In recent years, many researchers have renewed interest in the spatially integrated social sciences, following the call of 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, get data, and process them for further analysis. These steps involve essential geospatial operations, such as cropping, aggregating, or linking data, and 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.