Crime forecasting is a new area of research, following upon the success of crime mapping for support of tactical deployment of police resources. The major question investigated in this paper is whether it is possible to accurately forecast crime one month ahead at a “smallscale” aggregation, i.e., at the precinct level. In a case study of Pittsburgh, Pennsylvania, we contrast the forecast accuracy of standard, univariate time series models with non-modeling practices commonly used by police. Included is a comparison of seasonality estimates made by precinct versus the city as a whole. As suspected for the small-scale data of this problem, average crime count by precinct and crime type is the major determinant of forecast accuracy. A fixed effects regression model of absolute percent forecast errors shows that such counts need to be on the order of 30 or more to achieve accuracy of 20 percent error or less. A second major result is that practically any model-based forecasting approach is vastly more accurate than current police practices. Thirdly, this is the first empirical paper to investigate crime seasonality at the sub-city level. Our seasonality estimates provide evidence supporting the routine activities theory of crime, but not earlier theories.
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