Python spatial statistics

Python tools for spatial statistics spatiotemporal processes.


Pangeo: A community platform for Big Data geoscience

Pangeo is first and foremost a community promoting open, reproducible, and scalable science. This community provides documentation, develops and maintains software, and deploys computing infrastructure to make scientific research and programming easier. The Pangeo software ecosystem involves open source tools such as xarray, iris, dask, jupyter, and many other packages. There is no single software package called "pangeo"; rather, the Pangeo project serves as a coordination point between scientists, software, and computing infrastructure. On this website, scientists can find guides for accessing data and performing analysis using these tools (read the Guide for Scientists, browse the Pangeo Gallery, and learn about the Packages). Those interested in building infrastructure can find instructions for deploying Pangeo environments on HPC or cloud clusters (learn about the Technical Architecture or read the Deployment Setup Guides). For more general information, read About Pangeo, see the Funders and Collaborators, or read the Frequently Asked Questions. Welcome to the Pangeo community!

These folks support Dask, Xarray, and probably other famous pieces of python big data infrastructure.



…is a toolkit for high performance geospatial processing, modelling and analysis.

Some highlights of Geostack include:

  • Range of programmable geospatial operations based on OpenCL, including map algebra, distance mapping and rasterisation.
  • Data IO for common geospatial types such as geotiff and shapefiles with no dependencies.
  • Implicit handling geospatial alignment and projections, allowing easier coding of geospatial models.
  • Python bindings for interoperability with GDAL/RasterIO/xarray/NetCDF.
  • Built-in computational solvers including level set and network flow models.

More information and build guides are on our wiki.

Geostack can be installed for Python using conda.


PySAL. This seems to be a rich ecosystem; it is kind of dual to QGIS, in that it seems to put statistical analyses first and geography second. It has a lot of moving parts and it made of many libraries. Personally I am curious about their spatial Gibbs sampler.



Hales, Riley Chad, Everett James Nelson, Gustavious P. Williams, Norman Jones, Daniel P. Ames, and J. Enoch Jones. 2021. β€œThe Grids Python Tool for Querying Spatiotemporal Multidimensional Water Data.” Water 13 (15): 2066.
Rey, Sergio J., and Luc Anselin. 2010. β€œPySAL: A Python Library of Spatial Analytical Methods.” In Handbook of Applied Spatial Analysis, 175–93. Springer.

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