Pyneapple is an open-source Python library for scalable and enriched spatial data analysis. The regionalization module provides the algorithm for the generalized p-regions problem, the scalable max-p-regions solution, and the expressive max-p-regions problem solution. The spatial regression module provides the scalable Multi-scale Geographically Weighted Regression method. It is under development for the inclusion of newly proposed algorithms for scalable spatial analysis methods.
- pyneapple.regionalization.pruc P-regions with user-defined constraint
- pyneapple.regionalization.smp Scalable max-P regionalization
- pyneapple.regionalization.emp Max-P Regionalization with Enriched Constraints
All examples can be run interactively by launching this repository as a or opened using Jupyter Notebook.
You can try to install the package using the following command:
$ pip install git+https://github.com/MagdyLab/Pyneapple.git@main
You can also download the source distribution (.tar.gz) and decompress it to your selected destination. Open a command shell and navigate to the decompressed folder. Type:
$ pip install .
Pyneapple is under active development and contributors are welcome.
If you have any suggestions, feature requests, or bug reports, please open new issues on GitHub. To submit patches, please open a pull request. Once your changes get merged, you’ll automatically be added to the Contributors List.
If you are having trouble, please create an issue, or start a discussion.