Enhancing Deep Learning-based Crime Hotspot Predictions With Theory-based Environmental Risk Scores

Abstract

This study introduces a novel network-based crime risk score, the Street Segment Risk Score (SSRS), designed to enhance crime hotspot predictions on street networks. The SSRS evaluates the risk of individual street segments by incorporating the dynamic spatial influence of nearby urban features on local crime patterns. Our dataset comprises all reported incidents of robbery (n = 2,016) and theft (n = 31,493) from 2015 to 2018 in Chicago's Central Side (CS). We developed both daily and intraday crime hotspot prediction models that integrate the SSRS and compared their performance-with and without the SSRS-using two graph-based deep learning algorithms, Graph WaveNet (GWNet) and the Spatiotemporal Graph Convolutional Neural Network (STGCN); a traditional deep learning model, Long Short-Term Memory (LSTM); and two baseline methods, Multilayer Perceptron (MLP) and Spatiotemporal Network Kernel Density Estimation (STNetKDE). Results indicate that incorporating the SSRS improves daily robbery hotspot prediction accuracy by up to 5.3% and intraday theft prediction accuracy by as much as 33%. The proposed SSRS demonstrates strong potential to support more precise, street-level security interventions by enhancing daily and intraday crime hotspot predictions.