An approach for understanding offender modus operandi to detect serial robbery crimes

Abstract

Detecting serial crimes is one of the most challenging tasks in crime analysis. Linking crimes committed by the same criminal can improve the work efficiency of police offices and maintain public safety. Previous crime linkage studies have focused on the crime features of modus operandi (M.O.) but did not address the crime process. In this paper, we proposed an approach for detecting serial robbery crimes based on understanding offender M.O. by integrating crime process information. According to the crime narrative text, a natural language processing method is used to extract the action and object characteristics of the crime process, a dynamic time warping method was introduced in the similarity measurement of these characteristics, and an information entropy method was used to weight the similarity of the action and object characteristics to obtain the comprehensive similarity of criminals’ crime process. A real-world robbery dataset is employed to measure the performance of finding serial crimes after adding the crime process information. According to the results, information about the crime process obtained from the case narrative text has significant separability and can better characterize better the offender’s M.O. Five machine learning algorithms are used to classify the case pairs and identify serial cases and nonserial cases. Based on the crime features, the results show that the addition of crime process information can substantially improve the effect of detecting serial crimes.