Discovering the joint influence of urban facilities on crime occurrence using spatial co-location pattern mining

Abstract The presence or absence of some urban facilities can shape the spatial distribution of crime occurrence. Exploring the joint influence of various types of facilities on crime occurrence has been a major concern for both crime prevention and urban planning. Previous research (e.g., the spatial conjunctive analysis of case configurations) have tried to ascertain the joint influence of facilities by identifying the frequent spatial configurations (combinations of facility types) that exist near criminal incidents. However, such a method neglects the prevalence of facilities and the spatial autocorrelation of crime occurrence, thus resulting in some spurious conclusions. To resolve this problem, borrowing methods from spatial pattern recognition and ecology, this study applied the spatial co-location pattern mining and pattern reconstruction approach to identify statistically significant spatial configurations for crime occurrence. The results show that the adopted approach effectively eliminates the influence of independent facility types with abundant instances, thus revealing statistically significant spatial configurations with better accuracy. These identified high-risk spatial configurations both confirm and expand on previous research; these configurations can also make a positive contribution to crime prevention and urban planning.