The Effect of Climatic Elements on Creating Criminal Opportunities for the Theft (Case Study: Bandar Anzali)

Document Type : Original Article

Authors

1 Associate Professor, Department of Geography, Amin Comprehensive University of Police Sciences, Tehran, Iran.

2 Assistant Professor, Department of Basic Sciences, Amin Comprehensive University of Police Sciences, Tehran, Iran.

10.22124/gscaj.2025.25383.1265

Abstract

This study examined the impact of weather on theft crime and its spatial distribution. Crime occurs when offenders and potential targets converge. The research highlighted the role of climatic factors in creating opportunities for theft in the coastal city of Bandar Anzali between 2016 and 2020. The study adopted a descriptive-analytical approach with a spatiotemporal analysis perspective. The unit of analysis included all burglary data collected from the Police 110 database and climatic data obtained from a reliable meteorological source. Statistical analyses were conducted using ArcMap software. The Average Nearest Neighbor (ANN) index was employed to analyze the spatial distribution of theft data, while the Inverse Distance Weighting (IDW) interpolation method was used for crime mapping. Additionally, the Kernel Density Estimation (KDE) function was applied to estimate and predict theft crime density. The study also utilized the Ordinary Least Squares (OLS) model. The findings revealed that the highest occurrence of theft was in Zone 7 of Anzali, while the lowest was in Zone 1 of Ghāziān. The summarized results of the OLS regression model showed that climatic variables influenced theft crime, with some coefficients being positive and others negative. For instance, during spring, positive coefficients for minimum temperature were associated with an increase in theft, while negative coefficients for maximum temperature corresponded to a decrease in theft. Considering the Variance Inflation Factor (VIF) values, which were below 7.5, the regression model demonstrated reliable predictions for some variables. To assess the compatibility of climatic variables with theft crime in the geographical space, the results of the Breusch-Pagan (BP) test indicated that in all seasons, the p-value was greater than 0.05, showing the model's compatibility with the variables. This study clearly revealed that offenders adapt their criminal behaviors to the climatic conditions they encounter.

Keywords

Main Subjects


Asghari, Azad, Vaisiyan, Mohammad, & Akbari, Mahnaz. (2015). The effect of climate on the rate of theft crimes: A case study of Qorveh city. Seventh National Conference on Urban Planning and Management with Emphasis on Urban Development Strategies, Mashhad.[ in Persian]
Borna, Reza, Bakhtiarpour, Ali, Parvazi, Mahnaz, & Ghasemnia, Nabiollah. (2015). Investigating the impact of climatic elements on crime rates in Khuzestan Province. Police Geography Research Journal, 3(11), pp. 99–132. .[ in Persian]
Ebadi Nejad, Seyed Ali, & Bahoush, Mohammad. (2013). Environmental factors affecting the prevention of home burglary. Social Order Quarterly, 4(3), pp. 105–125. .[ in Persian]
Asrare, Abdolrahman, & Shambiati, Houshang. (2013). Environmentally influenced behaviors and strategies for controlling criminal phenomena. Azad Legal Research Journal, 6(19), pp. 1–30. .[ in Persian]
Khosravi Nia, Babak, & Moghli, Marzieh. (2011). The impact of climate on crime: A case study of Larestan. Physical Geography, 4(11), pp. 63–74. .[ in Persian]
Ziyari, Keramatollah, & Rokhsari, Hamid. (2019). Investigating the factors affecting hotel site selection and its spatial reflections in urban areas: A case study of Bandar Anzali city. Urban and Regional Geography and Planning, 9(32), pp. 145–170.[ in Persian]
Eskandari Nodeh, Mohammad, & Khoshdelan, Mojgan. (2012). Spatial analysis of population distribution and service distribution in Bandar Anzali city using TOPSIS model. Geography and Environmental Sustainability, 2(2), pp. 25–44. .[ in Persian]
Zeynali, Saie, Hoseinali, Farhad, Sadeghi Niaraki, Abolghasem, Kazemi Bidokhti, Mohammad, & Efti, Meysam. (2015). Spatial analysis of accidents at urban intersections using spatial autocorrelation methods and kernel density estimation. Geospatial Information Engineering, 3(2), pp. 21–42. .[ in Persian]
Iran Statistics Center Portal. .[ in Persian]
Abraham, J., & Ceccato, V. (2022). Crime and safety in rural areas: A systematic review of the English-language literature 1980–2020.Journal of Rural studies, 94, 250_273.
. https://doi.org/10.1016/j.jrurstud.2022.05.010 
Andresen, M. A., & Malleson, N. (2013). Crime seasonality and its variations across space. Applied Geography, 43, 25–35. https://doi.org/10.1016/j.apgeog.2013.06.007
  Anselin, L., & Arribas-Bel, D. (2013). Spatial fixed effects and spatial dependence in a single cross-section. Papers in Regional Science, 92(1), 3–17. https://doi.org/10.1111/j.1435-5957.2012.00480.x
 Blakeslee, D., Chaurey, R., Fishman, R., Malghan, D., & Malik, S. (2021). In the heat of the moment: Economic and non-economic drivers of the weather-crime relationship. Journal of Economic Behavior & Organization, 192. https://doi.org/10.1016/j.jebo.2021.11.003
  Breetzke, G. D., Polaschek, D. L. L., & Curtis-Ham, S. (2019). Does crime count? Investigating the association between neighbourhood-level crime and recidivism in high-risk parolees. Applied Geography, 102, 20–27. https://doi.org/10.1016/j.apgeog.2018.11.002
  Burkhardt, J., Bayham, J., Wilson, A., Carter, E., Berman, J. D., O'Dell, K., Ford, B., Fischer, E. V., & Pierce, J. R. (2019). The effect of pollution on crime: Evidence from data on particulate matter and ozone. Journal of Environmental Economics and Management, 98. https://doi.org/10.1016/j.jeem.2019.102267
  Chambru, C. (2020). Weather shocks, poverty and crime in 18th-century Savoy. Explorations in Economic History. https://doi.org/10.1016/j.eeh.2020.101353
  Cohen, J. G., Gorr, W. W., & Durso, C. M. (2003). Estimation of crime seasonality: A cross-sectional extension to time series classical decomposition. Working paper, H. John Heinz III School of Public Policy and Management, Carnegie Mellon University.
  Cohen, L. E., & Felson, M. (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 44(4), 588–608. https://doi.org/10.2307/2094589
  Crank, J. P., & Jacoby, L. S. (2015). Modeling the relationship between global warming, violence, and crime. In Crime, Violence, and Global Warming. Anderson Publishing Ltd. https://doi.org/10.1016/B978-0-323-26509-6.00004-8
  Fang, C., Liu, H., Li, G., Sun, D., & Miao, Z. (2015). Estimating the impact of urbanization on air quality in China using spatial regression models. Sustainability, 7(11), 15570–15592.
  Felson, M., & Boba, R. (2010). Crime and everyday life (4th ed.). Los Angeles, CA: Sage Publications.
Garg, T., McCord, G. C., & Montfort, A. (2018). Losing your cool: Psychological mechanisms in the temperature-crime relationship in Mexico. Working Paper.
Güneyli, H., & Ahmed, S. M. S. (2023). Detecting abnormal seismic activity areas of Anatolian plate and deformation directions using Python Geospatial libraries. Heliyon, 9 (3).
https://doi.org/10.1016/j.heliyon.2023.e14394
Hart, R., Pedersen, W., & Skardhamar, T. (2022). Blowing in the wind? Testing the effect of weather on the spatial distribution of crime using Generalized Additive Models. Crime Science, 11 (9).
https://doi.org/10.1186/s40163-022-00171-2
Heilmann, K., Kahn, M., & Tang, J. (2021). The urban crime and heat gradient in high and low poverty areas. Journal of Public Economics, 197. https://doi.org/10.1016/j.jpubeco.2021.104408
Hofer, I. M. J., Hart, A. J., Martín-Vega, D., & Hall, J. R. (2020). Estimating crime scene temperatures from nearby meteorological station data. Forensic Science International, 306.
https://doi.org/10.1016/j.forsciint.2019.110028
Hou, K., Zhang, L., Xu, X., Feng, Y., Baozhang, C., Hu, W., & Shu, R. (2023). High ambient temperatures are associated with urban crime risk in Chicago. Science of The Total Environment, 856 (Part 1). https://doi.org/10.1016/j.scitotenv.2022.158846
Crank, J. P., & Jacoby, L. S. (2015). Crime, violence, and global warming. Anderson Publishing, Ltd. https://doi.org/10.1016/B978-0-323-26509-6.00004-8
Jones, B. (2022). Dust storms and violent crime. Journal of Environmental Economics and Management, 111. https://doi.org/10.1016/j.jeem.2021.102590
Lewis, L. T., & Alford, J. J. (1975). The influence of season on assault. The Professional Geographer, 27 (2), 214–217.
Li, J., & Zhan, Z. (2022). Impact of COVID-19 travel-restriction policies on road traffic accident patterns with emphasis on cyclists: A case study of New York City. Accident Analysis & Prevention, 167, 1–15. https://doi.org/10.1016/j.aap.2022.106586
Lin, X., Zhang, J., & Jiang, S. (2022). Spatial and temporal correlations of crime in Detroit: Evidence from spatial dynamic panel data models. International Review of Law and Economics, 72
. https://doi.org/10.1016/j.irle.2022.106100
Linning, S. J. (2015). Crime seasonality and the micro-spatial patterns of property crime in Vancouver, BC and Ottawa, ON. Journal of Criminal Justice, 43(6), 544–555. https://doi.org/10.1016/j.jcrimjus.2015.05.007
Maiti, A., Zhang, Q., Sannigrahi, S., Pramanik, S., Chakraborti, S., Cerda, A., & Pilla, F. (2021). Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States. Sustainable Cities and Society, 68. https://doi.org/10.1016/j.scs.2021.102784
McDowall, D., Loftin, C., & Pate, M. (2012). Seasonal cycles in crime, and their variability. Journal of Quantitative Criminology, 28, 389–410. https://doi.org/10.1007/s10940-011-9145-7
Non-economic factors in violence: Evidence from organized crime, suicides and climate in Mexico. Journal of Economic Behavior & Organization, 168. https://doi.org/10.1016/j.jebo.2019.10.021
Oshan, T. M., Smith, J. P., & Fotheringham, A. S. (2020). Targeting the spatial context of obesity determinants via multiscale geographically weighted regression. International Journal of Health Geographics, 19, 1–17. https://doi.org/10.1186/s12942-020-00204-6
Quetelet, L. A. J. (1842). A treatise on man and the development of his faculties (pp. 82–96). Trans. Dr. R. Know, FRSE. Edinburgh: W. and R. Chambers. In M. A. Andresen, P. J. Brantingham, & J. B. Kinney (Eds.), Classics in Environmental Criminology (pp. 29–75). Boca Raton, FL: CRC Press.
Ranson, M. (2014). Crime, weather, and climate change. Journal of Environmental Economics and Management, 67(3), 274–302. https://doi.org/10.1016/j.jeem.2013.11.008
Shen, B., Hu, X., & Wu, H. (2020). Impacts of climate variations on crime rates in Beijing, China. Science of The Total Environment, 725, 1–20. https://doi.org/10.1016/j.scitotenv.2020.138190
Shiode, N., Shiode, S., & Nishi, H. (2023). Seasonal characteristics of crime: An empirical investigation of the temporal fluctuation of the different types of crime in London. Computational Urban Science, 3, Article 19. https://doi.org/10.1007/s43762-023-00094-x
Sorg, E. T., & Taylor, R. B. (2011). Community-level impacts of temperature on urban street robbery. Journal of Criminal Justice, 39(6), 463–470. https://doi.org/10.1016/j.jcrimjus.2011.08.004
Wang, J., Yu, Z., Lei, Z., Long, S., & Shaogang, Z. (2022). Study on the critical factors and hot spots of crude oil tanker accidents. Ocean & Coastal Management, 217, 1–20.
https://doi.org/10.1016/j.ocecoaman.2021.106010
White, J. (2014). Crime rates could rise as climate change bites. New Scientist, 221(2959), Page 12. https://doi.org/10.1016/S0262-4079(14)60464-4
Xu, R., Xion, X., Abramson, M., Yuming, G., & Jshanshan, L. (2020). Ambient temperature and intentional homicide: A multi-city case-crossover study in the US. Environment International, 143, Article 105992. https://doi.org/10.1016/j.envint.2020.105992