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The Social Life of Small Urban Spaces 2.0

Three Experiments in Computational Urban Studies
  • Javier Argota Sánchez-VaquerizoEmail author
  • Daniel Cardoso LlachEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1028)

Abstract

This paper introduces a novel framework for urban analysis that leverages computational techniques, along with established urban research methods, to study how people use urban public space. Through three case studies in different urban locations in Europe and the US, it demonstrates how recent machine learning and computer vision techniques may assist us in producing unprecedently detailed portraits of the relative influence of urban and environmental variables on people’s use of public space. The paper further discusses the potential of this framework to enable empirically-enriched forms of urban and social analysis with applications in urban planning, design, research, and policy.

Keywords

Data analytics Urban design Machine learning Artificial intelligence Big data Space syntax 

Notes

Acknowledgements

This research has been possible thanks to the Fulbright Foundation that supported my stay in the United States at the MS in Computational Design at CMU; to R. Delso, I. Romero and A. Gómez as partners for developing the whole computer vision algorithm; and to Metro 21 Institute for Smart Cities at CMU, Heinz Endowments, Pittsburgh Downtown Partnership and Pittsburgh Supercomputing Center for supporting the Market Square implementation in Pittsburgh.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Computational Design Laboratory, School of ArchitectureCarnegie Mellon UniversityPittsburghUSA

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