• (Spatial) Econometrics: OLS; ML; Lag/Error/Durbin Models
• Exploratory Spatial Data Analysis; Geographically Weighted Regression
• Mapping; Visualizations; Projections
• Hands-on R exercises
• R spatial environment & packages
• Usage of RStudio Professional IDE with remote pair programming support
• Interactive R Tutorials supported by RStudio Connect

Students will be able to apply to

• collect, prepare and analyze spatial data in R
• choose and apply spatial econometric methods
• understand limitations, drawbacks and common issues in spatial data analysis

Regular attendance is required (80%)

Hybrid/Blended Learning Format

1. In-class: Teacher presentation of basic concepts, discussion and review of coding sessions/assignments
2. Remote class: Guided coding sessions using RStudio Connect
3. Weekly assignments

1. Final Exam (60%)
2. Assignment (30%)
3. In-class discussion or student inputs (10%)

First come

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• Good knowledge of Econometrics I (OLS, ML)
• Basic R training (recommended, quick overview will be given in first unit)

Anytime: matmoser@wu.ac.at