Manipulating and illustrating large public datasets using advanced open source tools for spatial analysis
Tuesday, April 16 2019 09:30AM-04:30PM to Wednesday 17 April 2019 09:30-16:30
Teaching room - Redpath museum
Language of the workshop: English
In partnership with
Are you interested in global patterns of species distributions? How the human population size or the socioeconomic status of people in different parts of the world is changing over time? Or how this associated with environmental change, such as deforestation rates or climate change?
Fortunately, this information can be obtained from publicly available data sets, for example climate data from the past and prediction (e.g., worldclim.org), species distribution (IUCN), or data on human population size, land use, socioeconomic factors, or health (e.g., FAOSTAT, Worldbank). In this course, you will be provided with an overview about such datasets, and get an introduction how to obtain and work with them. Furthermore, we will discuss different ways of data visualizations that are helpful in this context, including the creation of geographic maps. As we will use various R packages (e.g., dplyr, sf, raster), a basic skillset with R is highly recommended; if you need to improve your R skills, please contact the course coordinators for recommendations for self-study.
The second day of this workshop will demonstrate the possibilities for the integration of different open source platforms for spatial analysis. A basic introduction to PostGIS will be provided, and the Processing platform in QGIS. Then, different possibilities for interactions between the various platforms will be discussed and shown.
- Basics and advantages of PostGIS
- Using PostGIS in QGIS
- Using PostGIS, QGIS, GDAL and GRASS within R
- Processing toolbox in QGIS. How to use R, GRASS and SAGA within QGIS
- Graphical modeling in QGIS
- Introduction to public data sets and which types of data that can be obtained from different sources
- Advantages and disadvantages of different datasets
- Different types of dataset structures
- Introduction to the concept of relational databases How the tidyverse (dplyr, tidyr, …) can be used to combine and manipulate datasets in R
- Using the sf package in combination with the tidyverse to create maps in R