Published on: April 2026
CRIMEWATCH AI: A REAL-TIME PREDICTIVE CRIME MAPPING AND PUBLIC SAFETY SYSTEM USING MACHINE LEARNING AND NLP
Rooben RS Moorthy M Venthan K Mahadeepak Kabi Bala B
Saranya L
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Abstract
CrimeWatch AI is a sophisticated intelligent system that utilizes Machine Learning (ML), Natural Language Processing (NLP), and geospatial analytics to enhance public safety in urban and rural areas. Through the combination of structured crime data and unstructured textual information such as police reports, news articles, and social media content, the system identifies concealed patterns in behavior that can lead to potential criminal activity. The system utilizes supervised learning algorithms such as Random Forest and Logistic Regression to detect crime-prone areas with accuracy and generate predictive insights for proactive intervention. Structured data analysis is used alongside NLP techniques, including tokenization, NER, sentiment analysis, and topic modeling for extracting meaningful contextual information from text. Using a locally deployed Large Language Model (LLM), the system extracts structured data such as crime type, location and severity from inputs in natural language. This integration improves the system's ability to identify emerging trends, identify critical entities, and capture spatial and temporal patterns in criminal activity. It also uses clustering techniques for hotspot detection and interactive graphing with tools for visualization of results (e.g, interactive heatmaps and trend graph).) Users and law enforcement agencies can easily understand the intricate data presented by CrimeWatch AI, thanks to its intuitive visualization dashboard. This is a major factor in their decision to use this technology. Integrating predictive analytics with real-time risk assessment, the system offers safe route recommendations to enhance navigation security. Experimental results indicate that the proposed system significantly improves prediction accuracy, enhances situational awareness, and enables data-driven decision-making. Generally speaking, CrimeWatch AI is a flexible and adaptable tool for contemporary crime analysis, with potential applications in smart city infrastructure, real-time surveillance systems, and intelligent public safety management.Keywords: Crime prediction, machine learning, NLP, predictive analytics, crime mapping, data mining; public safety systems; hotspot detection; artificial intelligence; and smart surveillance.
How to Cite this Paper
RS, R., M, M., Mahadeepak, V. K. & B, K. B. (2026). Crimewatch AI: A Real-Time Predictive Crime Mapping and Public Safety System using Machine Learning and NLP. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.573
RS, Rooben, et al.. "Crimewatch AI: A Real-Time Predictive Crime Mapping and Public Safety System using Machine Learning and NLP." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.573.
RS, Rooben,Moorthy M,Venthan Mahadeepak, and Kabi B. "Crimewatch AI: A Real-Time Predictive Crime Mapping and Public Safety System using Machine Learning and NLP." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.573.
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References
[1] Y. Wang, L. Zhang, and X. Li, “Crime prediction using machine learning and data mining techniques,” IEEE Access, vol. 6, pp. 12345–12356, 2018.[2] H. Chen, W. Chung, J. Xu, G. Wang, Y. Qin, and M. Chau, “Crime data mining: A general framework and some examples,” Computer, vol. 37, no. 4, pp. 50–56, 2004.
[3] D. Jurafsky and J. H. Martin, Speech and Language Processing, 3rd ed. Pearson, 2020.
[4] T. Mikolov et al., “Efficient estimation of word representations in vector space,” arXiv preprint arXiv:1301.3781, 2013.
[5] J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proc. NAACL-HLT, 2019, pp. 4171–4186.
[6] T. Brown et al., “Language models are few-shot learners,” in Advances in Neural Information Processing Systems (NeurIPS), 2020.
[7] L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
[8] F. Pedregosa et al., “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
[9] M. Ester, H. Kriegel, J. Sander, and X. Xu, “A density-based algorithm for discovering clusters in large spatial databases,” in Proc. KDD, 1996, pp. 226–231.
[10] R. Kitchin, “The real-time city? Big data and smart urbanism,” GeoJournal, vol. 79, no. 1, pp. 1–14, 2014.
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