🚨 Integrated Crime Intelligence System (ICIS)

💡 Project Overview

The Integrated Crime Intelligence System (ICIS) is an advanced, two-part platform developed for the Police Crime Branch. Its primary goal is to transition investigative processes from reactive data handling to proactive, intelligence-led policing by fusing disparate data sources and leveraging real-time AI capabilities.

The system consists of a powerful Web Application for deep analytical insights and a high-speed Mobile Application for critical field operations.

🎯 Key Features

1. Intelligence Automation & Analysis (Web App)

• Communication Network Analysis: Automated ingestion and parsing of Call Data Records (CDRs) to model and visualize complex criminal associations using a Graph Database (Neo4j).
• Geospatial Crime Mapping: Fuses ANPR hit logs and CDR locations onto interactive maps for spatial analysis, hot-spot identification, and movement tracking.
• Management Dashboards: Centralized views for tracking officer schedules, resource allocation, and case file progress.

2. Real-Time Field Operations (Mobile App)

• Mobile Vehicle Recognition (LPR): Utilizes the mobile device camera to capture license plates and instantly cross-reference them against watch lists via a high-speed AI API.
• Live Facial Recognition: Secure, on-site matching of captured suspect faces against the secure criminal database.
• Field Data Viewer: Provides authorized officers with secure, read-only access to critical case, vehicle, and suspect profiles.

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🛠️ Technology Stack

Component	Technology	Rationale
Mobile App	React Native	Cross-platform development (iOS/Android) ensuring fast access to device camera hardware.
Web App	React.js	Robust framework for complex, data-heavy, and highly interactive analysis dashboards.
Backend & APIs	Python (FastAPI)	Chosen for performance, speed, and standard use in Machine Learning/AI model deployment.
Relational DB	PostgreSQL with PostGIS	Secure, reliable, and essential for complex structured data and geospatial queries.
Graph DB	Neo4j	Optimized specifically for rapid link analysis, essential for visualizing communication networks.
Deployment	Docker	Used for containerization to ensure consistency, isolation, and simplified deployment.

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🚀 Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing.

Prerequisites

• Node.js (LTS)
• Python 3.9+
• Docker and Docker Compose

Installation Steps

1. Clone the Repository:

   git clone https://github.com/iSiRaH/Crimelink_Analyzer
   cd Crimelink_Analyzer

2. Start Database Containers: Navigate to the deployment/ directory and run the containers for PostgreSQL and Neo4j.

   docker-compose up -d

3. Setup Backend (AI Service):

   cd backend/
   pip install -r requirements.txt
   python manage.py runserver

4. Setup Web Application:

   cd web-app/
   npm install
   npm start

5. Setup Mobile Application: See the detailed setup instructions in mobile-app/README.md.

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📄 Project Structure & Timeline

The project was executed over a 13-week period (2025/10/22 – 2026/01/20), prioritizing foundational security and development before tackling complex AI integration.

Phase	Major Focus	WBS Tasks Included
1.0 - 2.0	Planning & Design	Requirements, Architecture, ERD, API Design
3.0 - 4.0	Core Development	DB Setup, Auth, CDR Analyzer, ML Model Training
5.0	Integration	Mobile-to-AI APIs, Maps, Network Visualization
6.0 - 7.0	Testing & Finalization	Unit/System Testing (UAT), Documentation, Reporting

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🔒 Security Note

The system utilizes OAuth 2.0 for token-based authentication and implements a strict Role-Based Access Control (RBAC) model. All data transmission is secured via TLS/SSL.