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Predictive Choropleth Maps Using ARIMA Time Series Forecasting for Crime Rates in Visegrád Group Countries

Author

Listed:
  • Usman Ghani

    (Doctoral School of Regional and Economic Sciences, Szechenyi Istvan Egyetem, Egyetem Ter, 9026 Gyor, Hungary)

  • Peter Toth

    (Doctoral School of Regional and Economic Sciences, Szechenyi Istvan Egyetem, Egyetem Ter, 9026 Gyor, Hungary)

  • Fekete David

    (Doctoral School of Regional and Economic Sciences, Szechenyi Istvan Egyetem, Egyetem Ter, 9026 Gyor, Hungary)

Abstract

Geographical mapping has revolutionized data analysis with the help of analytical tools in the fields of social and economic studies, whereby representing statistical research variables of interest as geographic characteristics presents visual insights. This study employed the QGIS mapping tool to create predicted choropleth maps of Visegrád Group countries based on crime rate. The forecast of the crime rate was generated by time series analysis using the ARIMA (autoregressive integrated moving averages) model in SPSS. The literature suggests that many variables influence crime rates, including unemployment. There is always a need for the integration of widespread data insights into unified analyses and/or platforms. For that reason, we have taken the unemployment rate as a predictor series to predict the future rates of crime in a comparative setting. This study can be extended to several other predictors, broadening the scope of the findings. Predictive data-based choropleth maps contribute to informed decision making and proactive resource allocation in public safety and security administration, including police patrol operations. This study addresses how effectively we can utilize raw crime rate statistics in time series forecasting. Moreover, a visual assessment of safety and security situations using ARIMA models in SPSS based on predictor time-series data was performed, resulting in predictive crime mapping.

Suggested Citation

  • Usman Ghani & Peter Toth & Fekete David, 2023. "Predictive Choropleth Maps Using ARIMA Time Series Forecasting for Crime Rates in Visegrád Group Countries," Sustainability, MDPI, vol. 15(10), pages 1-15, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8088-:d:1148169
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    References listed on IDEAS

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    1. Usman Ghani & Peter Toth & Dávid Fekete, 2022. "Incorporating Survey Perceptions of Public Safety and Security Variables in Crime Rate Analyses for the Visegrád Group (V4) Countries of Central Europe," Societies, MDPI, vol. 12(6), pages 1-19, November.
    2. Huddleston, Samuel H. & Porter, John H. & Brown, Donald E., 2015. "Improving forecasts for noisy geographic time series," Journal of Business Research, Elsevier, vol. 68(8), pages 1810-1818.
    3. Megan C Evans & Christopher Cvitanovic, 2018. "An introduction to achieving policy impact for early career researchers," Palgrave Communications, Palgrave Macmillan, vol. 4(1), pages 1-12, December.
    4. Kourtit, Karima & Nijkamp, Peter & Steenbruggen, John, 2017. "The significance of digital data systems for smart city policy," Socio-Economic Planning Sciences, Elsevier, vol. 58(C), pages 13-21.
    5. Mamta Mittal & Lalit Mohan Goyal & Jasleen Kaur Sethi & D. Jude Hemanth, 2019. "Monitoring the Impact of Economic Crisis on Crime in India Using Machine Learning," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1467-1485, April.
    6. Gorr, Wilpen & Olligschlaeger, Andreas & Thompson, Yvonne, 2003. "Short-term forecasting of crime," International Journal of Forecasting, Elsevier, vol. 19(4), pages 579-594.
    7. Gault, Martha & Silver, Eric, 2008. "Spuriousness or mediation? Broken windows according to Sampson and Raudenbush (1999)," Journal of Criminal Justice, Elsevier, vol. 36(3), pages 240-243, July.
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