Main page of this document: See

Analysing environmental data with GRASS GIS

Fun with GRASS GIS

In this session "Analysing environmental data with GRASS GIS" we will start with a few GRASS GIS basics. We then focus on analysing the ECA&D climatic data, looking at data organization, data aggregation, univariate statistics, raster map algebra, and zonal statistics. This part is followed by classification with machine learning (RandomForest Classifier) and concluded with a quick glance at linear and multiple regression in GRASS GIS.

The trainer

Markus Neteler is partner and general manager at mundialis GmbH & Co. KG, Bonn, Germany. From 2001-2015 he worked as a researcher in Italy. Markus is co-founder of OSGeo and since 1998, coordinator of the GRASS GIS development (for details, see his private homepage).

Session overview

In this session, we will go through the most commonly used spatial analysis techniques in GRASS GIS. The session is a hands-on with a set of exercises covering basic raster and vector processing capabilities of GRASS GIS.

  • Some quick GRASS GIS basics
  • Geospatial data import (aggregated ECA&D climatic data time series)
  • Simple exploratory data analysis
  • Zonal statistics: GRASS GIS and R with raster library
  • Machine Learning analysis to classify the European climatic zones based on ECA&D climatic data
  • Linear models in GRASS GIS and R

Software sources

You need to have GRASS GIS 7.6 or later installed.

See here for an overview (GRASS GIS, R, QGIS for different operating systems)

Course data download

For this exercise, we will use a GeoTIFF file and the already prepared GRASS GIS location ecad17_ll.

  • Download course ECA&D elevation GeoTIFF file here (or here)
  • Download the GRASS GIS location here (or here)
  • Unzip the file "" into the "$HOME/geodata/" folder (create folder if needed)
  • Unzip the file "" into the "$HOME/grassdata/" folder (create folder if needed)

A few GRASS GIS basics

This short section covers a few concepts you should know before continuing with the exercises. Please read on in

Analysing the ECAD climatic data

Please read on in

Classification with machine learning in GRASS GIS

Please read on in

Linear and multiple regression in GRASS GIS

Please read on in

Python and GRASS GIS

Some tricks are shown in


Original data sources

  • ECA&D V17 daily climatic data 1950-2017: aggregated in GRASS GIS to annual and monthly data of 30 years averages, 0.25° spatial resolution
  • Related European elevation model
  • NaturalEarth Admin0 country borders
  • Köppen climate types for the period 1901-2010 on different time scales by Chen et al 2013

Last changed: 2 Sep 2019

Creative Commons License "Analysing environmental data with GRASS GIS" by Markus Neteler, 2018

About | Privacy