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.
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.
Prerequisites / Notice
Familiarity with linear regression analysis (e.g. equivalent to the first part of the course 401-0649-00L Applied Statistical Regression) and with the software R (e.g. 401-6215-00L Using R for Data Analysis and Graphics (Part I), 401-6217-00L Using R for Data Analysis and Graphics (Part II)) are required for attending the course.
Performance assessment
Performance assessment information (valid until the course unit is held again)
The performance assessment is only offered at the end after the course unit. Repetition only possible after re-enrolling.
Mode of examination
written 150 minutes
Additional information on mode of examination
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.
Written aids
None
Online examination
The examination may take place on the computer.
Learning materials
No public learning materials available.
Only public learning materials are listed.
Groups
No information on groups available.
Restrictions
There are no additional restrictions for the registration.