Methods for testing for global and local autocorrelation in areal unit data.
Install esda by running:
preferred
$ conda install -c conda-forge esda
$ pip install esda
$ pip install git+https://github.com/pysal/esda@main
geopandas>=0.12libpysal>=??numpy>=1.24pandas>1.5scikit-learn>=1.2scipy>=1.9shapely>=2.0
numba>=0.57- used to accelerate computational geometry and permutation-based statistical inference.rtree>=1.0- required foresda.topo.isolation()
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.
If you are having issues, please talk to us in the esda Discord channel.
The project is licensed under the BSD 3-Clause license.
National Science Foundation Award #1421935: New Approaches to Spatial Distribution Dynamics