Abstract
A literature review of the important trends in predictive crime modeling and the existing measures of accuracy was undertaken. It highlighted the need for a robust, comprehensive and independent evaluation and the need to include complementary measures for a more complete assessment. We develop a new measure called the penalized predictive accuracy index (PPAI), propose the use of the expected utility function to combine multiple measures and the use of the average logarithmic score, which measures accuracy differently than existing measures. The measures are illustrated using hypothetical examples. We illustrate how PPAI could identify the best model for a given problem, as well as how the expected utility measure can be used to combine different measures in a way that is the most appropriate for the problem at hand. It is important to develop measures that empower the practitioner with the ability to input the choices and preferences that are most appropriate for the problem at hand and to combine multiple measures. The measures proposed here go some way towards providing this ability. Further development along these lines is needed.
Highlights
Because we focus on the measures of predictive accuracy or operational efficiency, we do not include measures of model fit, such as the BIC, in this review
Several of the accuracy measures proposed in the crime literature are based on one or more of these quantities. This includes the ‘hit rate’ [4,6,21,22,31,36,40,41,42,43], which is the proportion of crimes that were correctly predicted by the model out of the total number of crimes committed in a given time period (TP/(TP + false negatives (FNs))), and is typically applied to hotspots identified by the model
We propose a Penalized Predictive Accuracy Index (PAI) (PPAI) that penalizes the identification of a total hotspot area that is too small
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. As a result, building crime models that predict crime with a great degree of accuracy and are of practical use remains an ongoing research problem [1,2,3]. Another important issue is how to appropriately measure the accuracy of a crime model. The aims of this paper are first, to propose new, complementary measures that address some limitations of existing measures and second, to highlight some additional challenges and key considerations involved in developing and assessing predictive crime models.
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