Slate Magazine had this article on using computer models to predict where drug traffickers will move their shipment routes to in response to enforcement crackdowns in Mexico.
Dell uses Dijkstra’s algorithm first to model the routes that cost-minimizing traffickers would take on Mexico’s roadways and then to predict how these paths would change if disrupted by PAN victories along a route. It turns out that this model—combining simple assumptions about traffickers’ transport costs with an exercise in using Google Maps—is remarkably predictive of how trafficking routes are affected by PAN-led crackdowns that effectively sever paths on the road network: Drug confiscations in the communities where Dell predicts traffickers will relocate to following a crackdown increase by about 20 percent in the months following close PAN victories. It’s a reminder that crime fighting is a bit like Whac-A-Mole—smothering traffickers’ activities in one locale merely causes them to shift their operations elsewhere. Dell finds that drug-related homicides also go up in places that her model predicts will lie on traffickers’ new paths from Mexican drug labs to the U.S. border. (And she finds tentative evidence that towns on newly created routes see a decline in informal sector wages, presumably since drug traffickers also run protection rackets along their smuggling routes, which primarily victimize small shopkeepers and others in the informal economy.)
The whole piece is an interesting read on just how economics plays a role in criminal enterprises like drug trafficking. I think it also shows the promise that technology like GIS can have in understanding and ultimately interrupting these illicit economies.