Law Enforcement Experiments With Predictive Policing; Does It Work?

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Down the road from Ferguson, Mo., in the St. Louis suburb of Jennings, police precinct commander Jeff Fuesting had expressed interest in predictive policing, so when the department brought in HunchLab, it asked his precinct to roll it out first, reports The Marshall Project. They believed data could help their officers police better and more objectively. By identifying and aggressively patrolling “hot spots,” as determined by the software, the police wanted to deter crime before it ever happened. HunchLab, produced by Philadelphia-based startup Azavea, represents a new version of predictive policing, a method of analyzing crime data and identifying patterns that may repeat into the future.

HunchLab primarily surveys past crimes, but also digs into dozens of other factors like population density; census data; the locations of bars, churches, schools, and transportation hubs; schedules for home games, and even moon phases. Some of the correlations it uncovers are obvious, like less crime on cold days. Others are more mysterious: rates of aggravated assault in Chicago have decreased on windier days, while cars in Philadelphia were stolen more often when parked near schools. Research on the impact of predictive policing programs is still in its infancy. Last year, researchers at another predictive-policing organization, PredPol, published a study finding that sending patrol officers to several areas of Los Angeles predicted by their algorithm led to a reduction, on average, of more than four crimes per week in these neighborhoods. The researchers said the savings resulting from not having to investigate and prosecute crimes that otherwise may have happened could reach $9 million per year.

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