Subscribe / Unsubscribe Enewsletters | Login | Register

Pencil Banner

Predictive policing in the U.S. gets personal

Robert L. Mitchell | Oct. 25, 2013
Data mining can predict who will reoffend, not just where and when the crimes will occur.

The department hopes to stem the tide of violent crime, which has been increasing in recent years, Robbin says. But the jury is still out as to how effective it will be: "With gun crimes you have fewer incidents, so the predictions aren't as strong," she says. Within the predictions, she explains, the first three to five boxes are usually where experienced officers would expect trouble. But it's boxes 8, 9 and 10 on down the list that they never would have anticipated, she says.

Since implementing a similar predictive policing system four years ago, the City of Richmond, Va. has seen a significant reduction in all violent crimes and property-based crimes. The results were so good, in fact, that police chief Rodney Moore implemented a similar system soon after taking on his current position as chief of police in Charlotte-Mecklenburg, S.C. That system, developed by the commercial satellite imaging company Digital Globe, includes historical data and refreshes every two hours to adjust predictions for 39 response areas. "We've had a 20% reduction in violent crime and a 30% reduction in property crime," he says.

Dr. Colleen McCue, senior director of social science and quantitative methods at Digital Globe, has been modeling violent crime using machine learning for more than 20 years. "People are creatures of habit. That's what this all goes back to," she says, adding that models can also make predictions about specific offenders if there's enough previous criminal activity.

For example, a shooter in Northern Virginia a few years ago targeted government facilities and, with the Marine Corps Marathon coming up, authorities were anxious to anticipate his next move. McCue examined previous incidents involving the shooter and ran data to create a predictive model. They discovered that the shooter preferred a position 200 meters back from the target with close proximity to a highway or a major roadway.

Using 3-D spatial data she created a heat map showing all locations on the marathon route that met the criteria. Authorities positioned people there -- and nothing happened. While it's hard to know for sure if these actions thwarted the shooter, place preference came into play once again after it became clear that this shooter also had an affinity for cemeteries. Six months later he was apprehended at Arlington National Cemetery.

Predictive policing is a helpful tool, but you still need an analyst to interpret the data, rather than just depending on the system to push out all the answers, she adds. "Statistical-based approaches work in some cases, but in others [human] judgment still works better."

Charlotte-Mecklenburg, S.C. is now going beyond predicting where and when crime will occur to predict who is likely to reoffend. Instead of studying just crimes and locations to decide where crimes will occur, police departments make predictions using criminal histories to predict who will commit a crime. This approach -- making predictions about people with criminal records -- is one that both Los Angeles and Seattle have avoided due to public fears that the technology would be used to profile people based on race or the neighborhood in which they live.


Previous Page  1  2  3  Next Page 

Sign up for CIO Asia eNewsletters.