The analysis of criminal recidivism: a hierarchical model-based approach for the analysis of zero-inflated, spatially correlated recurring event data

Abstract The life course perspective in criminology has become prominent in recent years, offering valuable insights into various patterns of criminal pathways. Noticeably, the study of criminal trajectories aims to understand crime's beginning, persistence, and desistence. Central to this analysis is the identification of patterns in the frequency of criminal victimization and recidivism, along with the factors that contribute to them. Specifically, this work introduces a new class of models that overcome limitations in traditional methods used to analyse criminal recidivism. The proposed models are designed for recurrent events data characterized by excess of zeros and spatial correlation. In addition to their parametric counterparts, we propose flexible semi-parametric versions approximating the intensity function using Bernstein Polynomials. The performance of these models was evaluated in a simulation study with various scenarios, and we applied them to analyse criminal recidivism data in the Metropolitan Region of Belo Horizonte, Brazil. The results provide a detailed analysis of high-risk areas for recurrent crimes and the behaviour of recidivism rates over time. This research significantly enhances our understanding of criminal trajectories, paving the way for more effective strategies in combating criminal recidivism.