Bayesian Geographic Profiling: A Fundamental Limitation

Abstract

Geographic profiling is a criminal investigative technique that analyzes the locations of a crime series to determine the most probable area of offender residence. Police agencies employ the methodology for suspect prioritization and information management purposes in serial crime cases. Geoprofiles are probability maps generated by an algorithm that integrates distance decay functions originating from the point pattern of the connected crime sites. A more recent approach, known as empirical Bayes journey-to-crime estimation (or Bayesian geographic profiling), seeks to supplement these models with area-based historical offender and crime data. Spatial information from previous crime trips is used to calibrate analyses following the assumption that the unknown offender likely resides in the same neighborhood as past criminals who offended in the location of the new crime series. Inferring individual suspect rankings from historical area rankings, however, creates an ecological fallacy, and the greater the congruence between past offenders and future suspects, the more tautological the analysis. Although Bayesian models cannot be used to inform suspect prioritization—the main function of geographic profiling—the approach could have applicability for police strategies based on area prioritization. Surprisingly, this major limitation of the Bayes approach to geoprofiling has been ignored in the literature.