PyMEDM: Penalized Maximum-Entropy Dasymetric Modeling (P-MEDM) in Python

This is a GPU-ready Python port of PMEDMrcpp via jax and jaxopt. Support for usage on Windows is not guaranteed.

Installation

Conda-forge (recommended)

The pymedm feedstock is available via the conda-forge channel.

$ conda install --channel conda-forge pymedm

PyPI

pymedm is available on the Python Package Index.

$ pip install pymedm

Source

Directly via GitHub + pip

$ pip install git+https://github.com/likeness-pop/pymedm.git@develop

Download + pip

Download the source distribution (.tar.gz) and decompress where desired. From that location:

$ pip install .

Usage

• See usage examples in ./notebooks/

Development

1. Clone the repository to the desired location.
2. Install in editable mode
   • Navigate to where the repo was cloned locally.
   • Within that directory:

     $ pip install -e .
     

3. Open an Issue for discussion
4. In a branch off develop, implement update/bug fix/etc.
5. Create a minimal Pull Request with clear description linked back to the associated issue from (3.)
6. Wait for review from maintainers
7. Adjust as directed
8. Once merged, fetch down origin/develop and merge into the local develop
9. Delete the branch created in (4.)
10. Start over at (2.)

References

1. Leyk, S., Nagle, N. N., & Buttenfield, B. P. (2013). Maximum entropy dasymetric modeling for demographic small area estimation. Geographical Analysis, 45(3), 285-306.
2. Nagle, N. N., Buttenfield, B. P., Leyk, S., & Spielman, S. (2014). Dasymetric modeling and uncertainty. Annals of the Association of American Geographers, 104(1), 80-95.

Citation

If you find this package useful or use it an academic publication, please cite as:

• Tuccillo, J.V. and Gaboardi, J.D. (2025) pymedm. Computer Software. URL: https://github.com/likeness-pop/pymedm. DOI: 10.11578/dc.20250320.3