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This paper proposes a Bayesian approach to perform inference regarding thesize of hidden populations at analytical region using reported statistics. Todo so, we propose a specification taking into account one-sided errorcomponents and spatial effects within a panel data structure. Our simulationexercises suggest good finite sample performance. We analyze rates of crimesuspects living per neighborhood in Medell\'in (Colombia) associated with fourcrime activities. Our proposal seems to identify hot spots or "crimecommunities", potential neighborhoods where under-reporting is more severe, andalso drivers of crime schools. Statistical evidence suggests a high level ofinteraction between homicides and drug dealing in one hand, and motorcycle andcar thefts on the other hand.

Inferring hidden potentials in analytical regions: uncovering crime suspect communities in Medell\'in