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Pyneapple

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.

Modules

  • 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

Examples

All examples can be run interactively by launching this repository as a Binder or opened using Jupyter Notebook.

Requirements

Python

Notebook

Java

Installation

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 .

Contribute

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.

Support

If you are having trouble, please create an issue, or start a discussion.

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