Statistical Analysis of Crime Occurrence: Crimestatistics

Thomas M. Menino Convention & Exhibition Center 

Violent crime remains a major societal concern and a long-standing focus of intervention by the Chicago Police Department (CPD). While aggregated crime rates have generated important findings in quantitative criminology, crime events are inherently localized in space and time. Reliance on fixed administrative units and static population denominators induces substantial bias and instability, particularly for small areas and short time windows. We propose an adaptive estimation approach that addresses the denominator problem by fixing the number of events using an equal-count algorithm rather than a moving spatial window, and by using a Hilbert curve to preserve continuity in multidimensional spatiotemporal space. The resulting estimates are flexible, interpretable, and amenable to downstream modeling and visualization. The method is applied to CPD's violence-reduction dashboard data, particularly shootings and ShotSpotter detections. Results reveal clear seasonal patterns and high-resolution heat maps of mass shooting events. This approach provides a principled, reproducible alternative for high-resolution crime-rate estimation with broad applicability to spatiotemporal event data.

Spatiotemporal point processes

Adaptive rate estimation

Equal-count algorithms

Spatial indexing (Hilbert curve) 

Section on Statistical Computing