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

Abstract Based on crime attractor and displacement theories of environmental criminology, we specify a leading indicator model for forecasting serious property and violent crimes. The model, intended for support of tactical deployment of police resources, is at the micro-level scale; namely, one-month-ahead forecasts over a grid system of 104 square grid cells 4,000 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 contiguous grid cells of observation grid cells. Our validation case study uses 1.3 million police records including 16 individual crime types 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 3,774 forecast errors per forecast model. We estimated the leading indicator model using both an OLS linear regression model and a nonlinear neural network, plus included a proven univariate, extrapolative forecast method as a benchmark for a Granger causality assessment. The analytical approach to forecast validation is based on decision support requirements of police for crime prevention. Needed is information on large forecasted changes in crime. The leading indicator models have the comparative advantage over extrapolative methods of being able to forecast the largest changes in crime, those due to breaks in crime series data such as step jumps in the forecast period. Expectations for forecast results should be that they yield good if imperfect leads on where to deploy crime analysts, patrols and detectives;

[1]  Shashi Shekhar,et al.  Environmental Criminology , 2008, Encyclopedia of GIS.

[2]  Wilpen L. Gorr,et al.  Short-term forecasting of crime , 2003 .

[3]  Jerry H. Ratcliffe Burglary Reduction and the Myth of Displacement , 2002 .

[4]  W. Gorr,et al.  Application of Tracking Signals to Detect Time Series Pattern Changes in Crime Mapping Systems , 2003 .

[5]  Rene B.P. Hesseling,et al.  DISPLACEMENT: A REVIEW OF THE EMPIRICAL LITERATURE , 2006 .

[6]  G. H. Moore,et al.  The Leading Indicator Approach to Economic Forecasting--Retrospect and Prospect , 1982 .

[7]  Kent A. Harries,et al.  Mapping Crime: Principle And Practice , 1999 .

[8]  Norman R. Swanson,et al.  Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models , 1997 .

[9]  J. Wilson,et al.  BROKEN WINDOWS: THE POLICE AND NEIGHBOURHOOD SAFETY , 1982 .

[10]  M. Felson,et al.  Opportunity Makes the Thief Practical theory for crime prevention , 1998 .

[11]  C. Granger Investigating Causal Relations by Econometric Models and Cross-Spectral Methods , 1969 .

[12]  John E. Eck,et al.  Crime and place , 1995 .

[13]  Andreas M. Olligschlaeger ARTIFICIAL NEURAL NETWORKS AND CRIME MAPPING by , 1998 .

[14]  Patricia L. Brantingham,et al.  Patterns in Crime , 1984 .

[15]  Wilpen L. Gorr,et al.  Introduction to crime forecasting , 2003 .

[16]  Wilpen L. Gorr,et al.  ESTIMATING INTERVENTION EFFECTS IN VARYING RISK SETTINGS: DO POLICE RAIDS REDUCE ILLEGAL DRUG DEALING AT NUISANCE BARS? , 2003 .

[17]  Andreas Olligschlaeger Spatial Analysis of Crime Using GIS-Based Data: Weighted Spatial Adaptive Filtering and Chaotic Cell , 1997 .

[18]  Derek Deadman,et al.  Forecasting Residential Burglary , 2003 .

[19]  Vincent E. Henry The Compstat Paradigm: Management Accountability in Policing, Business and the Public Sector , 2002 .

[20]  Wesley G. Skogan,et al.  Fixing Broken Windows: Restoring Order and Reducing Crime in Our Communities by George L. Kelling and Catherine M. Coles:Life in the Gang: Family, Friends, and Violence , 1997 .

[21]  Lawrence E. Cohen,et al.  Social Change and Crime Rate Trends: A Routine Activity Approach , 1979 .

[22]  Hui Xiong,et al.  Geographical Analysis , 2008, Encyclopedia of GIS.

[23]  Richard Harries Modelling and predicting recorded property crime trends in England and Wales--a retrospective , 2003 .