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#geopython

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Today is the day! GeoPandas 1.1.0 is out, available on PyPI and conda-forge. This release brings feature parity with shapely 2.1 thanks to a long list of new methods, allows sparse and dense arrays as outputs of spatial index queries and comes with coverage simplification, among many other improvements and fixes. See the full list of changes at geopandas.org/en/stable/docs/c

If we have broken anything, please let us know :).

geopandas.orgChangelog — GeoPandas 1.1.0+0.gc36eba0.dirty documentation

Ever needed to simplify street networks? I did. And it is a pain. So we joined forces with @anavybor and @JamesGaboardi and wrote an algorithm that does that for us. And can do for you, as it is available as a Python package called `neatnet`.

Here's a short blog about it - martinfleischmann.net/simplifi

And here's, not so short preprint - arxiv.org/abs/2504.16198

But you probably want the package. That is here - uscuni.org/neatnet.

Happy coding!

"Spatial Data Science Languages: commonalities and needs" - that a preprint 11(!) of us wrote together as one of many outcomes of two workshops held in Münster (2023) and in Prague (2024). It summarised where we are, what we share between R, Python and Julia, what are the common challenges, lessons and recommendations - arxiv.org/abs/2503.16686

Big thanks belongs especially to @edzer who kickstarted the whole initiative! And to all the others who participated!

arXiv.orgSpatial Data Science Languages: commonalities and needsRecent workshops brought together several developers, educators and users of software packages extending popular languages for spatial data handling, with a primary focus on R, Python and Julia. Common challenges discussed included handling of spatial or spatio-temporal support, geodetic coordinates, in-memory vector data formats, data cubes, inter-package dependencies, packaging upstream libraries, differences in habits or conventions between the GIS and physical modelling communities, and statistical models. The following set of insights have been formulated: (i) considering software problems across data science language silos helps to understand and standardise analysis approaches, also outside the domain of formal standardisation bodies; (ii) whether attribute variables have block or point support, and whether they are spatially intensive or extensive has consequences for permitted operations, and hence for software implementing those; (iii) handling geometries on the sphere rather than on the flat plane requires modifications to the logic of {\em simple features}, (iv) managing communities and fostering diversity is a necessary, on-going effort, and (v) tools for cross-language development need more attention and support.

pygeoapi 0.19.0 released! This release provides numerous enhancements, updates and bug fixes. New features include dataset specific templating, Arabic translation, RTL support, transactional feature support for PostgreSQL, and a new OpenSearch provider.

github.com/geopython/pygeoapi/

#foss4g @osgeo @opengeospatial #geopython #ogcapi

GitHubRelease 0.19.0 · geopython/pygeoapiThe pygeoapi team announces the release of 0.19.0. This release provides numerous enhancements, updates and bug fixes. New features include dataset specific templating, Arabic translation, RTL sup...

We are honoured to announce that Tom Kralidis is the recipient of the
2024 Sol Katz Award presented on 6 December 2024 at #FOSS4G2024 in Belém, Brazil.

You may know Tom through his activities within the #GeoPython community. Tom also plays a critical role in promoting #OGC standards, throughout FOSS4G projects and connecting the OGC +
#OSGeo communities. He is active in the OSGeo Board & committees.

Read more about Tom Kralidis & the award osgeo.org/foundation-news/tom- #FOSS4G