2018/05/08 by Jana Svobodová, Svobodová, Jana
Computer Science · Economics, Econometrics and Finance · Mathematics · Medicine · Psychology · #Applications (stat.AP) #Artificial intelligence #Bayesian Methods and Mixture Models #Bayesian network #Bayesian probability #Computer science #Crime analysis #Criminology #Data mining #Data-Driven Disease Surveillance #FOS: Computer and information sciences #Machine learning #Methodology (stat.ME) #Offender profiling #Posterior probability #Profiling (computer programming) #Psychology #Spatial and Panel Data Analysis #stat.AP #stat.ME
paper · pdf · doi:10.48550/arxiv.1805.02993
arxiv created 2018/05/08 · openalex publication_date 2018/05/08 · arxiv updated 2018/05/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider the problem of geographic profiling and offer an approach to choosing a suitable model for each offender. Based on the analysis of the examined dataset, we divide offenders into several types with similar behavior. According to the spatial distribution of the offender's crime sites, each new criminal is assigned to the corresponding group. Then we choose an appropriate model for the offender and using Bayesian methods we determine the posterior distribution for the criminal's anchor point. Our models include directionality, similar to models of Mohler and Short (2012). Our approach also provides a way to incorporate two possible situations into the model - when the criminal is a resident or a non-resident. We test this methodology on a real data set of offenders from Baltimore County and compare the results with Rossmo's approach. Our approach leads to substantial improvement over Rossmo's method, especially in the presence of non-residents.