Need fast raster processing in R? π
Check out {fasterRaster} by Adam B. Smith, an R package designed for large spatial rasters and vectors through GRASS GIS, built to complement {terra} & {sf} π
Learn more: https://buff.ly/4htiGTR
[#]rspatial #rstats #GIS #grassgis
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The shapefiles are gone! π
The {spData} package has officially dropped all datasets in the shapefile format, moving toward modern and efficient geospatial data standards. ππ
Upgrade your workflows with {spData}: https://buff.ly/4htZ5D3
[#]rspatial #rstats #GIS #Geocompx
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Introducing {wbw}, a new geospatial powerhouse in the R ecosystem by Anatolii Tsyplenkov! πβ‘
{wbw} provides R bindings for Whitebox Workflows for Pythonβa fast & powerful library for hydrology, geomorphometry, remote sensing, and more (raster, vector, LiDAR).
Check it out: https://buff.ly/4gaWbCe
[#]rspatial #rstats #geocompx #GIS
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π¨ After years of development, the new version of #rstats tmap (4) is now available on CRAN! π Huge thanks to author Martijn Tennekes and all contributors.
π§ Install: install.packages("tmap")
π Vignettes: https://buff.ly/4aAYNbq
π Book Chapter: https://buff.ly/43TokaF
[#]maps #tmap #rspatial
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π Excited to share the 1st edition of Geocomputation with Python is complete! π
Learn geospatial with Pythonβfrom vectors to rasters.
Read it online: https://buff.ly/3NK2uBq
Get the book: https://buff.ly/42t4dD7
Blog post: https://buff.ly/42s5fiN
Open-source + community-driven!
[#]geocompx #geopython #GIS #SpatialData
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Can #rstats move Antarctica to the north pole? Of course.
Check Michael Sumner solution at https://buff.ly/3ZkHK8l
[#]rspatial #maps
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This review provides an overview of recent developments in computational methods in #LandscapeEcology
It covers topics like spatial patterns, connectivity, landscape genetics, and more.
Read the full paper: https://buff.ly/41xU6wx
[#]Ecology #ComputationalMethods #Research
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Looking for resources from the previous edition of the OpenGeoHub Summer School (2023)?
Tutorials, code, and recordings for Python, R, and Julia are available at https://buff.ly/44uKIaT
[#]rspatial #geopython #juliageo
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If you need a quick, but fairly deep introduction to GeoParquet -- I recommend you to listen to an episode of the MapScaping podcast with Kyle Barron.
https://buff.ly/3D23bn7
[#]gischat #rspatial #geopython
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ππ OpenGeoHub Summer School 2025
"Data Science for Earth Observation: Hands-on Training in Python, R, and Julia."
Workshops, ML competitions, demos, and more!
π 1-6 Sept 2025 | π Wageningen, Netherlands
π Apply by 18 Jan 2025: https://buff.ly/41C8y6y
[#]EarthObservation #DataScience #gischat
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Want to showcase your work on #MachineLearning in #Geography? π
Submit to Erdkunde's Special Issue: Machine Learning in Geography β Challenges & Perspectives.
π More info: https://buff.ly/3VLlPGq
[#]ml #spatialml #gischat
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π―π΅ Geocomputation with R is also available in Japanese. π―π΅
Read it at https://buff.ly/4f8dkfp
[#]rstats #rspatial #geocompx
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π Two blog posts about simulating spatial patterns with the kinetic Ising model.π
Part 1: https://buff.ly/3vbhvFJ
Part 2: https://buff.ly/3RNy1Ue
[#]rstats #rspatial #spatialpatterns
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"Exploration of spatial patterns in raster data" -- a talk about methods to describe, compare, and analyze spatial patterns.
Slides: https://buff.ly/3P3pnQF
[#]rstats #rspatial #gischat #spatialecology
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Advancing geocomputation through open-source software! πΊοΈ
Consider supporting our continued development of free, powerful spatial analysis resources, such as Geocomputation with R.
Learn how to contribute at https://buff.ly/3OMFPUU
[#]rstats #rspatial #geopython #gecompx
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The tmap R package now allows to add world borders to many standard projections.
Read more about it at: https://buff.ly/3ActgyD
[#]rstats #rspatial #tmap
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"Ising on the Cake" a blog post by John Peach about the Ising model, including various further references.
Read it at https://buff.ly/4h9aAkc
[#]isingmodel #spatialsimulations
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π A regular reminder: CRAN Task View: Analysis of Spatial Data is always open for contributions!π
Let us know about great tools for working with spatial data in R!
https://buff.ly/487HU6S
[#]rstats #rspatial #CRAN
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"Machine Learning Approaches for Working with Spatial Data"
Learn how to adapt your #machinelearning workflows for spatial data using #rstats and #rspatial.
Covers spatial feature engineering, cross-validation, and model explainability.
Url: https://buff.ly/4fCOd56
[#]3RqueR #RtieneuncoloRespecial
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π₯ The final blog post in my series on methods for comparing spatial patterns discusses its properties, applications, and extensions.π₯
Read it at https://buff.ly/40NozWQ
[#]spatialpatterns #gischat #geospatial
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