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

Maryam Amini, Saman Hatamerad, Hosein Mohammadi, Elham Nobahar, Hosein Asgharpour and Ali Afaghi
Additional contact information
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

Quarterly Journal of Applied Theories of Economics, 2025, vol. 12, issue 1, 185-212

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

Keywords: Crime; Non-parametric Bayesian Network; Spatial Causality (search for similar items in EconPapers)
JEL-codes: C11 C31 O18 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ris:qjatoe:021668

Access Statistics for this article

Quarterly Journal of Applied Theories of Economics is currently edited by Sakineh Sojoodi

More articles in Quarterly Journal of Applied Theories of Economics from Faculty of Economics, Management and Business, University of Tabriz Contact information at EDIRC.
Bibliographic data for series maintained by Sakineh Sojoodi ().