Automating Crime Informatics to Inform Public Policy

Abstract

Violent crime is a critically important community issue. Government has attempted to address this problem in a variety of ways, with varied levels of success. However, there are only a certain number and type of factors that can be addressed by government action; which are most important? In this paper we address this question by “reverse engineering” the crime prediction problem. Intuition suggests that the collection of factors most informative in predicting crime will include, as a subset, the primary causal factors of crime. If this is true, it makes sense to develop ways to identify and objectively quantify these most informative predictive factors. We characterize the K-metric (loosely related to the F-Measure) for assessing the effectiveness of measured features for crime prediction. This metric is used to substantially reduce the number of factors needed to capture the total information of a many-feature dataset. Further, all features in the set can be rank ordered by their K-metric values, providing an automated means of identifying and objectively quantifying potentially causal factors for intervention services.