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This webinar will offer a brief overview of the National Institute of Justice and the data science needs of the criminal justice field. In addition, it will provide details about the Crime Forecasting Challenge, including who can submit, how to retrieve datasets, and the submission categories. The overall goal of the Crime Forecasting Challenge is to harness recent advances in data science to drive innovation in algorithms that advance place-based crime forecasting.
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Professor Lawrence Sherman explains how policing can prevent far more crimes than prison per dollar spent. His analysis of the cost-effectiveness of prison compared to policing suggests that states can cut their total budgets for justice and reduce crime by reallocating their spending on crime: less prison, more police.
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NIJ Conference panelists will present the results of three studies that applied situational crime prevention (SCP) principles: (1) an evaluation of the Safe City initiative in Chula Vista, Calif., designed to combine the expertise and resources of local law enforcement, retailers and the community to increase the safety of designated retail areas; (2) a randomized controlled trial (in partnership with the Washington Metro Transit Police) that assessed the effectiveness of SCP to reduce car crime in Metro's parking facilities; and (3) an evaluation of the impact of SCP
This Crime File video reviews three studies evaluating the effectiveness of policing strategies.
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