Exploratory Spatial Data Analysis in PySAL

Methods for testing for global and local autocorrelation in areal unit data.

Documentation

• Home
• Tutorial
• API

Installation

Install esda by running:

conda-forge

preferred

$ conda install -c conda-forge esda

PyPI

$ pip install esda

GitHub

$ pip install git+https://github.com/pysal/esda@main

Requirements

• geopandas>=0.12
• libpysal>=4.12
• numpy>=1.24
• pandas>1.5
• scikit-learn>=1.2
• scipy>=1.9
• shapely>=2.0

Optional dependencies

• numba>=0.57 - used to accelerate computational geometry and permutation-based statistical inference.
• rtree>=1.0 - required for esda.topo.isolation()
• matplotlib - required for esda.moran.explore()

Contribute

PySAL-esda is under active development and contributors are welcome.

If you have any suggestion, feature request, or bug report, please open a new issue on GitHub. To submit patches, please follow the PySAL development guidelines and open a pull request. Once your changes get merged, you’ll automatically be added to the Contributors List.

Support

If you are having issues, please talk to us in the esda Discord channel.

License

The project is licensed under the BSD 3-Clause license.

Funding

National Science Foundation Award #1421935: New Approaches to Spatial Distribution Dynamics