Preface xiii Acknowledgements xv Introduction xvi Section I Urban Complexity 1 1 Urban Complexity 3Sean Hanna 2 Emergence and Universal Computation 15Cassey Lee 3 Fractals and Geography 31Pierre Frankhauser and Denise Pumain Project 1 Emergence and Urban Analysis 57Ljubomir Jankovic Project 2 The Evolution and Complexity of Urban Street Networks 63Nahid Mohajeri and Agust Gudmundsson Section II Machines that Think 69 4 Artificial Intelligence, Logic, and Formalising Common Sense 71John McCarthy 5 Defining Artificial Intelligence 91David B. Fogel 6 AI: From Copy of Human Brain to Independent Learner 121Shelly Fan 7 The History of Machine Learning and Its Convergent Trajectory Towards AI 129Keith D. Foote 8 Machine Behaviour 143Iyad Rahwan, Manuel Cebrian, Nick Obradovich, Josh Bongard, Jean-François Bonnefon, Cynthia Breazeal, Jacob W. Crandall, Nicholas A. Christakis, Iain D. Couzin, Matthew O. Jackson, Nicholas R. Jennings, Ece Kamar, Isabel M. Kloumann, Hugo Larochelle, David Lazer, Richard McElreath, Alan Mislove, David C. Parkes, Alex ‘Sandy’ Pentland, Margaret E. Roberts, Azim Shariff, Joshua B. Tenenbaum, and Michael Wellman Project 3 Plan Generation from Program Graph 167Ao Li, Runjia Tian, Xiaoshi Wang, and Yueheng Lu Project 4 Self-organising Floor Plans in Care Homes 171Silvio Carta, Stephanie St. Loe, Tommaso Turchi, and Joel Simon Project 5 N2P2 – Neural Networks and Public Places 177Roberto Bottazzi, Tasos Varoudis, Piyush Prajapati, and Xi Wang Project 6 Urban Fictions 183Matias del Campo, Sandra Manninger, and Alexandra Carlson Project 7 Latent Typologies: Architecture in Latent Space 189Stanislas Chaillou Project 8 Enabling Alternative Architectures 193Nate Peters Project 9 Distant Readings of Architecture: A Machine View of the City 201Andrew Witt Section III How Machines Learn 207 9 What Is Machine Learning? 209Jason Bell 10 Machine Learning: An Applied Mathematics Introduction 217Paul Wilmott 11 Machine Learning for Urban Computing 249Bilgeçağ Aydoğdu and Albert Ali Salah 12 Autonomous Artificial Intelligent Agents 263Iaroslav Omelianenko Project 10 Machine Learning for Spatial and Visual Connectivity 287Sherif Tarabishy, Stamatios Psarras, Marcin Kosicki, and Martha Tsigkari Project 11 Navigating Indoor Spaces Using Machine Learning: Train Stations in Paris 293Zhoutong Wang, Qianhui Liang, Fabio Duarte, Fan Zhang, Louis Charron, Lenna Johnsen, Bill Cai, and Carlo Ratti Project 12 Evolutionary Design Optimisation of Traffic Signals Applied to Quito City 297Rolando Armas, Hernán Aguirre, Fabio Daolio, and Kiyoshi Tanaka Project 13 Constructing Agency: Self-directed Robotic Environments 303Patrik Schumacher Section IV Application to the City 309 13 Code and the Transduction of Space 311Martin Dodge and Rob Kitchin 14 Augmented Reality in Urban Places: Contested Content and the Duplicity of Code 341Mark Graham, Matthew Zook, and Andrew Boulton 15 Spatial Data in Urban Informatics: Contentions of the Software-sorted City 367Marcus Foth, Fahame Emamjome, Peta Mitchell, and Markus Rittenbruch 16 Urban Morphology Meets Deep Learning: Exploring Urban Forms in One Million Cities, Towns, and Villages Across the Planet 379Vahid Moosavi 17 Computational Urban Design: Methods and Case Studies 393Snoweria Zhang and Luc Wilson 18 Indexical Cities: Personal City Models with Data as Infrastructure 409Diana Alvarez-Marin 19 Machine Learning, Artificial Intelligence, and Urban Assemblages 445Serjoscha Düring, Reinhard Koenig, Nariddh Khean, Diellza Elshani, Theodoros Galanos, and Angelos Chronis 20 Making a Smart City Legible 453Franziska Pilling, Haider Ali Akmal, Joseph Lindley, and Paul Coulton Project 14 A Tale of Many Cities: Universal Patterns in Human Urban Mobility 467Anastasios Noulas, Salvatore Scellato, Renaud Lambiotte, Massimiliano Pontil, and Cecilia Mascolo Project 15 Using Cellular Automata for Parking Recommendations in Smart Environments 473Gwo-Jiun Horng Project 16 Gan Hadid 477Sean Wallish Project 17 Collective Design for Collective Living 483Elizabeth Christoforetti and Romy El Sayah Project 18 Architectural Machine Translation 489Erik Swahn Project 19 Large-scale Evaluation of the Urban Street View with Deep Learning Method 495Hui Wang, Elisabete A. Silva, and Lun Liu Project 20 Urban Portraits 501Jose Luis García del Castillo y López Project 21 ML-City 507Benjamin Ennemoser Project 22 Imaging Place Using Generative Adversarial Networks (GAN Loci) 513Kyle Steinfeld Project 23 Urban Forestry Science 517Iacopo Testi Section V Machine Learning and Humans 521 21 Ten Simple Rules for Responsible Big Data Research 523Matthew Zook, Solon Barocas, Danah Boyd, Kate Crawford, Emily Keller, Seeta Peña Gangadharan, Alyssa Goodman, Rachelle Hollander, Barbara A. Koenig, Jacob Metcalf, Arvind Narayanan, Alondra Nelson, and Frank Pasquale 22 A Unified Framework of Five Principles for AI in Society 535Luciano Floridi and Josh Cowls 23 The Big Data Divide and Its Consequences 547Matthew T. McCarthy 24 Design Fiction: A Short Essay on Design, Science, Fact, and Fiction 561Julian Bleecker 25 Superintelligence and Singularity 579Ray Kurzweil 26 The Social Life of Robots: The Politics of Algorithms, Governance, and Sovereignty 603Vincent J. Del Casino Jr, Lily House-Peters, Jeremy W. Crampton, and Hannes Gerhardt Project 24 Experiments in Synthetic Data 615Forensic Architecture Project 25 Emotional AI in Cities: Cross-cultural Lessons from the UK and Japan on Designing for an Ethical Life 621Vian Bakir, Nader Ghotbi, Tung Manh Ho, Alexander Laffer, Peter Mantello, Andrew McStay, Diana Miranda, Hiroshi Miyashita, Lena Podoletz, Hiromi Tanaka, and Lachlan Urquhart Project 26 Decoding Urban Inequality: The Applications of Machine Learning for Mapping Inequality in Cities of the Global South 625Kadeem Khan Project 27 Amsterdam 2040 631Maria Luce Lupetti Project 28 Committee of Infrastructure 635Jason Shun Wong Index 639

Explore the applications of machine learning and artificial intelligence to the built environment Machine Learning and the City: Applications in Architecture and Urban Design delivers a robust exploration of machine learning (ML) and artificial intelligence (AI) in the context of the built environment. Relevant contributions from leading scholars in their respective fields describe the ideas and techniques that underpin ML and AI, how to begin using ML and AI in urban design, and the likely impact of ML and AI on the future of city design and planning. Each section couples theoretical and technical chapters, authoritative references, and concrete examples and projects that illustrate the efficacy and power of machine learning in urban design. The book also includes: An introduction to the probabilistic logic that underpins machine learningComprehensive explorations of the applications of machine learning and artificial intelligence to urban environmentsPractical discussions of the consequences of applied machine learning and the future of urban design Perfect for designers approaching machine learning and AI for the first time, Machine Learning and the City: Applications in Architecture and Urban Design will also earn a place in the libraries of urban planners and engineers involved in urban design.