AI-Driven-Crime-Analytics-Visualization-Platform

🛡️ 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