Spatial-Temporal Augmentation for Crime Prediction (Student Abstract)

Abstract

Crime prediction stands as a pivotal concern within the realm of urban management due to its potential threats to public safety. While prior research has predominantly focused on unraveling the intricate dependencies among urban regions and temporal dynamics, the challenges posed by the scarcity and uncertainty of historical crime data have not been thoroughly investigated. This study introduces an innovative spatial-temporal augmented learning framework for crime prediction, namely STAug. In STAug, we devise a CrimeMix to improve the ability of generalization. Furthermore, we harness a spatial-temporal aggregation to capture and incorporate multiple correlations covering the temporal, spatial, and crime-type aspects. Experiments on two real-world datasets underscore the superiority of STAug over several baselines.