EURO 2024 Copenhagen
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2876. Spatio-temporal analysis of variable-density clusters in hot spot policing

Invited abstract in session TA-6: Advancements of OR-analytics in statistics, machine learning and data science 12, stream Advancements of OR-analytics in statistics, machine learning and data science.

Tuesday, 8:30-10:00
Room: 1013 (building: 202)

Authors (first author is the speaker)

1. Ben Moews
Institute for Astronomy, University of Edinburgh

Abstract

The effective prevention of crime requires the availability of empirical data to optimise public resource utilisation and public safety outcomes. In this context, spatio-temporal incident reports can be used for newly developed variable-density cluster analysis approaches, for which empirical data collected over more than two decades in the City of Chicago is used. This provides insights into the evolution of crime type ratios in the twenty-first century so far, with particularly notable effects from the recent COVID-19 pandemic due to shifts in space occupancy, with primary functions of city areas playing an important role. An analysis of spatial autocorrelations at different distances, as a methodology transfer from cosmology, demonstrates variations in incident uniformity between clusters and outlier areas, and highlights the need to question the currently wide-spread practice of cluster epicentre policing. One of the points the discussion focusses on is the role of criminology-oriented optimisation as an aspect of community operational research. This is particularly relevant when it comes to acknowledging risks due to known and unknown data biases, for example due to extraneous and temporally constrained events as well as impacts from spatially varying police-community relations.

Keywords

Status: accepted

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