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Spatial interaction of crime incidents in Japan

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

Abstract

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

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    Cited by:

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    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. Alejandro Puerta & Andr'es Ram'irez-Hassan, 2020. "Inferring hidden potentials in analytical regions: uncovering crime suspect communities in Medell\'in," Papers 2009.05360, arXiv.org.
    5. Lin, Xu & Zhang, Jihu & Jiang, Shanhe, 2022. "Spatial and temporal correlations of crime in Detroit: Evidence from spatial dynamic panel data models," International Review of Law and Economics, Elsevier, vol. 72(C).
    6. 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.
    7. Ma, Yingying & Guo, Shaojun & Wang, Hansheng, 2023. "Sparse spatio-temporal autoregressions by profiling and bagging," Journal of Econometrics, Elsevier, vol. 232(1), pages 132-147.
    8. 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.
    9. Leiva, Mauricio & Vasquez-Lavín, Felipe & Ponce Oliva, Roberto D., 2020. "Do immigrants increase crime? Spatial analysis in a middle-income country," World Development, Elsevier, vol. 126(C).
    10. Ma, Yingying & Lan, Wei & Zhou, Fanying & Wang, Hansheng, 2020. "Approximate least squares estimation for spatial autoregressive models with covariates," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
    11. Puerta-Cuartas, Alejandro & Ramírez-Hassan, Andrés, 2025. "A spatial one-sided error model to identify where unarrested criminals live," Economic Modelling, Elsevier, vol. 142(C).
    12. Zhijian Wang & Yunquan Song, 2024. "Privacy-preserving parametric inference for spatial autoregressive model," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 33(3), pages 877-896, September.
    13. Morito Tsutsumi & Hajime Seya, 2008. "Measuring the impact of large‐scale transportation projects on land price using spatial statistical models," Papers in Regional Science, Wiley Blackwell, vol. 87(3), pages 385-401, August.

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