An optimised approach to near repeat analysis for intelligence driven crime linkage

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).