From an announcement from the QGIS mailing list: RQGIS has released a new version of RQGIS! RQGIS establishes an interface between R and QGIS, i.e. it allows the user to access the QGIS geoalgorithms from within R. With the new release, it is possible to run the most recent QGIS releases (>=2.18.2) with RQGIS.
The GRASS GIS development team recently released a new stable major release, GRASS GIS 7.2. The release brings more than 1900 fixes and improvements since the previous stable release 7.0.5. You’ll find a detailed overview of all the changes and improvements on this GRASS wiki page.
One important library change in a GRASS library is support for NULL file compression using the r.null function. This may not sound terribly exciting to all of you, but for those that have GRASS databases with large (number of) raster layers, this may save considerable space on the hard disk.
Continue reading “Saving space on your HD – null file compression in GRASS GIS 7.2”
I have been using the development version for some time now, and all I can say is that you definitely should give the new GRASS GIS 7.2.0RC1 release a try. It is, in my experience, very stable, and it provides more than 1900 stability fixes and manual improvements compared to the stable releases 7.0.x.
It also features a number of new modules. A favourite of mine is the new g.gui.datacatalog which makes it so much easier to browse, modify and manage GRASS maps across map sets and locations. Also very welcome is the new d.legend.vect module which can be used to display a vector legend in the active graphics frame. And for those that are into space-time analyses, there are also a number of new modules for the temporal framework.
For more information about all the improvements and changes, see the detailed announcement. And while you are at it, don’t forget to check out the add-ons, some great new ones have been added and updated in the last few months.
And last but not least, a big thanks to the developers!
In GRASS GIS you can upload raster values at positions of vector points to the attribute table of that vector point layer using the function v.what.rast. If you also interested in the raster category labels, you can have a look at r.what, which lets you query a raster map on their category values and category labels.
However, the results of r.what are written to a text file. If you want to upload raster values and labels to the attribute table of a point vector map, you can use v.in.ascii to import the text file created with r.what as a point vector layer in GRASS GIS.
Fairly straightforward, but wouldn’t it be even more convenient if you you had an option in r.what.rast to also upload the category labels? Continue reading “A GRASS GIS addon to upload raster values and labels to a point layer”
For species or vegetation modelling, one of the first choices to make is the selection of explanatory variables, which in most cases will include climatic or bioclimatic data sets. One of the most widely used global climate data sets in biogeographic and ecological research is from Worldclim (Hijmans et al., 2005). Alternative global rainfall data sets are from TAMSAT TARCAT (Maidment et al., 2014) and CHIRPS (Funk et al., 2014). The Worldclim data layers are based on an interpolation of average monthly climate data from weather stations. The other two data sets combine weather station data with satellite observations to improve accuracy where in situ rainfall measurements are sparse. All three data sets are available from the KITE resources website as part of the Africlim dataset (Platts et al. 2015). Continue reading “Climate data sets, which one to select?”
The Global Biodiversity Information Facility (GBIF) is an international open data infrastructure that allows anyone, anywhere to access data about all types of life on Earth, shared across national boundaries via the Internet. GBIF provides a single point of access through http://www.gbif.org/ to species records shared freely by hundreds of institutions worldwide. The data accessible through GBIF relate to evidence about more than 1.6 million species, collected over three centuries of natural history exploration and including current observations from citizen scientists, researchers and automated monitoring programs.
There are various ways to import GBIF data, including directly from the website as comma delimited file (csv) and using the v.in.gbif addon for GRASS (I’ll post an example using this addon at a later stage). Here, however, I’ll use the rgbif package for R to obtain the data. In the link section some tutorials are listed that illustrate the use of other R packages. Continue reading “Use R to get gbif data into a GRASS database”
In modelling, multicollinearity in the set of predictor variables is a potential problem. One way to detect multicollinearity is the variance inflation factor analysis (VIF). In GRASS GIS, the VIF for a set of variables can be computed using the r.vif addon. This addon furthermore let’s you select a subset of variables using a stepwise variable selection procedure, in which variables are removed till the highest VIF values is less than a user-defined threshold value. In this post I introduce the addon and provide some examples how to use the addon. Continue reading “VIF stepwise variable selection”