This project developed a reference guide for law enforcement agencies interested in predictive policing; it assesses the most promising technical tools for making predictions as well as the most promising tactical approaches for acting on predictions.
"Predictive policing" is defined as "the application of analytical techniques - particularly quantitative techniques - to identify likely targets for police intervention and prevent crime or solve past crimes by making statistical predictions." This study determined that predictive-policing methods can be divided into four broad categories: methods for predicting crimes, methods for predicting offenders, methods for predicting perpetrators' identities, and methods for predicting crime victims. Methods used in each of these predictive categories are outlined. For the predictive focus within each broad predictive category, tables distinguish "conventional crime analysis" (low to moderate data demand and complexity) and "predictive analytics" (large data demand and high complexity). In designing police interventions based on predictive analysis, this study conceives this as part of a "comprehensive business process." At the core of this process is a four-step cycle. The first two steps involve collecting and analyzing crime, incident, and offender data in order to develop predictions. The third step consists of conducting police operations that intervene to prevent predicted threats to public safety. The interventions must take into account the personnel and financial resources available. The fourth step involves the implementation of interventions. This includes rapid assessment that determines whether or not the implementation faithfully follows the intervention's design, as well as the impact of the intervention based on relevant data collection and analysis. The study also addresses predictive policing myths and pitfalls. The recommendations address police agencies (the buyer), vendors of predictive tools, and "crime fighters" (those who implement interventions based on predictive analysis). 31 figures, 9 tables, and a 223-item bibliography
Report (Grant Sponsored)
Date Published: September 1, 2013
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