**The Urban Book Series**

Wenzhong Shi · Michael F. Goodchild · Michael Batty · Mei-Po Kwan · Anshu Zhang Editors

# Urban Informatics

# The Urban Book Series

# Editorial Board

Fatemeh Farnaz Arefian, University of Newcastle, Singapore, Singapore; Silk Cities & Bartlett Development Planning Unit, UCL, London, UK

Michael Batty, Centre for Advanced Spatial Analysis, UCL, London, UK

Simin Davoudi, Planning & Landscape Department GURU, Newcastle University, Newcastle, UK

Geoffrey DeVerteuil, School of Planning and Geography, Cardiff University, Cardiff, UK

Andrew Kirby, New College, Arizona State University, Phoenix, AZ, USA

Karl Kropf, Department of Planning, Headington Campus, Oxford Brookes University, Oxford, UK

Karen Lucas, Institute for Transport Studies, University of Leeds, Leeds, UK

Marco Maretto, DICATeA, Department of Civil and Environmental Engineering, University of Parma, Parma, Italy

Fabian Neuhaus, Faculty of Environmental Design, University of Calgary, Calgary, AB, Canada

Steffen Nijhuis, Architecture and the Built Environment, Delft University of Technology, Delft, The Netherlands

Vitor Manuel Aráujo de Oliveira , Porto University, Porto, Portugal

Christopher Silver, College of Design, University of Florida, Gainesville, FL, USA

Giuseppe Strappa, Facoltà di Architettura, Sapienza University of Rome, Rome, Roma, Italy

Igor Vojnovic, Department of Geography, Michigan State University, East Lansing, MI, USA

Jeremy W. R. Whitehand, Earth & Environmental Sciences, University of Birmingham, Birmingham, UK

Claudia Yamu, Department of Spatial Planning and Environment, University of Groningen, Groningen, Groningen, The Netherlands

The Urban Book Series is a resource for urban studies and geography research worldwide. It provides a unique and innovative resource for the latest developments in the field, nurturing a comprehensive and encompassing publication venue for urban studies, urban geography, planning and regional development.

The series publishes peer-reviewed volumes related to urbanization, sustainability, urban environments, sustainable urbanism, governance, globalization, urban and sustainable development, spatial and area studies, urban management, transport systems, urban infrastructure, urban dynamics, green cities and urban landscapes. It also invites research which documents urbanization processes and urban dynamics on a national, regional and local level, welcoming case studies, as well as comparative and applied research.

The series will appeal to urbanists, geographers, planners, engineers, architects, policy makers, and to all of those interested in a wide-ranging overview of contemporary urban studies and innovations in the field. It accepts monographs, edited volumes and textbooks.

#### Now Indexed by Scopus!

More information about this series at http://www.springer.com/series/14773

Wenzhong Shi • Michael F. Goodchild • Michael Batty • Mei-Po Kwan • Anshu Zhang Editors

# Urban Informatics

Editors Wenzhong Shi The Hong Kong Polytechnic University Hong Kong, China

Michael Batty University College London London, UK

Anshu Zhang The Hong Kong Polytechnic University Hong Kong, China

Michael F. Goodchild University of California Santa Barbara, USA

Mei-Po Kwan The Chinese University of Hong Kong Hong Kong, China

ISSN 2365-757X ISSN 2365-7588 (electronic) The Urban Book Series ISBN 978-981-15-8982-9 ISBN 978-981-15-8983-6 (eBook) https://doi.org/10.1007/978-981-15-8983-6

© The Editor(s) (if applicable) and The Author(s) 2021. This book is an open access publication. Open Access This book is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this book are included in the book's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

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

# Acknowledgements

The publication of this book is supported by The Hong Kong Polytechnic University (1-847V, 1-ZVN6, 1-99YX, 1-99XK).

We would like to thank all the authors for their valuable contributions to this book. Also, we greatly appreciate the comments from anonymous reviewers for the chapters. We would like to thank Prof. Shaowen Wang, Prof. Shih-lung Shaw, and Dr. Yang Xu for their input to the book organization at the early stage and Ms. Mengfei Ma and Ms. Zihan Kan for their editorial assistance in all parts and in Part II of the book, respectively.

# Contents

x Contents

# About the Editors

Wenzhong Shi is the Otto Poon Charitable Foundation Professor in Urban Informatics, Chair Professor in GISci and remote sensing, Director of the Smart Cities Research Institute and Head of the Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University. He obtained his doctoral degree from the University of Osnabrück, Germany, in 1994. His current research interests include urban informatics for Smart Cities, GISci and remote sensing, focusing on analytics and quality control for spatial big data, object extraction, and change detection from satellite images and LiDAR data, integrated mobile mapping technology, and 3D and dynamic GISci modeling. He was elected as an academician to the International Eurasian Academy of Sciences in 2019, as a fellow of HKIS in 2019 and a fellow of RICS in 2018. He has published more than 400 scientific articles and 15 books. He has received a number of prestige awards, including an award from the International Society of Photogrammetry and Remote Sensing and the Natural Science Award from the State Council, China. He serves as President of the International Society for Urban Informatics. His full CV is at http://www.lsgi.polyu.edu.hk/academic\_staff/John. Shi/index.htm.

Michael F. Goodchild is Professor Emeritus of Geography at the University of California, Santa Barbara. Until 2012, he held the Jack and Laura Dangermond Chair of Geography and was Director of UCSB's Center for Spatial Studies. He received his BA degree from Cambridge University in Physics in 1965 and his Ph.D. in Geography from McMaster University in 1969. His research and teaching interests focus on issues in geographic information science, including uncertainty in geographic information, discrete global grids, and volunteered geographic information. He has directed or co-directed several large funded projects, including for the National Center for Geographic Information and Analysis, the Alexandria Digital Library, and the Center for Spatially Integrated Social Science. He was elected member of the US National Academy of Sciences in 2002, Foreign Member of the Royal Society, and Corresponding Fellow of the British Academy in 2010; and in 2007, he received the Prix Vautrin Lud. He has published over 550 books and articles. He moved to Seattle upon retirement in 2012 and currently holds part-time positions as Research Professor at Arizona State University and as Distinguished Chair Professor at The Hong Kong Polytechnic University. His full CV is at www.geog. ucsb.edu/\*good.

Michael Batty is Bartlett Professor of Planning, Chairman of the Center for Advanced Spatial Analysis at University College London, and Distinguished Chair Professor at the Hong Kong Polytechnic University. He received his BA degree from The University of Manchester in Town and Country Planning in 1966 and his PhD in Architecture from the University of Wales in 1984. He has worked on computer models of cities and their visualization since the 1970s and has published several books, such as Cities and Complexity (2005), The New Science of Cities (2013), and Inventing Future Cities (2018) all MIT Press. Prior to his current position, he was Professor of City Planning and Dean of the School of Environmental Design at the University of Wales at Cardiff from 1979 to 1990 and then Director

of the National Center for Geographic Information and Analysis at the State University of New York at Buffalo from 1990 to 1995. He was awarded the CBE in the Queen's Birthday Honours in 2004 and was the 2013 recipient of the Lauréat Prix International de Géographie Vautrin Lud. In 2015, he was awarded the Gold Medal of the Royal Geographical Society and in 2016 the Gold Medal of the Royal Town Planning Institute. He is a Fellow of the Royal Society and the British Academy.

Mei-Po Kwan is Choh-Ming Li Professor of Geography and Resource Management and Director of the Institute of Space and Earth Information Science at the Chinese University of Hong Kong. She was a Visiting Distinguished Chair Professor of Geography and Geographic Information Science at the Hong Kong Polytechnic University. She received her MA degree in Urban Planning in 1989 from the University of California, Los Angeles, and her Ph.D. in 1994 in Geography from the University of California, Santa Barbara. Her research interests include environmental health, human mobility, social and transport issues in cities, healthy cities, and GIScience. Kwan is an Elected Fellow of the UK Academy of Social Sciences, the John Simon Guggenheim Memorial Foundation, the American Association for the Advancement of Science, and the American Association of Geographers (AAG). She was included in the 2019 Highly Cited Researchers list of the Web of Science Group and has received many prestigious honors and awards, including the Distinguished Scholarship Honors from the AAG and a Research Award from the US University Consortium for Geographic Information Science. Kwan has published over 310 books, articles and book chapters. She has delivered over 240 keynote addresses and invited lectures in more than 20 countries. More information about Kwan is available at http://meipokwan.org.

Anshu Zhang is a Research Assistant Professor in the Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University. She received her B.Sc. degree in Geo-Information Technology in 2011 and her Ph.D. in Geographic Information Systems in 2017 from The Hong Kong Polytechnic University. Her research interests include spatial data mining, human mobility modeling and prediction, with the emphasis on improving the robustness and reliability of data analytics by approaches such as statistical tests, evolutionary computing, and explainable arti ficial intelligence. She was the Secretary of Working Group II/1, The International Society for Photogrammetry and Remote Sensing (2012 –2016). She has participated in multiple governmental funded projects and is the Principle Investigator of a sub-research program under the State Key R&D Scheme funded by the Ministry of Science and Technology of China. She received the China Science and Technology Progress Award in Surveying and Mapping (Grand Award) in 2017.

# **Chapter 1 Overall Introduction**

**Wenzhong Shi, Michael F. Goodchild, Michael Batty, Mei-Po Kwan, and Anshu Zhang**

**Abstract** Urban informatics is an interdisciplinary approach to understanding, managing, and designing the city using systematic theories and methods based on new information technologies. Integrating urban science, geomatics, and informatics, urban informatics is a particularly timely way of fusing many interdisciplinary perspectives in studying city systems. This edited book aims to meet the urgent need for works that systematically introduce the principles and technologies of urban informatics. The book gathers over 40 world-leading research teams from a wide range of disciplines, who provide comprehensive reviews of the state of the art and the latest research achievements in their various areas of urban informatics. The book is organized into six parts, respectively covering the conceptual and theoretical basis of urban informatics, urban systems and applications, urban sensing, urban big data infrastructure, urban computing, and prospects for the future of urban informatics. This introductory chapter provides a definition of urban informatics and an outline of the book's structure and scope.

W. Shi (B) · A. Zhang

A. Zhang e-mail<EMAIL_ADDRESS>
M. F. Goodchild University of California, Santa Barbara, USA e-mail<EMAIL_ADDRESS>
M. Batty Centre for Advanced Spatial Analysis, University College London, London, UK e-mail<EMAIL_ADDRESS>
M.-P. Kwan

Department of Land Surveying and Geo-Informatics and Smart Cities Research Institute, The Hong Kong Polytechnic University, Hong Kong, China e-mail<EMAIL_ADDRESS>
Department of Geography and Resource Management and Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China e-mail<EMAIL_ADDRESS>
# **1.1 Defining Urban Informatics**

Urban informatics is an interdisciplinary approach to understanding, managing, and designing the city using systematic theories and methods based on new information technologies, and grounded in contemporary developments of computers and communications. It integrates urban science, geomatics, and informatics: urban science provides studies of activities, places, and flows in the urban area; geomatics provides the science and technologies for measuring spatiotemporal and dynamic urban objects in the real world and managing the data obtained from the measurements; informatics provides the science and technologies of information processing, information systems, computer science, and statistics which support the quest to develop applications to cities.

The field covers many sectors that define city systems. Those sectors are often studied in their own right, such as transportation, housing, retail activity, physical infrastructure involving the distribution of waste, water, electricity, and other sources of energy, as well as demographic structure, economic location, urban development, and a host of related perspectives that pertain to cities and urban systems. What makes urban informatics different and complementary to these disciplinary approaches is the fact that computation is central to the way in which methods and models are used to generate a deeper understanding: of many problems that involve working out how cities function, how they generate different forms, how their dynamics reflects the ways in which they grow and decline, and how they mix, segregate, and polarize different populations and activities.

What makes urban informatics a particularly timely way of gathering together and fusing many interdisciplinary perspectives which involve computation is that in the last twenty years, computers have scaled down to the point where they can be used as sensors and embedded in a variety of physical infrastructures as well as being used in a mobile context by the population at large. This has meant that quite suddenly we are now endowed with streams of data about a city's functioning in real time, something that was not generally available hitherto when most of our methods of data collection were not automated through sensors. This has led to what is called big data—data that are generated in real time, with great variety, and hence almost limitless in volume. Such data may be the product of sensors that operate continuously and provide immediate updates to the system of our concern. For these data, we need new methods and models to help our understanding and to interpret old models that still have relevance. This has thrown the 24-hour city onto the agenda, and many of the chapters in this book reflect the fact that temporal dynamics is now a serious feature of this field of informatics. Time is now being deeply reflected in our models, whereas in the past the focus was more on spatial variety.

The field of urban informatics is still developing rapidly in its embrace of new sensing technologies, new kinds of spatial data science, new methods of analysis that range from traditional statistical methods as in spatial econometrics, all the way to new developments in machine learning, and multivariate analysis that enable analysts to explore big data in ways that have not been possible hitherto. In terms of the fields that are distinct within the contributions we have collected here, it is worth noting that new approaches to the structure, form, and dynamics of cities using mainly physical approaches are being used to define a new kind of urban science. New methods of urban analytics are being fashioned using these ideas, and the fact that we are now able to exploit real-time movement data from sensors—either fixed to monitor traffic or mobile to do the same through telephone calls and other social media—means that we have a much richer understanding of cities than anything we have been able to develop so far. Mobility studies have thus become central to urban informatics, while developments in the dynamics of infrastructure, urban pollution, and waste—in short, the metabolism of the city—are coming to the fore through urban analytics. A large part of urban informatics involves sensing at many spatial scales from satellite remote sensing to indoor navigation, while the development of the third dimension in cities in terms of sensing and visualization is now becoming routine. Stitching all these ideas together is another important function of urban informatics, while the development of what was seen as rather disconnected types of urban models—land use and transportation, urban microsimulation, cellular automata, and agent-based models—is now part of the wider agenda. Last but not least, the field also has regard to how its theories, models, and tools relate to wider questions of governance, risk, security, crime, health, and welfare, as well as geodemographics. All these features are encapsulated in our definition of urban informatics here, and we hope readers will thus be able to piece together their own big picture of the field as they navigate many contributions in this book.

# **1.2 The Background: The Origins of Urban Informatics**

The idea of publishing this book is rooted in the fast development of urban informatics in both academia and industry in the big data era. In academia, many universities have established programs to offer both undergraduate and postgraduate degrees related to urban informatics. Examples of such programs include a undergraduate program in Urban Informatics at Shenzhen University, an MSc program in Smart Cities and Urban Analytics at University College London (UCL), a graduate program in Applied Urban Science and Informatics at New York University, an MSc program in Urban Informatics at Northeastern University, an MSc program in Urban Informatics and Analytics at Warwick University, and an MSc program and a PhD research area in Urban Informatics and Smart Cities at The Hong Kong Polytechnic University (HKPU). These kinds of courses are rapidly expanding as different research groups recognize the importance of training and research in the ways in which urban informatics might be applied to contemporary urban problems. The common goal shared by these programs is to promote education and research activities to cope with various challenges in cities under the rapid global urbanization process. In industry, the smart city is a major new trend in urban development and management, and urban informatics is the core technology of smart cities. According to recent reports by Grand View Research and Zion Market Research, the global smart city market accounted for USD 955.3 billion in 2017 and is anticipated to reach USD 2.57 trillion by 2025. Such a huge and increasing market is driven by many factors, such as rapid growth of urban populations around the world and the need to foster sustainable urban development. However, there are very few books systematically introducing the principles and technologies of urban informatics, including urban science, urban systems and applications, urban sensing, urban big data infrastructure, and urban computing. There is an urgent need to edit and publish such books to equip the current and next-generation workforce with the knowledge to tackle the challenges that cities are facing. Our contribution here is to address this urgent need.

The publication of this book is among a series of activities carried out by HKPU for promoting urban informatics internationally. Other activities include initiating and organizing the International Conference on Urban Informatics (ICUI) series, establishing the International Society of Urban Informatics (ISUI) and International Journal of Urban Informatics (IJUI), developing a new MSc program and a PhD research area in Urban Informatics and Smart Cities, and founding the Smart Cities Research Institute for conducting cutting-edge research.

Hosted by the Department of Land Surveying and Geo-Informatics (LSGI), HKPU, ICUI provides a platform for leading scientists, young scholars, and researchers worldwide to share an interest in urban informatics. The first conference in the ICUI series was held in 2017, with around 40 presentations on topics in urban systems, urban sensing, spatiotemporal big data, urban computing, and urban solutions. The second conference was held in 2019 with the theme "Toward Future Smart Cities". Over 280 participants from 18 countries and institutions such as MIT, Harvard University, the University of Cambridge, UCL, ETH, and the Alan Turing Research Institute, joined the conference and delivered over 120 presentations on 18 topics. Also introduced in ICUI 2019 was the International Society of Urban Informatics (ISUI). ISUI aims to promote the international exchange of knowledge and experience in the field of urban informatics, helping its members to succeed in their professions through regional and international academic exchange programs, publications, and networks of cross-disciplinary experts.

A number of other universities in Hong Kong have also contributed to urban informatics and smart city development. For example, the University of Hong Kong has formed the Hong Kong Urban Labs, the Chinese University of Hong Kong has established the Institute of Future Cities, and the Hong Kong University of Science and Technology has developed the GREAT Smart Cities Institute. HKPU has been conducting research on various topics in urban informatics and has accumulated numerous theories, methods, advanced technologies, and successful application cases that provide updated materials for this book.

The book is based on invitations to over 40 world-leading scholars and their teams across a wide range of fields in urban informatics who were asked to write the chapters of this book. In the book, they not only give comprehensive reviews but also share their latest research achievements in various topics within urban informatics, as well as vivid examples of employing emerging urban informatics technologies for solving urban problems. Some of the chapters have been contributed by the participants of the ICUI series, but include new material rather than the presentations at these conferences.

This book is intended for use by researchers and students from a wide range of disciplines related to urban informatics, urban science, urban systems and applications, urban sensing, urban big data infrastructure, and urban computing. It will serve as a textbook for those undergraduate and graduate students majoring in urban informatics, studies in smart cities, transport and civil engineering, geography, geosciences, urban planning, geographic information science, environmental science, resources science, and land use. It can also be used as a reference book for practitioners and professionals in the governmental, commercial, and industrial sectors, such as urban planners, computer scientists, data scientists, geographers, policy makers, architect designers, surveyors, urban governors, and environmental scientists.

# **1.3 Structure of the Book**

This book has six parts that cover the latest developments in a wide range of topics in urban informatics. These topics include the conceptual and theoretical basis of urban informatics, applications of urban informatics in understanding and managing various urban systems, urban sensing, urban big data infrastructure, and urban computing. While the parts are related, they can be read in any order except Part I, which intends to provide an overview of the backgrounds of urban informatics and thus should be read before the other parts.

After the overall introduction, Part I (Dimensions of Urban Science) focuses on the conceptual and theoretical basis of urban science as it has evolved in the examination of the city as a system. It highlights contemporary theories of urban interactions, human dynamics, metabolisms, and the urban economy, and relates these to the wider vision of a new urban science for examining cities in the twenty-first century. The chapters in Part II (Urban Systems and Applications) discuss applications of urban informatics in understanding, analyzing, and managing various urban systems. These include applications in urban travel and human mobility, urban freight systems, crime and security, pollution monitoring, energy systems, health and well-being, risk and resilience, as well as urban governance. The state-of-the art urban informatics are used to identify the problems and provide viable solutions for those problems. The chapters in Part III (Urban Sensing) describe existing and new methods of urban sensing, including remote sensing, ground-based sensors, global navigation satellite systems (GNSS), mobile mapping technologies, indoor positioning technologies, user-generated content, and other developments that have a considerable potential for advancing urban science.

Part IV (Urban Big Data Infrastructure) focuses on issues related to the new developments in urban big data infrastructure, including those concerning big data, geoprivacy, 3D city modeling, 3D cadastre, rule-based modeling, cyber infrastructure, spatial search, and urban IoT. These new developments will likely contribute to significant progress in urban informatics and in urban science more broadly. The chapters in Part V (Urban Computing) cover various topics in urban informatics from the perspectives of computer science and urban modeling. Specific research or application areas examined include visual analytics, cloud and mobile computing, data mining, artificial intelligence (AI) and deep learning, agent-based modeling, microsimulation, Cellular Automata modeling, and transportation modeling. The chapters highlight the development and use of computing technologies, principles, and models for urban contexts and applications. Part VI (The Value of Urban Informatics) concludes the book with a broadly based and forward-looking discussion by Michael F. Goodchild on the goals of urban informatics, the potential for unintended consequences, and possible approaches to accountability.

# **1.4 Retrospective and Prospective**

In the third decade of the twenty-first century, we find ourselves with a well-developed ability to acquire vast amounts of information about the city and with the tools to perform a wide range of analyses. Projects under way in world cities such as Beijing, London, New York, Hong Kong, and Singapore are described at many points in the chapters of this book, and there is every reason to believe that the burgeoning field of urban informatics will continue to grow. But while the reader will find rich detail in the pages that follow, he or she will also recognize that what is being described is a first-world activity, largely confined to the Global North. What all of this means for the Global South remains an issue that is scarcely addressed, and we can only speculate as to what is likely to happen if this omission continues.

Urban informatics is a young field, and not surprisingly it is difficult to organize into self-contained subfields. The reader will become well aware of this issue as he or she navigates the parts of the book and encounters issues such as urban mobility or urban heat islands in different chapters and parts and in different contexts. Hopefully, a better and more robust conceptual model of urban informatics will emerge in time, as the field matures and as its principles become more clearly articulated. We look forward to one or more future textbooks that distill the field into a simple, concise, and theory-based structure. For now, however, the approach has to be more encyclopedic.

What else is missing? First is a sense of history, of how earlier cities dealt with their limited information resources and their lack of the tools to make sense of what they had. John Snow's map of the London cholera outbreak of 1854 was a masterful exercise in inference (Johnson 2007); while the concept of the smart lamppost has a fascinating precursor in the Pluto lamps that were installed in London in the late 1890s (https://www.british-history.ac.uk/survey-london/vol47/pp52-83). We should be able to learn much from a counterfactual approach from earlier times. Second is a sense of what the future may hold in the way of unintended consequences, gaming of technology, and subversion. The history of information technologies is rich in examples of breakthroughs gone astray, finding application for purposes that are malicious and dystopic. Many of the chapters are full of enthusiasm and excitement

#### 1 Overall Introduction 7

for the positive potential of urban informatics and understandably do not dwell on the negative. These possibilities are addressed at the end of the book in Part VI. Finally, as in any data-intensive field there will always be a need to address uncertainty, and associated issues of data provenance and measurement error, especially given the spatiotemporal focus of the field. Dealing with uncertainty is not simply a matter of putting a plus or minus on each item of data, given the strong existence of statistical dependence in both spatial and temporal domains. To quote Korszybski (1933), the map is not the territory; the data are only an approximation and representation of reality.

# **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Part I Dimensions of Urban Science**

# **Chapter 2 Introduction to Urban Science**

**Michael Batty**

**Abstract** This introduction outlines a portfolio of theory and methods in the chapters that develop a basic urban science for urban informatics. Inductive and deductive methods for generating data, analytics, and urban simulation, form the focus. In this first Part of the book, the emphasis is on mobility, space-time theory, energy and infrastructure, the spatial economy, and the role of modelling in understanding and planning the smart city.

There are many different but related disciplinary perspectives underpinning urban informatics, and each of these brings a different science to bear on the tools and techniques which form the core of this new domain. In this introduction, we will not sketch all of these different approaches, for many of these will be developed throughout this book. Here, we will simply outline some of the basic physical theories that pertain to the structure of cities, in particular how the form of the city and its functions influence the location of different activities and the ways in which these activities are linked together. We call this "urban science," which is a little more comprehensive than particular sciences relevant to cities, which relate to ecology, energy, social structure, economic development, and so on, and which develop theories and concepts of these particular subsystems in greater depth. Urban science deals with generic theories of how cities are structured and how they grow and evolve in time, how they change qualitatively with respect to growth, and how their populations organize themselves in space. These features often reveal the kinds of problems that urban planning is designed to alleviate, and in this context, the ways in which urban informatics might progress physical planning can be rooted in some of the theories and principles which urban science is able to elucidate.

Like any science, urban science articulates relationships that define the components of the city using quantitative methods which are generally validated by observations that are drawn from actual cities. In short, the conventional scientific method is key to developing the best tools and techniques that comprise urban informatics. The

M. Batty (B)

© The Author(s) 2021

Centre for Advanced Spatial Analysis, University College London, London, UK e-mail<EMAIL_ADDRESS>
W. Shi et al. (eds.), *Urban Informatics*, The Urban Book Series, https://doi.org/10.1007/978-981-15-8983-6\_2

tool set that is evolving rapidly is based on the classic distinctions between methods that are used to infer order and pattern in data drawn from the city, as well as testing hypotheses that are framed about this order and pattern with respect to data about the city. In short, these tools are based on generating theory through induction or testing theory through deduction. The scientific method usually involves both induction that generates ideas, often alongside deductions from these ideas which in turn are tested. The loop that defines this method is continuous as new ideas are evolved, improved, or discarded, revealing whether or not they are fit for purpose. But at any point in this cycle, these theories need to be translated into forms that are useful in applying the methods of urban informatics. Indeed, the first substantive chapter by Daniel Zünd and Luis Bettencourt illustrates how we can capture data in real time from various objects in the city and by using machine learning, can generate patterns that define how the form of the city can be interpreted. In a later chapter, Shih Lung Shaw illustrates how a series of models about the dynamics of the city can be defined in terms of how the city changes in space and time, with the models then validated in classic deductive terms. Thus, induction and deduction are both brought to bear on the development of urban informatics.

This entire area is dominated by many new methods emanating from computer science, which in turn have developed as computers have scaled down to the point where we can use them to sense any movement and change in the built environment. These sensors may be fixed or mobile, but they have given rise to new data sets that measure how different components in the city change through time. This has led to very large data volumes that tend to produce highly unstructured data that we can only interpret using new methods of pattern recognition and statistical analysis that search for pattern and order in the data. These data are often called 'big' in that they pertain to individual movements and decisions in real time and are only bounded by the time the sensors are active. In this way, data streams can be continuous, and if they grow to terabyte or petabyte levels, we need new and different techniques to explore them, that is, to find the pattern in such data. This is in stark contrast to traditional data sets in cities that usually do have structure, as they are collected in one-off fashion through interview or census. The focus in this book on techniques that involve machine learning and data search has emerged primarily from the need to find structure in data that in their raw form are often completely unstructured. At the same time, increasing amounts of data which might become big can be fashioned from individuals generating their own data either individually or through crowdsourcing. Crowdsourcing has always been used to collect some data, but the existence of new information technologies to support such sourcing has given a new momentum to this kind of data collection.

The elements of urban science that the chapters in this first part of the book address deal with urban morphology, which defines the form and function of the city in terms of location and interactions. Morphology is developed in terms of a threefold characterization of the size, scale, and shape of the city, and much of urban informatics addresses ways in which we might improve the city by changing and manipulating these dimensions. Mobility is the generic area that has grown to encompass the relations between locations and interactions, and this immediately raises the role of networks at different hierarchical levels in the city, as well as the flows that are directed by these networks. Transportation modeling encompasses the best-developed set of tools in this domain, and many of the chapters here allude to such modeling. The relations that bind all these ideas together and are the essence of urban science are scaling, which formalizes the way the hierarchy of elements of different sizes and scales, such as neighborhoods and districts, function within the city. The classic signature of such scaling is the power law, which is ubiquitous as a measure of nonlinearity in urban systems; and in the next chapter, these ideas are spelt out in more detail. In absorbing the contents of this book, readers will find that they emerge in many different guises.

With respect to what follows in this first part, Daniel Zünd and Luís Bettencourt illustrate how it is possible to sense the most obvious objects in a small town in the Galapagos Islands using a blanket coverage and street-view-like cameras. This produces data that can be mined for the more abstract morphology of the place, showing how a judicious mix of user-generated content can be used to sense the spatial structure of the town. Shih Lung Shaw then provides a detailed review of different dynamic models of cities based on urban systems dynamics, cellular automata, and agent-based simulations, setting this in the wider context of human dynamics at the individual person level, and space-time theory as originally developed by Torsten Hägerstrand. The use of new technologies in unpacking individual movements is explored by Martin Raubal, Dominik Bucher, and Henry Martin, who show how personalized tracking can be scaled to look more generally at mobile decisionmaking, complementing the two previous chapters, with the focus very much on urban dynamics, spatial structure, and individual mobility.

The argument then changes direction. Sybil Derrible, Lynette Cheah, Mohit Arora, and Lih Wei Yeow explore urban metabolism that they articulate using input–output relations and flows of energy and materials that define linkages between many different components of the urban system. These models are static in that they simulate flow at a cross section in time, and although the authors provide an example based on Singapore, they illustrate how problematic it is to generalize these kinds of models to embrace the fine spatial scale. Ying Jin then explores a simple spatial econometric model which looks at GDP in Guangzhou province in China, where he uses the classic measure of gravitational potential or accessibility to relate this to the way the urban system functions with respect to innovative economic activities. This has important implications for future planning of industrial development in the region. Helen Couclelis then concludes this part by standing back and speculating on how all these trends in digital modeling at different scales pertain to the planning of future cities, particularly smart cities. This serves as gentle closure to the ideas in this first part of the book, which establishes many of the theoretical concepts to be picked up and operationalized in the chapters that follow.

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# **Chapter 3 Defining Urban Science**

**Michael Batty**

**Abstract** This introductory chapter provides a brief overview of the theories and models that constitute what has come to be called urban science. Explaining and measuring the spatial structure of the city in terms of its form and function is one of the main goals of this science. It provides links between the way various theories about how the city is formed, in terms of its economy and social structure, and how these theories might be transformed into models that constitute the operational tools of urban informatics. First the idea of the city as a system is introduced, and then various models pertaining to the forces that determine what is located where in the city are presented. How these activities are linked to one another through flows and networks are then introduced. These models relate to formal models of spatial interaction, the distribution of the sizes of different cities, and the qualitative changes that take place as cities grow and evolve to different levels. Scaling is one of the major themes uniting these different elements grounding this science within the emerging field of complexity. We then illustrate how we might translate these ideas into operational models which are at the cutting edge of the new tools that are being developed in urban informatics, and which are elaborated in various chapters dealing with modeling and mobility throughout this book.

# **3.1 A Science of Cities**

There are many sciences that encompass our understanding of cities. In this introductory chapter, we seek to define the range of scientific disciplines and perspectives that underpin theories pertaining to urban form, social structure, and the built environment in contemporary cities. The science that we will present is based on abstracting the critical functions that determine processes of change that characterize cities, processes such as the way markets operate; the way goods, people, and information are distributed across networks; the economic rationale for the location of activities in cities; and the way these functions and processes grow and change as cities get

M. Batty (B)

Centre for Advanced Spatial Analysis, University College London, London, UK e-mail<EMAIL_ADDRESS>
bigger or smaller. There are many sciences of the city that are not included in our remit, such as those involving the physics of the built environment, the ecology of cities, and the way climate impacts on city form and function; and there are many aspects of the social domain such as political actions and social mixing that are not considered in this review. But it is important at the outset to be clear about the limits to this science (Lobo et al. 2020). The purpose of this chapter is to suggest a wide variety of scientific ideas that support the quest for establishing urban informatics.We loosely define it here as the technologies and tools as well as the data that enable our city science to be embodied in the models and simulations that are used to improve the management and planning of cities and regions across many different scales and topic areas (Batty 2019).

Urban informatics has emerged as a coherent field largely due to the scaling down of computers and sensors to the point where they can be embedded at very high densities in every part of the urban environment. This includes mobile devices that people activate and operate, as well as fixed sensors that record data pertaining to their functions, often in real time. Urban informatics thus covers a wide range of digital data, from that which is collected in traditional terms from universal or sample censuses at typically low frequencies such as years or decades, all the way to realtime big data streams that are captured at very high frequencies and which provide a portrait of how the city is changing continuously. This field not only covers data, but it also embraces the tools and models that are collectively referred to as urban analytics. In all these tools, we need good theory, and thus, it is the purpose of this chapter to sketch the rudiments of a city science that covers both low- and high-frequency processes in cities, as well as methods of representing and visualizing the form these processes take when we are able to incorporate them in models, simulations, and predictions.

Accordingly we begin by exploring the nature of the city as a system, which was the dominant way of articulating its structure and dynamics in the middle years of the past century. This will establish the key components of cities and how they function at different levels of organization arranged in hierarchical fashion. This then leads us to extend our knowledge to systems of cities, although in this book we will only occasionally refer to such extended systems when we explore cities at regional and national levels. In reviewing these ideas, we introduce the notion that cities can also be seen as systems that emerge from a multitude of local individual decisions, implemented from the bottom up. These generate order from the apparent chaos of non-coordination, and this grounds the study of cities and this science as one of the main exemplars of complexity theory. The theories that have emerged from this focus on systems and complexity are often referred to loosely as social physics in analogy to mechanical systems, and we review these before we develop two key constructs that define the essence of this science of cities: scale and size. The way a city's spatial form—often through its geometry—is reflected in its functions generates the key properties of cities that are articulated in theories about how cities function economically and socially. We then present these functions, linking these to the networks and flows that form the cement that binds the various subsystems, components, and the city's elements together. Many of these models form the basis of operational applications, and we will note a wide variety of these simulations to give readers some idea of the range of possibilities in using simulation in urban informatics. We will then conclude with some speculations about how these theories, as viewed in terms of urban informatics, influence the distribution of different types of cities world-wide and the way in which they can be used to develop tools to improve the quality of life and sustainability of cities through the development of urban informatics.

# **3.2 City Systems and Systems of Cities**

Up to the beginning of the industrial revolution, all cities evolved from some central location where people came together to trade or to rule. From ancient times, populations clustered around these central places and cities developed in such a way that competition for locating closer to the center depended upon the ability of those who engaged in production to capture sufficient demand for their goods to be able to outbid others with respect to the price of space and proximity. Although this model was distorted in the early industrial revolution with the exploitation of fossil fuels around which cities also grew, the notion of the city having a dominant core with bands of different land-use activities or land uses surrounding it, became the received wisdom for how cities came to be formed . As transportation routes bringing producers and consumers to the center to engage in trade could not be built everywhere, cities also developed in radial fashion, with the dominant model being the radially concentric form that was most clearly articulated by Park and Burgess (1925) in their classic studies of Chicago.

The system underlying this model is much more complex, in that different subsystems exist, each with a radially concentric form at different hierarchical levels. These form neighborhoods, districts, communities, villages, and even small towns within bigger cities, and as the city grows and evolves, these hubs or clusters become ever more differentiated. In short, these subsystems form highly structured networks which in turn mirror a hierarchy of different functions, each serving local areas. The kinds of models that have been developed, and are still widely applied, simulate flows of people and goods between different places within the city, using analogies from gravitation that mirror the increasing deterrence effects that distance imposes on movement. The standard model divides the city into different locations (or zones) which we can label *i* and *j*, and we assume that a generic flow between these locations *Ti j* is a direct function of the size of places *i*, *Oi* and *j*, *Dj* and an inverse function of the distance or some function of spatial impedance *di j* between them. The typical model is

$$T\_{ij} \sim O\_i D\_j f\left(d\_{ij}\right) \tag{3.1}$$

and this is still widely applied to simulate transportation in cities, migration between cities, flows of expenditure to retail centers, and many other flow systems that define how the subsystems of the city engage with one another across many different hierarchical levels. A key element in this new science of cities is that patterns of spatial interaction also reflect underlying networks, and that the activities at different specific locations can be simulated as being proportional to the flows that emanate from all locations. From Eq. (3.1), these accumulations of flow at different locations might be predicted as proportional to the relevant activities as

$$\begin{aligned} P\_i \propto \sum\_j T\_{ij} &\sim O\_i \sum\_j D\_j f\left(d\_{ij}\right) \\ P\_j \propto \sum\_i T\_{ij} &\sim D\_j \sum\_i O\_i f\left(d\_{ij}\right) \end{aligned} \tag{3.2}$$

where *Pi* and *Pj* might be defined as some measure of population size at their respective locations.

The models in Eq. (3.2) are in essence measures of potential—in analogy to gravitation once again—or accessibility, and measure the relative nearness of all places to each place in question (Stewart 1947; Hansen 1959). The models developed by Jin (Chap. 8), which measure hotspots with respect to income and GDP, are in this tradition. In fact, this generic model can be made subject to constraints on locations in various ways. The usual version of the model used for transportation modeling is to make sure the trip distribution produced by the model in Eq. (3.1) meets the constraints on the size of trips generated at origins and attracted to destinations. This is the so-called doubly constrained model. If there are constraints solely on the origins or the destinations, these are singly constrained models, and it is possible to use them to predict the cumulative flow of trips at origins or destinations; in this sense, these are location models. If there are no constraints on either origins or destinations, the model in Eq. (3.1) predicts the location of activities such as the populations given by Eq. (3.2). This is the unconstrained model. This family of models and other variants was introduced by Wilson (1971) and has become the de facto standard in spatial interaction modeling.

This link between location and spatial interaction is key to the science that we are referring to. We can in fact generalize these ideas to many cities—to systems of cities as Berry (1964) first referred to them—in that although functions such as retailing specialize across a hierarchy within individual cities, this same sort of differentiation exists between cities. It was Christaller (1933) who first defined the hierarchy of cities with respect to the different functions different-sized cities have, using the idea that the bigger the city, the more specialist services it could provide—largely through its division of labor. The population would demand more specialist services in the bigger cities, and this would imply that the bigger city would need a much bigger hinterland to capture this demand than smaller cities. This would then be reflected in the area of the hinterland and thus implies a hierarchy of cities based on nested hinterlands associated with different city sizes, and a decreasing number of large cities and their hinterlands as the demand for more and more specialist functions grew. Christaller did two things with these ideas. He first demonstrated that this pattern of nested hinterlands could be observed in the relatively well-developed landscape of Bavaria, while his second contribution was to abstract these hinterlands into a regular hierarchy of hexagonal market areas that could be nested and which reflected a progression of ever fewer but bigger central places. In fact, the model is one of the cornerstones of human geography, and it is consistent with much of location theory (Isard 1956), with spatial interaction models, with network representations of cities, and with the development of urban economics (Alonso 1964).

If we order the cities in such a system by size from the largest to the smallest, we can then rank them, and when we examine this ranking, it is easy to show that these sizes follow an inverse scaling relation which is often assumed to be an inversepower law. Of course, the frequency of cities of the same size increases with rank in this theoretical central place system based on regular-nested hexagons, but if we consider that some noise is always present in such an evolving system, then it is not difficult to imagine that we get a smoother continuum, and it is this that has been used to demonstrate a strong relationship between city size and rank. It was Zipf (1949) who first popularized this relationship, and we can give some form to this by first thinking about the size of the various neighborhoods within a single city using the model that we introduced in Eqs. (3.1) and (3.2). Let us assume that the destination activity in Eq. (3.2), that is, *Pj* , can be ordered from largest to smallest. Then, we can use the index 1, 2,..., *n* to define these cities where *P*(1)*<sup>j</sup>* = *P*(max)*<sup>j</sup>* and *P*(1)*<sup>j</sup>* > *P*(2)*<sup>k</sup>* > *P*(3)*<sup>z</sup>* > ···. We can dispense with the index *j* because we are now rank-ordering the locations with respect to size, not location. The formal relation which has been demonstrated many times in many places for locations within cities and also between cities themselves—Zipf's Law or the rank-size rule—can thus be stated as:

$$P(r) \propto 1/r^a \tag{3.3}$$

where *r* is the rank of the location or city with population *P*(*r*) and α is a parameter which defines the slope of the power law. In fact, the strict form of Zipf's law is where α = 1 but most applications suggest that this parameter differs from 1. This is due to the relative stage which particular cities have reached in the evolutionary process, the fact that the distribution of cities is not in a steady state, and the fact that the spatial regions over which the relationship is defined, are not usually closed in any sense.

# **3.3 Urban Growth: Urbanization from the Bottom Up**

The models that define the city in terms of spatial interaction are essentially static, in that they articulate the workings of the city at a cross section in time. There is little concern for process other than developing average relationships that encapsulate the entire historical development of the city at the given point in time, and there is little concern for urban growth and change. As soon as the models from social physics were applied and adapted to urban applications, there was a move to embed and extend them to deal with related dynamic processes. Some of these applications simply used the models to simulate a series of cross sections and to explore the time series that was generated, but some have been used to simulate the actual changes as increments in each time interval, which provides a more basic representations of the dynamics. However, these kinds of application do not embrace the fundamentals of urban dynamics, and other models which are essentially temporal have been adopted.

Many of these models articulate the city not as a mechanism but as an organism, evolving like a biological system rather than being manufactured like a machine. In this sense, cities are represented not as aggregates of populations but as sets of individuals—agents—that act purposively in making decisions pertaining to urban development. Thus cities develop from the bottom up rather than being organized or planned from the top down. There are many models of how city populations grow and change but in aggregate, it looks now as though world population, whose growth until quite recently appeared to be exponential or even super-exponential, is likely to become logistic with the total population stabilizing by the end of the century. This of course is one prediction too far, but it appears currently to be the most likely, and in some respects, the growth of cities is following a similar trend. Big cities are getting bigger, but they are achieving this by fusing with other cities, generating polycentric urban landscapes while still attracting population, but at a decreasing rate. Cities are thus fusing into larger urban agglomerations, but their dynamics is much more mixed than following simple exponential and capacitated-exponential curves. A number of models that illustrate chaotic patterns of urban growth have been suggested, and although none of these have been operationalized for real cities, other than as thought experiments illustrated by stylized facts, they have provided an arsenal of tools for studying nonlinear dynamical systems that underpin many of the tools and techniques presented in the rest of this book.

As cities grow in size, they change qualitatively, generating economies and diseconomies of scale that do not cancel each other out. As cities get bigger, they bring more specialized people together, and as central place theory reveals, the bigger cities are much more specialized and serve a much larger population than the smaller ones. Their economies of scale are reflected in the fact that big cities are more innovative, more creative, and consequently often more wealthy, and there is considerable evidence that as cities grow, they do indeed become more than proportionately richer, creative, and innovative. But at the same time, there are diseconomies of scale which relate to more-than-proportionately increasing levels of crime, lower incomes among the poorest, and increasing inequalities between rich and poor. These relationships are captured in the key relationship between the income of a city *Y* (*t*) and its population *P*(*t*) that can be written as:

$$Y(t) \sim P(t)^{\beta}, \ \beta > 1\tag{3.4}$$

where β is a measure of the economies of scale. If β < 1, then the model in Eq. (3.4) illustrates that income increases less than proportionately with population size. This in fact is unlikely, but if we were to break the population down into different groups, then the poorest group would have to get more than proportionately much greater when cities increase in size for the relationships in Eq. (3.4) to hold. This sort of model was originally developed to look at growth in biological systems, but it presents a good analog of economies of scale, and has been widely applied to examples of ancient and modern city systems as well as firms, individual incomes, and a host of related socio-economic phenomena (West 2017).

In fact, this allometric model has not been developed temporally for individual cities or sets of cities, and there is considerable debate about the effect of scale economies, as the underlying processes which lead to this are defined away by such models; as such they remain implicit in these formulations. In fact, there is still a dearth of dynamic models that represent the way cities evolve, although with the development of complexity theory, there are several key dimensions to the way we now characterize these dynamics. There are no well-worked-out dynamics that coincide with the processes that determine how cities grow and evolve, and this is as much because there are very few good, robust theories that we have been able to discover to date. This is also because of our inability to observe such processes at first hand and compile good data. Urban systems like many social systems are highly resistant to detailed observation and show a degree of invisibility that is much more problematic than in many physical systems where we are able to instrument most features of any relevance.

Complexity theory does, however, reveal certain features of cities that define the limits to our existing models. Cities are always in disequilibrium and this is the new normal, as if it was anything other than that hitherto. In fact, cities are far from equilibrium, in that equilibrium is an abstract concept that in some models represents a long-term steady state, but in most models cannot be defined and probably does not exist. As cities grow from the bottom up, patterns emerge at higher levels. Although there are features of self-similarity at these different levels that we can grasp and sometimes articulate in terms of fractal phenomena, it is often difficult to tie the patterns that we see in cities at different levels to specific bottom-up processes. In this sense, history is all important as we perceive an average randomness in how decisions about urban development are made at the lowest levels. Decisions are for the most part rational if they are unpacked to the level at which they become understandable, but the physical limits of the city and the way we interact socially are such that these constrain what is possible and enable the emergence of order at all levels. In this sense, history matters just as much as geography does. As we implied above, our models and theories need to rapidly reflect the fact that the systems we are dealing vary in space and time. Our abilities to improve the quality of life in cities must take account of such variations which of course reflect underlying human behaviors. In short, in any complex system, there is a degree of historical path dependence that reflects the fact that decisions, although rational, are not necessarily ordered in any obvious way.

There are some processes that are now quite well defined such as those that reveal remarkably clear organization based on decisions that are initially random. For example, the model of segregation first developed by Schelling (1978) demonstrates that if a population system composed of agents are initially randomly distributed, but these agents have distinct preferences to always live with as many of their own kind around them, then if agents begin to move when this is not the case, very quickly an extreme pattern of segregation can evolve. The degree of extremeness—like ghettoization or gentrification in modern cities—appears to be entirely unwarranted, given that the agents have a very mild preference to live side by side with those of their own kind (being quite content to have an equal number of their own kind as well as an equal number of other kinds around them). The reason for this segregation, then, is that there is no coordination at the micro-level. Individuals move of their own accord when they see those around them dominating the neighborhood. It is processes like these that we need to identify in cities because part of our quest to make cities less polarized, more efficient, and to increase the quality of life, are closely bound up with this kind of decision making.

All issues pertaining to complexity influence our current thinking about cities (Batty 2005), but the theories we have about how the city system functions are still quite rudimentary. Many of the models we have hinted at so far are being developed for individual sectors and distinct dynamic processes, and many are being adapted to deal with short- as well as long-term change in the high- as well as the lowfrequency city. For example, in this book, there are several chapters that deal with mobility and new data sets that pertain to networks and flows, and the models in this chapter are reflected in these. To an extent, urban informatics is much more about tools, techniques, and models than about theories, although theory is essential to constructing the bigger picture of how this domain can improve our understanding, prediction, and design of future cities. In the next section, we will pull the ideas of the previous two sections together, emphasizing how these models can be consistently linked in terms of what we know about scale and size, networks, and flows.

# **3.4 Scale and Size, Networks, and Flows**

To all intents and purposes, by the end of the century, everyone will be living in cities of one size or another, where the distribution of sizes will follow the rank-size rule. The biggest cities will be up to 100 million in population, but all of these will be urban agglomerations that consist of polycentric hierarchies of smaller cities, towns, and villages that have fused together. But as we have shown in the previous two sections, the size of a city can also be measured with respect to its local morphology, its geometry, and the distances that define the bounds over which people will interact intensively to enact the business of the city. Since the industrial revolution and the invention of new technologies for mobility and interaction, all cities are part of a global urban form where distances, travel costs, travel times, and like measures of impedance condition the interactions and networks that bind all cities together. In

#### 3 Defining Urban Science 23

short, we can no longer think of cities as being freestanding entities; they are now networked in ways that make it ever more difficult to disentangle them from one another.

The ideas that we have introduced all pertain to different levels of size and scale. A metropolitan area for example has a certain population size, a density which is some measure of size with respect to unit area, and various distances from its core to its boundary. There is a common force which relates scale to size, and this is referred to in statistical physics as scaling. In essence, it means that as a city grows in size, density, in the length of its perimeter, and in the distances travelled within it, we can identify a common scaling that enables us to represent these various properties with respect to size. As we change their size, then the quantities involve scale in a relatively simple way. We can demonstrate this quite easily with respect to the various models that we have introduced. Starting with the standard spatial interaction model in Eq. (3.1), we can now write it in more specific terms using the inverse-power function of distance as follows:

$$T\_{ij} \sim O\_i \mathcal{D}\_j d\_{ij}^{-\mathcal{Y}} \tag{3.5}$$

If we increase the scale of the city by a factor λ, which to fix ideas, we might consider being equal to 2, this will change the model to:

$$T\_{ij} \sim \lambda^{-\gamma} T\_{ij} \sim \lambda^{-\gamma} O\_i D\_j d\_{ij}^{-\gamma} = O\_i D\_j (\lambda d\_{ij})^{-\gamma} \tag{3.6}$$

We have doubled the distance, but the number of trips has not halved, for the nonlinearity applied in the model reduces the number of trips by the factor λ−<sup>γ</sup> . If we define an inverse square law of distance γ = 2, then the number of trips reduces by a factor of 4. In the same way, if our model incorporated economies of scale ϑ and μ which we apply to the origin and destination attractors as

$$T\_{ij} \sim O\_i^\vartheta D\_j^\mu d\_{ij}^{-\nu} \tag{3.7}$$

and if we scale these attractors by (ξ *Oi*) <sup>ϑ</sup> and - *Dj* μ , then we can easily show that the trips also scale in a nonlinear way, but remain proportionate to the existing flows.

When we look at the distribution of population sizes and any of the cumulative flows that can be predicted from the model in Eqs. (3.5) or (3.6), we have also noted in Eq. (3.3) that these follow an inverse-power law in the form of the rank-size rule. If we scale the rank of the cities by a rate α, then the rank-size relation becomes:

$$
\lambda^{-a}P(r) \sim (\lambda r)^{-a} = \lambda^{-a}(r^{-a}) \sim P(r) \tag{3.8}
$$

The same kind of self-similar scaling is evident in any power-law relationship such as the urban allometric relationship in Eq. (3.4). If the population in all cities grows by a factor λ, then

$$
\lambda^\beta Y(t) \sim (\lambda P(t))^\beta = \lambda^\beta P(t)^\beta \sim Y(t) \tag{3.9}
$$

It is also worth noting that several key relationships which emerge from urban economics, such as the relationship between the density of population, rents charged, and indeed income itself, vary with respect to distance in the city. The long-standing observation that densities and rents decline inversely with distance from the core of the city has been widely modeled using inverse relationships as either a negative exponential or a power law. The density ρ*<sup>i</sup>* (population *Pi* divided by area *Ai*) defined as

$$\rho\_i = \prescript{P\_i}{}{\}\_{A\_i} \sim \exp(-\varphi d\_i) \quad \text{or} \quad \rho\_i = \prescript{P\_i}{}{\}\_{A\_i} \sim d\_i^{-\psi} \tag{3.10}$$

is also scaling, as a simple change in the scale of distance in either of these relationships in Eq. (3.10) would show. These relationships indicate that as size increases in cities, quantities such as income, the numbers of trips, etc., increase or decrease more or less than proportionately, and this indicates that as cities grow or decline, there are qualitative changes that are likely to change the kinds of informatics that are appropriate. This is certainly true of issues concerning economic development, the provision of transportation, and the ability of the city to generate wealth, innovations, and new industries (Bettencourt 2021).

In some senses, what we know about the pattern of locations and interactions in cities is reflected in the underlying networks that support them. There are a multitude of such networks, other than the most obvious and visible systems that transport people and goods using different technologies or modes, but many are hard to observe and measure, particularly those that involve information, such as email, Web access, social media, even telephone, television, and countless other media. All of these networks have scaling properties that suggest that the distribution of their hubs in terms of their indegrees and outdegrees—the number of links that enter or leave the hubs or nodes defining these networks—follow rank-size distributions, and the number of clusters in such networks by size also follow similar inverse-power laws (Barabási 2018). In many of the chapters in this book that deal with mobility, networks form the basis of the various simulations, and the properties introduced here are key to the way such flows are measured and modeled.

# **3.5 The Development of Operational Urban Models**

The theories and models that we have introduced form many of the elements of more comprehensive urban models that deal with various sectors of the urban system. Most models developed so far tend to be those that deal with the low-frequency city, but some of these tools, particularly those dealing with flows and networks which involve transportation, are being developed to deal with movements over short periods of time, focusing on real-time movements, usually on a daily basis. There are at least four classes of model that we can define as the pillars of urban science with respect to urban informatics: first, those that depend on aggregate populations and activities which we call land-use transportation interaction (LUTI models), physical urbandevelopment models using cellular automata (CA models), agent-based models that deal with disaggregate populations of individuals moving and making decisions through time (ABM models), and dynamic models that deal with individual decisionmaking, focusing largely on mobility and geodemographics such as microsimulation models (Chap. 44).

The generic spatial interaction model in Eq. (3.1) and its derivatives, such as accessibility potentials in Eq. (3.2), lie at the heart of many land-use transportation models that essentially stitch together several such models to replicate the locations and interactions between many population and employment sectors of the urban system. These models were first developed as pure transportation models and then extended to deal with land uses and activities in the 1960s. The problems they encountered were due to limits on computation which have now largely disappeared, but more important were the limitations of good theory and of course data. Data still remain an enormous problem, for data on spatial movements have always been hard to get, notwithstanding new sources from real-time capture on mobile devices. The fact that such models and their variants only simulate the city at a cross section in time spurred the development of more dynamic urban models, and in the later years of the last century, models based not on simulating the dynamics of population and employment location but on urban land use more generally at the physical level were developed. These models were largely based on cellular automata whose roots lie in complexity theory and in physical diffusion processes (such as forest fires). Because they focus literally on the physical development of land-use change, they are not easily linked to the numerical characterization of the city in terms of population, employment, income, and related properties. As such, rather than providing operational applications, CA processes as articulated in this genus of model find their use in more specific processes such as traffic simulation at the level of detailed flows.

In the quest for better representations, much more disaggregate models are being built using two different but complementary approaches: agent-based modeling and microsimulation. In terms of ABM models, urban models formulated in this way at the operational level are highly detailed with large data requirements on the behaviors of individual decision makers, usually households and firms, but most suffer from difficulties over developing good theory for the key urban dynamics processes at work in cities. As such, many models tend to be pilots and demonstrations, prototypes used to illustrate what is possible, and very few reach the level of full operationality. UrbanSim and PECAS are exceptions. The fourth class of model based on microsimulation uses techniques based on constructing synthetic populations which are more tolerant of the lack of data pertaining to individual behaviors. Such simulations reflect probability distributions pertaining to the attributes of individuals in a population, and such profiles are used to construct synthetic estimates of populations according to a series of conditional probabilities. There are two subtypes of model, the first being traditional microsimulation models reflecting population profiles in terms of geodemographics. The second set are rather different in that these have been quite widely developed for transportation modeling. These are loosely referred to as activity models, where households generate decisions about trip-making over the course a day, and the probabilities associated with such decision making translate into trip patterns at a very detailed level, such that these are much more powerful than detailed traffic-flow models. MATSIM is one of the best-known such models, although others such as SimMobility, SimAgent, and so on have been developed. All of these models derive from TRANSIMS, the original Los Alamos microsimulation of traffic flow. There are a number of reviews of all these models, and the reader is referred to Batty (2008), Wegener (2014), and Moeckel et al. (2018) for definitions, theoretical expositions, and applications.

In the rest of this book, these dimensions of urban science map out into many areas of urban informatics, and it is worth noting some of the key chapters that relate to this science before we conclude. In terms of modeling, all four of the areas that we have just defined are covered in detail in the chapters at the end of the book, in Part 5 where Eric Miller deals with transportation modeling (Chap. 47), Anthony Yeh with CA modeling (Chap. 45), Andrew Crooks and his co-authors (Chap. 46) with agent-based modeling, and Mark Birkin (Chap. 44) with microsimulation. Mobility of course runs through all these themes and is dealt with from different perspectives in several parts of the book, particularly by Shih-Lung Shaw (Chap. 5) and Martin Raubal and his co-authors (Chap. 6) in Part 1, by Marta Gonzalez et al. (Chap. 11) linking mobility to urban science in Part 2, Chiang Kai-Wei et al. (Chap. 25) explaining developments in mobile mapping in Part 3, methods for spatial search by Liping Di and Eugent Yu (Chap. 37) in Part 4, and with respect to the visualization of movement data by Gennady Andrienko et al. (Chap. 40) in Part 5. Sybil Derrible et al. (Chap. 7) and Budhendra Bhaduri et al. (Chap. 18) examine energy and infrastructure in their contributions in Parts 1 and 2, respectively. In terms of an overview, urban informatics is such a broad area that many of the authors here develop the big picture from their own perspectives. But in particular, Helen Couclelis (Chap. 9) sets all this in context of the smart city in Part 1, and Michael Goodchild provides the wider perspective for how this whole area of urban informatics is addressing questions of new and big data and geographic information science in Part 6.

# **3.6 Future Directions in Urban Informatics**

There are many aspects of urban systems which we have not addressed in this brief review of what constitutes urban science. There is a general question as to how the tools and techniques of urban informatics apply to different types and sizes of cities in different cultures and societies. Much of urban studies is focused on such comparative analysis from the point of view of social and economic differences, and there are implications for the use of urban informatics in different sizes of city with different social cultures, political regimes, and governance. In particular, the distinction between the Global North and Global South is important, and there are already attempts at extending the ideas of city science to these domains, as in the reports from Acuto et al. (2018) and Lobo et al. (2020). Urban science deals with how we define cities in terms of their spatial scale and their boundaries, and in this sense, the size of the city is all important with respect to the kinds of models and techniques that spin off from the ideas introduced in this chapter and elaborated in the rest of this book.

The theories that we have hinted at in this introductory chapter are by no means complete and never will be. Cities are driven by individuals, and complexity theory tells us that they grow and evolve from the bottom up. If there is a hidden hand in this process, it is in the fact that we appear to be able to produce quite ordered structures from our actions that in many respects are quite independent of each other. How we intervene in such complex systems is highly problematic, and urban informatics is in the front line of how we move toward a planning system that is effective in developing more sustainable, equitable, and efficient cities. This book introduces a very wide range of tools that can be used at many points in the planning and policy process, and a major focus needs to be on developing models and techniques that are able to adapt to new changes that continue to beset cities, as well as new technologies that are being introduced ever more rapidly.

# **References**

Schelling TC (1978) Micromotives and macrobehavior. W W Norton Company, New York, NY

Zipf GK (1949) Human behavior and the principle of least effort. Addison-Wesley, Cambridge, MA

**Michael Batty** is Bartlett Professor of Planning and Chairman of the Centre for Advanced Spatial Analysis at University College London. He is also a Distinguished Chair Professor at The Hong Kong Polytechnic University. He is a Fellow of the Royal Society and the British Academy.

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Chapter 4 Street View Imaging for Automated Assessments of Urban Infrastructure and Services**

**Daniel Zünd and Luís M. A. Bettencourt**

**Abstract** Many forms of ambient data in cities are starting to become available that allows tracking of short-term urban operations, such as traffic management, trash collections, inspections, or non-emergency maintenance requests. However, arguably the greatest promise of urban analytics is to set up measurable objectives and track progress toward systemic development goals connected to human development and sustainability over the longer term. The challenge for such an approach is the connection between new technological capabilities, such as sensing and machine learning and local knowledge, and operations of residents and city governments. Here, we describe an emerging project for the long-term monitoring of sustainable development in fast-growing towns in the Galapagos Islands through the convergence of these methods. We demonstrate how collaborative mapping and the capture of 360-degree street views can produce a general basis for a broad set of quantitative analytics, when such actions are coupled to mapping and deep-learning characterizations of urban environments. We map and assess the precision of urban assets via automatic object classification and characterize their abundance and spatial heterogeneity. We also discuss how these methods, as they continue to improve, can provide the means to perform an ambient census of urban assets (buildings, vehicles, services) and environmental conditions.

# **4.1 Introduction**

Many forms of ambient data in cities are starting to allow tracking of short-term operations and services (Park et al. 2014; Townsend 2015). Uses of these technologies range from facilitating traffic management to air quality control, or the management of non-emergency requests (Park et al. 2014; O'Brien 2015). However, arguably one

D. Zünd (B) · L. M. A. Bettencourt

Mansueto Institute for Urban Innovation and Ecology and Evolution, The University of Chicago, Chicago, USA

e-mail<EMAIL_ADDRESS>
L. M. A. Bettencourt e-mail<EMAIL_ADDRESS>
of the greatest promises of urban analytics is to set up measurable objectives and track progress toward systemic development goals connected to human development and sustainability over the longer term (Brelsford et al. 2017). A main challenge to achieving long-term monitoring of processes in urban settings is the convergence of new technology, local knowledge, and the operations of residents and local governance. Whereas these objectives already constitute challenges for developed cities, they are even more daunting in developing country settings (Praharaj et al. 2017). In rapidly developing cities, data are often far less abundant or even non-existent. Additionally, urban environments often change at a much faster pace and in informal ways (Sarin 2016).This makes it much more difficult to track change, and specifically, to generate statistical progress in development trajectories toward sustainable development goals (Randhawa and Kuma 2015; Komninos 2015).

A good case study to research the potential of new technology in semi-informal settings, and the impact it has on managing and tracking the progress of long-term goals, are the Galapagos Islands. The archipelago, famous for its unique ecosystems, lies about 1000 km off the Pacific coast of Ecuador (the blue square in Fig. 4.1). Though most of the islands remain a natural reserve, the human presence on land and sea is growing very quickly, with four fast-growing towns concentrating most of the immigrant human population. The remote location and the unique coupled urban– natural system of these islands constitute a particularly interesting and poignant setting to study the development trajectories of urbanization (Batty et al. 2019).

**Fig. 4.1** The Galapagos Islands are an archipelago in the midst of the Pacific Ocean (blue square). Their secluded location, fast-growing towns, and unique ecosystems offer a particularly interesting and poignant setting for developing models of sustainable development for coupled urban–natural systems. The manageable size of these urban areas makes it possible to study novel methods of collaborative data collection and the convergence of new technology and local knowledge. We exemplify the method on the capital of the islands, Puerto Baquerizo Moreno on San Cristóbal, depicted in the inset. Map designs are from Mapillary (2019) and OpenStreetMap (2019)

From a modeling perspective, the islands provide a unique setting due to their remote location, and the fact that all materials and goods in and out the system are registered upon arrival or departure, just as are people's migration (Bettencourt 2019), provides a good basis for assessing the impact of the island system on its external environment and vice versa.

Together with the emergence of a plan to harmonize tourism with sustainable stewardship of the local charismatic ecosystem (Rousseaud et al. 2017), the towns in the Galapagos Islands provide a unique chance to study novel approaches to urban planning, urban management of resource flows, and tracking of development toward sustainability goals (Batty et al. 2019).

We will focus in this study on the second largest town in the Galapagos, Puerto Baquerizo Moreno, which is also the regional capital and has a population of about eight thousand residents (Andrade and Ferri 2019). The town is located on the eastern part of the Archipelago, on the island of San Cristóbal, as depicted in Fig. 4.1. In terms of materials, the island is relatively independent of the other islands in the archipelago since it has its own harbor and airport that directly connect it to continental Ecuador where most people, construction materials, energy, and consumer goods originate.

Historically, the island of San Cristóbal has not been the archipelago's main tourist hotspot. However, since the airport opened in 1986, the island is increasingly attractive to a growing number of tourists—as can be seen by the number of arrivals at the airport—which shows a higher growth rate than the total growth rate of tourist arrivals across the Galapagos Islands (Izurieta 2017). The annual increase of 3.72% in tourism (about 225 thousand visitors in 2015; Izurieta 2017) creates a growing economy on the islands, but also places pressure on the urban–natural interfaces of the islands. These pressures and possible solutions remain hard to track in detail, therefore precluding a balanced path where economic opportunities may be expanded, while ecosystems in the islands are protected.

Thus, innovative approaches that track the growth and effects of urbanization on the islands are becoming paramount. Here, we exemplify how collaborative data collection and new imaging and artificial intelligence technology can support this process in the context of an emerging project for long-term sustainable development of the Galapagos Islands.

# **4.2 Data Collection and Object Localization**

The rapid development of computer vision and object recognition has opened up efficient ways to process large image datasets (Chen et al. 2016). For urban science and policy, these capabilities have great potential to follow the trajectory of the built infrastructure and to assess the heterogeneity of urban assets and services, including the consumption of energy and materials. However, data about these issues are often lacking, outdated, or too coarse in many developing urban areas. This is even more so the case for remote locations, such as the towns in the Galapagos Islands and specifically, the town of Puerto Baquerizo Moreno. Before we started the project of monitoring the town's built environment, very few data were available online (about a dozen images) of which only a few depicted the island's urban areas.

Monitoring the urban development, however, asks for data that capture the urban fabric as a whole and over time. In the following, we introduce a method that makes it possible to document the whole town within only a few days' work and with only minimal initial investments, thus making collaborative data collection possible. The data pipeline consists of three main steps, of which two are fully automated. The first involves capturing street-level photographs, and the second analyzes single images in order to recognize and segment objects, as depicted on the right panel of Fig. 4.2. The third step consists of identifying the same object in different images and geolocating its position in space and time.

The most time-consuming step is the collection of enough imagery to cover the whole town. The process is entirely parallelizable and can involve a group of people or vehicles. There must be enough overlap in the images so that the geolocation of objects is possible and thus becomes unambiguous. Figure 4.3 depicts an example where a store sign was recognized in six different images.

In this study, we used a 360-degree action camera able to automatically take images with a chosen temporal frequency. The camera is capable of taking images

**Fig. 4.2** Street-level imagery can be captured with relatively simple tools. For this study, we collected data by attaching a 360-degree sports camera on a helmet and rode a bicycle through the town. The imagery is available through Mapillary's (2019) user interface, as depicted on the left panel. The right panel shows processed and segmented imagery. The automatic object classification identifies structures and objects out of almost three-dozen categories. However, on the island, the algorithms sometimes fail to properly identify certain objects. For example, the sidewalk on the right is classified as ground. Nevertheless, the methods provide a powerful tool to assess urban features in developing towns experiencing rapid change

**Fig. 4.3** The imagery covers most of the accessible street network of Puerto Baquerizo Moreno on San Cristóbal, Galapagos. The green dots show the locations of all 360-degree imagery produced by us. When a series of images are available along a street, objects can be identified and geolocated. The inset depicts a situation in which the same store sign is recognized in six different images in the right inset panel, taken from slightly different locations, of which three are shown in the left inset panel. Map designs are from Mapillary (2019) and OpenStreetMap (2019)

that cover the whole surrounding from the current location which, with some postprocessing, produced globes at each location. We attached the camera to a helmet and drove around the town with it. Since the camera also added the GPS coordinates to each image's metadata, we were able to cover about 75 km of geotagged image globes within only a couple of days. The collected imagery accounts for more than 10,000 images, of which many overlap and provide a good dataset for the next steps in the data pipeline. Each location of a 360-degree image is depicted by a trace of green dots in Fig. 4.3.

We executed steps two and three in collaboration with Mapillary (2019), a technology company dedicated to creating crowdsourced street view maps. Mapillary provides an engine that automatically processes uploaded images, including a user interface to walk from one image to the next and, thus, ultimately throughout the entire city. The left side of Fig. 4.2 depicts the interface that is accessible to the public. The images are further processed using computer vision and object recognition algorithms, of which many have been developed and optimized by the Mapillary research teams (Bulo and Kontschieder 2016; Bulo et al. 2017; Cariucci et al. 2017; Neuhold et al. 2017). The algorithms segment the images and add semantic information to different parts of the visual field.

The field of computer vision and object recognition has made significant strides in recent years by using deep-learning algorithms to perform image segmentation (Krylov et al. 2018). However, these techniques are not yet perfect and the resulting semantic information extracted from images is often only an approximation to reality. For street-level data, this is especially the case for areas that differ from the data that were used to train the object recognition classifier. Nevertheless, the algorithms are able to recognize core properties in the imagery, as depicted in the right inset panel of Fig. 4.2.

When the same object is recognized in several images, it can be geolocated uniquely in space. Figure 4.3 shows an example where a single store sign is recognized in six different images located in the right inset, three of which are shown in the left inset panel. The task of geolocating objects from different images at street level involves several major technical challenges. Besides aggregating the same object present in several images, the main challenge in processing crowdsourced streetlevel data is the varying qualities of the imagery, such as blurring or restricted field of view, and variability in camera positions. The latter is important, since high-quality geolocation depends on the camera position relative to the object in the field of view for accurate triangulation and location (Krylov and Dahyot 2018).

Despite these challenges, the engine was able to geolocate almost 12,000 objects in the small town of Puerto Baquerizo Moreno, including 777 trash cans, 343 store signs, 412 advertisement signs, and 224 driveways. These are the classes of objects that we use in the next section to derive the functions of certain parts of the town and to exemplify the conclusions that can be drawn from these methods, as they continue to improve.

# **4.3 Deriving Urban Functions from Object Statistics**

The collection of data and the identification and localization of objects in space provides a basic functional mapping of an urban area. The spatial distribution of different classes of objects makes it possible to study the location and functions of different districts. For example, the density distribution of store signs in Fig. 4.4b shows the areas in Puerto BaquerizoMoreno that provides a range of specific services, typically associated with tourism (Andrade and Ferri 2019).

Figure 4.4 shows two object–class density distributions that are good indicators of residential areas: the distributions of trash cans and driveways (subfigures (a) and (c)). Trash cans in residential areas of Puerto Baquerizo Moreno are standardized vessels with a unique shape and color combination. Each household is required to have their trash cans outside of the building, close to the street for easy access for trash collectors. They additionally serve as public trash bins. The trash bins in tourist areas are different, not as prominently placed, and often obfuscated. The segmentation engine has problems identifying them as such, but this is also a clear sign of a different look and function and of an intentional effort to deal with the issue differently. The waterfront area with the most tourist services is much denser than 4 Street View Imaging for Automated Assessments of Urban … 35

**Fig. 4.4** Geolocated objects help to identify and locate different properties of the town. The figures depict the distribution of **a** trash cans, **b** store signs, **c** driveways, and **d** advertisement signs. The distribution of the trash cans shows the importance of local knowledge. The ones identified by the segmentation are private trash cans, whereas the public ones are not recognized and are largely in the business parts of town, close to the sea and indicated by a high volume of shop signs in **b**. The driveways in **c** indicate a lower density of houses in those areas, since they are set back from the street. The advertisement signs in **d** have a similar pattern as the store signs in **b**, but are more uniformly distributed, mainly along principal roads. Map designs are from Stamen Design (2019)

the rest of the town. The buildings are often located next to the street and not set back. This is indicated by the abundance of driveways in the residential area in the northeast and their absence in the denser locations, such as the area central of the town toward the sea. Figure 4.4c depicts this clearly.

The last indicator we want to point out in this study is the distribution of advertisement signs. Their spatial distribution is depicted in Fig. 4.4d. According to the density distributions of advertisement signs, there are three main patterns specific to places with a large accumulation of advertising signs. The first pattern is where most tourists spend their time within the town and also where most restaurants and tourist services are located, corresponding to the highest density of store signs in Fig. 4.4b.

The second area with a high density of advertisements consists of the main thoroughfares that cut through the town from east to west, each a one-way street. Within the town, these are the streets where most shops frequented by locals are located. The main street also connects further to the only other settlement on the island and is the only street that cuts through the San Cristóbal from east to west. This road constitutes the main axis in the town, together with the street that is orthogonal to it and starts at the airport on the left of the map. However, these signals are not as clear as for other indicators.

The third cluster, the one with the highest density of advertising signs according to the data, is located at the international convention center close to the center top of the image. This cluster has to be regarded with care, because many of our data collection trips started here, so that the region is oversampled in terms of imagery. The data-processing engine has some difficulties to cope with this sampling effect, separates advertisement signs that are the same, and geolocates them in very similar locations.

The above interpretations of the different density distributions in Fig. 4.4 are clearly highly reliant on local knowledge. For example, the unique form and shape of the private trash cans are not a general pattern across different urban systems, but a very local feature. There would not have been an obvious conclusion from the extracted data without knowledge of local choices, habits, and rules.

# **4.4 Discussion**

Recent technological advancements are paving the way to novel ways of monitoring, studying, and assessing characteristics and change in urban environments that are closer to the human experience. Our present study shows how collecting street view imagery and identifying and locating associated functional objects require little initial investment. These methods are also suitable for collaborative approaches involving both image collection and interpretation of resulting spatial statistics. Thus, this type of result demonstrates that concepts of smart cities and the collection of extensive and detailed ambient urban data are no longer restricted to large investments and efforts by large corporations or universities, but are also feasible in developing towns by relatively small numbers of people.

It is desirable that local citizens take a greater part in this type of process for a number of different reasons. First, on purely technical grounds, an ongoing data collection effort helps improve the system's evidence pool in terms of coverage and accuracy of object identification statistics. Second, local knowledge is critical for good urban planning and policy, and there have been thus far few systematic strategies that combine data and technology with people's local experiences. Third, and most important, data collections by corporations and governments rarely speak to the perspective and priorities of local communities, who, in the case of sustainable development, have a clear stake in the future of their environment and can act as the best stewards of its well-being (Burke et al. 2006). Fourth, the use of methods such as the ones discussed here provides a number of interesting educational and training opportunities that can contribute to the growth of local human capital and may have spillovers to other innovative local practices.

There are still a number of technical obstacles for turning the pilot described here into an effective system that can speak to these objectives. Object recognition in images of developing cities is far from working perfectly. This is likely due to biases in training of the artificial intelligence algorithms with imagery from more formal environments, such as cities of the Global North. As a result, the present algorithms often fail to extract all semantic information from the images in the Galapagos and thus fail to achieve high levels of accuracy in object recognition and segmentation. Nevertheless, the methods already offer powerful tools in their current state, so that we can reasonably expect that they will improve in the near future as more evidence from informal and variable environments becomes part of training corpora.

Aspects of algorithms that need improvement are likely related to increased knowledge of geographic and cultural contexts. We have seen for example that the recognition of sidewalks remains difficult as these rather irregular spaces are often classified as parts of the streets or simply as ground. Another example is the classification of beaches. In the data, we collected on the Galapagos Islands, sand beaches are often classified as snow. Simple contextual clues would certainly improve this type of classification.

Nevertheless, the methodology provides initial stages of potentially powerful artificial intelligence tools to assess the assets of cities and towns and to study the development trajectory of urban microenvironments. This will become even more powerful in the future, as the algorithms become capable of more fine-grained object classification and segmentation in a ways that can track, for example, construction processes and the materials and costs involved.

A big impact in future studies of urban areas will arise from extracting threedimensional (3D) city models (Schläpfer et al. 2015) from the type of imagery produced and analyzed in this study. In combination with more traditional aerial and remote sensing (Qin and Fang 2014; Weng et al. 2018) and citizen engagement, high-quality 3D models of whole towns and cities are just now becoming accessible also in fast-changing settings in the developing world (see also Chap. 34). The simplicity and generalizability of data collection demonstrated here provide a way to easily and quickly track these development trajectories in ways that are closer to the experience of individuals and households living and working in these environments, and at the same time allow us to characterize material and information flows through these systems across scales.

# **References**

Schläpfer M, Lee J, Bettencourt LMA (2015) Urban skylines: building heights and shapes as measures of city size. arXiv preprint arXiv:1512.00946

Stamen Design (2019) maps.stamen.com. Accessed 2019-03-04

Townsend A (2015) Cities of data: examining the new urban science. Public Cult 27(2):201–212 Weng Q, Quattrochi D, Gamba PE (eds) (2018) Urban remote sensing. CRC Press, Boca Raton, FL

**Daniel Zünd** is a Postdoctoral Fellow at the Mansueto Institute for Urban Innovation and a Postdoctoral researcher in Ecology and Evolution at the University of Chicago. He holds a Ph.D. in Architecture and Urban Planning, as well as a Master's degree in Computer Science.

**Luís M. A. Bettencourt** is the Inaugural Director of the Mansueto Institute for Urban Innovation and Professor of Ecology and Evolution at the University of Chicago, as well as an External Professor of Complex Systems at the Santa Fe Institute.

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Chapter 5 Urban Human Dynamics**

**Shih-Lung Shaw**

**Abstract** Urban areas are places where people concentrate in a relatively high density built environment to carry out a wide range of activities. Each urban area should provide adequate infrastructure and services to support the needs of its population. Since various resources, services, and facilities are at different locations, urban areas manifest a complex system of flows of people, goods, and information to support the economic, social, cultural, and political systems in human society. These activities, flows, and systems are driven by various processes and exhibit various spatiotemporal patterns that are the outcomes of human dynamics. However, how we investigate the various dynamic processes and complex systems in urban areas has been and continues to be a challenging research topic. Urban human dynamics cover multiple aspects and can be studied from different perspectives. This chapter discusses urban dynamics and human dynamics in terms of their respective approaches and methods, along with some selected examples. It then connects urban human dynamics research with urban informatics to highlight their relationships and how together they could lead to urban areas that can better serve human needs and improve the quality of life.

# **5.1 Introduction**

Urban areas are places where people concentrate in a relatively high density built environment to carry out a wide range of activities. The terms urban area and city are often used interchangeably. The National Geographic Society, for example, indicates that "An urban area is the region surrounding a city" (https://www.nationalgeograp hic.org/encyclopedia/urban-area/). Each urban area requires adequate infrastructure and services such as electricity, water, sewer, transportation, schools, hospitals, shops, and parks to support the needs of its population. Since various resources, services, and facilities are at different locations, urban areas therefore have a complex system of flows of people, goods, and information to support their economic, social, cultural,

S.-L. Shaw (B)

Department of Geography, University of Tennessee, Knoxville, USA e-mail<EMAIL_ADDRESS>
and political systems. These activities, flows, and systems are driven by various processes and exhibit various spatiotemporal patterns that are the outcomes of urban human dynamics. It should be noted that urban human dynamics also constantly evolve across space and over time with changing technologies, environmental issues, and social values.

According to the United Nations Educational, Scientific and Cultural Organization (UNESCO, https://www.unesco.org/education/tlsf/mods/theme\_c/popups/mod 13t01s009.html) and Our World in Data (https://ourworldindata.org/urbanization), the trend to global urbanization has dramatically accelerated in the past several decades. Approximately 30% of world population lived in urban areas in 1950 and around 55% in 2019. This urbanization trend is expected to continue, and it is estimated that close to 70% of the world's population are likely to live in urban areas by 2050. With this trend, many existing cities must grow bigger to accommodate the increasing population. Given the fact that many big cities already face significant challenges with respect to their current population size, how to accommodate a continuously increasing urban population without sacrificing our general quality of life has become an important and urgent research topic.

Urban areas have long been considered to be dynamic and complex in nature (Crosby 1983; Batty 2003). Batty (2005) suggested that the emphasis of urban models is no longer on spatial interaction but on development dynamics and local movement. However, how to investigate the various dynamic processes and complex systems in urban areas has been and remains a challenging research topic. Urban human dynamics cover multiple aspects and can be studied with different perspectives. In general, we can divide research in urban human dynamics into two major types, urban dynamics research, and human dynamics research. Urban dynamics research tends to focus on the evolution of an urban area in terms of its growth, change, and decline. In this case, the focus is mainly on the urban area itself, and human activities often are considered implicitly through the outcomes of human activities such as land-use types. For example, we can study how a city evolves spatially through its landuse change patterns over time in terms of its growth, change, and decline. Urban dynamics research also can investigate the dynamics among a system of urban areas such as studying various types of flows among a set of cities. In this case, the focus is mainly on the interactions between cities. Human dynamics research, on the other hand, has a focus on humans per se and studies the dynamics of human activities and interactions that lead to various flows and patterns in an urban area or between urban areas. Although urban dynamics and human dynamics are closely related to each other and should not be treated as two independent types of dynamics in urban areas, this chapter discusses each of these two types of urban human dynamics separately since they tend to use different research approaches and research methods.

# **5.2 Urban Dynamics**

One way of studying complex and dynamic urban areas is to employ general systems theory (von Bertalanffy 1968; Straussfogel 1991; Alfeld 1995; Xie 1996). General systems theory considers a system comprising of a set of interdependent subsystems. A system, which can be more than the sum of its parts, exhibits emerging patterns from the interactions of its parts. Changes in one subsystem can affect other subsystems as well as the system as a whole. Forrester (1969), who is considered the founder of system dynamics, published a book titled *Urban Dynamics* in 1969. He states that "In this book, the nature of the urban problem, its causes, and possible corrections are examined in terms of interactions between components of the urban system" (Forrester 1969, p. ix). Forrester uses computer simulations to study the life cycle of an urban area to reveal its dynamic characteristics. This was an early effort at studying urban dynamics with a computer simulation approach to systematically examine the structure, growth, stagnation, and revival of urban areas.

Due to the influence of Forrester's approach in investigating urban dynamics, two volumes of *Readings in Urban Dynamics* were subsequently published in 1974 and 1975, respectively (Mass 1974; Schroeder et al. 1975). These two volumes include articles that cover conceptual issues, models, and applications of various aspects of urban dynamics as well as responses to the criticisms of the approach presented in Forrester's book. For example, Forrester uses a five-step process to reach his conclusions about the dynamics of a typical inner area of a US city, his example loosely related to Boston. The first step chooses certain basic variables to represent the social and economic composition of an urban area, followed by a second step of using specific equations to describe the development of an urban area. The third step introduces public policies to modify the development expressed in the equations, which then leads to the fourth step of deriving the development outcomes due to the public policies introduced into the equations. The fifth step compares the different development outcomes and recommends the public policy that would generate a desirable development outcome. Kadanoff (1971) pointed out several shortcomings of Forrester's approach, which includes (1) Forrester's model fails to include city– suburban interactions, (2) migration is the only interaction between an urban area and the outside world in Forrester's model, and (3) Forrester's model focuses mainly on predictive methods and does not give sufficient attention to the goals behind the normative approach. Kadanoff (1971, p. 262) then concluded that "I would reject the conclusions, but accept the model as an appropriate basis for further work."

In response to these criticisms, Forrester (1974, p. vii) wrote: "With the publication of *Readings in Urban Dynamics*, it seems important to emphasize that the original *Urban Dynamics* model represented more a viewpoint and a methodology for analyzing urban behavior than a single, finished model. *Urban Dynamics* was a first step in a continuously evolving set of ideas about social systems. The urban dynamics approach has several major distinguishing features. First, it focuses primarily upon the *interrelationships* between economic, political, psychological, and sociological variables rather than analyzing in detail any one subsystem of the urban environment. Second, it deals with the long-term evolution of an urban area; it treats the positive feedback processes that lead to urban growth as well as the nonlinearities and negative feedback processes that arise to limit growth. Finally, it provides a formal means for testing the implications of our collective assumptions about urban behavior." The above statements provide a clear picture of Jay Forrester's approach to studying urban dynamics; it is associated with general systems theory and uses computer simulations to examine the interrelationships among different subsystems of an urban area. More importantly, the computer simulation approach suggested by Forrester has been pursued by many other researchers in their investigation of urban dynamics, although different simulation models have been used in various studies.

# *5.2.1 Cellular Automata for Urban Dynamics Research*

Cellular automata (CA), which were developed in the 1940s by Ulam (1950) and von Neumann (1966), are frequently used to model and simulate urban dynamics. Following these ideas, Tobler (1979) proposed a cellular geography that uses cellular spaces in geographic modeling. A cellular space can be considered as a twodimensional grid, and each cell in the grid has a *state* that is determined by the states of its neighboring cells. The *neighbor* of a given cell can be defined in different ways, by either the four cells sharing a common side (known as von Neumann neighborhood) or the eight cells that share a common side or a common corner of a given cell (known as the Moore neighborhood). A *transition rule* then determines how the state of a cell changes into a different state from time *t* to time *t* + 1 based on the specific configurations of the states of its neighboring cells. For example, a transition rule could convert a given cell from the state of non-residential at time *t* to residential at time *t* + 1 if three of its four neighboring cells have a state of residential at time *t*. Cells, states, neighbors, and transition rules therefore serve as the foundation of cellular automata models.

There are two characteristics of cellular automata that are attractive to geographical problems (White and Engelen 1993). First, cellular automata divide a study area into a grid that is intrinsically spatial. Second, cellular automata can generate very complex forms from very simple rules that are useful to study complex spatial phenomena. In other words, simple local changes due to interactions among the neighboring cells in a CA model could lead to complex emergent global patterns (Wolfram 1983, 1984). CA models therefore can reflect micro–macro interactions in a simple and direct way, and the key contribution of CA models is to provide insights into how urban systems work rather than offer a simulation tool of urban dynamics (Couclelis 1985). This presents a way of linking the processes operating at different scales to tackle a major research challenge in many fields that attempt to link forms to processes and address local to global structures (Batty and Xie 1994; Emmeche 1994). In fact, Jacobs (1961) suggested that the observed disorders in urban areas could be viewed as organized complexity due to a deeper order reflecting their diversity. Cellular automata models enable us to investigate urban dynamics from local processes in order to understand global complex patterns and to gain insights into the evolution of various aspects of urban dynamics.

Chapin andWeiss (1968) first applied the concepts of cellular automata to an urban land development model, and Tobler (1970) employed the idea of cellular space to simulate urban growth in the Detroit region, although both studies did not use the term cellular automata. Tobler (1970, p. 234) suggested that "the utmost effort must be exercised to avoid writing a complicated model. … Because a process appears complicated is also no reason to assume that it is the result of complicated rules." White and Engelen (1993) argued that most geographic theories, such as central place theory and urban economic models as embodied in the Alonso-Muth land-use theory, are static in nature and assume a state of stable equilibrium, which is contrary to our common sense and experience that all urban areas are undergoing continual growth, change, decline, and restructuring. White and Engelen (1993) consequently developed a CA model that generates fractal patterns of land use from relatively simple rules of spatial behavior in order to address the issue of complexity in urban structure. The objective of this study is to gain insight into the underlying reasons behind the evolution of land-use structures and to demonstrate the existence of a complex fractal order of land-use patterns. Their findings suggest that complexity is a necessary feature of cities. When cities are too simple in their structure, they probably will not evolve successfully and could cease to function effectively. This study is a good example of using a CA model to assess the complexity of urban structure and to establish general guidelines for planning policy.

Couclelis (1985) pointed out that the standard cell-space model has many limitations to its usefulness for tackling real-world geographic problems. These limitations include the infinite plane, neighborhood stationarity, spatial homogeneity, spatial and temporal invariance of transition rules, and closure to external events that are directly related to the basic assumptions of cell-space models. Batty and Xie (1994, p. S46) also suggested that a major problem of applying CA models to urban systems is that "It is most unlikely that urban systems can be simulated entirely at the local scale, but the value of this approach lies in focusing our attention on this scale and the extent to which a hierarchy of processes and scales is essential to understanding how cities work." Xie (1996) discussed improvements to CA models over the years and proposed a generalized model for cellular urban dynamics, named dynamic urban evolutionary modeling (DUEM), to demonstrate the theoretical integrity and technical merit of the CA approach for urban dynamic applications. One major contribution of DUEM is to adopt a hierarchical system of CA spaces consisting of neighborhood, field, and region that can be used to simulate interactions between cell space, model space, and geographic space to overcome some limitations of the conventional cell-space models. DUEM further connects with a geographic information system (GIS) to benefit from GIS data, analysis, and visualization capabilities.

Anthony Yeh, Xia Li and their collaborators have used cellular automata models extensively to study urban dynamics. Li and Yeh (2000) developed a constrained CA model within a raster GIS that includes local, regional, and global constraints to regulate cellular space and defines gray cells as representing the percentages of urban land development at any iteration of the CA model. Yeh and Li (2001) further used a constrained CA model and a raster GIS to simulate seven different types of urban forms and developments ranging from compact-monocentric to very highly dispersed development patterns. Their model considers various criteria such as urban forms, environmental suitability, and land consumption for the purpose of planning sustainable cities. They also combined CA models with computational intelligence methods such as neural networks (Li and Yeh 2001), ant colony optimization (Liu et al. 2008), and artificial immune systems (Liu et al. 2010) to investigate complex urban systems. Santé et al. (2010) offered a helpful review of urban cellular automata models applied to the simulation of real-world urban processes with respect to their capabilities and limitations. They also conclude that the widespread use of CA models is due to their simplicity. In the meantime, the simplicity of CA models is also the main weakness that limits their ability to represent real-world phenomena. Another major shortcoming is the lack of a standard method for the definition of transition rules in urban CA models which represent the complexity of the processes.

# *5.2.2 Other Urban Dynamics Approaches*

Batty (2008) indicated that traditional urban models treated cities as aggregate equilibrium systems and mainly used spatial interaction. The approach changed in the late twentieth century to consider urban dynamics more as evolving complex systems whose structure emerges from the bottom-up. In his book *Cities and Complexity: Understanding Cities with Cellular Automata, Agent-Based Models, and Fractals*, Batty (2007) presented agent-based models as another useful approach to study complex urban dynamics as urban planning moves from a top-down centralized perspective to a bottom-up decentralized perspective. An agent-based model (ABM) consists of autonomous agents, which can be either individual or collective entities, with defined behaviors to simulate the effects on emerging system patterns from the actions and interactions of the autonomous agents. One key difference between cellular automata models and agent-based models is that agents in ABM are free to move and interact with each other and the environment. The goal of agent-based models is mainly to gain insights into the collective behavior of agents that follow simple behavioral rules. Huang et al. (2014) reviewed 51 agent-based residential choice models in three research domains, which are (1) urban land-use models based on classical theories, (2) different stages of the urbanization process, and (3) integrated agent-based and microsimulation models, to offer a retrospective on developments in agent-based models (ABMs) of urban residential choices. This review paid special attention to the progress of the representation of agent heterogeneity, the extent of land-market representation, and the method of measuring the extensive model outputs. They concluded that "Urban land-use models can benefit from agent-based modeling by incorporating heterogeneous intelligent agents and explicit modeling of an institution that stands behind land exchange" (Huang et al. 2014, p. 681).