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Materials for the Course “Advanced Topics in Spatial Data” as part of the Summer School for Women in Political Methodology (July 24, 2026)
Anne-Kathrin Stroppe (anne-kathrin.stroppe@gesis.org)
Thanks a lot to Stefan Jünger (stefan.juenger@gesis.org) \& Dennis Abel (dennis.abel@gesis.org) who have co-authored these slides.
Many of the phenomena political scientists study don’t sit independently in space: Voting outcomes, policy adoption, protest behaviour, and migration decisions all
tend to cluster, affect neighbouring areas, or diffuse across borders. This spatial dependence is both a risk and an opportunity. It can undermine the assumptions our
models rely on, but the very same structure can also become the main interest of the mechanisms we want to explain. In this session we will move through three relationships researchers can have with spatial dependence. First, space can act as a threat, in which case we diagnose dependence, and use spatial error models so it no longer biases our models. Second, space can be a process in its own right, in which case we model diffusion and spillover as the substantive phenomena we actually want to explain. Third, space can serve as leverage, in which case we exploit spatial structure as a source of identifying variation for causal claims.
## What do you find here?
This page comprises the official course repository with the most recent changes to our materials. You can find all the course data, slides, and exercises here. The section below links the slides and exercises that will open them directly in the browser as HTML files. They are also stored in the folders ./slides/ and ./exercises. You can also find all the data in the folder ./data. They comprise the following official (Open Data) sources:
German Census 2022 data are provided by the [Federal Statistical Office Germany, Wiesbaden 2025](https://ergebnisse.zensus2022.de/)
Shapefiles, voting data, and car-related data for Cologne are gathered from the [Open Data Portal Cologne](https://www.offenedaten-koeln.de/)
**Please make sure that if you reuse any of the provided data to cite the original data sources.**