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David O’Sullivan shows how spatial autocorrelation makes sampling fundamentally important: even when two surfaces contain the same values, their spatial arrangement means different sampling schemes can “see” very different patterns 🗺️
URL: https://geospatialstuff.com/posts/2025/11/14/gia-chapter-2A-spatial-autocorrelation/index.html
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Last month I had the pleasure of attending the Advances in Spatial Machine Learning 2026 workshop.
It provided an excellent setting for in-depth discussions, shared learning, and exchange of ideas.
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Thank you to everyone who came to discuss my PICO at #EGU26 today 👋
“Assessing residual spatial autocorrelation in machine learning models”
Slides & details: https://jakubnowosad.com/egu2026/
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GDAL can fix invalid geometries in a few different ways, depending on what you want to preserve. The main options are a “linework” approach that sticks closely to the original input, and a “structure” approach that prioritises clean, valid polygons 🛠️
Since GDAL 3.12, you can also run this directly from the command line with `gdal vector make-valid`.
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My presentation at #EGU26 (Vienna):
> Assessing residual spatial autocorrelation in machine learning models
📅 6 May | ⏰ 16:30 CEST
📍 PICO2.6, spot 2See you there!
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Final deadline extension 📢
Special Issue: Coding Earth: Open Source Solutions in Physical Geography (Progress in Physical Geography: Earth and Environment)We already have a dozen or so submissions and look forward to more.
Submit by 30 June 2026!
https://journals.sagepub.com/home/ppg
#CallForPapers #OpenScience #PhysicalGeography #EarthScience
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a5R brings the A5 pentagonal geospatial index to R.
Equal-area pentagonal cells across 31 resolutions, encoded as 64-bit integers, with millimetre-level precision at the finest scale 🗺️
R package by Hugh Graham; a5 by Felix Palmer
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Geospatial conferences in 2026
A curated and growing list of events in GIS and spatial data science🔗 https://github.com/Nowosad/conferences_2026
Which ones are you planning to attend?
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We just published a JOSIS paper on what spatial data science languages have in common and what they still need. Insights from across the R, Python & Julia ecosystems.
URL: https://doi.org/10.5311/JOSIS.2025.31.462
#SpatialDataScience #GISchat #OpenSource #RSpatial #GeoPython #JuliaGeo
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The spcosa package provides an R framework for spatial coverage sampling.
Explore examples at https://git.wur.nl/Walvo001/spcosa
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A growing list of 2026 geospatial conferences is live 🌍
URL: https://github.com/Nowosad/conferences_2026
If you know of additional GIS or remote-sensing events, please contribute. PRs and suggestions are welcome.
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Call for papers: Coding Earth — Open Source Solutions in Physical Geography for Progress in Physical Geography ⚡
Show how open-source tools, coding workflows, and open science are reshaping physical geography.
https://journals.sagepub.com/pb-assets/PDF/PPG_Coding_Earth_SI_CFP-1759916968.pdf
Deadline: 1 March 2026.
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New R package: rgeomorphon 📦 by Andrew Brown
Classifies terrain forms using a parallel C++ implementation of the geomorphon algorithm.
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Great new resource from Roger Bivand (NHH, June 2024): slides on spatial econometrics and ML for economic & social research.
URL: https://rsbivand.github.io/nem24_talk/bivand_nem24_pres.pdf
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📍 Registration is open for Spatial Data Science across Languages (SDSL) 2025 – Sept 17–18 (+19), Salzburg, Austria.
Connect R, Python, Julia & more in spatial science.
🔗 https://forms.gle/E9fpG88V2VQQKmjk9 -- Apply for on-site by mid-July – limited spots.
#SDSL2025 #SpatialDataScience #OpenSource #RSpatial #Geopython #Juliageo
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🚨 CFP: Our special issue *Coding Earth: Open Source Solutions in Physical Geography* is now open! 🌍💻
We’re seeking papers on open-source tools, coding workflows, and critical reflections in open georesearch.
🗓️ Deadline: Dec 18, 2025
🔗 https://journals.sagepub.com/home/ppg -
🚀 New preprint! "Spatial Data Science Languages: Commonalities and Needs" 🌍
Exploring challenges & insights from #Rstats #Python & #JuliaLang for spatial data handling—geodetic coords, data cubes, and more!
🔗 Read here: https://arxiv.org/html/2503.16686v1
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Explore the blog series on comparing spatial patterns in raster data using R. 🌍📊
- Techniques for analyzing continuous and categorical data
- Handling overlapping and arbitrary regions
- Advanced methods for comparing spatial patternsFind the full series at https://buff.ly/s35030O
#GIS #Rstats #SpatialAnalysis #RasterData #GeospatialAnalysis #rspatial
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Here’s a useful list of Diamond Open Access Journals by Lorena Abad, covering topics like Geoinformatics, Remote Sensing, Geomorphology, and more.
Feel free to add suggestions via PR.
Check it out: https://buff.ly/3WxYxnT
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✨ The GeoPAT 2 software allows the segmentation/regionalization of large spatial raster data.✨
Now, thanks to D G Rossiter, it can now be installed on MacOS.
You can find all of the instructions and other links at https://buff.ly/3LEzA4b.
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🛰️ A new paper "scikit-eo: A Python package for Remote Sensing Data Analysis" on a tool for #LULC analysis with various machine learning and neural networks algorithms.🛰️
Article: https://doi.org/10.21105/joss.06692
Software: https://yotarazona.github.io/scikit-eo/ -
New NLCD products for the year 2021 are now available, and starting from 2024, there will be a new land cover product for the conterminous United States at 30-meter spatial resolution and on an annual time step for the years 1985-2023.
Read more at https://www.usgs.gov/centers/eros/news/nlcd-2021-now-available