GESIS Training Courses

Scientific Coordination

Alisa Remizova
Tel: +49 221 47694-475

Administrative Coordination

Noemi Hartung

Geodata and Spatial Regression Analysis

Mannheim B6, 4-5
General Topics:
Course Level:
Software used:
Students: 330 €
Academics: 495 €
Commercial: 990 €
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Lecturer(s): Tobias Rüttenauer

About the lecturer - Tobias Rüttenauer

Course description

In recent years, more and more spatial data has become available, providing the possibility to combine otherwise unrelated data, such as survey data with contextual information, and to analyze spatial patterns and processes (e.g., spillover effects or diffusion).
Many social science research questions are spatially dependent such as voting outcomes, housing prices, protest behavior, or migration decisions. Observing an event in one region or neighborhood increases the likelihood that we observe similar processes in proximate areas. As Tobler's first law of geography puts it: “Everything is related to everything else, but near things are more related than distant things”. This dependence can stem from spatial contagion, spatial spillovers, or common confounders. Therefore, basic assumptions of standard regression models are violated while analyzing spatial data. Spatial regression models can be used to detect this spatial dependence and explicitly model spatial relations, identifying spatial spillovers or diffusion.
The main objective of the course is the theoretical understanding and practical application of spatial regression models. This course will first give an overview on how to perform common spatial operations using spatial information, such as aggregating spatial units, calculating distances, merging spatial data as well as visualizing them. The course will further focus on the analysis of geographic data and the application of spatial regression techniques to model and analyze spatial processes, and furthermore, the course addresses several methods for defining spatial relationships. Hereby, the detection and diagnostic of spatial dependence as well as autocorrelation are demonstrated. Finally, we will discuss various spatial regression techniques to model processes, clarify the assumptions of these models, and show how they differ in their applications and interpretations.
Organizational structure of the course
  • Aim of course: 3 hours of lecture + 3 hours of lab exercise every day
  • Lab time: work on hands-on exercises with real-world data, discuss unclear topics of lecture
  • Support: Support exercises during lab, discussion and consultations of own projects

  • Target group

    Participants will find the course useful if:
  • Working with spatial data
  • Analyzing spatial research questions
  • Using spatial econometrics
  • Combining different data sources with geo-coordinates

  • Learning objectives

    By the end of the course participants will:
  • Manipulate and combine spatial data sources
  • Detect and visualize spatial patterns and relationships
  • Analyze spatial processes
  • Construct spatial weights matrices
  • Apply and interpret spatial econometric models

  • Prerequisites

  • Profound knowledge of standard regression estimators (OLS and Maximum Likelihood)
  • Advanced knowledge of R
  • Basic knowledge of matrix algebra and statistics
  • Basic knowledge of GIS in R would be preferable
    Software and hardware requirements
    R with spatial software packages (sf, gstat, mapview, spdep, spatialreg) should be installed. These require some spatial libraries: GDAL, GEOS, PROJ, S2.


    Recommended readings