Clustering Areal Units at Multiple Levels of Resolution to Model Crime in Philadelphia

TL;DR

This paper introduces a Bayesian non-parametric method for clustering urban regions at multiple resolutions to better understand and model the complex spatial patterns of crime in Philadelphia.

Contribution

It develops a novel approach that simultaneously clusters areal units at different resolutions, addressing spatial heterogeneity and barriers in urban crime analysis.

Findings

Effective in capturing spatial heterogeneity in crime data

Improves understanding of urban crime patterns

Demonstrates robustness through synthetic data evaluation

Abstract

Estimation of the spatial heterogeneity in crime incidence across an entire city is an important step towards reducing crime and increasing our understanding of the physical and social functioning of urban environments. This is a difficult modeling endeavor since crime incidence can vary smoothly across space and time but there also exist physical and social barriers that result in discontinuities in crime rates between different regions within a city. A further difficulty is that there are different levels of resolution that can be used for defining regions of a city in order to analyze crime. To address these challenges, we develop a Bayesian non-parametric approach for the clustering of urban areal units at different levels of resolution simultaneously. Our approach is evaluated with an extensive synthetic data study and then applied to the estimation of crime incidence at various levels of resolution in the city of Philadelphia.