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Handling and Mapping Spatial Data


Nov 6-8, 2023

Lang EN Workshop language is English

Prof. Cleo Bertelsmeier, UNIL
Sébastien Ollier, UNIL
Olivia Bates, UNIL


Sébastien Ollier, UNIL
Olivia Bates, UNIL


Over the last few decades geospatial analysis has progressed at an astonishing rate. Spatial data analysis is no longer the preserve of those with expensive hardware and software like ArcGis: anyone can now download and run high-performance spatial libraries. Open source Geographic Information Systems (GIS), such as QGIS, have made geographic analysis accessible worldwide. But GIS programs tend to emphasize graphical user interfaces with the unintended consequence of discouraging reproducibility. R, by contrast, emphasizes the command line interface: although it is not dedicated to mapping, it presents a lot of facilities to handle and map spatial data, a key step for data analysis in ecology. Participants will learn a range of spatial skills in R, including: reading, writing and manipulating geographic data; making static and interactive maps and applying geocomputation to solve real-world problems. We will illustrate these concepts using different examples from multiple fields of ecology and evolution. Students will learn how to use the main packages to manipulate spatial data in R (sf, terra, mapsf, tmap, leaflet, shiny). They will train on case studies coming from literature. At the end of the course, they should therefore feel empowered with a strong understanding of the possibilities opened up by R's impressive spatial capabilities, new skills to solve real-world problems with geographic data, and the ability to communicate their work with maps and reproducible code.
The course will be organized around these three sessions:

  • Handling spatial data in R: attribute and spatial operation, raster-vector interaction (3h of course and 3h of practice)
  • Mapping spatial data in R: projecting spatial data, building a nice map for publication, building an interactive map for managing data (3h of course and 3h of practice)
  • Geocomputation and spatial exploration: exploring spatial structure on a map, interpolation methods (3h of course and 3h of practice)

Basic knowledge of R is required. Participants must come with their laptops with the last version of R installed


University of Lausanne



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