Crime in Lima: an approximation with district data

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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...

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Detalles Bibliográficos
Autor: Hernández Breña, Wilson
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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spelling 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
identifier_str_mv 10.21754/iecos.v18i0.1182
dc.language.none.fl_str_mv spa
eng
language spa
eng
dc.relation.none.fl_str_mv 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
rights_invalid_str_mv Derechos de autor 2017 Wilson Hernández Breña
https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
audio/mpeg
audio/mpeg
dc.publisher.none.fl_str_mv Universidad Nacional de Ingeniería
publisher.none.fl_str_mv Universidad Nacional de Ingeniería
dc.source.none.fl_str_mv revista IECOS; Vol. 18 (2017); 192-237
Revista IECOS; Vol. 18 (2017); 192-237
2788-7480
2961-2845
10.21754/iecos.v18i0
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collection Revistas - Universidad Nacional de Ingeniería
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