Study on crime trend analysis and countermeasures based on law enforcement databases

In order to study the trend of criminal behavior from the perspective of temporal and spatial distribution, this paper adopts the time series decomposition method and the nearest neighbor index method to obtain the temporal and spatial distribution patterns of cases. A multi-view simultaneous convolution algorithm is designed to extract and fuse features from three perspectives: time, space and type to predict crime trends. Based on the City B law enforcement database, a study was conducted to explore the temporal heterogeneity of burglary crimes by combining two-dimensional color matrix plots and multi-timescale trend plots. Standardized crime rates and spatial autocorrelation tests are used to reveal the spatial aggregation patterns of burglary crimes. Spatio-temporal features are fused based on type nodes to dissect the potential patterns of burglary crimes. The prediction results are evaluated in terms of performance and prevention suggestions are made for burglary crime trends. The results indicate that there are roughly three levels of time periods in the distribution of crimes in a day. The lowest number of burglary incidents is from 0-6 hours, the relatively high number is from 6-18 hours, and the highest number of hours is at 18-24 hours. In view of the spatial and temporal characteristics of burglary, the strategy of “multi-point joint defense and cooperative action” can effectively prevent and reduce the occurrence of burglary cases by enhancing patrols and strengthening the security of key areas.