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LEADS Scholar Spotlight -- Predictive Policing Algorithms

NCJ Number
Date Published
May 2018
1 page
In this video and associated transcript, Shon Barnes, a deputy police chief with the Salisbury Police Department (North Carolina), explains how the National Institute of Justice's (NIJ's) LEADS Program (Law Enforcement Advancing Data and Science) provided him with the knowledge and experience needed to launch his agency's predictive policing effort.
He explains that predictive policing algorithms involve collecting data and putting it into a system that will indicate when and where crime is going to occur. The purpose of such algorithms is to guide law enforcement agencies in the deployment of their resources in areas where crime is predicted to occur, so as to improve the cost-effectiveness of policing. Barnes' agency conducted a predictive policing quasi-experiment, based on the way the agency is structured. One group of officers applied the "treatment," which was the use of the predictive policing algorithms in deploying officers to areas where crime was predicted to occur. Another group of officers was deployed to "hot spots" where crime was already concentrated. Subsequent analysis indicated that those who patrolled in areas predicted to have crime increases used fewer officers than were involved in "hot spots" policing, had more activity, and had a greater reduction in crime over the same time period. Barnes attributes the training he received in the LEADS Program to enabling him to conduct data collection and analysis for predictive policing, which resulted in his agency achieving more cost-effective policing.

Date Published: May 1, 2018