A growing interest in economics and the social sciences in Earth observation (EO) data has led to a broad thematic spectrum of publications in recent years. They range from studying environmental attitudes and behavior, economic development, conflicts and causes of flight, and electoral behavior. However, working with EO data requires advanced knowledge of geospatial data processing. Social science researchers face many obstacles in applying and using these data, resulting from 1) a lack of technical expertise, 2) a lack of knowledge of data sources and how to access them, 3) unfamiliarity with complex data formats, such as high-resolution, longitudinal raster datacubes, and 4) a lack of expertise in integrating the data into existing social science datasets. After all, despite the increased interest in the data, for the majority of researchers in the social sciences, complex geospatial data derived from remote sensing represents a black box.

 

This course aims to address this gap. We will focus on data access from large databases via APIs, data wrangling of raster data and datacubes, and introduction of workflow for data integration with users' datasets, such as survey data. This course is advanced and suitable for students and scientists who feel familiar with R and have some basic knowledge of working with geodata.

 

Organizational structure of the course

The best way to learn geospatial data processing 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 that they work on alone. The solution of the exercises will be discussed before the start of the next lecture part.

The Introduction to Geospatial Techniques for Social Scientists in R workshop, offered by GESIS at the beginning of April 2025, is a suitable basis for the current workshop.

 

Software requirements

Course participants will need a computer or laptop with R (https://cran.r-project.org/) and RStudio installed (https://www.rstudio.com/). Both programs are free and open source.