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

TL;DR

This paper introduces a Bayesian method to estimate hidden populations in regions, applied to crime suspect communities in Medellín, revealing spatial crime patterns and potential under-reporting hotspots.

Contribution

It develops a novel Bayesian framework incorporating spatial effects and one-sided errors for estimating hidden populations using reported data.

Findings

Identifies crime hotspots and under-reporting areas in Medellín.

Reveals strong interaction between homicide, drug dealing, and thefts.

Demonstrates good finite sample performance in simulations.

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.