Data mining goes mainstream

Rodney Monroe, the police chief in Richmond, Va., describes himself as a lifelong cop whose expertise is in fighting street crime, not in software. His own Web browsing, he says, mostly involves checking golf scores.

But shortly after he became chief in 2005, a crime analyst who had retired from the force convinced him to try some clever software. The programs cull through information that the department already collects, like "911"and police reports, but add new streams of data--about neighborhood demographics and payday schedules, for example, or about weather, traffic patterns and sports events--to try to predict where crimes might occur.

"It sounded nutty at first," Monroe recalled, "but the more and more you get into it, the more sense it makes.”

The technology, for example, pointed to a high rate of robberies on paydays in Hispanic neighborhoods, where fewer people use banks and where customers leaving check-cashing stores were easy targets for robbers. Elsewhere, there were clusters of random-gunfire incidents at certain times of night. So extra police were deployed in those areas when crimes were predicted.