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Spatial interaction of crime incidents in Japan


  • Kakamu, Kazuhiko
  • Polasek, Wolfgang
  • Wago, Hajime


We analyze the development of 18 types of criminal records in Japan for the period 1991–2001 across 47 prefectures with spatial lag and spatio-temporal heteroscedasticity. We explore the hypothesis that crime data are related to socio-economic variables in Japan. We extend the Bayesian approach of LeSage [J.P. LeSage, Bayesian estimation of spatial autoregressive models, Int. Regional Sci. Rev. 20 (1997) 113–129] for spatio-temporal Bayesian models. Additionally we analyze unobserved heteroscedasticity in the panel model by variance inflation factors as in Geweke [J. Geweke, Bayesian treatment of the independent Student-t linear model, J. Appl. Econ. 8 (1993) 19–40]. Positive and significant spatial dependencies can be found for 12 types of crimes and the influence of the socio-economic variables varies over the type of crimes.

Suggested Citation

  • Kakamu, Kazuhiko & Polasek, Wolfgang & Wago, Hajime, 2008. "Spatial interaction of crime incidents in Japan," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(2), pages 276-282.
  • Handle: RePEc:eee:matcom:v:78:y:2008:i:2:p:276-282 DOI: 10.1016/j.matcom.2008.01.019

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    References listed on IDEAS

    1. Gary S. Becker, 1974. "Crime and Punishment: An Economic Approach," NBER Chapters,in: Essays in the Economics of Crime and Punishment, pages 1-54 National Bureau of Economic Research, Inc.
    2. Holloway, Garth & Shankar, Bhavani & Rahman, Sanzidur, 2002. "Bayesian spatial probit estimation: a primer and an application to HYV rice adoption," Agricultural Economics, Blackwell, vol. 27(3), pages 383-402, November.
    3. Chib, Siddhartha & Greenberg, Edward, 1995. "Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models," Journal of Econometrics, Elsevier, vol. 68(2), pages 339-360, August.
    4. Geweke, J, 1993. "Bayesian Treatment of the Independent Student- t Linear Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 19-40, Suppl. De.
    5. Cornwell, Christopher & Trumbull, William N, 1994. "Estimating the Economic Model of Crime with Panel Data," The Review of Economics and Statistics, MIT Press, vol. 76(2), pages 360-366, May.
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    Cited by:

    1. Seya, Hajime & Tsutsumi, Morito & Yamagata, Yoshiki, 2012. "Income convergence in Japan: A Bayesian spatial Durbin model approach," Economic Modelling, Elsevier, vol. 29(1), pages 60-71.
    2. Yutaka Hamaoka, 2009. "Spatial Diffusion of Innovation: A Spatial Panel Analysis of Electronic Toll Collecting Transponders in Japan," Keio/Kyoto Joint Global COE Discussion Paper Series 2009-017, Keio/Kyoto Joint Global COE Program.
    3. Kakamu, Kazuhiko & Yunoue, Hideo & Kuramoto, Takashi, 2014. "Spatial patterns of flypaper effects for local expenditure by policy objective in Japan: A Bayesian approach," Economic Modelling, Elsevier, vol. 37(C), pages 500-506.
    4. Kenichi Mizobuchi & kazuhiko kakamu, 2007. "Simulation Studies on the CO2 Emission Reduction Efficiency in Spatial Econometrics: A case of Japan," Economics Bulletin, AccessEcon, vol. 18(4), pages 1-9.
    5. Liu, Shuangzhe & Ma, Tiefeng & Polasek, Wolfgang, 2014. "Spatial system estimators for panel models: A sensitivity and simulation study," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 101(C), pages 78-102.


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