Interaction of Crime Risk across Crime Types in Hotspot Areas

Abstract

Repeat and near-repeat victimization are important concepts in the study of crime. The incidence of repeat offenses within a single type of crime has been confirmed. However, the study of the circumstances existing across crime types requires further investigation. This article investigates whether the phenomenon of near-repeat crime exists in different types of crime by studying the spread of crime risk within different crime types. Taking Suzhou City as the research area, a DBSCAN-based algorithm is proposed, which can detect a large number of important and stable hotspots through the multi-density self-adaptation of algorithm parameters. Pearson correlation is used to analyze the risk correlation between different types of crime. In different crime hotspots, the types of crime and the spread of crime risk among different types is also different. After a crime occurs, identifying the risk can aid crime prevention.