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An urban network percolation based spatiotemporal analysis of crime hotspot using directed acyclic graphs

Author

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  • Perez, Yuri
  • Gonçalves, Luis Fernando
  • Pereira, Fabio Henrique

Abstract

This paper presents an exploration of crime data through the application of complex network methods. Our primary objective is to analyze spatiotemporal crime dynamics using a complex network model based on the percolation of directed acyclic graphs, introducing a dual criterion of spatial and temporal proximity. Leveraging a dataset comprising vehicle theft incidents from 2014 to 2023, sourced from the Department of Public Safety of São Paulo, our empirical investigation highlights a downward trend in criminal activity from 2014 to 2020, with a substantial increase in 2023. Notably, the geographical stability of crime clusters across different time scales emerges as a significant finding. We detected varying frequencies and spatial densities throughout the city, suggesting a complex, heterogeneous array of crime patterns. Our model offers a promising avenue for shaping policing strategies based on spatial density and temporal patterns.

Suggested Citation

  • Perez, Yuri & Gonçalves, Luis Fernando & Pereira, Fabio Henrique, 2025. "An urban network percolation based spatiotemporal analysis of crime hotspot using directed acyclic graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 669(C).
    • Handle: RePEc:eee:phsmap:v:669:y:2025:i:c:s0378437125001621
    DOI: 10.1016/j.physa.2025.130510
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