Geospatial Resources
This was part of the geospatial package.
byandell.github.io/Documentation
Books and Online Docs
- Data Carpentry Workshop
- Spatial Data Science book by Edzer Pebesma and Roger Bivand
- Geospatial Vector Data in R with SF (Coding Club)
- Online Comparisons of Some Spatial Packages
- ArcGIS Online
- Free online accounts, with new features added over time
- QGIS
R Packages
Packages to Create Data Cube Layers
- gdalcubes
- https://r-spatial.org/
- https://rspatial.org/
- tidyterra
- https://dieghernan.github.io/tidyterra/
- Extension of the
tidyverseforSpatRasterandSpatVectorobjects of theterrapackage
Packages to Access Data
- rstac: Access, search and download from SpatioTemporal Asset Catalog (STAC)
- osmdata: Download and import of ‘OpenStreetMap’ (‘OSM’) data
- geos: R API to the Open Source Geometry Engine (‘GEOS’)
- landsat: Processing of Landsat and other multispectral satellite imagery
Previous package rgdal is now obsolete. Unsure about status of raster package.
Data Repositories
- https://data-library.esiil.org
- See list on menu of
https://cu-esiil.github.io/hackathon2023_datacube/code_for_building_cube/Pull_flood_data/
These have been compiled in datasets.csv.
Data are stored in different coordinating systems, which makes it important to transform between them. Some common ones:
- EPSG:4326: WGS84 = World Geodetic System 1984
- EPSG:32618: WGS84 for UTM zone 18N (North America)
- EPSG:32730: WGS84 for UTM zone 20S (South America)
Geospatial Workshops
ESIIL Hackathon, CU Boulder, November 15-17, 2023
ESIIL_Art_Data_Cube.Rmd: Yandell edit of Ty Tuff’s The Art of Making a Datacube
Geospatial Data Carpentry Workshop, UW-Madison, June 5-8, 2023 https://go.wisc.edu/i4gsfr
Geospatial.Rmd: Rmarkdown from Workshop
Geospatial Download for Data Carpentry Workshop
The data have been organized in The Carpentries nicely in FigShare as workshop data from carpentries site. See also the https://datacarpentry.org/geospatial-workshop/ page section on Data and more information at https://datacarpentry.org/geospatial-workshop/data.html. The data seem to come from NEON Raster Intro page NEON Raster 00: Intro to Raster Data in R, via Download Dataset. The data are from two field sites:
- Harvard Forest (HARV)
- San Joaquin Experimental Range (SJER)
The key raster data are the following “geotif” files:
- HARV_dsmCrop.tif
- HARV_dsmCrop.tif
- HARV_DSMhill.tif
It should be possible using some of the commands in the The art of making a data cube to elegantly download needed data on the fly.