EXPLORING ECONOMIC AND SPATIAL DEPENDENCIES OF CRIME RATES IN EUROPE AT THE NUTS-3 LEVEL
Keywords:
Crime, economics, spatial analysis, economic development.Abstract
The paper is focused on the spatial exploratory analysis of data related to crime and economic development in EU on the NUTS-3 level. NUTS is the statistical territorial classification of EU and EUROSTAT and its 3rd level includes the smallest regions. The analysis has three steps. First of all, the most commonly used indicators in studies investigating the relationship crime-economic conditions were identified. In the second stage, after search for these indicators in EUROSTAT NUTS-3 level datasets the research dataset was established. Finally, the data is geographically referenced and tests for spatial dependencies and local correlation of some indicators are introduced. Hierarchical clustering of indicators is used both for 2009 and 2010. The research shows the existence of flows and inequalities of data, as well as absence of data on NUTS-3 level for important indicators, despite their presence on higher levels of the territorial classification. Regardless of these shortcomings, the exploratory spatial analysis generates the idea to continue the research on the relations between infrastructural indicators such as distance to ports and highways and crime rates. The mapping of identified clusters shows the existence of stable geographically formed groups of regions from similar clusters. Another positive result is the possibility to classify, visualize and study the similarities and differences in EU smallest statistical regions.
Downloads
References
Anselin L. et al. (2000), Spatial Analyses of Crime, "Criminal Justice", vol. 4, no. 2, pp. 213–262.
Google Scholar
Arnio A. N., Baumer E. P. (2012), Demography, Foreclosure, and Crime: Assessing Spatial Heterogeneity in Contemporary Models of Neighborhood Crime Rates, "Demographic Research", vol. 26, pp. 449–488.
Google Scholar
Becker G. S. (1968), Crime and Punishment: An Economic Approach, "The Journal of Political Economy", vol. 76, no. 2, pp. 169–217.
Google Scholar
Benson B. L., Zimmerman P. R. (2010), Handbook on the Economics of Crime, Edward Elgar Publishing, Northampton, MA.
Google Scholar
Bjerk D. J. (2006), The Effect of Segregation on Crime Rates, (in:) American Law & Economics Association Annual Meetings, The Berkeley Electronic Press, http://law.bepress.com/cgi/viewcontent.cgi? article=1693&context=alea (access: July 2, 2014).
Google Scholar
Brand S. et al. (2000), The Economic and Social Costs of Crime, Economics and Resource Analysis, Research, Development and Statistics Directorate, Home Office, London.
Google Scholar
Cahill M., Mulligan G. (2007), Using Geographically Weighted Regression to Explore Local Crime Patterns, "Social Science Computer Review", vol. 25, no. 2, pp. 174–193.
Google Scholar
CPWG (2013), Crime and Place Working Group Bibliography, Center for Evidence-Based Crime Policy, Department of Criminology, Law and Society at George Mason University, http://cebcp.org/wp-content/cpwg/Place-Based-Bibliography (May 2, 2014).
Google Scholar
Ehrlich I. (1973), Participation in Illegitimate Activities: A Theoretical and Empirical Investigation, Social Science Research Network, Rochester, NY. SSRN Scholarly Paper, http://papers.ssrn.com/ abstract=961495 (July 17, 2014).
Google Scholar
European Union, EUROSTAT (2011), Regions in the European Union: Nomenclature of Territorial Units for Statistics: NUTS 2010/EU-27, EUR-OP, Luxembourg.
Google Scholar
Feitosa F. F. et al. (2012), Countering Urban Segregation in Brazilian Cities: Policy-Oriented Explorations Using Agent-Based Simulation, "Environment and Planning-Part B", vol. 39, no. 6, p. 1131.
Google Scholar
Freeman R. B. (1999), The Economics of Crime, (in:) Ashenfelter O., Layard R., Card D. (eds.), Handbook of Labor Economics, Elsevier, pp. 3529–3571, http://econpapers.repec.org/bookchap/eeelabchp/3-52.htm (access: July 17, 2014).
Google Scholar
Gollini I. et al. (2013), GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models, arXiv.org, Cornell, University Library, http://arxiv.org/abs/1306.0413 (access: May 23, 2014).
Google Scholar
Lauridsen J. T., Zeren F., Ari A. (2013), A Spatial Panel Data Analysis of Crime Rates in EU, Department of Business and Economics, University of Southern Denmark, http://static.sdu.dk/mediafiles/ A/2/1/%7BA21BF15B-1A84-4BA6-B4A7-3AAA6E3FA6AC%7Ddpbe2_2013.pdf (access: July 10, 2014).
Google Scholar
Leitner M. (2013), Crime Modeling and Mapping Using Geospatial Technologies, Springer Science & Business Media, London.
Google Scholar
Mohler G. O. et al. (2011), Self-Exciting Point Process Modeling of Crime, "Journal of the American Statistical Association", vol. 106, no. 493, pp. 100–108.
Google Scholar
Paradis E. (2009), Moran’s Autocorrelation Coefficient in Comparative Methods, “R Foundation for Statistical Computing, Vienna”, http://star-www.st-andrews.ac.uk/cran/web/packages/ape/vignettes/MoranI.pdf (access: July 16, 2014).
Google Scholar
Patterson E. B. (1991), Poverty, Income Inequality, and Community Crime Rates, "Criminology", vol. 29, no. 4, pp. 755–776.
Google Scholar
Silber J., Fluckiger Y., Reardon S. F. (2009), Occupational and Residential Segregation, Emerald Group Publishing, London.
Google Scholar
Webber A. (2010), Literature Review Cost of Crime, Attorney General & Justice, New Sowth Wales, http://www.crimeprevention.nsw.gov.au/agdbasev7wr/_assets/cpd/m660001l2/cost%20of%20crime%20literature%20review.pdf (access: July 27, 2014).
Google Scholar
Witte A. D., Tauchen H. (1994), Work and Crime: An Exploration Using Panel Data, National Bureau of Economic Research, Cambridge, MA, http://www.nber.org/papers/w4794 (access: July 17, 2014).
Google Scholar
