Discovering Spatial-Temporal Indication of Crime Association (STICA)

The importance of combining spatial and temporal aspects has been increasingly recognized over recent years, yet pertinent pattern analysis methods in place-based crime research still need further development to explicitly indicate spatial-temporal localities factors’ influence ranges. This paper proposes an approach, Spatial-Temporal Indication Crime Association (STICA), facilitate identifying the main contributing factors crime, which are operated at diverse scales. method’s rationale is progressively discern zones with patterns. A specific implementation STICA by kernel density estimation, k-median-centers clustering, thematic mapping, applied understand burglary urban peninsula, China. empirical findings include: (1) both time-stable time-varying can be indicated disparities patterns for different based on results. (2) range these enlighten understanding interactions generating patterns, especially regards how temporally transient spatially global produce a locally crime-ridden zone through mediation stable factors. (3) results reveal contextual effects factors, great value improve modeling As demonstrated, approach effective exploring shown potential providing new vision research.