A couple of weeks ago Miller-McCune magazine had an interesting piece about LAPD's development of a predictive policing program. From the story:
In some ways, the notion of predicting where crimes will happen based on where they’ve happened in the past is fairly obvious. If there have been a lot of muggings on one particular street for the last 50 weeks, there will probably be some next week. Cops know that, of course. But the idea is to make those assumptions and guesses more accurate and to turn up patterns that aren’t so readily apparent.
Corporations have long used such predictive analytics to anticipate consumer demand and have found that cross-pollinating data can yield unexpected results. A famous example comes from Wal-Mart’s analysis of what its customers in coastal areas stock up on before hurricanes. The list includes duct tape and bottled water, of course, but also a surprise item: strawberry Pop-Tarts.
Analyzing crime data can similarly yield counterintuitive conclusions, researchers have found. Most people think good lighting makes an area safer, for instance, but studies have found that it actually increases the chances of being victimized, says P. Jeffrey Brantingham, a UCLA anthropology professor. It seems that muggers want to be able to see their potential targets clearly.
There have been a number of stories in the news lately about predictive policing. I have even posted about a few of them before. I do also see the promise in these programs. If my computerized crystal ball prognosticated that a particular neighborhood was ripe for burglaries on a particular week, it would be a whole lot easier to dedicate limited resources to dealing with that problem.
I am also heartened that a focus of some of these experiments is the problem of burglaries. This crime has such a historically low clearance rate (about 10% or less here in Texas) that anything that helps to improve that performance is a good thing.