An outlier-based data association method for linking criminal incidents

Serial criminals are a major threat in the modern society. Associating incidents committed by the same offender is of great importance in studying serial criminals. In this paper, we present a new outlier-based approach to resolve this criminal incident association problem. In this approach, criminal incident data are first modeled into a number of cells, and then a measurement function, called outlier score function, is defined over these cells. Incidents in a cell are determined to be associated with each other when the score is significant enough. We applied our approach to a robbery dataset from Richmond, VA. Results show that this method can effectively solve the criminal incident association problem.