aleksanderbl29/landbrugsdata

Et projekt for at formidle data om dansk landbrug

Landbrugsdata

The purpose of this project is to organize information about the Danish agricultural sector and make it universally accessible and useful.

🚀 Quick Start

Backend Setup

cd backend
python -m venv venv
source venv/bin/activate # or venv\Scripts\activate on Windows
pip install -r requirements.txt
uvicorn src.main:app --reload

Frontend Setup

cd frontend
npm install
npm start

Visit:

• Frontend: http://localhost:3000
• API Documentation: http://localhost:8000/docs

🏗️ Architecture

Backend (FastAPI + Python)

├── src/
│ ├── sources/ # Data source implementations
│ │ ├── base.py # Base source class
│ │ ├── parsers/ # API/WFS sources
│ │ └── static/ # Static file sources
│ ├── main.py # FastAPI application
│ └── config.py # Configuration

Frontend (React + TypeScript)

frontend/
└── src/
├── components/ # React components
├── api/ # Backend API client
├── hooks/ # Custom React hooks
└── types/ # TypeScript definitions

📊 Data Sources

Data should have one or more of the following attributes to be useful:

• CVR number (company registration number)
• CHR number (herd registration number)
• geospatial coordinates (point or polygon)
• enhedsnummer (CVR individual identifier)
• bfe number (cadaster number)

Live Sources

1. Agricultural Fields (WFS)
   • Updates: Weekly (Mondays 2 AM UTC)
   • Content: Field boundaries, crop types

Static Sources

All static sources are updated through manual pull requests:

• Animal Welfare: Inspection reports and focus areas
• Biogas: Production data and methane leakage reports
• Fertilizer: Nitrogen data and climate calculations
• Herd Data: CHR (Central Husbandry Register)
• Pesticides: Usage statistics (2021-2023)
• Pig Movements: International transport (2017-2024)
• Subsidies: Support schemes and project grants
• Visa: Agricultural visa statistics
• Wetlands: Areas and carbon content

🤝 Contributing

Prerequisites

• Python 3.9+
• Node.js 16+
• GDAL library
• Git

Development Workflow

1. Fork the repository
2. Create a feature branch:

   git checkout -b feature/your-feature-name

3. Make your changes
4. Run tests:

   # Backend
   cd backend
   pytest
   
   # Frontend
   cd frontend
   npm test

5. Submit a pull request

Adding New Data Sources

1. Choose the appropriate directory:

   • backend/src/sources/parsers/ for API/WFS sources
   • backend/src/sources/static/ for static files

2. Create a new parser class:

   from ..base import Source
   
   class YourSource(Source):
       async def fetch(self) -> pd.DataFrame:
           # Implement data fetching
           pass

3. Add configuration in backend/src/config.py

4. Update frontend types in frontend/src/types/

5. Add visualization in frontend/src/components/

🔄 Data Updates

• WFS Sources: Automatic weekly updates (Mondays 2 AM UTC)
• Static Sources: Manual updates via pull requests
• Deployment: Automatic to Google Cloud Run

🔧 Environment Setup

Backend (.env)

GOOGLE_CLOUD_PROJECT=your-project-id
GCS_BUCKET=your-bucket-name

Frontend (.env)

REACT_APP_API_URL=http://localhost:8000

📚 Documentation

• Backend API Documentation
• Frontend Component Documentation
• Data Source Specifications

🐛 Troubleshooting

Common issues and solutions:

1. GDAL Installation

   # Ubuntu/Debian
   sudo apt-get install gdal-bin libgdal-dev
   
   # macOS
   brew install gdal

2. API Connection Issues

   • Verify backend is running
   • Check CORS settings
   • Confirm environment variables

📝 License

This work is licensed under a Creative Commons Attribution 4.0 International License (CC-BY).

🙏 Acknowledgments

• Danish Agricultural Agency
• Danish Environmental Protection Agency
• Danish Energy Agency
• SIRI