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Analysis of Influential Factors of Violent Crimes and Building a Spatial Cluster in South Korea

Analysis of Influential Factors of Violent Crimes and Building a Spatial Cluster in South Korea This study analyzed the spatial distribution of violent crime (murder, robbery, rape, assault, and larceny) in Korea and the relationship between violent crime and the governance, administrative, physical, and socio-economic factors of local communities. The occurrence of violent crime was approached from the perspective of the community, not from a personal perspective, based on the theoretical ecological perspective. In addition, an analysis model (spatial lag model) designed to analyze spillover effect between neighboring communities. For the analysis, this study used the data of 56 sub-local governments of Seoul Metropolitan City and Gyeonggi Province in 2015.The analysis results are as follows: First, this study identified five major violent crime occurrence situations through descriptive statistical analysis. Second, the hot-spot and cold-spot of violent crime were derived through exploratory spatial analysis (Moran's I, LISA). Third, this study derived the relationship between the incidence of violent crime and the governance, administrative, physical, and socioeconomic factors of the community through spatial regression analysis based on the spatial lag model. Specifically, the valid factors influenced on the five major violent as follows: variables of local security council in space effect and governance; variables of crime monitoring facilities and crime agency in administrative capacity; variables of detrimental facilities density in physical environment; variables of race heterogeneity and family disorganization(divorce rate) in socio-economic environments. This study presented policy implications based on the above analysis results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Spatial Analysis and Policy Springer Journals

Analysis of Influential Factors of Violent Crimes and Building a Spatial Cluster in South Korea

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Publisher
Springer Journals
Copyright
Copyright © Springer Nature B.V. 2019
Subject
Social Sciences; Human Geography; Landscape/Regional and Urban Planning; Regional/Spatial Science
ISSN
1874-463X
eISSN
1874-4621
DOI
10.1007/s12061-019-09327-1
Publisher site
See Article on Publisher Site

Abstract

This study analyzed the spatial distribution of violent crime (murder, robbery, rape, assault, and larceny) in Korea and the relationship between violent crime and the governance, administrative, physical, and socio-economic factors of local communities. The occurrence of violent crime was approached from the perspective of the community, not from a personal perspective, based on the theoretical ecological perspective. In addition, an analysis model (spatial lag model) designed to analyze spillover effect between neighboring communities. For the analysis, this study used the data of 56 sub-local governments of Seoul Metropolitan City and Gyeonggi Province in 2015.The analysis results are as follows: First, this study identified five major violent crime occurrence situations through descriptive statistical analysis. Second, the hot-spot and cold-spot of violent crime were derived through exploratory spatial analysis (Moran's I, LISA). Third, this study derived the relationship between the incidence of violent crime and the governance, administrative, physical, and socioeconomic factors of the community through spatial regression analysis based on the spatial lag model. Specifically, the valid factors influenced on the five major violent as follows: variables of local security council in space effect and governance; variables of crime monitoring facilities and crime agency in administrative capacity; variables of detrimental facilities density in physical environment; variables of race heterogeneity and family disorganization(divorce rate) in socio-economic environments. This study presented policy implications based on the above analysis results.

Journal

Applied Spatial Analysis and Policy – Springer Journals

Published: Sep 27, 2020

References

  • Local indicators of spatial association—LISA
    Anselin, L
  • Spatial dependence in linear regression models with an introduction to spatial econometrics
    Anselin, L; Bera, AK
  • Nodes, paths and edges: Considerations on the complexity of crime and the physical environment
    Brantingham, PL; Brantingham, PJ
  • Anticipating the displacement of crime using the principles of environmental criminology
    Brantingham, PJ; Brantingham, PL
  • Which measures of school quality does the housing market value?
    Brasington, D
  • The paradox of social organization: Networks, collective efficacy, and violent crime in urban neighborhoods
    Browning, CR; Dietz, RD; Feinberg, SL
  • Situational crime prevention
  • “Modeling offenders” decisions: A framework for research and policy
    Clarke, RV; Cornish, DB
  • Violence and local development in Fortaleza, Brazil: A spatial regression analysis
    Oliveira, VH; Medeiros, CN; Carvalho, JR
  • Linear models with spatially distributed data: Spatial disturbances or spatial effects?
    Doreian, P
  • Estimating linear models with spatially distributed data
    Doreian, P
  • Practical handbook of spatial statistics
    Griffith, DA
  • New directions in social disorganization theory
    Kubrin, CE; Weitzer, R
  • Spatial statistics and models
    Miron, J
  • Neighborhood inequality, collective efficacy, and the spatial dynamics of urban violence
    Morenoff, JD; Sampson, RJ; Raudenbush, SW
  • Social change and crime in Russia – A test of institutional anomie theory
    Sang-Weon Kim
  • Juvenile delinquency and urban areas
    Shaw, CR; McKay, HD
  • Deviant places: A theory of the ecology of crime
    Stark, R
  • Neighborhood social capital and crime victimization: Comparison of spatial regression analysis and hierarchical regression analysis
    Takagi, D; Ikeda, KI; Kawachi, I
  • Handbook of quantitative criminology
    Tita, GE; Radil, SM
  • A computer movie simulating urban growth in the Detroit region
    Tobler, WR
  • Burglary victimization in England and Wales, the United States and the Netherlands: A cross-national comparative test of routine activities and lifestyle theories
    Tseloni, A; Wittebrood, K; Farrell, G; Pease, K
  • Broken windows
    Wilson, JQ; Kelling, GL