Short-term forecasting of crime

The major question investigated is whether it is possible to accurately forecast selected crimes 1 month ahead in small areas, such as police precincts. In a case study of Pittsburgh, PA, we contrast the forecast accuracy of univariate time series models with naı̈ve methods commonly used by police....

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Bibliographic Details
Published in:International journal of forecasting 2003-10, Vol.19 (4), p.579-594
Main Authors: Gorr, Wilpen, Olligschlaeger, Andreas, Thompson, Yvonne
Format: Article
Language:English
Subjects:
Accuracy
Crime
Crime forecasting
Estimating techniques
Forecasting
Small area estimation
Studies
Time series
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Summary:The major question investigated is whether it is possible to accurately forecast selected crimes 1 month ahead in small areas, such as police precincts. In a case study of Pittsburgh, PA, we contrast the forecast accuracy of univariate time series models with naı̈ve methods commonly used by police. A major result, expected for the small-scale data of this problem, is that average crime count by precinct is the major determinant of forecast accuracy. A fixed-effects regression model of absolute percent forecast error shows that such counts need to be on the order of 30 or more to achieve accuracy of 20% absolute forecast error or less. A second major result is that practically any model-based forecasting approach is vastly more accurate than current police practices. Holt exponential smoothing with monthly seasonality estimated using city-wide data is the most accurate forecast model for precinct-level crime series.
ISSN:0169-2070
1872-8200
DOI:10.1016/S0169-2070(03)00092-X