2026/03/28 by Sujeong Lee, Won Chang, Jorge Mateu +2 · 1 voice
Computer Science · #Bayesian Methods and Mixture Models
paper · doi:10.1093/jrsssa/qnag052
openalex created_date 2025/10/10 · openalex publication_date 2026/03/28 · openalex updated_date 2026/07/28
Abstract Analysing 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 (DP) mixture model on a linear network to analyse crime events in Valencia, Spain. We propose a Bayesian hierarchical model with a DP 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 centres and the city’s amenities, which provides an intuitive explanation of criminal contagion.