Multivarite areal aggregated crime analysis through cross correlation

Abstract

Intelligent crime analysis allows for a greater understanding of the dynamics of unlawful activities. Discovering crime and spatial features that exhibit strong correlation allows a deeper insight into the complex question of crime analysis. To effectively search heterogeneous data types for cross correlation, a spatial multivariate association measure can be used. We demonstrate a bivariate spatial association approach for crime analysis that can be extended to extract multivarite cross correlation. It is able to extract the top-k and bottom-k associative features from areal aggregated datasets. Experimental results demonstrate our approach using real crime datasets.