Leading Indicators and Spatial Interactions: A Crime‐Forecasting Model for Proactive Police Deployment

Abstract

We develop a leading indicator model for forecasting serious property and violent crimes based on the crime attractor and displacement theories of environmental criminology. The model, intended for support of tactical deployment of police resources, is at the microlevel scale; namely, 1‐month‐ahead forecasts over a grid system of 141 square grid cells 4000 feet on a side (with approximately 100 blocks per grid cell). The leading indicators are selected lesser crimes and incivilities entering the model in two ways: (1) as time lags within grid cells and (2) time and space lags averaged over grid cells contiguous to observation grid cells. Our validation case study uses 1.3 million police records from Pittsburgh, Pennsylvania, aggregated over the grid system for a 96‐month period ending in December 1998. The study uses the rolling‐horizon forecast experimental design with forecasts made over the 36‐month period ending in December 1998, yielding 5076 forecast errors per model. We estimated the leading indicator model using a robust linear regression model, a neural network, and a proven univariate, extrapolative forecast method for use as a benchmark in Granger causality testing. We find evidence of both the crime attractor and displacement theories. The results of comparative forecast experiments are that the leading indicator models provide acceptable forecasts that are significantly better than the extrapolative method in three out of four cases, and for the fourth there is a tie but poor forecast performance. The leading indicators find 41–53% of large crime volume changes in the three successful cases. The corresponding workload for police is quite acceptable, with on the average 5.2 potential large change cases per month to investigate and with 31% of such cases being positives.