Forecasting Spatio-Temporal Variation in Residential Burglary with the Integrated Laplace Approximation Framework: Effects of Crime Generators, Street Networks, and Prior Crimes

Objectives We investigate the spatio-temporal variation of monthly residential burglary frequencies across neighborhoods as a function of crime generators, street network features and temporally and spatially lagged burglary frequencies. In addition, we evaluate the performance of the model as a forecasting tool. Methods We analyze 48 months of police-recorded residential burglaries across 20 neighborhoods in Amsterdam, the Netherlands, in combination with data on the locations of urban facilities (crime generators), frequencies of other crime types, and street network data. We apply the Integrated Laplace Approximation method, a Bayesian forecasting framework that is less computationally demanding than prior frameworks. Results The local number of retail stores, the number of street robberies perpetrated and theclosenessof the local street network are positively related to residential burglary. Inclusion of a general spatio-temporal interaction component significantly improves forecasting performance, but inclusion of spatialproximityor temporalrecencycomponents does not. Discussion Our findings on crime generators and street network characteristics support evidence in the literature on environmental correlates of burglary. The significance of spatio-temporal interaction indicates that residential burglary is spatio-temporally concentrated. Our finding thatrecencyandproximityof prior burglaries do not contribute to the performance of the forecast, probably indicates that relevant spatio-temporal interaction is limited to fine-grained spatial and temporal units of analysis, such as days and street blocks.

Automated summary

The study investigated the spatio-temporal variation of residential burglary frequencies in 20 neighborhoods in Amsterdam over 48 months, considering crime generators, street network features, and lagged burglary frequencies. Results showed that the number of nearby retail stores, street robberies, and street network closeness were positively related to residential burglary. The inclusion of a general spatio-temporal interaction component significantly improved forecasting performance.