Crime data mining: A general framework and some examples

Concern about national security has increased significantly since the terrorist attacks on 11 September 2001. The CIA, FBI, and other federal agencies are actively collecting domestic and foreign intelligence to prevent future attacks. These efforts have in turn motivated local authorities to more closely monitor criminal activities in their own jurisdictions. A major challenge facing all law-enforcement and intelligence-gathering organizations is accurately and efficiently analyzing the growing volumes of crime data. For example, complex conspiracies are often difficult to unravel because information on suspects can be geographically diffuse and span long periods of time. Detecting cybercrime can likewise be difficult because busy network traffic and frequent online transactions generate large amounts of data, only a small portion of which relates to illegal activities. Data mining is a powerful tool that enables criminal investigators who may lack extensive training as data analysts to explore large databases quickly and efficiently.(1) Computers can process thousands of instructions in seconds, saving precious time. In addition, installing and running software often costs less than hiring and training personnel. Computers are also less prone to errors than human investigators, especially those who work long hours. We present a general framework for crime data mining that draws on experience gained with the Coplink project (http://ai.bpa.arizona.edu/coplink), which researchers at the University of Arizona have been conducting in collaboration with the Tucson and Phoenix police departments since 1997.