As Malaysia faces growing urbanization and rising crime rates, there is an urgent need for smarter, data-driven approaches to manage and mitigate crime. This paper presents the Integrated Crime Risk Assessment System (ICRAS) Framework, designed to transform how we assess and respond to crime risks across the country. By bringing together a mix of crime data, social indicators, and advanced predictive analytics, ICRAS provides an innovative way to identify high-risk areas, track crime trends, and prioritize resources effectively. Built on a scalable data platform, ICRAS integrates multiple data sources to ensure timely updates and flexible risk metrics. With machine learning at its core, this system can analyze patterns across locations, times, and situations, giving law enforcement a dynamic tool for proactive crime prevention. This paper explores the theory behind ICRAS, the rigorous methods used in its development, and the results from pilot testing, showing how it aligns with Malaysia’s national crime prevention goals. Initial findings suggest that ICRAS holds real promise as a game-changer in crime prevention, offering Malaysian authorities a powerful tool to predict and reduce crime and foster safer communities.
Catlett, C., Cesario, E., Talia, D., & Vinci, A. (2019). Spatio-temporal crime predictions in smart cities: A data-driven approach and experiments. Pervasive and Mobile Computing, 53, 62–74.
Hew, W. W. L., Low, B. Y., Goh, G. G. G., & Lau, S. H. (2020). Crime, urban flight and societal wellbeing: A case of Malaysia. Journal of Environmental Treatment Techniques, 8(1), 35–40.
Fitzpatrick, D. J., Gorr, W. L., & Neill, D. B. (2019). Keeping score: Predictive analytics in policing. Annual Review of Criminology, 2(1), 473–491.
Marzbali, M. H., Abdullah, A., & Tilaki, M. J. M. (2019). Crime prevention through environmental design (CPTED) in Malaysia: Development of a tool to measure CPTED implementation in residential settings. In Rebuilding Crime Prevention Through Environmental Design (pp. 153–183). Routledge.
Maidin, M. R., Hussin, M. F., Zakaria, N. A. Z., Ab. Rahim, S. A. E., & Samsudin, K. (2024). The need for integrated crime risk assessment systems using data-driven strategies for crime prevention in Malaysia. International Journal of Academic Research in Business and Social Sciences, 14(11), 996–1005.
Butt, U. M., Letchmunan, S., Hassan, F. H., Ali, M., Baqir, A., & Sherazi, H. H. R. (2020). Spatio-temporal crime hotspot detection and prediction: A systematic literature review. IEEE Access, 8, 166553–166574.
Meier, R. F., Kennedy, L. W., & Sacco, V. F. (2018). The process and structure of crime: Criminal events and crime analysis. Routledge.
Maswod, S. R. (2021). Towards a productive relationship between police and community safety leaders in racialized socially disadvantaged neighbourhoods: Measuring the impact of formal, partnership-based community crime prevention organizations.
Okoro, I., & Yusuf, A. (2024). AI-driven crime prevention strategies: Integrating big data analytics and predictive analytics. Asian American Research Letters Journal, 1(3).
Caplan, J. M., Kennedy, L. W., Drawve, G., & Baughman, J. H. (2021). Data-informed and place-based violent crime prevention: The Kansas City, Missouri risk-based policing initiative. Police Quarterly, 24(4), 438–464.
Syed, S., & Albalawi, E. M. (2024). Transforming law enforcement: Exploiting big data science and data analytics for precision decision-making and crime pattern anticipation in police operations.
Adilah, F. B., Mohd Zahir, M. Z., Mohd. Ali, H., & Hassan, M. S. (2023). A study of Malaysian anti-money laundering law and the impact on public and private sector. Journal of Money Laundering Control, 26(4), 831–844.
Azero, M. A., Abdullah, S. N. A. K., Zakaria, Z., Haris, H., Yusoff, Y. H., & Alam, P. (2024). The nexus of cybercrime and money laundering: A conceptual paper. Accounting and Finance Research, 13(2), 167–167.
Saravanan, P., Selvaprabu, J., Arun Raj, L., Khan, A., & Sathick, K. (2021). Survey on crime analysis and prediction using data mining and machine learning techniques. In Advances in Smart Grid Technology: Select Proceedings of PECCON 2019—Volume II (pp. 435–448). Springer Singapore.
Ahmad, A., Masron, T., Kimura, Y., Barawi, M. H., Jubit, N., Junaini, S. N., ... & Bismelah, L. H. (2024). Unveiling urban violence crime in the state of Selangor, Kuala Lumpur and Putrajaya: A spatial–temporal investigation of violence crime in Malaysia’s key cities. Cogent Social Sciences, 10(1), 2347411.
Daud, N. A., Samsuri, N. A., & Hussein, S. S. (2023, September). JUVDATA: Data visualization of juvenile crime in Malaysia. In International Conference on Mathematics, Statistics, and Computing Technology (pp. 327–349). Singapore: Springer Nature Singapore.
Mussiraliyeva, S., & Baispay, G. (2024). Leveraging machine learning methods for crime analysis in textual data. International Journal of Advanced Computer Science & Applications, 15(4).
Oatley, G. C. (2022). Themes in data mining, big data, and crime analytics. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 12(2), e1432.
Ghazi, G., Zainodin, W. H. W., & Yahya, A. H. (2024). Leveraging VSP community policing apps: Royal Malaysia Police’s strategic communication approach to crime prevention.
Ghazali, A. F., Suhaimi, A., Noor, N. M. M., & Retnowardhani, A. (2023). A framework of spatial decision support system (SDSS) for crime investigations. International Journal of Business and Technology Management, 5(2), 1–7.
Roosli, R. (2017). A Malaysian study of mixed methods: An example of integrating quantitative and qualitative methods. Cambridge Scholars Publishing.
Zainol, M. R., Khamis, K. A., & Zakaria, Z. (2023, December). Unearth the urban street crime in Malaysia: A thematic analysis. In International Conference on Mathematical and Statistical Physics, Computational Science, Education and Communication (ICMSCE 2023) (Vol. 12936, pp. 349–358). SPIE.
McDaniel, J. L., & Pease, K. (Eds.). (2021). Predictive policing and artificial intelligence. Routledge, Taylor & Francis Group.
Maidin, M. R., Hussin, M. F., Zakaria, N. A. Z., Ab.Rahim, S. A. E., & Samsudin, K. (2024). A Comprehensive Framework for Crime Risk Assessment in Malaysia: Integrating Data-Driven Insights for Enhanced Public Safety. International Journal of Academic Research in Business and Social Sciences, 14(11), 2863–2874.
Copyright: © 2024 The Author(s)
Published by Knowledge Words Publications (www.kwpublications.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode
