Academic journal article Geographical Analysis

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

Academic journal article Geographical Analysis

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

Article excerpt

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.

Introduction

Geography has become increasingly important in law enforcement and crime prevention. Criminology has long focused on individual propensities toward crime, but it was only during the last few decades that the criminogenic features of settings began to take on importance in research and practice. Environmental criminology gained in development, empirical verification, and practical applications by police (Cohen and Felson 1977; Brantingham and Brantingham 1981, 1984; Cornish and Clarke 1986; Eck and Weisburd 1995). Places, besides persons, became targets for allocation of police resources, and fields including crime mapping (Harries 1999), geographic profiling (Rossmo 2000), and (most recently) crime forecasting (Gorr and Harries 2003) arose in support of the new-found law enforcement opportunities.

This article introduces a leading-indicator crime-forecasting model for proactive policing and crime prevention, building on the work of Olligschlaeger (1997, 1998). Police, like other professionals delivering services, generally know the current locations and intensities of demand for services. Indeed, crime mapping based on near-real-time input of police reports has made the current picture for police more complete, integrating data from various officers, shifts, and neighborhoods. With the current situation in hand, the next step and most difficult new information to obtain is making forecasts of large changes in crime. If it were possible to obtain such forecasts, in the short term of up to a month ahead, then police could focus crime analysts' activities and build up intelligence on highlighted areas, target patrols, reallocate detective squads, and carry out other police interventions to prevent crimes.

Attempting to make accurate forecasts of the relatively rare, large changes in crime from month to month is an ambitious and difficult undertaking; however, the expectations of police can adapt to accepting good leads mixed in with false positives. For example, if 50% of forecasted large changes actually have large changes, then we claim that this would be an excellent result. Such forecasts would provide an entirely new kind of valuable information for police. …

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