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Monitoring the Impact of Economic Crisis on Crime in India Using Machine Learning

Author

Listed:
  • Mamta Mittal

    (G B Pant Government Engineering College)

  • Lalit Mohan Goyal

    (Bharti Vidyapeeth’s College of Engineering)

  • Jasleen Kaur Sethi

    (GGSIPU)

  • D. Jude Hemanth

    (Karunya University)

Abstract

Trends of crimes in India keep changing with the growing population and rapid development of towns and cities. The rise in crimes at any place especially crimes against women, children and weaker sections of the society is a worrying factor for the Indian Government. In India, the crime data is maintained by National Crime Records Bureau as well as an application called Crime Criminal Information System is available to make inquiry and generate reports for the crime data. To curb crime, the Police need countless hours to go through the crime data and determine the various factors that affect it. Therefore, there is necessity of tools which can automatically predict the factors that effects the crimes effectively and efficiently. The field of machine learning has emerged in the recent years for this purpose. In this paper, various machine learning techniques have been applied on crime data to monitor the impact of economic crisis on the crime in India. The effect of unemployment rates and Gross District Domestic Product on theft, robbery and burglary has been monitored across districts of various states in India. Further, Granger causality between crime rates and economic indicators has also been calculated. It has been observed from the experimental work that unemployment rate is the major economic factor which affects the crime rate, thus paving the path to control the crime rate by raising more opportunities for the employment.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:compec:v:53:y:2019:i:4:d:10.1007_s10614-018-9821-x
    DOI: 10.1007/s10614-018-9821-x
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    References listed on IDEAS

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    1. Granger, C. W. J., 1988. "Causality, cointegration, and control," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 551-559.
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    Cited by:

    1. Huafang Xie & Lin Liu & Han Yue, 2022. "Modeling the Effect of Streetscape Environment on Crime Using Street View Images and Interpretable Machine-Learning Technique," IJERPH, MDPI, vol. 19(21), pages 1-22, October.
    2. Golnoosh Babaei & Shahrooz Bamdad, 2021. "A New Hybrid Instance-Based Learning Model for Decision-Making in the P2P Lending Market," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 419-432, January.
    3. Natalia Sypion & Pawel Terefenko & Michael Leitner & Marek Dutkowski & Andrzej Lysko & Tomasz M. Komorowski, 2024. "What Determines Crime: Prosperity or Poverty?," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 394-424.
    4. Pritam, Kocherlakota Satya & Sugandha, & Mathur, Trilok & Agarwal, Shivi, 2021. "Underlying dynamics of crime transmission with memory," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    5. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
    6. 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.

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