Python tools for spatial statistics spatiotemporal processes.
Pangeo
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.
Geostack
…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
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.
QGIS
See spatial statistics.
Incoming
Google Earth Engine is easy to access from colaboratory, e.g. ee-api-colab-setup.ipynb; there is an amount of geospatial imagery processing in there.
TransBigData – for Transportation Spatio-Temporal Big Data
TransBigData is a Python package developed for transportation spatio-temporal big data processing and analysis. TransBigData provides fast and concise methods for processing common traffic spatio-temporal big data such as Taxi GPS data, bicycle sharing data and bus GPS data. It includes general methods such as rasterization, data quality analysis, data pre-processing, data set counting, trajectory analysis, GIS processing, map base map loading, coordinate and distance calculation, and data visualization.
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