From a widely-cited article in the latest issue of The Economist:
The police department of Richmond, Virginia, has pioneered the use of network-analysis software to predict crimes. Police officers know that crime increases at certain times, such as on paydays and when there is a full moon. But the software lets them analyse the social networks around suspects, such as dealings with employers, collection agencies and the Department of Motor Vehicles. The goal, according to Stephen Hollifield, the department’s technology chief, is to “pull together a complete picture” of suspects and their social circle.
Party plans turn out to be a particularly useful part of this picture. Richmond’s police have started monitoring Facebook, MySpace and Twitter messages to determine where the rowdiest festivities will be. On big party nights, the department now saves about $15,000 on overtime pay, because officers are deployed to areas that the software deems ripe for criminal activity. Crime has “dramatically” declined as a result, says Mr Hollifield.
I’d like to have a better sense of what the numbers are here. “Big party nights” sounds a bit too imprecise to me. If every night were a big party night, and I can assure you that every night is not a big party night, we’d be talking about savings of $5,475,000 a year. The Richmond Police Department will spend roughly $75 million this year. Much bigger savings are necessary. But I certainly like the idea of economizing on labor costs through the use of a low-cost technology.
As the article makes clear, network-analysis software can make contributions in many other public policy domains. One are to explore is school discipline. By using this tools to identify and manage disruptive students, all students could see a marked increase in time devoted to effective instruction. And by creating a more satisfying work environment for teachers, this could, in turn, reduce the pressure to increase cash compensation.