Machine Learning and the City

Applications in Architecture and Urban Design
Herausgegeben:Carta, Silvio

• Broschiertes Buch

Produktbeschreibung

Machine Learning and the City

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 learning
_ Comprehensive explorations of the applications of machine learning and artificial intelligence to urban environments
_ Practical 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.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Wiley & Sons / Wiley-Blackwell
• Artikelnr. des Verlages: 1W119749630
• 1. Auflage
• Seitenzahl: 672
• Erscheinungstermin: 17. Mai 2022
• Englisch
• Abmessung: 244mm x 170mm x 36mm
• Gewicht: 1140g
• ISBN-13: 9781119749639
• ISBN-10: 1119749638
• Artikelnr.: 62348835

Autorenporträt

Silvio Carta is an architect and Associate Professor at the University of Hertfordshire, UK. His research interests include digital architecture, data-driven approaches and computational design. Silvio is the author of Big Data, Code and the Discrete City. Shaping Public Realms (Routledge 2019).

Inhaltsangabe

Preface xiii

Acknowledgements xv

Introduction xvi

Section I Urban Complexity 1

1 Urban Complexity 3
Sean Hanna

2 Emergence and Universal Computation 15
Cassey Lee

3 Fractals and Geography 31
Pierre Frankhauser and Denise Pumain

Project 1 Emergence and Urban Analysis 57
Ljubomir Jankovic

Project 2 The Evolution and Complexity of Urban Street Networks 63
Nahid Mohajeri and Agust Gudmundsson

Section II Machines that Think 69

4 Artificial Intelligence, Logic, and Formalising Common Sense 71
John McCarthy

5 Defining Artificial Intelligence 91
David B. Fogel

6 AI: From Copy of Human Brain to Independent Learner 121
Shelly Fan

7 The History of Machine Learning and Its Convergent Trajectory Towards AI 129
Keith D. Foote

8 Machine Behaviour 143
Iyad 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 167
Ao Li, Runjia Tian, Xiaoshi Wang, and Yueheng Lu

Project 4 Self-organising Floor Plans in Care Homes 171
Silvio Carta, Stephanie St. Loe, Tommaso Turchi, and Joel Simon

Project 5 N²P² - Neural Networks and Public Places 177
Roberto Bottazzi, Tasos Varoudis, Piyush Prajapati, and Xi Wang

Project 6 Urban Fictions 183
Matias del Campo, Sandra Manninger, and Alexandra Carlson

Project 7 Latent Typologies: Architecture in Latent Space 189
Stanislas Chaillou

Project 8 Enabling Alternative Architectures 193
Nate Peters

Project 9 Distant Readings of Architecture: A Machine View of the City 201
Andrew Witt

Section III How Machines Learn 207

9 What Is Machine Learning? 209
Jason Bell

10 Machine Learning: An Applied Mathematics Introduction 217
Paul Wilmott

11 Machine Learning for Urban Computing 249
Bilgeça Aydodu and Albert Ali Salah

12 Autonomous Artificial Intelligent Agents 263
Iaroslav Omelianenko

Project 10 Machine Learning for Spatial and Visual Connectivity 287
Sherif Tarabishy, Stamatios Psarras, Marcin Kosicki, and Martha Tsigkari

Project 11 Navigating Indoor Spaces Using Machine Learning: Train Stations in Paris 293
Zhoutong 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 297
Rolando Armas, Hernán Aguirre, Fabio Daolio, and Kiyoshi Tanaka

Project 13 Constructing Agency: Self-directed Robotic Environments 303
Patrik Schumacher

Section IV Application to the City 309

13 Code and the Transduction of Space 311
Martin Dodge and Rob Kitchin

14 Augmented Reality in Urban Places: Contested Content and the Duplicity of Code 341
Mark Graham, Matthew Zook, and Andrew Boulton

15 Spatial Data in Urban Informatics: Contentions of the Software-sorted City 367
Marcus 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 379
Vahid Moosavi

17 Computational Urban Design: Methods and Case Studies 393
Snoweria Zhang and Luc Wilson

18 Indexical Cities: Personal City Models with Data as Infrastructure 409
Diana Alvarez-Marin