Crime data warehousing and crime pattern discovery

Abstract

The structure of crime activity logs stored in police databases is not designed for decision support systems and hence not for complex crime analysis. This paper shows how crime log data could be converted to significantly useful information using data warehousing and data mining techniques. Crime incident data and other relevant data are organized with applying data warehousing concepts. Spatial association rule mining is used for finding interesting local relationship patterns of crime incidents with other spatial features. This paper shows how to materialize spatial relationship information between crime activities and task-relevant other features into data marts, and to discover interesting crime patterns from the data mart using a spatial association rule mining technique. A proof of concept is carried out with real crime data and points of interest in a study area to illustrate and evaluate the proposed approach. The case study results show the usefulness of such data warehousing and spatial association rule mining for crime data analysis.