pypopRF is a Python package for population prediction and dasymetric mapping using machine learning techniques. It provides a comprehensive toolkit for processing geospatial data, training models, and generating high-resolution population distribution maps.
- Feature extraction from multiple geospatial covariates
- Random Forest-based population prediction with automatic feature selection
- Parallel processing support for large datasets
- Dasymetric mapping for high-resolution population redistribution
- Visualization tools for analysis and validation
- Command-line interface for easy project management
pip install pypoprfFull documentation is available at https://wpgp.github.io/pypopRF/
The documentation includes:
- Detailed installation instructions
- Usage guide and examples
- Input data requirements
- Configuration options
- Troubleshooting guide
# Create a new project
pypoprf init my_project# Run with configuration file
pypoprf run -c my_project/config.yaml- Clone the repository:
git clone https://github.com/wpgp/pypopRF.git
cd pypopRF- Create and activate virtual environment:
python -m venv venv
source venv/bin/activate # Linux/Mac
# or
venv\Scripts\activate.bat # Windows- Install in development mode:
pip install -e ".[dev,docs]"Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.
This project is licensed under the MIT License - see the LICENSE file for details.
If you use pypopRF in your research, please cite:
@software{pypoprf2025,
author = {Priyatikanto R., Nosatiuk B., Zhang W., McKeen T., Vataga E., Tejedor-Garavito N, Bondarenko M.},
title = {pypopRF: Population Prediction and Dasymetric Mapping Tool},
year = {2025},
publisher = {GitHub},
url = {https://github.com/wpgp/pypopRF}
}- Developed by WorldPop SDI