The Role of Data Analytics in Crime Prediction and Prevention

  • Authors

    • Divya Sharma HR Manager, Cognizant, India. Author
    • Naveen Kumar Product Manager, Zoho Corporation, India. Author

    DOI:

    https://doi.org/10.67228/30715636/IJETMR-2021PII3D1R

    Published 10-05-2021

  • Crime Prediction, Predictive Policing, Data Analytics, Machine Learning, Crime Prevention, Big Data, Geographic Information Systems, Smart Cities, Artificial Intelligence, Law Enforcement Analytics

    Issue

    Vol. 4 No. 2 (2021)

    Section

    Articles

    How to Cite

    [1]
    D. Sharma and N. Kumar, “The Role of Data Analytics in Crime Prediction and Prevention”, IJETMR, vol. 4, no. 2, pp. 01–17, Oct. 2021, doi: 10.67228/30715636/IJETMR-2021PII3D1R.

  • Abstract

    Crime remains a persistent societal challenge affecting safety, economic growth, and social stability worldwide. With rapid digitalization and the expansion of available data, data analytics has emerged as a powerful tool for enhancing crime prediction and prevention. By analyzing historical crime records, demographic and geographic data, behavioral patterns, and real-time sensor inputs, predictive systems can identify trends and forecast potential criminal activity. This paper examines analytical frameworks and computational methods used in predictive policing, including machine learning, statistical modeling, geographic information systems (GIS), clustering techniques, neural networks, and deep learning architectures. Unlike traditional reactive policing models, predictive analytics supports proactive strategies by identifying high-risk locations, time patterns, and potential offenders in advance. The study also addresses critical ethical concerns such as privacy, algorithmic bias, transparency, and accountability. Findings indicate that data-driven policing improves hotspot identification, resource allocation, and response efficiency compared to conventional approaches. The paper concludes by emphasizing the importance of integrated, scalable systems and highlights future research directions involving artificial intelligence and IoT-based surveillance for smart city crime prevention.  systems, explainable AI models, and privacy-protective data analytics mechanisms.

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