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statistics and classes for exploratory spatial data analysis
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pysal/esda
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Methods for testing for global and local autocorrelation in areal unit data.
Installesda by running:
preferred
$ conda install -c conda-forge esda$ pip install esda$ pip install git+https://github.com/pysal/esda@maingeopandas>=0.12libpysal>=4.12numpy>=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()matplotlib- required foresda.moran.explore()
PySAL-esda is under active development and contributors are welcome.
If you have any suggestion, feature request, or bug report, please open a newissue on GitHub. To submit patches, please follow the PySAL developmentguidelines and open apull request. Once your changes get merged, you’ll automatically be added to theContributors List.
If you are having issues, please talk to us in theesda Discord channel.
The project is licensed under theBSD 3-Clause license.
National Science Foundation Award #1421935:New Approaches to Spatial Distribution Dynamics
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statistics and classes for exploratory spatial data analysis