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Administrative Coordination

Claudia O'Donovan-Bellante
Tel: +49 621 1246-221

Geospatial Techniques for Social Scientists in R (Online-Workshop!)

Lecturer(s):
Dr. Stefan Jünger, Anne-Kathrin Stroppe

Date: 08.02 - 09.02.2021 ics-file

Location: Online via Zoom / Course language: English

About the lecturer

Course description

When social scientists aim to use geospatial data, they must rely on specialized tools, called Geographic Information Systems (GIS). However, the world of GIS is complicated, since often only foreign software solutions provide a comprehensive collection of available geospatial techniques. Fortunately, nowadays, social scientists can also use the statistical software R as a proper GIS. Thus, this course will teach how to exploit R and apply its geospatial techniques in a social science context. We will learn about the most common data formats, their quirks, and their application. Most importantly, the course will present data sources, how to get the data and wrangle them for further analysis.  Central are geospatial operations, such as cropping, aggregating or linking data. Finally, what is of interest for many researchers is creating maps, which is also straightforward in R.


Target group

Beginners or advanced users of R who want to learn (more) about geospatial data, their use and the creation of maps.


Learning objectives

Participants will learn how to process geospatial data in R using the most recent packages and routines available. At the end of the course, they will be familiar with the different data formats and structures, and they will feel comfortable exploiting geospatial data in R for their own (research) purposes. Their most visual skill will be creating quick and dirty maps and advancing them in great detail using geometric operations.


Prerequisites

Participants should at least have some basic knowledge of R, its syntax and internal logic. Generally, it is helpful to have some sort of affinity for using script-based languages (R, Stata, Python) and wrangling of complex data structures.


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