In recent years, many researchers have renewed interest in the spatially integrated social sciences, following the call for a 'spatial turn' among plenty of its subdisciplines. However, to process, visualize, and analyze geospatial data, social scientists must first be trained in specialized tools called Geographic Information Systems (GIS). The good news is: While this may have been an unacquainted undertaking until recently, the familiar open-source statistical language R can now serve as a full-blown GIS for many research applications.

This course will teach its participants how to exploit R to apply its geospatial techniques in a social science context. We will learn about the most common data formats, their characteristics, and their applications. Most importantly, the course will present available data sources and how to get data and process them for further analysis. These steps involve essential geospatial operations, such as cropping, aggregating, or linking data, and they are the first fundamental steps of modeling and assessing spatial interdependence. The course will be hands-on, so it also includes one of the most rewarding tasks of working with geospatial data: visualizing them through maps.

 

Organisational structure of the course

The best way to learn new things in R is to try things out and apply the presented concepts. Therefore, we will have a mixture of lectures and hands-on exercises. More specifically, each topic will be introduced in a lecture by the instructors. Participants will then receive a set of exercises on each topic. The solution to the exercises will be discussed before the start of the next lecture part.

Participants will find the course useful if:

By the end of the course, participants will:

Software and hardware requirements

R and RStudio. A list of packages will be provided in the weeks before the workshop.