EDENext Training Program - Spatial Data and GIS, Year 2 (2012)

The second year EDENext Spatial Data and GIS residential workshop was hailed a great success and built apon the skills learnt in the first year of the course.  The course took place in Antwerp, Belgium from the 4th-6th of December inclusive.   The improved skill levels of the participants were obbvious from the first day and the EDENext DMT Team would like to congratulate the participants on their obvious application to the distance learning course throughout the previous year.

The focus of this years course was to introduce a number of tools and approaches that can be used on the spatial data that the students now have the skills to collect and mange within a GIS environment.  The course utilised GIS software, modelling software and scripting platforms.  What was very satisfying to see was that on the final day of the course students were using all three environments on their own datasets, moving datasets confidently from GIS for viewing and setting extents, R for batch processing clipping and some modelling and then into Vecmap for further modelling.

The distance learning part of the course will continue in the new year.  Details of its content will be confirmed shortly.

Links to the software used during the workshop are given below:

Workshop syllabus

  1. Getting back into QGIS and GRASS - This topic recapitulates the basic and advanced functionalities of QGIS and GRASS. This is illustrated using the case study provided by the ROBO group. Els Ducheyne

  2. Using R: Data import - How to import data for species distribution modelling. Els Ducheyne

  3. Using R: Species distribution Modelling - Using the presence data collected during the MODIRISK project, this topic shows how ENFA can be applied in R. In the next step, the absence data are included in the data set for Random Forest Classication.  In the final step, the accuracy of the output models is assessed. Els Ducheyne

  4. Using R: Spatial Autocorrelation - An illustrated session on how to measure spatial autocorrelation and how to account for it into linear models using R. Marius Gilbert

  5. VECMAP modelling - In this section, it is shown how VECMAP can be used for species distribution modelling. David Morley

  6. Useful Scripts in R - In this topic we will post useful scripts in R.  Els Ducheyne

Background Reading

This section links to the background reading and resources recommended for each of the topics of the workshop.

Getting back into QGIS and GRASS

The case study used in this module looked at creating a GIS layer of an index of the likely number of people to visit locations in forest covered areas in belgium.  Details from the following paper were used for background information.

  • Van Herzele & Wiedemann (2003); A monitoring tool for the provision of accessible and attractive urban green spaces; Landscape and Urban Planning; Vol 63, Issue 2, 15 April 2003, Pages 109–126; http://dx.doi.org/10.1016/S0169-2046(02)00192-5

Using R: Data Import

Using R: Species Distribution Modelling

  • ENFA Modelling???

  • Elith et al (2006); Novel methods improve prediction of species’ distributions from occurrence data; Ecography Volume 29, Issue 2, pages 129–151, April 2006; DOI: 10.1111/j.2006.0906-7590.04596.x

  • Random Forest???

  • Peters et al (2011); Absence reduction in entomological surveillance data to improve niche-based distribution models for Culicoides imicola; Prev Vet Med. 2011 Jun 1;100(1):15-28. doi: 10.1016/j.prevetmed.2011.03.004. Epub 2011 Apr 14.

Using R: Spatial Autocorrelation


Vecmap Modelling

  • VECMAP help file for Non Linear Discriminant Analysis

Useful Scripts in R