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.
Similar content being viewed by others
Notes
In order to measure spatial autocorrelation, a spatial weight matrix must be established that defines the adjacent relationships between regions. There are three methods for building spatial weight matrices: contiguity, distance, and k-nearest neighbors. In this study, spatial weight matrix using contiguity was built, and the method of QUEEN in detail, the adjacent dimension was set to be set at 1.
References
Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.
Anselin, L., & Bera, A. K. (1998). Spatial dependence in linear regression models with an introduction to spatial econometrics. Statistics Textbooks and Monographs, 155, 237–290.
Beck, U. (2008). Korea is a “very special” dangerous society. Chosun News 2008. 4.1 01: 19. http://news.chosun.com/site/data/html_dir/2008/04/01/2008040100139.html
Brantingham, P. L., & Brantingham, P. J. (1993). Nodes, paths and edges: Considerations on the complexity of crime and the physical environment. Journal of Environmental Psychology, 13(1), 3–28.
Brantingham, P. J., & Brantingham, P. L. (2003). Anticipating the displacement of crime using the principles of environmental criminology. Crime Prevention Studies, 16, 119–148.
Brasington, D. (1999). Which measures of school quality does the housing market value? Journal of Real Estate Research, 18(3), 395–413.
Browning, C. R., Dietz, R. D., & Feinberg, S. L. (2004). The paradox of social organization: Networks, collective efficacy, and violent crime in urban neighborhoods. Social Forces, 83(2), 503–534.
Cheong, J-S., & Wook, K. (2013). Causal relationship between structural characteristics of metropolitan neighborhoods and homicide. The Korea Contents Association, 13(3), 152–161.
Clarke, R. V. G. (Ed.). (1997). Situational crime prevention (pp. 53–70). Monsey: Criminal Justice Press.
Clarke, R. V., & Cornish, D. B. (1985). “Modeling offenders” decisions: A framework for research and policy. Crime and Justice, 6, 147–185.
Cohen, L. E., & Felson, M. (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 588–608.
de Oliveira, V. H., de Medeiros, C. N., & Carvalho, J. R. (2017). Violence and local development in Fortaleza, Brazil: A spatial regression analysis. Applied Spatial Analysis and Policy, 12(1), 147–166.
Doreian, P. (1980). Linear models with spatially distributed data: Spatial disturbances or spatial effects? Sociological Methods & Research, 9(1), 29–60.
Doreian, P. (1981). Estimating linear models with spatially distributed data. Sociological Methodology, 12, 359–388.
Griffith, D. A. (1996). The need for spatial statistics. In Practical handbook of spatial statistics.
Guerry, A. M. (1833). Essai sur la statistique morale de la France. Clearwater.
Jeffery, C. R. (1971). Crime prevention through environmental design (Vol. 91). Beverly Hills, CA: Sage Publications.
Kim, J-O., & Seong Y-E. (2006). A study on the influence between noxious environment and juvenile delinquency. Korean Criminal Psychology Review, 2, 203–225.
Kubrin, C. E., & Weitzer, R. (2003). New directions in social disorganization theory. Journal of Research in Crime and Delinquency, 40(4), 374–402.
Kyeong-Seok JEONG. (2010). The Spatio-temporal patterns of urban crime and its influencing factors using integrated model of spatial crime analysis. Doctoral dissertation of Gyeongsang National University, 151.
Lee, H.-Y., & Noh, S. C. (2013). Advanced statistical analysis: Theory and practice. Moonwoosa.
Miron, J. (1984). Spatial autocorrelation in regression analysis: A beginner’s guide. In Spatial statistics and models (pp. 201–222). Dordrecht: Springer.
Morenoff, J. D., Sampson, R. J., & Raudenbush, S. W. (2001). Neighborhood inequality, collective efficacy, and the spatial dynamics of urban violence. Criminology, 39(3), 517–558.
Newman, O. (1972). Defensible space (p. 264). New York: Macmillan.
Quetelet, A. (1835). Physique sociale, ou essai sur le développement des facultés de l’homme (Vol. 2). C. Muquardt.
Sang-Weon Kim. (2006). Social change and crime in Russia – A test of institutional anomie theory. Korean Journal of Sociology, 40(4), 223–254.
Park, S-H. (2011). Assessing the criminal opportunities and neighborhood effects on household victimization. Korean Criminological Review, 327–357.
Shaw, C. R., & McKay, H. D. (1972). Juvenile delinquency and urban areas (Revised ed.).
Stark, R. (1987). Deviant places: A theory of the ecology of crime. Criminology, 25(4), 893–910.
Takagi, D., Ikeda, K. I., & Kawachi, I. (2012). Neighborhood social capital and crime victimization: Comparison of spatial regression analysis and hierarchical regression analysis. Social Science & Medicine, 75(10), 1895–1902.
Tita, G. E., & Radil, S. M. (2010). Spatial regression models in criminology: Modeling social processes in the spatial weights matrix. In Handbook of quantitative criminology (pp. 101–121). New York: Springer.
Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(sup1), 234–240.
Tseloni, A., Wittebrood, K., Farrell, G., & Pease, K. (2004). Burglary victimization in England and Wales, the United States and the Netherlands: A cross-national comparative test of routine activities and lifestyle theories. British Journal of Criminology, 44(1), 66–91.
Wilson, J. Q., & Kelling, G. L. (1982). Broken windows. Atlantic Monthly, 249(3), 29–38.
Zimbardo, P. G. (1969). The human choice: Individuation, reason, and order versus deindividuation, impulse, and chaos. In Nebraska symposium on motivation. University of Nebraska Press.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Lee, D., Lee, D. Analysis of Influential Factors of Violent Crimes and Building a Spatial Cluster in South Korea. Appl. Spatial Analysis 13, 759–776 (2020). https://doi.org/10.1007/s12061-019-09327-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12061-019-09327-1