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

Alejandro Puerta-Cuartas and Andr\'es Ram\'irez-Hassan

Papers from arXiv.org

Abstract: This paper proposes a Bayesian approach to perform inference regarding the size of hidden populations at analytical region using reported statistics. To do so, we propose a specification taking into account one-sided error components and spatial effects within a panel data structure. Our simulation exercises suggest good finite sample performance. We analyze rates of crime suspects living per neighborhood in Medell\'in (Colombia) associated with four crime activities. Our proposal seems to identify hot spots or "crime communities", potential neighborhoods where under-reporting is more severe, and also drivers of crime schools. Statistical evidence suggests a high level of interaction between homicides and drug dealing in one hand, and motorcycle and car thefts on the other hand.

Date: 2020-09
New Economics Papers: this item is included in nep-ecm and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2009.05360

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