Crime analysis through spatial areal aggregated density patterns

Abstract

Intelligent crime analysis allows for a greater understanding of the dynamics of unlawful activities, providing possible answers to where, when and why certain crimes are likely to happen. We propose to model density change among spatial regions using a density tracing based approach that enables reasoning about large areal aggregated crime datasets. We discover patterns among datasets by finding those crime and spatial features that exhibit similar spatial distributions by measuring the dissimilarity of their density traces. The proposed system incorporates both localized clusters (through the use of context sensitive weighting and clustering) and the global distribution trend. Experimental results validate and demonstrate the robustness of our approach.