An optimised approach to near repeat analysis for intelligence driven crime linkage. Issue 1 (2nd January 2022)

• Record Type: Journal Article
• Authors: Spaulding, Jamie S.; Morris, Keith B.
• Is Part Of: Journal of policing, intelligence and counter terrorism. Volume 17:Issue 1(2022)
• Page Start: 24
• Page End: 47
• Publication Date: 2022-01-02
• Subjects: Crime -- space–time clustering -- near repeat -- open source -- network; 363.3205
• DOI: 10.1080/18335330.2021.1945663
• Languages: English
• ISSNs: 1833-5330

Abstract

ABSTRACT: Law enforcement and security agencies around the globe have integrated geospatial analysis into their intelligence workflow to profile serial offenders, track suspects, and direct crime reduction/prevention efforts. Expansion to spatio-temporal analyses may yield significant and relevant information to better understand the underlying factors of crime. Among the current spatio-temporal methods to associate crimes is near repeat analysis. The premise of the near repeat phenomenon is that if a given location is the target of a crime, nearby locations will have an increased chance of being targeted for a limited time with the level of risk decaying with distance from the original target and over time. Robust analytical methods were developed to discover and further understand spatio-temporal clustering of crime incidents. The open source nature of these functions facilitate transparency and reproducibility in the analytical method and implementation across agencies/police management systems. Firstly, a new method for near repeat analysis is presented which expands current techniques through graphical linkage of crime incidents given spatio-temporal proximity. Next, this method is used to evaluate the prevalence of near repeats across cities of scale. Given this, a method for determining optimal parameters is presented and utilised to determine the optimal parameters (inter-incident time/distance).