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Spatial Statistics
Last Updated: 2026-02-05 16:37:28
Abstract
In many research fields, spatially referenced data are collected. 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 purposes.
Objective
The course will provide an overview of the basic concepts and stochastic models that are commonly used to model geostatistical data sets. In addition, the participants will learn a number of geostatistical techniques and acquire some familiarity with software that is useful for analysing 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 random processes, modelling large-scale spatial patterns by 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 worked-out solutions to them will be provided.
Literature
P.J. Diggle & P.J. Ribeiro Jr. 2007. Model-based Geostatistics. Springer
General Information
- Language
- English
- Levels
- WBZ
- Frequency
- Every two years
Examination
- Type
- ungraded semester performance
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise |
Spatial Statistics
Does not take place this semester.
Block course in HS 2024
|
No time listed | 14 h semesterly |