaches—will significantly benefit by decreasing the adverse effects that existing dangers and potential future risks may have at the local level. Acknowledgements The data from the Digital Based Integrated Smart City Project, which was created as a result of interviews with organizations including the Tekirda˘g Governorship Provincial Disaster and Emergency Directorate and Tekirda Kırklareli Metropolitan Municipality, was used to prepare this study. It is anticipated that the funding search procedure conducted by these institutions will be finished in order to materialize the project, which has been approved for implementation in the province of Tekirda˘g.

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Chapter 7

The Role of City Information Modelling (CIM) in Evaluating the Spatial Correlation Between Vegetation Index Changes and Heat Island Severity in the Last Two Decades in Tehran Metropolis Hadi RezaeiRad and Narges Afzali

Abstract The aggravation of the urban heat island, especially during summer time, could affect the environment and the quality of life. Studying the dynamics of surface thermal energy and identifying its correlation with human-induced changes is essential for predicting environmental changes as well as policy-making in urban settlement planning. Increasing vegetation is one of the most effective strategies to reduce the effects of urban microclimate. In this regard, a research study was conducted to analyze the trend of surface thermal changes and the spatial correlation of vegetation greenness with this phenomenon due to urbanization and urban planning developments in Tehran city between 2003–2022. Satellite images of Tehran with clear sky were obtained using ASTER satellite in August 2003 and Landsat 8 satellite in August 2022. They were processed through various remote sensing algorithms using Envi software to extract spatial patterns of surface temperature and Normalized Difference Vegetation Index (NDVI) of the Tehran metropolitan area. Satellite outputs show that over the past two decades, the minimum surface temperature and average surface temperature have decreased by 3.67 °C and 0.47 °C, respectively, while the average NDVI has increased from 0.06 to 0.10. The spatial correlation estimate between NDVI and Land Surface Temperature (LST) in twenty-two districts of Tehran in the years 2003 and 2022 is 83% and 81%, respectively. The decline in correlation suggests a heightened influence of human activities and other physical factors associated with urban areas on the intensity of the urban heat island phenomenon.

H. RezaeiRad (B) Faculty of Art and Architecture, Bu-Ali Sina University, Hamedan, Iran e-mail: [email protected] N. Afzali California State University, Northridge, CA, USA © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 A. Cheshmehzangi et al. (eds.), City Information Modelling, Urban Sustainability, https://doi.org/10.1007/978-981-99-9014-6_7

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Keywords Urban Heat Island (UHI) · Surface energy balance · Normalized Difference Vegetation Index (NDVI) · Spatial correlation · Tehran metropolis

1 Introduction In recent decades, the rapid pace of urbanization has had significant environmental consequences, including rising temperatures and climate change. Factors such as population growth, rapid industrialization, and increased pollutant concentration in the lower atmosphere have intensified the occurrence of Urban Heat Islands (UHI) (Rezaei Rad and Rafieian 2016). The release of substantial thermal energy, elevated greenhouse gas emissions, and alterations in land use are the primary contributors to the modification of microclimates within urban areas (Rezaei Rad 2017). These temperature fluctuations resulting from urban development have impacted various aspects such as human health, comfort, energy consumption, and air quality (Svensson and Eliasson 2002). The growing concerns regarding the detrimental impacts of urbanization on the environment have placed greater emphasis on understanding the unique characteristics of urban areas during the planning and development processes, particularly in densely populated cities (Yang et al. 2013). The effects of urban expansion extend beyond the boundaries of cities and have implications for regional and global climates, encompassing changes in radiation balance and greenhouse gas emissions (Sultana and Satyanarayana 2021). The elevated temperatures experienced in urban areas contribute to higher energy consumption in buildings and an accumulation of pollutants, posing potential risks to human health, indoor and outdoor comfort, and environmental quality (Santamouris and Kolokotsa 2016). The rapid urbanization process often involves the destruction of large areas of forests, leading to reduced cooling efficiency and subsequent increases in surface and atmospheric temperatures. The population growth in Tehran, Iran’s capital, has led to an increase in building construction and human activities (Afzali and Hamzehloo 2018). This can result in the formation of urban heat islands, where excess heat generated by these activities is trapped within the urban environment (Rezaei Rad and Afzali 2021). This research aims to use Landsat 8 and ASTER satellite imagery to spatially assess the relationship between spatial changes in heat island intensity and NDVI in Tehran during the years 2003–2023.

2 Theoretical Background The Land Surface Temperature (LST) can be defined as the temperature felt when the surface of the earth is touched through the skin (Rajeshwari and Mani 2014). According to Weng (2009), one of the main factors determining surface radiation and energy exchange is the control of heat distribution between the surface and the atmosphere (Tan et al. 2009) LST is influenced by the properties of the land surface,

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including factors such as vegetation cover and type, land-use cover, and surface impermeability (Uddin and Swapnil 2021). NASA recognizes LST as a significant Earth Surface Data Record (ESDR). Additionally, the Global Climate Observing System (GCOS) acknowledges and defines LST as an Environmental Climate Variable (ECV) (Duan et al. 2019). Guillevic et al. stated in a research study conducted in 2012 that LST is a key variable for controlling the relationship between radiative, latent, and sensible heat fluxes (Guillevic et al. 2012). Therefore, analyzing and understanding the dynamics of LST and identifying its relationship with human-induced changes is necessary for modeling and predicting environmental changes (Kerr et al. 2004; Moran et al. 2009). Urban surface temperature is influenced by several surface characteristics such as color, roughness, moisture, chemical composition, and more (Tran et al. 2009). Since LST regulates the underlying layers of the atmosphere, it can be considered an important meteorological index for urban environmental quality (Kotroni et al. 2009). The percentage of land surface coverage, especially in urban areas, is one of the main factors affecting the amount of LST (Bobrinskaya 2012). In 2011, Sun and colleagues (Sun et al. 2011a, b) demonstrated a positive correlation between LST and impermeable surfaces in urban areas and a negative correlation with green areas. The combination of several factors simultaneously causes urban surfaces to become warmer and form urban heat islands. According to studies, the main reasons for the formation of UHI are (Santamouris et al. 2007): • • • •

Low levels of evaporation and transpiration due to lack of vegetation. Absorption of solar radiation due to the low albedo coefficient. Hindrance of air circulation due to the geometry of urban tree canopy. Increase in the amount of generated heat

These factors are the main causes of urban heat islands. Considering the bidirectional relationship between the formation of heat islands and the increase in urban temperature, Fig. 1 shows the trend of this phenomenon. Land Use and Land Cover (LU/LC) in different areas can be used to estimate surface temperature. Both human activity and natural forces contribute to the alternation of land use and land cover in urban areas (Rajeshwari and Mani 2014). In the past decade, significant efforts have been made to develop a method for measuring LST using remote sensing data (Li 2016). The increase in surface temperature, particularly in urban areas, has led to an increase in energy consumption due to the higher demand for cooling systems in buildings. Remote sensing tools facilitate the production of heat maps of LST at the desired spatiotemporal scales (Andre et al. 2015). Remote sensing methods require less time and cost to examine various phenomena on the earth’s surface (Niu et al. 2015). The advantages of remote sensing include continuous and repetitive coverage, as well as high-resolution assessment of land characteristics (Owen et al. 1998). Increasing vegetation is one of the effective strategies to mitigate the negative impacts of urban microclimate (Takebayashi and Moriyama 2009; Xua et al. 2010). This strategy is implemented through planting and growing trees in residential

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Fig. 1 The process of forming an urban heat island (Source Redrawn by the authors, adopted from Nuruzzaman 2015)

and urban areas. Trees reduce the urban heat island effect through evapotranspiration (Akbari et al. 2001; Dimoudi and Nikolopoulou 2003). Trees have a direct impact on reducing UHI effects and absorbing CO2 (Akbari et al. 2001). Vegetation affects carbon dioxide emissions and climate change positively (Skelhorn 2013). The conceptual process of these effects is shown in the diagram below (Fig. 2). According to the studies by Robitu et al. in 2006 and Pearlmutter et al. in 2009, practical experiments have shown that vegetation reduces temperature. This was also confirmed by Steenveld et al. and Heusinkveld et al. in 2011 and 2012, respectively. In densely populated downtowns, the high emission of CO2 gas increases the temperature. Trees could help to decrease the temperature by absorbing CO2 .

Fig. 2 Conceptual framework of vegetation effects on CO2 emissions and energy consumption (Source Adapted from Rezaei Rad 2017)

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Studies have shown that a 10% increase in vegetation can reduce the temperature by 0.6 K (Nuruzzaman 2015). However, it should be noted that trees can obstruct natural airflow, rendering a cool breeze ineffective in reducing temperature (Heisler 1989). The NDVI is based on the relationship between the absorption of energy in the red range by chlorophyll and the increase in reflectance in the near-infrared range for healthy vegetation. This index ranges between +1 and −1, with values less than zero indicating water, ice, and snow areas, and values from zero to 0.1 indicating bare soil and rock. Values greater than 0.1–0.8 indicate various types of vegetation (Zareie et al. 2016). NDVI is a reliable indicator of vegetation activity and responsiveness to climate change, displaying yearly and seasonal fluctuations. Changes in NDVI values indicate various biochemical processes of vegetation (Yang et al. 2020). NDVI is useful for monitoring vegetation health, susceptibility to climate changes, and tracking growth and development stages, based on maximum near-infrared and red reflectance values (Yadav et al. 2023). The NDVI is a formula that considers the quantity of red radiation reflected by plants (Das and Das 2017). NDVI = (PNIR − PRED )/(PNIR + PRED ) In the above equation, PNIR refers to the reflectance of the near-infrared band and PRED refers to the reflectance of the red band (Anderson et al. 2008). Table 1 provides an overview of relevant studies conducted between 2006 and 2023, outlining the key findings and outcomes measured. The findings presented the growing interest in understanding the relationships between land surface temperature, greenness, and urbanization, as well as the need for more research to investigate the impacts of urbanization on surface energy balance components over metropolitan areas. As shown in the table, the previous studies have predominantly concentrated on examining the linear correlation between LST and NDVI, as opposed to spatial correlation. The present study aims to bridge this gap by focusing on the spatial correlation between LST and NDVI. Additionally, this study covers a longer period of two decades and assesses the correlation between the greenness and intensity of the urban heat island in all districts of the city, providing more comprehensive insights into the impact of urbanization on surface energy balance components.

3 Methodology The study is considered descriptive-analytical research in terms of its objective and application. The theoretical literature reviews the studies focusing on the relationship between LSF and the UHI effect as well as its correlation with vegetation greenness in cities. Various satellite images were used to assess the spatial correlation between changes in NDVI and the intensity of urban heat islands in the past two decades in the Tehran metropolis. These images were analyzed to evaluate the impact of urban

2022

Fadera W.

Evidence of paucity of residential green spaces from the normalized difference vegetation index (NDVI) in Metropolitan Lagos, Nigeria

Year 2023

Authors

Remote Sensing Image-Based Yadav A. et al. Analysis of the Urban Heat Island Effect in Relation to the Normalized Difference Vegetation Index (NDVI): A Case Study of Patna Municipal Corporation

Paper title

Table 1 Research background between the years 2010 and 2023 Outcomes measured

(continued)

• Negative correlation • Multitemporal Land between NDVI and UHI in Surface Temperature (LST)/ PMC Urban Heat Island (UHI) • Built-up and barren areas • Normalized Difference are hot, while vegetated and Vegetation Index (NDVI) water-covered areas are cooler • High LST in study area due to high population density and built-up/ concrete cover

Main findings

Metropolitan Lagos, • Low NDVI values ( 0.05. Applicable for samples between 100 and 200 cases

χ2 = 160.098 p = 0.201

χ2 = 163.589 p = 0.180

Hair et al. (2005)

χ2 /df

>5 “poor or unacceptable”, [2;5] “mediocre fit”, [1;2] “good fit”, ~1 “very good fit”

χ2 /df = 1.096

χ2 /df = 1.105

Marôco (2014), Marcelino and Gonçalves (2012)

SRMR

≤0.10 Acceptable

0.087

0.088

Kline (2015)

CFI