Micro-Level Incident Analysis using Spatial Association Rule Mining

Criminological research and theory have traditionally focused on individual offenders and macro-level analysis to characterize crime distribution. However local aspects of crime activities have been also recognized as important factors in crime analysis. It is an interesting problem to discover implicit local patterns between crime activities and environmental factors such as nearby facilities and business establishment types. This work presents micro-level analysis of criminal incidents using spatial association rule mining. We show how to process crime incident points and their spatial relationships with task-relevant other spatial features, and discover interesting crime patterns using an association rule mining algorithm. A case study was conducted with real incident records and points of interest in a study area to discover interesting relationship patterns among crimes, their characteristics, and nearby spatial features. This study shows that our approach with spatial association rule mining is promising for micro-level analysis of crime.