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Sophisticated analysis of data can sometimes tell police where criminals are headed. It's academic now, but the LAPD plans to get involved.
August 21, 2010|By Joel Rubin, Los Angeles Times
The future of crime fighting begins with a story about strawberry Pop-Tarts, bad weather and Wal-Mart.
With a hurricane bearing down on the Florida coast several years ago, the retail giant sent supply trucks into the storm to stock shelves with the frosted pink pastries. The decision to do so had not been made on a whim or a hunch, but by a powerful computer that crunched reams of sales data and found an unusual but undeniable fact: When Mother Nature gets angry, people want to eat a lot more strawberry Pop-Tarts.
Officials in the Los Angeles Police Department are using the anecdote to explain a similar, but far more complicated, idea that they and researchers say could revolutionize law enforcement.
"As police departments have gotten better at pushing down crime, we are looking now for the thing that will take us to the next level," LAPD Chief Charlie Beck said. "I firmly believe predictive policing is it."
Predictive policing is rooted in the notion that it is possible, through sophisticated computer analysis of information about previous crimes, to predict where and when crimes will occur. At universities and technology companies in the U.S. and abroad, scientists are working to develop computer programs that, in the most optimistic scenarios, could enable police to anticipate, and possibly prevent, many types of crime.
Some of the most ambitious work is being done at UCLA, where researchers are studying the ways criminals behave in urban settings.
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Using LAPD data, Mohler tested his computer model on several thousand burglaries that occurred in a large section of the San Fernando Valley throughout 2004 and 2005. The results, he said, were far more effective than anything on the market today.
The program divided the Valley area into patrol zones that were each roughly the size of several neighborhood blocks and then calculated which zones had the highest probability of experiencing burglaries the next day.
In one test, in which Mohler assumed there were enough cops to patrol 10% of the area, the model accurately identified the zones where the officers should have gone in order to thwart about a quarter of all the burglaries that occurred that day.
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