Project Report - AI-Driven Emergency Response & Crime Intelligence System

1. Introduction

The increasing volume and complexity of emergency call data, particularly from Dial‑100 systems, has posed significant challenges in timely analysis and response at the district and station levels. Similarly, the manual review of FIRs and lack of correlation with live data sources delay effective law & order decision-making.

To address these gaps, this project proposes the implementation of an AI-powered system that automates the processing of Dial‑100 call logs and FIR records. Using Natural Language Processing (NLP) and large language models (LLMs), the system converts unstructured inputs into structured intelligence, supporting real-time hotspot detection, classification of incidents, and early warning alerts to aid proactive policing.

2. Objective

To develop a unified intelligence system that integrates Dial‑100 emergency call data and FIR records, leveraging AI and NLP for automatic classification, geospatial tagging, pattern recognition, and command-level insights. The system aims to enhance situational awareness, enable early detection of law and order concerns, and provide timely, evidence-backed support to district-level leadership.

3. Core Functional Modules

3.1 Real-Time Data Ingestion & Preprocessing

Automated retrieval of Dial‑100 call logs at 10-minute intervals.
Extraction of key metadata including timestamps, location coordinates, caller details, and jurisdiction.
Preprocessing of raw text to remove noise, standardize language, and prepare for AI classification.
Integration of FIR records from police MIS or e-FIR systems for combined analysis.

3.2 AI-Powered Classification & Entity Extraction

Classification of both calls and FIRs into specific crime/dispute categories such as theft, domestic violence, assault, etc.
Named Entity Recognition (NER) to identify and extract names, landmarks, locations, vehicles, IPC/CrPC sections, modus operandi (MO), and related entities.
Automatic assignment of severity ratings on a scale of 1–5 for operational prioritization.

4. Geospatial Intelligence

4.1 Multi-Source Hotspot Detection

Generation of interactive heatmaps based on:
- Real-time Dial‑100 inputs (citizen-reported concerns).
- FIR data (officially registered incidents).
- Combined intensity mapping for more accurate decision-making.
Clustering by location coordinates or police jurisdiction.

4.2 Jurisdictional Tagging

Automatic mapping of incidents to relevant:
- Police Stations
- Circles or Sub-Divisions
- Battalion or paramilitary jurisdictions (if applicable)

5. Command Dashboards & Reports

5.1 District Command Dashboard

Live display of:
- Call and FIR counts by category
- Severity indices
- Police station-wise distributions
Filtering by date range, geography, issue type, and severity.

5.2 Automated Reporting

Daily and weekly summary generation in PDF/HTML formats.
Reports include:
- Top five hotspots
- Crime trends and temporal shifts
- Correlation analysis between FIRs and Dial‑100 calls
Automatic dissemination to district officers via Email, SMS, or WhatsApp.

6. Analytical and Predictive Capabilities

6.1 Crime Pattern & Modus Operandi Analysis

Identification of:
- Repeat offenders
- Common time windows for crimes (e.g., weekend spikes)
- Recurring MOs
Use of historical FIR trends to auto-mark known hotspots.

6.2 Early Warning System (EWS)

Alerts generated when:
- Sudden increase in FIRs or calls is observed in specific areas.
- Clusters of violence-related incidents emerge.
- Certain individuals, communities, or locations are repeatedly flagged.
Examples include:
- “Three communal incidents in Sector-9 in 48 hours – Risk of escalation”
- “Increase in chain snatching near XYZ junction – Recommend intensified patrol”

6.3 Basic Crime Forecasting

Use of temporal and spatial trends to project potential crime increases.
- Example: “Land disputes projected to rise in Mandal-A during harvest season”

7. Human-in-the-Loop Enhancements

7.1 Role-Based Review Interface

Enables field officers (e.g., Inspectors, DSPs) to review and correct low-confidence AI classifications.
Corrections stored for ongoing model retraining and accuracy improvement.

7.2 Investigation Support Dashboard

Ability to cross-link FIRs by:
- Similar accused names
- Repeated crime patterns or MOs
Crime heatmaps showing spatial patterns like residence-to-crime proximity.

7.3 Performance Metrics Dashboard

Tracks key performance indicators such as:
- Delay between Dial‑100 call and FIR registration
- Case closure ratios
- Ratio of calls to registered FIRs at each police station

8. System Resilience, Notifications & Compliance

8.1 Reliability & Error Handling

Queue-based processing with retry mechanisms.
Logs flagged for:
- Data corruption
- Missing or incomplete metadata
Complete audit trails maintained for data flow and classification history.

8.2 Alerts & Notifications

Configurable alerts triggered based on:
- Sudden rise in specific crime categories
- Escalating situations within defined areas
Alerts sent via SMS, Email, or WhatsApp to designated personnel.

8.3 Data Protection & Access Control

Masking of sensitive victim and juvenile data.
Strict enforcement of Role-Based Access Control (RBAC).
System logs all user access and actions for accountability.

9. Conclusion

This AI-based intelligence platform is a step toward data-driven, proactive policing. It will not only automate the review of Dial‑100 and FIR records but also equip field leadership with real-time insights, geographic crime mapping, and pattern analysis to maintain public safety and respond rapidly to emerging challenges. Future extensions may include voice-to-text integration, full predictive policing modules, and integration with beat-level patrol data.