Crossref DOIMemberCiteFactoropen accessissn
SJIF Impact Factor 8.659 | ISSN: 2582-3930
IJSREM Logo

International Journal of Scientific Research in Engineering and Management

(Gateway to Global Scholars - An International, Scholarly, Multi-Disciplinary, Open Access Journal)

Peer-Reviewed Journal Designed to Meet UGC's Quality Parameters

HomeCurrent IssuePast IssuesPay OnlineConferencesEditorial BoardAbout UsContact Us
IJSREM

Loading article...

Follow Us

  • Google Scholar
  • ResearchGate
  • Academia.edu
  • Mendeley
  • LinkedIn
  • Facebook
  • Instagram

Journal Policies

  • Peer Review Policy
  • Publication Ethics
  • Copyright Infringement
  • Refund & Cancellation Policy
  • Terms and Conditions
  • Privacy Policy

Journal Identifiers & Licenses

  • ISSN Verified: 2582-3930ISSN Verified: 2582-3930
  • Crossref DOI: 10.55041Crossref DOI: 10.55041
  • SJIF Ranking: 08.659SJIF Ranking: 08.659
  • Licensed under CC BY 4.0Licensed under CC BY 4.0
  • ISO Certification: 9001:2015ISO Certification: 9001:2015
  • Indexed & Cited In: Google ScholarIndexed & Cited In: Google Scholar

Contact Us

International Journal of Scientific Research in Engineering and Management (IJSREM)

#62/1, New No 7, 1st Cross, 2nd Main, Ganganagar, R T Nagar, Bangalore North, Bangalore, Karnataka, India – 560032
[email protected]+91 93911 67991
logos
logos
Working Hours

Mon – Sat: 9:00 AM – 6:00 PM

Sunday: 10:00 AM – 4:00 PM

+91 93911 67991

© 2026 IJSREM – International Journal of Scientific Research in Engineering and Management. This is an open-access journal. Articles are published under the Creative Commons Attribution (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

DisclaimerPrivacy PolicyTerms and Conditions
ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM62491

Crime Analytics Pro: An Integrated Machine Learning and Geospatial Intelligence Platform for Predictive Crime Forecasting and Hotspot Detection

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM62491

Volume:

10 |

5 |

May 2026

Pages:

1-8

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM62491

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.

Share this article:

Authors

PAG

Prof. Abhay Gaidhani

Computer Engineering

Sandip Institute Of Technology And Research Centre Nashik Maharashtra

Download Author Certificate
DSR

Darshan Sudhir Revagade

Computer Engineering

Sandip Institute Of Technology And Research Centre Nashik Maharashtra

Download Co-Author Certificate
JAm

Jay Arun mahajan

Computer Engineering

Sandip Institute Of Technology And Research Centre Nashik Maharashtra

Download Co-Author Certificate
AVw

Ajay Vishnu wagh

Computer Engineering

Sandip Institute Of Technology And Research Centre Nashik Maharashtra

Download Co-Author Certificate
HAB

Harshal Annasaheb Bramhane

Computer Engineering

Sandip Institute Of Technology And Research Centre Nashik Maharashtra

Download Co-Author Certificate

Author Services & Downloads

Full ArticlesAcceptance Certificate

Abstract

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

Index Terms—Crime Prediction Machine Learning Random Forest Time Series Forecasting Hotspot Mapping Streamlit Geospatial Analysis Predictive Policing DBSCAN Public Safety Analytics.

Cite This Article

Prof. Abhay Gaidhani, Darshan Sudhir Revagade, Jay Arun mahajan, Ajay Vishnu wagh, Harshal Annasaheb Bramhane, "Crime Analytics Pro: An Integrated Machine Learning and Geospatial Intelligence Platform for Predictive Crime Forecasting and Hotspot Detection," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-8, May 2026. https://doi.org/10.55041/IJSREM62491

Full Article/Conclusion

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].

References

[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.

Editorial & Review Transparency

Publication Date

5/5/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

Documents

Review Report (Confidential)
Copyright Form (Office)

Article Metrics & Impact

Article Views

0

Total views since publication

PDF Downloads

7

Total downloads since publication

Geographic Reach

5

Indexing, Archiving & Preservation

Google Scholar
Academia
ResearchGate
Crossref
SSRN
Semantic Scholar
Thomson Reuters
WorldCat
Mendeley
OpenAlex
EBSCO
Zenodo

Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.

Ethics, Compliance & Transparency

Plagiarism Screening

This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.

Conflict of Interest

The authors declare no conflict of interest related to this research work.

Funding Information

No funding information available for this research.

Similar & Recommended Articles

Similar Articles by Topic

Related research in the same subject area

Loading similar articles...