Exploring the homogeneity of theft offenders in spatio-temporal crime hotspots

Abstract

Offender homogeneity occurs when the same criminal group is composed of offenders with similar attributes (e.g., socio-economic-demographics). Exploring the homogeneity of offenders within spatio-temporal crime hotspots (STCHs) is useful for understanding not only the generational mechanisms of crime hotspots, but also has crime prevention implications. However, the homogeneity of offenders within STCHs has not been explored in criminological studies hitherto. Indeed, current techniques of STCH detection are limited to using statistical clustering methods in existing studies that lack the ability to identify the shape of STCHs or the distribution and variety of offences/offender activity with them. In this study, we utilise a spatio-temporal clustering algorithm called ST-DBSCAN to determine STCHs. We then propose novel entropy-based indices that measure the similarity of offenders (and offences) within STCHs. The method is demonstrated using theft crime records in the central area of Beijing, China. The results show that theft in the city is concentrated in a narrow space and time span (STCHs) and that within these associated offenders with similar social demographics, referred to as homogeneous offender groups are detectable.