Analytics Help Police Stop Crime Before it Happens
Two California cities are pioneers in "predictive policing." Analysis of historical crime data produces digital maps that show officers where to patrol to prevent burglaries and vehicle thefts.
Tue, November 15, 2011
CIO — Capt. Sean Malinowski of the Los Angeles Police Department (LAPD) has just done something once unimaginable for a commanding officer: He's given up control of deploying his beat officers to a computer.
Malinowski, commanding officer of the LAPD's Foothill Community Police Station, is a pioneer in the field of "predictive policing." That means using predictive analytics to analyze data, such as the times and locations of past crimes, to forecast where and when certain crimes are likely to happen in the future so police can stop them before they occur.
"We're doing a rigorous examination, an experiment, for the next three months of predictive analytics and for the first time we're going to rely 100% on the computer to forecast property crimes, which are the lion's share of our crime," he says. The experiment began Nov. 6.
Malinowski says he's willing to make some sacrifices in terms of control if it means reducing crime in his jurisdiction.
"That's unusual for me to do because, as a [commanding officer], I like to be in control of things, especially the mission," he says. "But I'm going to give that up and I'm going to let the computer generate the geographic assignment of the missions."
Across the country, police departments must fight crimes in the face of decreasing budgets and manpower. But in Los Angeles and Santa Cruz, Calif., the police departments are turning to new technologies like predictive analytics to help them save time and money by enabling them to prevent crime by more effectively deploying patrol officers.
The LAPD and the Santa Cruz Police Department are using a crime-fighting tool developed by researchers—social scientists and mathematicians—at the University of California Los Angeles (UCLA) to target property crimes such as home and business burglaries, as well as vehicle thefts and break-ins.
Like Predicting Earthquakes
The tool, which identifies criminal hotspots, is modeled on a mathematical algorithm used to predict earthquakes and their aftershocks because the researchers discovered that, just as aftershocks are in close proximity to the initial earthquake, criminals tend to commit crimes in close proximity to past crimes.
The technology grew out of a long-standing UCLA-based project looking at the mathematics of crime, says P. Jeffrey Brantingham, one of the UCLA researchers and an associate professor of anthropology at the school. For the first six years of the seven-year project, researchers focused on trying to figure out what models do a good job determining how and why crime patterns form in the way they do. Now that they've developed those models, the researchers are putting them into practice.
