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#geopython

1 post1 participant0 posts today
💧🌏 Greg Cocks

Training - Geo-Python and Automating GIS Processes (‘AutoGIS’)
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autogis-site.readthedocs.io/en <-- shared opensource course material
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“Free courses teaching GIS automation via Python from the University of Helsinki. Called "Geo-Python and Automating GIS Processes (‘AutoGIS’)"; both the course materials and lectures are open access…”
#GIS #spatial #mapping #python #training #online #learning #tutorial #opensource #free #automation #openaccess #course #lectures #AI #coding #geopython #LLM #continuedlearning #AutoGIS #github
@UniversityOfHelsinki

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GeoPandas

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
#geopython
Martin Fleischmann

Ever needed to simplify street networks? I did. And it is a paingeopythonoined 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!

#python#networks#geopython
Martin Fleischmann

"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.
#rspatial#geopython#juliageo
Alexandre B A Villares 🐍

It is super cool to be listed as a "contributor" for having chimed in (constructively) on a project's issue :D

github.com/nathanrooy/taxicab/

#taxicab is a library that improves on #OSMnx route-finding function, giving more useful results, specially on short routes.

GitHubRelease OSMNX 2.0 update · nathanrooy/taxicabAddresses the following: OSMNX 2.0 updates (#15) thanks @rubchacon and @villares Chronological point ordering (#10) thanks @YannickAaron
#geopython#python
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