Permanent and transitory crime risk in variable-density hot spot analysis

TL;DR

This study analyzes crime hot spots in Chicago over two decades, revealing how crime types and spatial patterns evolved, especially during COVID-19, informing better crime prevention strategies and highlighting data bias challenges.

Contribution

It introduces a variable-density cluster analysis of crime data, examining long-term changes and pandemic impacts on crime composition and spatial distribution.

Findings

COVID-19 significantly affected crime share composition.

Differences in addressing specific crime types.

Variations in incident distribution across spatial scales.

Abstract

Crime prevention measures, aiming for the effective and efficient spending of public resources, rely on the empirical analysis of spatial and temporal data for public safety outcomes. We perform a variable-density cluster analysis on crime incident reports in the City of Chicago for the years 2001--2022 to investigate changes in crime share composition for hot spots of different densities. Contributing to and going beyond the existing wealth of research on criminological applications in the operational research literature, we study the evolution of crime type shares in clusters over the course of two decades and demonstrate particularly notable impacts of the COVID-19 pandemic and its associated social contact avoidance measures, as well as a dependence of these effects on the primary function of city areas. Our results also indicate differences in the relative difficulty to address specific crime types, and an analysis of spatial autocorrelations further shows variations in incident uniformity between clusters and outlier areas at different distance radii. We discuss our findings in the context of the interplay between operational research and criminal justice, the practice of hot spot policing and public safety optimization, and the factors contributing to, and challenges and risks due to, data biases as an often neglected factor in criminological applications.