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Parameters Affecting Crime in Iran with non-Parametric Bayesian Network Approach and Spatial Causality

Author

Listed:
  • Maryam Amini

    (PhD in Economics, Department of Economics, University of Isfahan, Isfahan, Iran)

  • Saman Hatamerad

    (Assistant Professor, Department of Economics, University of Zanjan, Zanjan)

  • Hosein Mohammadi

    (PhD Student in Economics, Faculty of Economics and Management, University of Tabriz, Tabriz, Iran)

  • Elham Nobahar

    (Associate Professor of Economics, Department of Economics, Faculty of Economics and Management, University of Tabriz, Tabriz, Iran)

  • Hosein Asgharpour

    (Professor of Economics, Faculty of Economics and Management, University of Tabriz, Tabriz, Iran)

  • Ali Afaghi

    (PhD in Electrical Engineering, Department of Electrical Engineering, University of Tabriz, Tabriz, Iran)

Abstract

One of the challenges of the last century, particularly in developing countries, has been the significant increase in crime rates. Iran, as a developing country, is not exempt from this rule. In this regard, this research investigates the socio-economic parameters affecting theft in 429 cities in Iran in 2015. For this purpose, five socio-economic indicators have been used, including the unemployment rate, industrialization rate, economic participation rate, divorce rate, and urbanization rate. In this study, two non-parametric Bayesian network models and spatial causality have been used. The results of Bayesian non-parametric network analysis show that except for the rate of economic participation, other variables have a direct effect on theft. On the other hand, the most important variables affecting theft are the urbanization rate and unemployment rate. The results obtained from the spatial causality method also confirm those of the Bayesian non-parametric network method, taking into account the spatial effects and the neighborhood between cities

Suggested Citation

  • Maryam Amini & Saman Hatamerad & Hosein Mohammadi & Elham Nobahar & Hosein Asgharpour & Ali Afaghi, 2025. "Parameters Affecting Crime in Iran with non-Parametric Bayesian Network Approach and Spatial Causality," Quarterly Journal of Applied Theories of Economics, Faculty of Economics, Management and Business, University of Tabriz, vol. 12(1), pages 185-212.
    • Handle: RePEc:ris:qjatoe:021668
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    Keywords

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    JEL classification:

    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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