🛡️ Bharat Raksha - Karnataka Police Intelligence Command Portal
Bharat Raksha is a premium, high-fidelity crime analytics and predictive risk modeling dashboard designed for Investigating Officers of the Karnataka State Police (KSP). It features real-time spatio-temporal tracking, machine learning-driven risk forecasting, criminal syndicate link analysis, and socio-economic correlation metrics.
🚀 How to Run the App
The project is equipped with a lightweight, robust Node.js server.
Prerequisites
Node.js installed on your machine.
Launch Instructions
Open a terminal in the project directory: C:\Users\HP.gemini\antigravity\scratch\crime-analytics-platform
Start the local server:
bash
node server.js
Open your browser and navigate to: 👉
http://localhost:8000 (or
http://127.0.0.1:8000)
🔒 Officer Gatekeeper Access
The dashboard is secured by a full-screen gatekeeper portal overlay. To log in or register a new session, use the official clearance passcode:
Intelligence clearance passcode: RAKSHA-2026
Simulated Credentials for Testing:
Officer Name: Inspector Kiran Gowda
KSP Service ID: KSP-7754-C
Badge Number: 504
Clearance Range: Bengaluru South
Upon validation, the gatekeeper portal runs a simulated biometric thumb/retinal scan animation with high-tech visual lasers and synthesized audio effects (Web Audio API), then unlocks the main command center.
📦 Key Capabilities & Features
Dashboard Overview:
Spatio-Temporal Mapping: Interactive GIS map displaying crime locations. Supports toggleable Case Markers (color-coded by severity) and Hotspot Density (Heatmap) views.
Metrics Panel: Real-time counters showing Total Crimes, AI Anomaly Flags, Recidivism threat index, and case clearance rate.
Operations Filter: Drill down by District Range, Crime Category, Severity Level, and Case Status.
Report Incident Modal: Register new case files.
AI Predictive Risk & Alerts:
ML Risk Predictor: Evaluates risks by sector and time of day (Day, Evening, Late-Night).
Socio-Economic Sliders: Dynamically override Poverty, Unemployment, and Education indices to forecast risk variance.
Dynamic Gauge: Real-time gauge rendering risk scores and breakdown indexes (Violent, Property, Cybercrime).
AI Anomaly Feed: Lists live clusters, repeating offenders, and uncharacteristic crime spikes.
Criminal Network Link:
Syndicate Link Graph: Interactive node chart representing relationships between criminals. Powered by an offline vis-network engine.
Offender Dossiers: Click suspect nodes to view photos, aliases, bios, risk parameter graphs, and past crime timelines.
Socio-Economic Correlation:
Regression Scatter Plot: Renders the relationship between crime count and socio-economic variables with a calculated AI Linear Regression fit line.
Table View: Dynamic listings of district-specific poverty, unemployment, and education parameters.
🛠️ Offline & Network Resilience (lib/)
To guarantee operation in internet-restricted or offline environments (e.g. command rooms), all third-party libraries have been bundled locally inside the lib/ directory:
Leaflet GIS Map: lib/leaflet.js & lib/leaflet.css
Leaflet Heatmap: lib/leaflet-heat.js
Chart.js Graphs: lib/chart.js
Vis-Network diagrams: lib/vis-network.min.js
No external network connections or CDNs are required, making the local console extremely robust.
📁 File Structure
text
crime-analytics-platform/
├── index.html # Main HTML layout, inline SVG crests, and modals
├── styles.css # Cyberpunk dashboard styling, animations, laser scan effect
├── app.js # Main Controller (state management, event hooks, AudioContext)
├── ai-engine.js # Predictive risk scoring models & anomaly rules
├── data.js # Database registry of incidents, criminals, and districts
├── server.js # Node.js static file web server
├── lib/ # Bundled offline JS and CSS libraries
└── README.md # Documentation file
license: mit
