Research on Crime Hotspot Pattern Based on Spatiotemporal Matrix

Abstract

In this study, Central city of C City was selected as the research area, and 36884 cases of robbery and theft in two years were taken as the research object. The spatial and temporal distribution characteristics of the cases were analyzed systematically from the macro and local micro scales. According to Pai index, nearest neighbor index, Gini Coefficient and regression function of case distance, crime hotspots were divided into six different spatiotemporal matrix patterns, which were applied to each hot spot grid in central city. In order to optimize the allocation of police resources, and to restrain and reduce the occurrence of crimes, the paper put forward the corresponding prevention and control countermeasures according to the spatial and temporal distribution of robbery and theft cases under different modes.