This article presents the strengths and weaknesses of various crime forecasting algorithms and argues that, to be efficient and realistic, models should be used in conjunction with other measures.
Hot-spot maps regularly aid many policing resource allocation decisions in today’s data-driven age. However, it is unclear what forecasting algorithm(s) should be used to create these maps. To address this gap, we must be able to assess how “good” a generated hot-spot map is. Currently, four main metrics are used for evaluation: the prediction accuracy index (PAI), the recapture rate index (RRI), the prediction efficiency index (PEI), and the prediction efficiency index* (PEI*). This article discusses PAI, RRI, and PEI’s strengths and weaknesses, articulates and justifies PEI*, and demonstrates the differences in calculations and interpretations of each metric. The authors argue that PEI* measures the efficiency of a crime forecasting algorithm while being operationally realistic and should be used in conjunction with other appropriate measures. (Published Abstract Provided)
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