After four months of development the new update release GRASS GIS 7.2.1 is available. It provides more than 150 stability fixes and manual improvements compared to the first stable release version 7.2.0. An overview of new features in this release series is available at New Features in GRASS GIS 7.2. See here the original announcement on the GRASS GIS website.
GRASS GIS offers some useful but basic plotting options for raster data. However, for plotting of data in attribute tables and for more advanced graphs, we need to use other software tools. In this tutorial I explore some of the possibilities offered by Pandas plot() and how we can further tune plots using matplotlib / pyplot library.
In this post I show how to import an attribute table of a vector layer in a GRASS GIS database into a Pandas data frame. Pandas stands for Python Data Analysis Library which provides high-performance, easy-to-use data structures and data analysis tools for the Python programming language. For people familiar with R, the Pandas data frame is an object similar to the R data frame. They are a lot like the most common way in which spreadsheets are used, with the data presented in rectangular form with columns holding variables and rows holding observations. An important characteristic is that the data frame, like a spreadsheet, can hold different types of data in different columns: numbers, character data, dates and so on. Continue reading “GRASS and Pandas – from attribute table to pandas dataframe”
Exploring species-environment relationships is important for amongst others habitat mapping, biogeographical classification, conservation, and management. And it has become easier with (i) the advance of a wide range of tools, including many open source tools, and (ii) availability of more relevant data sources. For example, there are many tools with which it is relatively easy to create a wide range of derived terrain variables using digital elevation (DEM) or bathymetric (DBM) models. However, the ease of use of many of these tools, especially when used by non-experts, may lead to the selection of arbitrary or sub-optimal set of variables. In addition, derived variables will often be highly correlated (Lecours et al. 2017).
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.
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!
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?”