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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63438
Volume:
10 |
5 |
May 2026
Pages:
1-5
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.
MASTER OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERING TECHNOLOGY
MASTER OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERING TECHNOLOGY
A Python-based analytical tool called the Crime Data Explorer and Hotspot Detector was created to effectively analyze, visualize, and explain crime data from Indian cities. Timely decision- making and effective crime prevention are hampered by the fragmented, non-interactive, and difficult-to-understand. nature of traditional crime records. By offering an interactive platform that uses data-driven methods and machine learning algorithms specifically, K-Means clustering to identify and display crime hotspots. according to crime frequency and severity, this system tackles these problems. Admin and User are the two main components that comprise the platform. To ensure system integrity and support, the Admin module. enables safe user, FAQ, and query management. The User module offers interactive dashboards, charts, and maps that reveal trends over time, hotspot detection, gender-based victimization patterns, and crime analysis by city. Through the integration of statistical analysis, clustering. algorithms, and interactive visualization, the system facilitates the acquisition of significant insights, encourages data-driven decision-making, and raises awareness of community safety among law enforcement agencies, lawmakers, researchers, and the general public. In general, the Crime Data Explorer and Hotspot Detector. creates safer and better-informed urban environments by converting unprocessed crime data into useful insight.
KEYWORDS
In conclusion the developed analysis of crimes and hotspot detection system successfully demonstrates. how data-driven Technology can improve public safety and enable informed decision-making. By applying the K-means clustering algorithm to crime data. the technique offers helpful visual insights into crime-prone areas making It makes it simpler for people to spot patterns and trends. The platform ensures smooth interaction. in addition to its analytical capabilities with features like secure user authentication FAQ access and an efficient. query-response system between users and administrators. Its modular architecture strong security features and intuitive interface make. it dependable and adaptable for real-time applications. All things considered, this system is a helpful instrument for keeping. an eye on crimes increasing awareness improving communication. and helping users or authorities implement preventative safety measures.
[1]. Crime Hotspot Detection using Optimized K-means Clustering and Machine Learning Techniques; Authors: Bonam K., Burra R.; Publication: 2023 4th International Conference. on Electronics, Communication and Aerospace Technology (ICECA) (IEEE); DOI: 10.1109/ICESC57686.2023.10193563; E-ISSN: 2767-5444. [2]. Crime data analysis for hotspot detection and visualization. interface; Authors: P Gautam, S Khan, S Mysore; Publication: 2025 — AIP Conference Proceedings, DOI: 10.1063/5.0285964, E-ISSN: 0094-243X. [3]. Intelligent Crime Hotspot Detection and Real-Time Tracking. Using Machine Learning; Authors: Rashid Ahmad, Asif Nawaz, Ghulam Mustafa, Tariq Ali, Mehdi Tlija, Mohammed A. El-Meligy, Zohair Ahmed; Publication: Computers, Materials & Continua, Vol. 81, No. 3 (2024); DOI: 10.32604/cmc.2024.056971; [4]. Crime Hotspot Detection and Monitoring Using Video Based Event Modeling and Mapping. Techniques; Authors: Zou Beiji, Nurudeen Mohammed, Zhu Chengzhang, Zhao Rongchang; Publication: International Journal of Computational Intelligence Systems, Vol 10 (2017) pp 962–969. [5]. Crime Detection Technique Using Data Mining and K-Means; Authors: Khushabu A. Bokde, Tiksha P. Kakade, Dnyaneshwari S. Tumsare, Chetan G. Wadhai; Publication: International Journal of Engineering Research. & Technology (IJERT), Vol. 7 Issue 02 (February 2018); DOI: 10.17577/IJERTV7IS020110; E-ISSN: 2278-0181.
Publication Date
5/18/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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