The use of predictive analysis in spatiotemporal crime forecasting: Building and testing a model in an urban context

Abstract

Police databases hold a large amount of crime data that could be used to inform us about current and future crime trends and patterns. Predictive analysis aims to optimize the use of these data to anticipate criminal events. It utilizes specific statistical methods to predict the likelihood of new crime events at small spatiotemporal units of analysis. The aim of this study is to investigate the potential of applying predictive analysis in an urban context. To this end, the available crime data for three types of crime (home burglary, street robbery, and battery) are spatially aggregated to grids of 200 by 200 m and retrospectively analyzed. An ensemble model is applied, synthesizing the results of a logistic regression and neural network model, resulting in bi-weekly predictions for 2014, based on crime data from the previous three years. Temporally disaggregated (day versus night predictions) monthly predictions are also made. The quality of the predictions is evaluated based on the following criteria: direct hit rate (proportion of incidents correctly predicted), precision (proportion of correct predictions versus the total number of predictions), and prediction index (ratio of direct hit rate versus proportion of total area predicted as high risk). Results indicate that it is possible to attain functional predictions by applying predictive analysis to grid-level crime data. The monthly predictions with a distinction between day and night produce better results overall than the bi-weekly predictions, indicating that the temporal resolution can have an important impact on the prediction performance.