Digital Policing & Crime Reporting System

Abstract

The system proposed offers features such as filing FIRs and complaints online with OTP, based verification, secure handling of documents and multimedia evidence, and live tracking of the case status. A role, based system guarantees that access is appropriately granted to citizens, police officers, and administrators. In contrast to traditional systems, this platform uses GIS, based crime mapping to illustrate the crime distribution and locate the most dangerous areas, thus empowering the police authorities with better situational awareness. Besides, the system features AI, driven conceptual elements for crime categorization and anomaly detection, thus enabling crime analysts to make data, based decisions and conduct predictive analysis of the future. The security and safety of the data are guaranteed by encrypted communication, access control with authentication, and digital record management that is well, organized. The system is a scalable and modular architecture that can be easily integrated with national e, governance initiatives and digital policing infrastructures. Through the experimental evaluation and the functional testing, it has been revealed that the users\' accessibility has been improved, reporting delays have been lessened, and transparency has been increased compared to traditional crime reporting methods. The proposed system is a step towards intelligent, citizen, centric, and technology, enabled policing that is practical in modern law enforcement environments.

Introduction

The paper presents a Digital Crime Reporting and Policing System designed to replace traditional station-based crime reporting, which is often slow, paper-heavy, and prone to underreporting. Conventional methods suffer from inconvenience, lack of transparency, fear of retaliation, and jurisdictional limitations, leading to delays in investigations and weak citizen trust.

Key Issues in Existing Systems

Earlier web-based FIR systems improved accessibility and reduced paperwork but mostly:

• Only supported basic complaint submission and tracking
• Lacked advanced analytics and GIS crime mapping
• Had limited security, privacy protection, and intelligence features
• Did not provide real-time situational awareness for policing

Recent research highlights the need for secure, scalable, and data-driven platforms using encryption, role-based access, and intelligent analytics, aligned with legal reforms like e-FIR and Zero FIR under BNSS 2023.

Proposed System Overview

The proposed system is a secure, web-based platform that enables:

• Online FIR and complaint registration
• Real-time status tracking
• Role-based modules for Citizens, Police, and Administrators
• Secure authentication (OTP, encrypted access, role control)
• Centralized data management

It also introduces:

• GIS-based crime mapping to identify hotspots
• AI-based analytics for crime classification and anomaly detection (decision support)

Architecture & Implementation

The system follows a 3-layer architecture:

• Presentation layer (user dashboards)
• Application layer (authentication, complaint handling, workflows)
• Data layer (MongoDB storage of FIRs, users, evidence, GPS data)

Technologies used include:

• React (frontend)
• MongoDB (database)
• Google Maps API (GIS mapping)
• Secure APIs and encrypted communication for data protection

Key Results

Testing shows:

• Improved transparency and faster complaint handling
• Effective real-time tracking of FIR status
• GIS mapping helps identify crime hotspots
• AI dashboards support resource allocation and risk visualization

Unlike earlier systems, this platform integrates secure reporting + spatial analysis + analytics in one system, supporting data-driven policing.

Limitations

• Tested on synthetic rather than real-world large-scale data
• AI features are only decision-support, not fully predictive
• No integration yet with national systems like CCTNS/NCRB
• Limited mobile/offline support and scalability testing

Conclusion

This article described how we designed and implemented an advanced Digital Crime Reporting and Policing System which is secure and scalable and addresses the gaps of traditional as well as online crime reporting mechanisms. The proposed system through the online FIR and complaint registration, role, based access for citizens, police, and administrators, and secure digital evidence handling features enhances accessibility, transparency, and operational efficiency as compared to manual station, centric processes [1], [3], [4]. The addition of GIS, based crime visualization component also facilitates enhanced situational awareness by enabling law enforcement agencies to locate crime, prone areas and track spatial crime patterns in real time [5], [7]. We conducted experimental evaluation of the system functionalities using synthetically generated datasets that demonstrated the system\'s capability of handling end, to, end complaint processing, real, time status tracking, and analytical dashboard visualization. Differently from many existing systems that focus only on complaint submission and tracking [1], [2], our proposed platform combines secure reporting workflows with spatial analysis and decision, support functionalities. The combined model complies with the latest studies which stress the importance of data, driven and citizen, centric digital policing solutions [6], [8]. Our system, however, is at present a prototype and was tested in a controlled environment only. The future plan is to employ real crime data and further enhance the system features by the incorporation of advanced machine learning models for analytical purposes.

References

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Copyright

Copyright © 2026 Uday ., Yuvraj Singh , Shivam Chauhan, Sudheer Yadav, Shruti Jain. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.