By operationalizing two theoretical frameworks, the authors forecasted crime hot spots in Colorado Springs.
First, they used a population heterogeneity (flag) framework to find places where the hot spot forecasting was consistently successful over months. Second, they used a state dependence (boost) framework of the number of crimes in the periods prior to the forecasted month. This algorithm was implemented in Microsoft Excel®, making it simple to apply and completely transparent. Results showed high accuracy and high efficiency in hot spot forecasting, even when the data set and the type of crime used in the study were different from what the original algorithm was based on. Results imply that the underlying mechanisms of serious and non-serious crime for forecasting were different from each other. The authors also found that the spatial patterns of forecasted hot spots were different between calls for service and crime event. The study recommends that future research consider both flag and boost theories in hot spot forecasting. (Publisher abstract provided)
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