CityPulse - Urban Analytics Platform

A comprehensive data science and machine learning powered urban analytics platform for analyzing city-level datasets, generating visualizations, and running predictive models.

🏙️ Features

• Data Ingestion: Upload CSV files or connect to APIs (OpenWeather, Government Open Data)
• Interactive Visualizations: Heatmaps, graphs, and geospatial maps
• Natural Language Queries: Ask questions about your data in plain English
• Machine Learning: Forecasting, clustering, and anomaly detection
• Real-time Dashboards: Interactive web interface with filters and controls

🚀 Quick Start

1. Install Dependencies

   pip install -r requirements.txt

2. Set up Environment Variables

   cp .env.example .env
   # Edit .env with your API keys and database settings

3. Initialize Database (Optional - for persistent storage)

   python scripts/init_db.py

4. Run the Application

   # Streamlit Interface
   streamlit run app/streamlit_app.py
   
   # Or Flask API
   python app/flask_app.py

📁 Project Structure

citypulse/
├── app/                    # Web applications
│   ├── streamlit_app.py   # Main Streamlit interface
│   ├── flask_app.py       # Flask API backend
│   └── components/        # Reusable UI components
├── src/                   # Core application logic
│   ├── data/             # Data processing modules
│   ├── ml/               # Machine learning models
│   ├── visualization/    # Plotting and mapping
│   └── api/              # External API integrations
├── data/                 # Data storage
│   ├── raw/              # Raw uploaded datasets
│   ├── processed/        # Cleaned datasets
│   └── models/           # Trained ML models
├── config/               # Configuration files
├── scripts/              # Utility scripts
├── tests/                # Unit tests
└── docs/                 # Documentation

🛠️ Technology Stack

• Backend: Python, Flask, FastAPI
• Frontend: Streamlit, React (optional)
• Data Science: Pandas, NumPy, GeoPandas, Scikit-learn
• Visualization: Plotly, Folium, Matplotlib, Seaborn
• ML: Prophet, TensorFlow, Statsmodels
• Database: PostgreSQL with PostGIS extension
• APIs: OpenAI, OpenWeather, Government Open Data

📊 Example Use Cases

1. Traffic Analysis: Upload traffic data to generate congestion heatmaps
2. Air Quality Forecasting: Predict AQI levels using time series models
3. Crime Hotspot Detection: Use clustering to identify high-crime areas
4. Real Estate Trends: Analyze property prices across different neighborhoods

🔧 Configuration

Copy .env.example to .env and configure:

• Database connection settings
• API keys (OpenWeather, OpenAI, etc.)
• Application settings

📈 Development

1. Setup Development Environment

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt

2. Run Tests

   python -m pytest tests/

3. Code Formatting

   black src/ app/
   flake8 src/ app/

🌍 Data Sources

• OpenWeatherMap API
• Government Open Data portals
• Google Maps API
• User-uploaded CSV files
• Real-time sensor data

📝 License

MIT License - see LICENSE file for details.

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

📞 Support

For questions and support, please open an issue on GitHub or contact the development team.