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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM62126

ONLINE CRIME REPORTING

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM62126

Volume:

10 |

4 |

April 2026

Pages:

1-11

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

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.

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Authors

R

Raja

BE

Angel college of engineering and technology

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K

Karthikeyan

BE

Angel college of engineering and technology

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M

Murugan

BE

Angel college of engineering and technology

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S

Sankaranarayanan

BE

Angel college of engineering and technology

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V

Vishnupriya

BE

Angel college of engineering and technology

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Abstract

Public crime reporting remains inefficient due to limited accessibility and slow response mechanisms. This paper presents an Online Crime Reporting System — a Flutter-based web application with a FastAPI backend — that allows citizens to submit geo-tagged crime reports with location, category, description, and image evidence. The system integrates an AI-driven spatial analysis layer using Kernel Density Estimation for risk mapping, DBSCAN clustering for hotspot detection, and a predictive model for forecasting future crime activity. Results are visualized on an interactive map with real-time heatmaps, danger zones, and crime markers. Role-based access separates citizen and admin workflows, with JWT-secured endpoints throughout. The system provides a practical, accessible tool for community-driven crime intelligence to support law enforcement decision-making.

KEYWORDS

Keywords: crime reporting Flutter Fast API KDE DBSCAN spatial analysis web application public safety

Cite This Article

Raja, Karthikeyan, Murugan, Sankaranarayanan, Vishnupriya, "ONLINE CRIME REPORTING," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, pp. 1-11, April 2026.

Full Article/Conclusion

This paper presented an Online Crime Reporting System that addresses the limitations of existing reporting mechanisms by combining a Flutter-based mobile application with a FastAPI backend and an AI-driven spatial intelligence engine. Citizens can submit geo-tagged crime reports directly from their smartphones, while administrators verify and monitor crime patterns through an interactive map rendering KDE risk surfaces, DBSCAN hotspots, danger zones, and future risk predictions in real time. Results confirmed that the system produces meaningful spatial outputs and delivers a practical, accessible platform that bridges citizen participation and intelligent crime analysis to support proactive law enforcement

References

·        [1] S. Chainey and J. Ratcliffe, GIS and Crime Mapping, Wiley, 2005. ·        [2] M. S. Gerber, "Predicting crime using Twitter            and        kernel                             density estimation," Decision Support Systems, vol. 61, pp. 115–125, 2014. ·        [3] S. V. Nath, "Crime pattern detection using data mining," Proc. IEEE/WIC/ACM Int. Conf. Web Intelligence, 2006. ·        [4] S. Chainey, L. Tompson, and S. Uhlig, "The utility of hotspot mapping for predicting spatial patterns of crime," Security Journal, vol. 21, no. 1–2, pp. 4–28, 2008. ·        [5] G. O. Mohler et al., "Self-exciting point process modeling of crime," Journal of the American Statistical Association, vol. 106, no. 493, pp. 100–108, 2011. ·        [6] M. Ester, H. Kriegel, J. Sander, and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise," Proc. KDD, pp. 226–231, 1996. ·        [7] T. H. Grubesic, "On the application of fuzzy clustering  for  crime  hot  spot detection," Journal                                     of              Quantitative Criminology, vol. 22, no. 1, pp. 77–105, 2006. ·        [8] W. G. Perry et al., Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations, RAND Corporation, 2013. ·        [9] P. Wang et al., "Crime rate inference with big data," Proc. ACM SIGKDD, pp. 635–644, 2016. ·        [10] S. Alotaibi and D. Roussinov, "User acceptance  of  smart  government services," Proc. IEEE Int. Conf. e-Science, 2016.

Editorial & Review Transparency

Publication Date

4/30/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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