🏙️ Urban Sentinel

AI-Powered Urban Intelligence for Smarter Cities

🎯 The Vision

Urban blight costs cities billions annually in reduced property values, increased crime, and community displacement. Traditional approaches are reactive—we only act after decay has already set in, when it's most expensive to fix.

Urban Sentinel flips this model. Our AI system predicts at-risk neighborhoods up to in advance, enabling proactive interventions that save communities and millions in taxpayer dollars.

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✨ What Makes This Different

🔮 Predictive, Not Reactive — See the future of your city before it unfolds
🎯 94.4% Accuracy — Trained on 5+ years of real Toronto data
⚡ Real-Time Intelligence — Interactive risk visualization at 30fps
🗺️ Actionable Insights — Click any neighborhood for detailed risk analysis
💰 Cost-Saving — Prevent problems before they become expensive to fix

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🔬 The Tech Behind the Magic

Machine Learning Engine

• Enhanced LightGBM with cross-validation and early stopping
• 10,659 risk predictions across Toronto's urban grid
• Feature engineering from 311 service complaints, temporal patterns, and geographic correlations
• 2014-2019 data for comprehensive training

Frontend

React + TypeScript + Mapbox GL JS
Performance-optimized rendering (30fps on any device)
Glass-morphic design with dynamic risk filtering

Backend

FastAPI + Python + GeoPandas
Real-time ML inference pipeline
Spatial data processing and GeoJSON generation

Infrastructure

Docker containerization for seamless deployment
Hot-reload development environment
Cross-platform compatibility

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📊 By the Numbers

Metric	Value	Impact
Model Accuracy	94.4% ROC-AUC	Industry-leading precision
Risk Predictions	10,659	Complete Toronto coverage
Data Span	2014-2019	Decade of insights
Prediction Horizon	2+ years	Early intervention window
Response Time	<500ms	Real-time intelligence

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🛠️ Quick Start

Prerequisites

• Docker & Docker Compose
• Node.js 16+ (for local development)
• Python 3.9+ (for local development)

One-Command Launch

# Clone and run the entire stack
git clone https://github.com/your-username/Urban-Sentinel.git
cd Urban-Sentinel
docker-compose up

That's it! Urban Sentinel will be running at:

• 🌐 Frontend: http://localhost:3000
• 🔧 Backend API: http://localhost:8000

Development Setup

# Frontend
cd frontend
npm install
npm start

# Backend
cd backend
pip install -r requirements.txt
python api.py

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📋 Project Structure

Urban-Sentinel/
├── 🎨 frontend/          # React + TypeScript UI
│   ├── src/components/   # Landing page, risk map, filters
│   └── src/services/     # API integration
├── 🧠 backend/           # FastAPI + ML pipeline
│   ├── api.py           # REST API endpoints
│   ├── model.py         # LightGBM training & inference
│   └── geojson.py       # Spatial data processing
├── 📊 datasets/          # Toronto 311 service data
└── 🐳 docker-compose.yml # One-command deployment

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🗺️ How It Works

1. Data Ingestion — Process Toronto's 311 service requests (2014-2019)
2. Feature Engineering — Extract temporal patterns, complaint clusters, geographic correlations
3. Model Training — Enhanced LightGBM with stratified sampling and early stopping
4. Risk Prediction — Generate urban decay forecasts for each grid cell
5. Visualization — Interactive Mapbox display with real-time filtering and statistics

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🎨 Screenshots

Landing Page

Clean, professional design that builds trust with city officials

Interactive Risk Map

Toronto's urban grid color-coded by blight risk — red zones need immediate attention

Detailed Analytics

Click any area for comprehensive risk breakdown and trend analysis

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🌟 What's Next

• 🌍 Multi-city expansion — Chicago, Detroit, New York
• 📱 Mobile app for field workers and community engagement
• 🛰️ Satellite imagery integration for enhanced predictions
• 📊 Economic impact modeling to quantify intervention ROI
• 🔗 API ecosystem for integration with existing city systems