CrimeScape: Analysis of socio-spatial associations of urban residential motor vehicle theft.

This paper focuses on advancing the traditional association rule mining (ARM) approach to capture the rich, multidimensional and multiscalar context that is anticipated to be associated with residential Motor Vehicle Theft (MVT) across urban environments. We tackle the challenge to materialize complex social and spatial components in the mining process and present a novel interactive visualization based on social network analysis of rules and associations to facilitate the analysis of mined rules. The spatial ARM (SARM) findings successfully identify many socio-spatial associations to MVT prevalence and establish their relative influence on crime outcome in a case study.