Short-term forecasting of crime

Short-term forecasting of crime

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Article ID: iaor20043669
Country: Netherlands
Volume: 19
Issue: 4
Start Page Number: 579
End Page Number: 594
Publication Date: Oct 2003
Journal: International Journal of Forecasting
Authors: , ,
Keywords: time series & forecasting methods
Abstract:

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 naïve 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 in 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.

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