Regression Analysis for Spatial Data

Work load

Classes: 12 sessions (90 minutes): 6 lecture sessions and 6 R Tutorials

Assignments: Two graded R assignments to be solved individually. About 5 hours for each assignment.

Paper Replication: About 10 hours.


Students are expected to have basic knowledge of probability theory, econometrics, and matrix algebra. The course is based on the program R. We ask students to bring their laptops to class. R and R Studio are available for free on Windows computers and Macs.


Course content

This course focuses on the visualization and modeling of spatial data. Examples are taken from different research areas such as political science, empirical international trade, criminology, and real estate. It offers a detailed explanation of individual estimation methods and their implementation in R. In this course, students will learn

What students do NOT learn in this course:



Day 1

Lecture 1: 09:15 – 12:00

R Tutorial 1: 13:00 – 15:00


Day 2

Lecture 2: 09:15 – 12:00

R Tutorial 2: 13:00 – 15:00


Day 3

Lecture 3: 09:15 – 12:00

R Tutorial 3: 13:00 – 15:00


Day 4

Lecture 4: 09:15 – 12:00

R Tutorial 4: 13:00 – 15:00


Day 5

Lecture 5: 09:15 – 12:00

R Tutorial 5: 13:00 – 15:00





LeSage, J., and R.K. Pace (2009), “Introduction to Spatial Econometrics”. CRC Press.


Supplementary / voluntary

Elhorst, J.P. (2014), “Spatial Econometrics: From Cross-Sectional Data to Spatial Panels”, Springer.


Holly, S., M.H. Pesaran, and T. Yamagata (2011), “The Spatial and Temporal Diffusion of House Prices in the UK”, Journal of Urban Economics 69, 2–23.


LeSage, J. (2014), “What Regional Scientists Need to Know about Spatial Econometrics”, The Review of Regional Studies 44, 13–32.


Mandatory readings before course start



Examination part

The course contains two graded R assignments and one graded paper replication. The assignments and the paper replication are to be solved individually.


R Assignment 1: 20%

R Assignment 2: 20%

Paper Replication: 60%


Supplementary aids

For solving the R assignments and the paper replication, any source from academic papers, text books and other sources from the internet are allowed.


Examination content