Paging Minority Report! Algorithms can now help predict where and when crimes will happen.
From Scientific American:
Any good cop knows his precinct’s honeypots, the places where crime is most common and arrests easiest to make. But Cunningham’s street savvy is being aided tonight by a crime forecast made by sociologists, investigators, mathematicians and a roomful of computers. The partnership between the Memphis Police Department (MPD) and the University of Memphis is called Blue CRUSH (for Crime Reduction Utilizing Statistical History), and the campaign is credited with helping to slash the numbers of major property and violent offenses by 26 percent citywide since the initiative was launched in 2006. Car break-ins, muggings and murders have plunged by 40 percent.
Number crunching is nothing new in police work—witness the New York City Police Department’s widely imitated CompStat program, which provided officials with frequently updated maps of high-crime areas when it launched in the mid-1990s. In the past few years, though, so-called predictive policing has grown ever more sophisticated. The most ambitious criminologists are no longer content to analyze data from the past—they are trying to predict the future.
Predictive policing is one of the hottest topics in law enforcement today, with more than a dozen experimental efforts under way in the U.S. and Europe. The dirty secret of the futuristic approach, though, is that nobody knows for certain that it works. The causes of crime are multifactorial and complex, making it difficult to pinpoint which strategies are best to combat it. Criminologists are only beginning to separate the effects of predictive police work from the myriad other factors that lower crime, such as the aging of the American population. All the experts know for certain is that police are doing something right. Across the U.S., crime is down to its lowest levels in four decades.
If algorithms can predict crime, why not cases?
In Memphis I attended a weekly Blue CRUSH TRAC—that is, Tracking for Responsibility, Accountability and Credibility— meeting. In a large conference room, the city’s eight precinct commanders took the podium in turn to discuss the latest crime in their areas. The projection screen behind them displayed maps marked with crime-symbolizing icons—fists, broken windows and little thieving men—each one representing a single offense in the past week.
Predictive-policing methods make use of far more variables than the times and locations of recent crimes, however. In Memphis an analyst might first pull up a map showing recent burglaries. He could then display the home addresses of all the students that the school district had reported as being recently absent. A third layer of data would indicate which of the truants had past convictions for burglary. When everything lines up—burglaries near the home of a truant student with a criminal record—it is time to hit the street and try to catch the thief in the act. Or show up at the truant’s house. “You go to do a knock and talk, and, lo and behold, you find stolen stuff stacked all around the building,” says John Harvey, manager of the Real Time Crime Center.
These algorithms have also begun to integrate the latest theories of criminologists. For example, conventional wisdom holds that savvy criminals do not return to the scenes of their crimes. But successful burglars do exactly that, according to U.C.L.A.’s Brantingham and George O. Mohler, a mathematician at Santa Clara University, who analyzed thousands of burglary incident and arrest reports from the LAPD to arrive at their findings. “From the offender’s point of view, going back to the house you broke into yesterday is a good strategy,” Brantingham says. “You know what’s in the house. You know how to get in and out quickly.” What is more, they found, the burglary risk also goes up considerably for other neighborhood houses because they often have similar layouts and types of possessions, making them attractive targets.