A Spatio-Temporal Dirichlet Process Mixture Model on Linear Networks for Crime Data

TL;DR

This paper introduces a Bayesian spatio-temporal Dirichlet process mixture model tailored for linear networks, enabling detection of crime hotspots and analysis of their relation to urban features in Valencia, Spain.

Contribution

It presents a novel hierarchical Bayesian model that incorporates network geometry and automatically detects crime clusters, improving understanding of crime dynamics on city networks.

Findings

Identified crime hotspots aligned with urban amenities.

Revealed patterns of criminal contagion and clustering.

Provided visualizations to aid crime prevention strategies.

Abstract

Analyzing crime events is crucial to understand crime dynamics and it is largely helpful for constructing prevention policies. Point processes specified on linear networks can provide a more accurate description of crime incidents by considering the geometry of the city. We propose a spatio-temporal Dirichlet process mixture model on a linear network to analyze crime events in Valencia, Spain. We propose a Bayesian hierarchical model with a Dirichlet process prior to automatically detect space-time clusters of the events and adopt a convolution kernel estimator to account for the network structure in the city. From the fitted model, we provide crime hotspot visualizations that can inform social interventions to prevent crime incidents. Furthermore, we study the relationships between the detected cluster centers and the city's amenities, which provides an intuitive explanation of criminal contagion.