One of the first cases of ‘predictive policing
The police officers arrived at the parking garage in downtown Santa Cruz and spotted two women behaving suspiciously. No crime had been committed, but peering through the windows of the parked cars was sketchy enough. The officers questioned the women: one had outstanding warrants; the other was in possession of illegal drugs.
What’s strange about this scenario is that no one had called the cops. In fact, the cops didn’t even know that the women would be there, just that the probability of a crime being committed at that location, at that time of day, was especially high. In one of the first cases of ‘predictive policing,’ law enforcement were able to calculate where the criminals would be and arrest them before the crime could be committed.
Oh yeah, totally “Minority Report,” absolutely “Numb3rs.”
Except it’s not Hollywood, it’s real. In July the Santa Cruz Police Department began experimenting with an interesting bit of software developed by scientists at Santa Clara University. The researchers behind the software are like an intellectual “Oceans Eleven” team of specialists: two mathematicians, an anthropologist and a criminologist. They’ve combined their cerebral forces to come up with a mathematical model that takes crime data from the past to forecast crimes in the future. The basic math is similar to that used by seismologists to predict aftershocks following an earthquake (also a handy bit of software in southern California).
Large earthquakes are unpredictable, but the aftershocks that follow are not and their occurrence can be predicted with mathematical models. It occurred to Dr. George Mohler, one of the Santa Clara mathematicians, that criminal activity might not be random and that, similar to aftershocks, some crimes might be predicted by other crimes that precede them. The reasoning is based on the assumption that crimes are clustered – it’s what police call ‘hotspots.’ Burglaries will occur in the same area and at the same houses because the vulnerabilities of that area will be known to the burglars. Gang violence is also clustered. A gang shooting will often trigger retaliatory shootings.
Using the aftershocks-inspired algorithms Dr. Mohler and his team came up with a model, then sought to test it. In collaboration with the LAPD they plugged in data on 2,803 residential burglaries occurring within a block of the San Fernando valley 11 miles by 11 miles throughout 2004. For a given day the software calculated the top 5 percent of city blocks most likely to be burglarized. The results convinced the LAPD that, had they been using the program, they could have prevented a quarter of burglaries across the entire test region for that day.
The current, real world test of the software involves generating a map of the city areas most likely to be burglarized, the time of day they are most likely to get hit, and deploying personnel accordingly. The software is recalibrated every day when burglaries from the previous day are added to the dataset. They don’t actually expect to catch people in the act, but to deter more crimes with more effective patrolling. The test that is underway will be evaluated at six months, but already the data is encouraging. Zach Friend, crime analyst for Santa Cruz police, confirmed to the New York Times that the program led to five arrests in July. Even more impressive, compared to July 2010 burglaries, the number of July 2011 burglaries are down 27 percent. Whether or not that trend holds remains to be seen, but so far it appears that being in the wrong place at the right time works.
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