2020/09/11 by Puerta, Alejandro, Andrés Ramírez Hassan, Ramírez-Hassan, Andrés
Mathematics · Medicine · Social Sciences · #COVID-19 epidemiological studies #Crime Patterns and Interventions #Data-Driven Disease Surveillance #Econometrics (econ.EM) #FOS: Economics and business
paper · pdf · doi:10.48550/arxiv.2009.05360
openalex publication_date 2020/09/11 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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 Medellín (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.