New R package: forestdata makes it easy to download forestry and land cover data from multiple sources (Copernicus, ESRI, EU-Trees4F, and more). Supports sf, SpatRaster, and tidy outputs.
Explore it here: https://cidree.github.io/forestdata/
#rspatial
0 posts0 participants0 posts today
I finally manage to watch @paleolimbot presentation at @RConsortium on "scaling the #Rspatial ecosystem" !
https://www.youtube.com/watch?v=tjNEoIYr_ag
Quick subjective key points:
- Use the database Luke and learn a bit SQL (I was already converted)
- the diversity of R packages to do some workflows also represent the diversity of standards (s.f.) and steps to reach similar results
- wkt_filter seems very nice (I was using "query" and GDAL/SQL instead)
Replied in thread
@MichaelTBacon @eliocamp here it seems #Rspatial is innocent, #GDAL error is helpful here.
I should start collecting all the ways in which #RSpatial can go wrong spectacularly.
Chapter 7: Coordinate Reference Systems 
This chapter explains how to work with coordinate reference systems (CRSs) in R. Learn how to reproject vector and raster data, and understand how CRS choices affect spatial analysis.
Seems @loreabad6 passed away yesterday. Apparently she was hit by a vehicle while riding a bicycle.
Yet another amazing person leaves us at a very young age. RIP.
Chapter 1: Introduction to Geocomputation 
What is geocomputation? Why use R for spatial analysis? 
This chapter traces its history, connects it to FOSS4G, and explores R’s power for geospatial work. Learn about R-spatial’s evolution! 
"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 - https://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.
A new version of {maptiles} has been released on CRAN. 
You can now access the high-resolution (or retina) tiles distributed by some providers.
{maptiles} is a package for downloading and composing raster tiles with R.
https://github.com/riatelab/maptiles
Like in previous years, I have used some of the amazing maps of @terence as examples of cartography in R in my book An Introduction to Spatial Data Analysis and Statistics
paezha.github.ioChapter 2 Basic Operations and Data Structures in R | An Introduction to Spatial Data Analysis and Statistics: A Course in RThis book was created as a resource for teaching applied spatial statistics at McMaster University by Antonio Paez, with support from Anastassios Dardas, Rajveer Ubhi, Megan Coad and Alexis Polidoro. Further testing and refinements are due to John Merrall and Anastasia Soukhov. The book is published with support of an Open Educational Resources grant from MacPherson Institute, McMaster University.
New blog post about {mapsf}, an R package for thematic mapping.
https://rcarto.github.io/posts/mapsf_v0.12.0/
A recent update has made 2 new features available: extraction of borders between polygons and creation of pencil drawing patterns.
Introducing rJavaEnv: A new R package to manage multiple Java JDKs for your R projects! 
https://github.com/e-kotov/rJavaEnv
#rstats #gischat #rspatial #rJava
Using #r5r and #OpenTripPlanner, or another rJava/Java dependent package, this tool for you!
Thread 
Wanting to get started with #rspatial for the geosciences? Attending #EGU24?
Then come and join the "Intro to R for the Geosciences" short course on April 16th, 7PM.
Happy that we @inbo supported faster qgis_process + #GRASSGIS Provider + overall startup time in #QGIS 3.36 !
https://changelog.qgis.org/en/qgis/version/3.36/#faster-grass-provider-startup
Great work @nyalld! 
This significantly benefits startup time of R #qgisprocess 
https://r-spatial.github.io/qgisprocess
Feature request: https://github.com/qgis/QGIS/issues/54563.
changelog.qgis.orgProjecta: Entries -
Hello! Hey there! 
I'm a biomedical engineer and informaticist curious about all intersections of data and society 
I work in #Philly at the Penn Urban Health Lab on #RSpatial projects centering urban nature, racial justice, and health equity 
I'm also an #RStats educator passionate about #DataLiteracy and #FOSS as tools to build power in communities 
You can learn more about me at https://silviacanelon.com, and I hope to learn more about you here! 
silviacanelon.comSilvia Canelón, PhD
First CRAN release of #qgisprocess became reality: use QGIS processing algorithms in R !
Website: https://r-spatial.github.io/qgisprocess
CRAN page: https://cran.r-project.org/package=qgisprocess
Some features:
- needs no QGIS project, just use R
- integrates with sf, terra, stars, raster for input/output
- supports QGIS processing plugins
Acknowledgements in the reply!
New blog post! Learn how to use #rstats to load and clean and play with all the location history data Google keeps about you and look at some neat plots and tables about our 5,000-mile summer road trip along the way #dataviz #rspatial https://www.andrewheiss.com/blog/2023/07/03/using-google-location-history-with-r-roadtrip/
Major Announcement
We created a new home for all of our resources on reproducible geographic data analysis, modeling, and visualization with open source software!
Visit https://geocompx.org/.