One of the fun things about being the CIO for a state was interacting with the cops at Public Safety. They were great people and had a completely different outlook from your typical geek. It was frustrating sometimes though to see where technology could add tremendous value to what they did and not see it being employed. One such area was GIS. From Wired magazine comes another example of how tools that business takes for granted could be applied to police work with significant effect.
Cloudy, With a Chance of Theft by Wilpen Gorr is about using business intelligence and forecasting methodologies that are commonplace in business to forecast crime. He says:
[W]e found that the way to predict lawlessness is to identify and track leading indicators. Companies look at consumer spending data; meteorologists keep tabs on barometric pressure. In our case, we studied soft crimes such as mischief, disorderly conduct, and trespassing. An increase often precedes a rise in hard crimes like burglary and assault.
This isn't too surprising since that's essentially the technique Giulliani used in NYC to drive the crime rate down: hit hard on soft crimes and the hard crimes take care of themselves. This, according to the article, is like "a giant game of Whack-A-Mole." Its reactive rather that proactive. Being proactive frees up resources to do other things, like homeland security. Sounds like a better use of our tax dollars than just blindly hiring lots more police and blanketing the whole city.