SPATIOTEMPORAL-TEXTUAL POINT PROCESSES FOR CRIME LINKAGE DETECTION

• 成果类型: Article
• 署名作者: Zhu, Shixiang; Xie, Yao
• 署名单位: University System of Georgia; Georgia Institute of Technology
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1538
• 发表日期: 2022
• 页码: 1151-1170
• 关键词: process models

摘要

Crimes emerge out of complex interactions of human behaviors and situations. Linkages between crime incidents are highly complex. Detecting crime linkage, given a set of incidents, is a highly challenging task since we only have limited information, including text descriptions, incident times, and locations. In practice, there are very few labels. We propose a new statistical modeling framework for spatiotemporal-textual data and demonstrate its usage on crime linkage detection. We capture linkages of crime incidents via multivariate marked spatiotemporal Hawkes processes and treat embedding vectors of the free-text as marks of the incident, inspired by the notion of modus operandi (M.O.) in crime analysis. Numerical results, using real data, demonstrate the good performance of our method as well as reveals interesting patterns in the crime data: the joint modeling of space, time, and text information enhances crime linkage detection, compared with the state-of-the-art, and the learned spatial dependence from data can be useful for police operations.