Forecasting Nationwide Crime at the Block Level

Forecasting Nationwide Crime at the Block Level Using a Hierarchical Graph Attention Network Model

Abstract

Crime forecasting is crucial for maintaining societal safety. This study interprets the criminological theory of Repeat and Near Repeat Victimization (RNRV) using a deep learning model, the Hierarchical Graph Attention Network (HGAT), to forecast occurrence of traffic, violent, sexual, income-generating and child-target crimes nationwide (6,220,112 sections) at a one-block level (0.25 km × 0.25 km) one week in advance. The Predictive Accuracy Index and Root Mean Square Error scores were used as forecast performance index. Result shows that the HGAT model achieved a significant improvement in forecast accuracy compared to state-of-the-art forecasting techniques. The HGAT model continued to forecast 1,724 municipalities with high accuracy for 14 consecutive weeks for the above five types of crimes. Because of its improved forecast accuracy and generalizability, the HGAT model will become the new state-of-the-art model for crime forecasting.