Risk terrain modeling and socio-economic stratification: identifying risky places for violent crime victimization in Bogotá, Colombia

This research focused on the effect of the built environment on Bogotá’s violent crime by using the Risk Terrain Modeling (RTM) technique. The current study used 17 ecological variables, including micro-level data on the spatial distribution of socio-economic strata, and the location of an array of...

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Autor principal: Giménez-Santana, Alejandro (Autor)
Otros Autores: Caplan, Joel M. ; Drawve, Grant R.
Tipo de documento: Electrónico Artículo
Lenguaje:Inglés
Publicado: 2019
En: European journal on criminal policy and research
Año: 2018, Volumen: 24, Número: 4, Páginas: 417–431
Acceso en línea: Volltext (Resolving-System)
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Sumario:This research focused on the effect of the built environment on Bogotá’s violent crime by using the Risk Terrain Modeling (RTM) technique. The current study used 17 ecological variables, including micro-level data on the spatial distribution of socio-economic strata, and the location of an array of businesses and other features of the landscape. As suggested by the results of this study, the spatial distribution of violent crime in Bogotá is highly correlated with the allocation of socio-economic strata throughout its geography. A statistically valid RTM analysis identified the micro-level risk factors associated with three types of violent crime incidents, namely homicide, assault, and theft incidents. These results suggest that future violent crime incidents are more likely to occur at a reduced number of high-risk micro-places. Moreover, while homicide and assault incidents were more likely to cluster within the poorest areas of the city, theft incidents presented a higher risk of victimization near the city center, where economic activity and suitable targets concentrate. This study offers a unique account regarding the effect of socio-economic segregation on violent crime victimization across Bogotá’s geography and within different socio-economic strata classifications.
ISSN:1572-9869
DOI:10.1007/s10610-018-9374-5