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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62491
Volume:
10 |
5 |
May 2026
Pages:
1-8
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Engineering
Sandip Institute Of Technology And Research Centre Nashik Maharashtra
Computer Engineering
Sandip Institute Of Technology And Research Centre Nashik Maharashtra
Computer Engineering
Sandip Institute Of Technology And Research Centre Nashik Maharashtra
Computer Engineering
Sandip Institute Of Technology And Research Centre Nashik Maharashtra
Computer Engineering
Sandip Institute Of Technology And Research Centre Nashik Maharashtra
Crime Analytics Pro is an advanced, web-based analytical platform designed to forecast and visualize crime patterns using machine learning (ML), time series forecasting, and geospatial intelligence. The system empowers law enforcement agencies and urban planners with tools to analyze historical crime data, predict future trends, and identify spatial hotspots for proactive intervention. Built on a modular Python architecture using Scikit-learn, Folium, Statsmodels, Plotly, and Streamlit, the platform integrates ensemble ML algorithms (Random Forest, Gradient Boosting), unsupervised clustering (KMeans, DBSCAN), and seasonal time-series decomposition. Evaluation on benchmark crime datasets—including the Chicago Crime Dataset comprising over 7 million records—demonstrates a prediction accuracy of 87.4%, hotspot detection precision of 93.7%, and forecasting reliability with RMSE≤ 5 and R2 ≥0.85. Comparative analysis against CHART [1], Liu et al. ST-GCN [2], and Mandalapu et al. [3] confirms that Crime Analytics Pro achieves superior or competitive performance while uniquely offering a non-technical user interface and a 12-month forecast horizon. By bridging data science and public safety, this work addresses key research gaps including the lack of unified, interpretable, and user-accessible crime intelligence platforms for real-world law enforcement use.
KEYWORDS
Crime Analytics Pro demonstrates the effective integration of ML, geospatial intelligence, and time series forecasting in a scalable, interpretable, and user-accessible crime prediction plat-form. Key contributions include: (1) supervised ML achieving 87.4% prediction accuracy; (2) DBSCAN-based hotspot detec-tion with 93.7% precision; (3) time series forecasting up to 12 months with R2≥0.85; (4) automated preprocessing pipelines handling datasets exceeding 900,000 records; and (5) Streamlit dashboards rated 4.7 / 5 by non-technical users. All defined suc-cess criteria are met. The platform enables law enforcement to shift from reactive policing to evidence-based proactive crime prevention, contributing meaningfully to the goal of data-driven urban governance [11].
[1] A. Al-Shehari, R. Alsini, H. Al-Qudah, N. L. Fitriyani, M. Ghulam, and M. Syafrudin, “CHART: Intelligent crime hotspot detection and real-time tracking using machine learning,” Computers, Materials & Continua, vol. 81, no. 3, pp. 4287–4308, 2024. [2] M. Liu, L. Wu, Y. Yang, and S. Li, “Research on a crime spatiotemporal prediction method integrating informer and ST-GCN: A case study of four crime types in chicago,” Big Data and Cognitive Computing, vol. 8, no. 7, p. 179, 2024. V. Mandalapu, L. Elluri, P. Vyas, and N. Roy, “Crime prediction using machine learning and deep learning: A systematic review and future directions,” IEEE Access, vol. 11, pp. 60470–60489, 2023. Y. Chen, J. Li, X. Wang, and J. Zhang, “A systematic review of multi-scale spatio-temporal crime prediction methods,” ISPRS International Journal of Geo-Information, vol. 12, no. 6, p. 209, 2023. [5] M. Yasir, M. Habib, W. Ahmad, and M. Y. Khan, “Em-pirical analysis for crime prediction and forecasting using machine learning and deep learning techniques,” IEEE Ac-cess, vol. 11, pp. 43468–43485, 2023. [6] J. Bernardino-Angulo, J. P. Escobedo-Cabello, E. Báez-García, and R. Tejeida-Padilla, “Deep learning for crime forecasting of multiple regions, considering spatial–temporal correlations between regions,” Computers, vol. 13, no. 6, p. 144, 2024. [7] X. Wang, Y. Chen, Y. Liu, and H. Xiong, “Interpretable ma-chine learning models for crime prediction,” Information Sciences, vol. 634, pp. 141–158, 2023. [8] S. Ahmad, I. Gondal, J. Kamruzzaman, and W. Mahmood, “Deep learning based crime prediction models: Exper-iments and analysis.” arXiv preprint arXiv:2407.19324, 2024. [9] Z. Wang, Y. Li, X. Su, G. Gao, P. Wang, and Y. Liang, “Uncertainty-aware crime prediction with spatial temporal multivariate graph neural networks,” in Proc. 33rd ACM Int. Conf. Information and Knowledge Management (CIKM 2024), 2024. arXiv:2408.04193. [10] X. Li, J. Li, J. Li, and Y. Li, “Research on crime risk prediction model based on machine learning,” in Proc. 13th Int. Conf. Computing and Pattern Recognition (ICCPR 2024), pp. 325–330, 2024.
Publication Date
5/5/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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