Brantingham says his approach is less about adapting the software for L.A. and more about spotting predictable patterns of universal human behavior. “People tend to utilize their local environment, so they don’t travel long distances to do things like buying milk,” he says. In the same way, he adds, “most burglars victimize places that are very close to where they live, or where they work, or where they hang out.”

With demand on the rise, IBM is betting big on predictive analytics software. Over the past four years, the company has invested $14 billion in more than 24 acquisitions to expand its analytics division, according to Robert Reczek, an IBM communications executive. Reczek says that more than 200 IBM mathematicians focus exclusively on analytics.

Police departments aren’t IBM’s only projected clients. The software also has applications ranging from preventing Medicare fraud to spotting phony university admissions data to detecting information leaks within federal agencies, says William Haffey, director of sales engineers at SPSS, the division of IBM that developed the software.

Sometimes, these programs pick up unpredictable warnings. For example, police might catch an uptick in shoplifting at the local mall “if it turned out that if it had been raining for three days, and it stopped raining and it happened to be the Saturday of a three-day weekend,” says Haffey.

But as promising as predictive analytics sound, simply buying the software is no panacea. After the Florida Department of Juvenile Justice purchased some from SPSS a few years ago, the job of projecting next year’s crime rates shrank from a day to a few hours, says the department’s chief of research and planning, Mark Greenwald. Still, Greenwald would need a much larger budget to start making the kinds of detailed predictions seen in Richmond or Memphis.

“I think it has been useful, at least for our general trend forecast, and from my perspective it’s very easy to use,” he says. “But it has a lot of functionality that I haven’t been able to tap into yet, because of staffing issues.” In other words, the technology won’t do much good if departments don’t have enough technical staff to keep feeding the model with the latest data—or if they don’t have enough cops to hit the hot spots when warnings flash.