Inferring hidden potentials in analytical regions: uncovering crime suspect communities in Medell\'in
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...
| Authors: | ; |
|---|---|
| Format: | Electronic Book |
| Language: | English |
| Published: |
2020
|
| In: | Year: 2020 |
| Online Access: |
Volltext (kostenfrei) |
| Check availability: | HBZ Gateway |
| Summary: | 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 |
|---|