Crime in Lima: an approximation with district data
Descripción del Articulo
Lima suffers from a high crime rate, but one that is heterogeneously distributed throughout its districts. However, little is known about one of the basic questions regarding crime in the city: what causes crime among and across these districts? We constructed a data pool consisting of six years of...
| Autor: | |
|---|---|
| Formato: | artículo |
| Fecha de Publicación: | 2017 |
| Institución: | Universidad Nacional de Ingeniería |
| Repositorio: | Revistas - Universidad Nacional de Ingeniería |
| Lenguaje: | español inglés |
| OAI Identifier: | oai:oai:revistas.uni.edu.pe:article/1182 |
| Enlace del recurso: | https://revistas.uni.edu.pe/index.php/iecos/article/view/1182 |
| Nivel de acceso: | acceso abierto |
| Materia: | Crimen clústeres distritos Crime clusters districts |
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Crime in Lima: an approximation with district dataCrimen en Lima: una aproximación con datos distritalesHernández Breña, WilsonCrimenclústeresdistritosCrimeclustersdistrictsLima suffers from a high crime rate, but one that is heterogeneously distributed throughout its districts. However, little is known about one of the basic questions regarding crime in the city: what causes crime among and across these districts? We constructed a data pool consisting of six years of data from the Encuesta Nacional de Programas Estratégicos (2010-2016) in order to obtain a representative sample for 35 districts in Lima (N=53,787). This allowed us to respond to the study’s two main objectives: (1) analyze the extent of the heterogeneity of crime (and its cau-ses) among Lima’s districts (cluster analysis) and (2) identify the drivers that cause certain districts to have higher crime rates than others (multilevel modeling). Results show that we should not treat Lima as a homogenous city in terms of crime rate. Rather, we found that the city’s districts could be classified into three groups (Latent Protection, Limited Protection and Permanent Defenselessness). We found that the theories of the origins of crime that we assessed in each group (social disorganization, routine activity theory, and social capital) differed in relation to the type of district. The policy implications of this research highlight the multicausality of crime, suggest improvements and assessments of police participation at the local level, as well as improving local management of economic incentives.Lima no solo mantiene niveles delictivos preocupantes sino también heterogéneos entre sus distritos. Pese a ello, son pocas las respuestas que se han dado a la pregunta más elemental: ¿qué causa el crimen en los distritos de Lima? Se usó el pool de datos de los siete años de la Encuesta Nacional de Programas Estratégicos (2010-2016) a fin de obtener artificialmente una muestra representativa de 35 distritos de Lima (N=53,787). Solo así fue posible responder a los dos objetivos de esta investigación: (1) analizar qué tan homogéneo es el crimen (y sus causas) entre un distrito y otro en Lima (análisis de clúster) y (2) identificar las razones que hacen que un distrito de Lima tenga más victimi-zación que otro (modelación multinivel). Los resultados indican que, con el propósito de explicar las causas del crimen, es incorrecto tratar a Lima como un bloque homogéneo de distritos; estos, por el contrario, se pueden clasificar en tres grupos: protección latente, protección limitada y desprotección abierta, cada uno con una relación distinta con las tres teorías del crimen evaluadas (desorganización social, actividades rutinarias y capital social). Las implicancias apuntan a brindar mayor importancia a la multicausalidad del delito, mejorar y evaluar la participación local de la policía y contar con una mejor gestión de los incentivos económicos entregados a las municipalidades.Universidad Nacional de Ingeniería2017-07-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPeer ReviewedEvaluado por paresapplication/pdfaudio/mpegaudio/mpeghttps://revistas.uni.edu.pe/index.php/iecos/article/view/118210.21754/iecos.v18i0.1182revista IECOS; Vol. 18 (2017); 192-237Revista IECOS; Vol. 18 (2017); 192-2372788-74802961-284510.21754/iecos.v18i0reponame:Revistas - Universidad Nacional de Ingenieríainstname:Universidad Nacional de Ingenieríainstacron:UNIspaenghttps://revistas.uni.edu.pe/index.php/iecos/article/view/1182/3157https://revistas.uni.edu.pe/index.php/iecos/article/view/1182/3158https://revistas.uni.edu.pe/index.php/iecos/article/view/1182/3159Derechos de autor 2017 Wilson Hernández Breñahttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessoai:oai:revistas.uni.edu.pe:article/11822025-01-20T04:24:47Z |
| dc.title.none.fl_str_mv |
Crime in Lima: an approximation with district data Crimen en Lima: una aproximación con datos distritales |
| title |
Crime in Lima: an approximation with district data |
| spellingShingle |
Crime in Lima: an approximation with district data Hernández Breña, Wilson Crimen clústeres distritos Crime clusters districts |
| title_short |
Crime in Lima: an approximation with district data |
| title_full |
Crime in Lima: an approximation with district data |
| title_fullStr |
Crime in Lima: an approximation with district data |
| title_full_unstemmed |
Crime in Lima: an approximation with district data |
| title_sort |
Crime in Lima: an approximation with district data |
| dc.creator.none.fl_str_mv |
Hernández Breña, Wilson |
| author |
Hernández Breña, Wilson |
| author_facet |
Hernández Breña, Wilson |
| author_role |
author |
| dc.subject.none.fl_str_mv |
Crimen clústeres distritos Crime clusters districts |
| topic |
Crimen clústeres distritos Crime clusters districts |
| description |
Lima suffers from a high crime rate, but one that is heterogeneously distributed throughout its districts. However, little is known about one of the basic questions regarding crime in the city: what causes crime among and across these districts? We constructed a data pool consisting of six years of data from the Encuesta Nacional de Programas Estratégicos (2010-2016) in order to obtain a representative sample for 35 districts in Lima (N=53,787). This allowed us to respond to the study’s two main objectives: (1) analyze the extent of the heterogeneity of crime (and its cau-ses) among Lima’s districts (cluster analysis) and (2) identify the drivers that cause certain districts to have higher crime rates than others (multilevel modeling). Results show that we should not treat Lima as a homogenous city in terms of crime rate. Rather, we found that the city’s districts could be classified into three groups (Latent Protection, Limited Protection and Permanent Defenselessness). We found that the theories of the origins of crime that we assessed in each group (social disorganization, routine activity theory, and social capital) differed in relation to the type of district. The policy implications of this research highlight the multicausality of crime, suggest improvements and assessments of police participation at the local level, as well as improving local management of economic incentives. |
| publishDate |
2017 |
| dc.date.none.fl_str_mv |
2017-07-01 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer Reviewed Evaluado por pares |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
https://revistas.uni.edu.pe/index.php/iecos/article/view/1182 10.21754/iecos.v18i0.1182 |
| url |
https://revistas.uni.edu.pe/index.php/iecos/article/view/1182 |
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10.21754/iecos.v18i0.1182 |
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spa eng |
| language |
spa eng |
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https://revistas.uni.edu.pe/index.php/iecos/article/view/1182/3157 https://revistas.uni.edu.pe/index.php/iecos/article/view/1182/3158 https://revistas.uni.edu.pe/index.php/iecos/article/view/1182/3159 |
| dc.rights.none.fl_str_mv |
Derechos de autor 2017 Wilson Hernández Breña https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
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Derechos de autor 2017 Wilson Hernández Breña https://creativecommons.org/licenses/by/4.0 |
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openAccess |
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application/pdf audio/mpeg audio/mpeg |
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Universidad Nacional de Ingeniería |
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Universidad Nacional de Ingeniería |
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revista IECOS; Vol. 18 (2017); 192-237 Revista IECOS; Vol. 18 (2017); 192-237 2788-7480 2961-2845 10.21754/iecos.v18i0 reponame:Revistas - Universidad Nacional de Ingeniería instname:Universidad Nacional de Ingeniería instacron:UNI |
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Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).