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701-0104-00L 3 Credits BSC , MSC D-USYS , D-BAUG , D-MATH , D-INFK , D-ITET

Statistical Modelling of Spatial Data

Lecturers & Examiners: Dr. Andreas Jürg Papritz
VVZ CR n/a

Last Updated: 2026-02-05 15:55:14

Abstract

In environmental sciences one often deals with spatial data. When analysing such data the focus is either on exploring their structure (dependence on explanatory variables, autocorrelation) and/or on spatial prediction. The course provides an introduction to geostatistical methods that are useful for such analyses.

Objective

The course will provide an overview of the basic concepts and stochastic models that are used to model spatial data. In addition, participants will learn a number of geostatistical techniques and acquire familiarity with R software that is useful for analyzing spatial data.

Content

After an introductory discussion of the types of problems and the kind of data that arise in environmental research, an introduction into linear geostatistics (models: stationary and intrinsic random processes, modelling large-scale spatial patterns by linear regression, modelling autocorrelation by variogram; kriging: mean square prediction of spatial data) will be taught. The lectures will be complemented by data analyses that the participants have to do themselves.

Resources

Lecture Notes

Slides, descriptions of the problems for the data analyses and solutions to them will be provided.

Literature

P.J. Diggle & P.J. Ribeiro Jr. 2007. Model-based Geostatistics. Springer.

General Information

Language
English
Levels
BSC , MSC
Frequency
Yearly recurring

Examination

Type
end-of-semester examination
Mode
written 150 minutes
Aids
None
Digital
The exam takes place on devices provided by ETH Zurich.
Online exam of 150 minutes duration, cf.Link. The exam is open-book. Participants are allowed to use any resources they find useful, such as course material, results from web searches, etc. to work out the solutions to the tasks. However, they must not use the help of any human being by any way of communication. Examined material: assigned sections of textbook; material of slides and of course notes with solutions to problems provided for the data analyses.

Course Components

Type Title Time & Place Hours
lecture with exercise Statistical Modelling of Spatial Data
  • Wed 08:15-10:00 (CHN F 46)
2 h weekly

Offered In