Use R to get gbif data into a GRASS database



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 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 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”

VIF stepwise variable selection


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”

Sample raster values at point location in QGIS – yet another way

A while back I wrote how one can sample raster values at point locations in QGIS using the Saga function ‘Add grid values to point’, which is available in the processing toolbox. Recently it was reported here that values uploaded from a floating raster layer are rounded up to an integer. I tried it myself in Saga, and it seems to work fine for me. But if you are running in problems with a function, it is good to remember that in QGIS you have have access to many libraries from multiple software tools. This means there are often more than one way to get things done.

And indeed, the processing toolbox offers another tool to sample raster values at point location; the GRASS GIS v.what.rast.points function. In this post I describe how to use this tool directly in GRASS, but you can use the tool in QGIS as well, as illustrated below. Continue reading “Sample raster values at point location in QGIS – yet another way”

Update of r.forestfrag addon

I just updated the r.forestfrag addon for GRASS GIS. The addon, which I described before, was developed based on an script by Sylla consult,  and can be used to characterizes the degree of fragmentation following the approach proposed by Riiters et al. (2000). It identifies six categories of fragmentation, viz., interior, perforated, edge, transitional, patch, and undetermined, based on the amount of forest and its occurrence as adjacent forest pixels within a user-defined distance (i.e., the user sets the size of moving window used to compute the index).  Continue reading “Update of r.forestfrag addon”

QGIS 2.14 ‘Essen’ has been released

The QGIS development team has released version 2.14 of QGIS. This is a special release since it is designated an ‘LTR’ (Long Term Release), which means it will be supported with back ported bug fixes for one year.

This new version has a host of new features and improvements. See the visual changelog for a nice overview and head over to the QGIS home page to get your copy (it may not yet be available for all platforms. If not, revisit the page in a few days).

As a very happy user of QGIS, I would like to express my sincere thanks to the developers for their hard work, and to the sponsors (listed on the page with visual change log in case you are interested who has made this new version possible).

Plot of temporal data sets in GRASS GIS

One of the new modules in GRASS GIS 7.03 (RC1) is g.gui.tplot. It is part of the temporal data processing framework (TGRASS) introduced with GRASS GIS 7.0 and lets you plot the values of one or more temporal datasets for a specific point. It furthermore allows plotting data of vector dataset for a defined categories and attribute. In this post I’ll use the tool to plot changes in the annual mean temperature and annual minimum temperature of the coldest month in the last century, using the CRU climate data set. First I’ll provide a brief overview of how to import and prepare the data and finally how to plot a time series using the g.gui.tplot module. Continue reading “Plot of temporal data sets in GRASS GIS”