GESIS Training Courses
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Scientific Coordination

Alisa Remizova

Administrative Coordination

Claudia O'Donovan-Bellante

Geodata and Spatial Regression Analysis

About
Location:
Mannheim B6, 4-5
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Fees:
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 states: “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, the basic assumptions of standard regression models are violated when 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 of 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; furthermore, the course addresses several methods for defining spatial relationships. Hereby, the detection and diagnosis 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.
 
Organisational structure of the course
The course follows the structure of 3 hours of lecture + 3 hours of lab exercise every day. The lab time includes, among others, working on hands-on exercises with real-world data and discussing unclear topics of the lectures. The lecturer is available for support during the lab, discussions, and consultations of participants' projects.
 


Target group

Participants will find the course useful if they are:
  • 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 be able to:
  • 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 (among others, experience with spatial data, such as loading and transforming data, and familiarity with the SF package). For an introduction or to refresh your knowledge, you may refer to https://r-spatial.org/book/ or attend the Intoduction to Geospatial Techniques for Social Scientists in R workshop offered by GESIS prior to the current workshop.
  •  
    Software and hardware requirements
     R with spatial software packages (sf, gstat, mapview, spdep, spatialreg). These require some spatial libraries: GDAL, GEOS, PROJ, S2.


    Schedule

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