Microcredential komex Geographical Information Systems and Geospatial Data Analysis

This three-day in-person course provides a hands-on introduction to working with geospatial data and GIS in R for PhD students in the social sciences.

What Is This Course About?

Many social science research questions have a spatial dimension: where people are located, how places differ, how close observations are to each other, and how outcomes vary across space. This three-day in-person course introduces students to the basics of working with geospatial data and geographic information systems in R. Through practical examples, participants will learn how to import, manage, combine, visualize, and interpret spatial data for their own research projects.

Learning Goals

• Understand core concepts of GIS (geographic information systems) and geospatial data
• Create clear and informative maps for exploratory analysis and communication
• Learn to transform and combine different types of spatial data
• Recognize common challenges in working with spatial data (projection errors, spatial mismatches, modifiable areal unit problem)
• Gain an overview of more advanced spatial topics like spatial autocorrelation diagnostics, Moran’s I, and spatial regression models

Recommended Readings for the Course

• Pebesma, Edzer, and Roger Bivand (2025) Spatial Data Science: With Applications in R. Chapman & Hall.
• Lovelace, Robin, Jakub Nowosad, and Jannes Muenchow (2025) Geocomputation with R. 2nd ed. Chapman & Hall/CRC.
• Bolstad, Paul, and Steven Manson (2022) GIS Fundamentals: A First Text on Geographic Information Systems. 7th ed. Eider Press.
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Assignments for the Course

Daily assignments (not graded) consisting of theoretical and practical problems that the students solve throughout the course.

Who Is Your Instructor?

Niklas Hänze is a postdoctoral researcher at the Cluster of Excellence The Politics of Inequality and the Department of Politics and Public Administration at the University of Konstanz. He has expansive experience in working with geospatial data for social science research projects and regularly incorporates complex geospatial environmental data in his work. Website: https://nhaenze.github.io/ Bluesky: https://bsky.app/profile/nikhaenze.bsky.social

Schedule

9-10.30h: Course

10.30-11h: Break

11-12.30h: Course

12.30-13.30h: Lunch break

13.30-14.30h: Applied work on geodata problems

14:30-15:30: Office hour

In addition, students should plan for sufficient time to do the assigned daily homework and read the assigned literature in advance.