Scientific Coordination
Alisa Remizova
Administrative Coordination
Claudia O'Donovan-Bellante
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Geodata and Spatial Regression Analysis
About
Location:
Mannheim B6, 4-5
Mannheim B6, 4-5
General Topics:
Course Level:
Format:
Software used:
Duration:
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Fees:
Students: 330 €
Academics: 495 €
Commercial: 990 €
Keywords
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Lecturer(s): 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:
Learning objectives
By the end of the course, participants will be able to:
Prerequisites
Software and hardware requirements
R with spatial software packages (sf, gstat, mapview, spdep, spatialreg). These require some spatial libraries: GDAL, GEOS, PROJ, S2.