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Urban street networks shape patterns of mobility, accessibility, and opportunities for crime. This study investigates the spatiotemporal patterns of vehicle-related crime in São Paulo, Brazil, with a particular focus on the relationship between the characteristics of the city’s street network and crime. Drawing on the analysis of geocoded police records of vehicle thefts and robberies (e.g. cars, motorcycles), the study examines how different dimensions of urban form, such as connectivity, street hierarchy, permeability, density, the presence of street barriers, and topography, are associated with the occurrence of these crimes. By considering how the street network may simultaneously influence opportunities for movement, escape, and guardianship. For this purpose, spatial and temporal analyses were conducted to identify concentrations of these crimes across the city, and machine learning models were applied to determine which street network features are most strongly associated with their occurrence, while controlling for criminogenic conditions conditioned by land-use and socio-economic variables.

Speakers: Prof. Luis Gustavo Nonato & Raissa Rosa dos Santos Januário, University of Sao Paulo at Sao Carlos and Prof. Vania Ceccato, KTH Royal Institute of Technology

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