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.
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.
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.
Slides, descriptions of the problems for the data analyses and solutions to them will be provided.
P.J. Diggle & P.J. Ribeiro Jr. 2007. Model-based Geostatistics. Springer.
Bivand, R. S., Pebesma, E. J. & Gómez-Rubio, V. 2013. Applied Spatial Data Analysis with R. 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 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.
Additional information on mode of examination
Written exam of 120 minutes duration. No aids allowed. Examined material: assigned sections of textbooks; material of slides and of course notes with solutions to problems provided for data analyses. A former exam is provided on the Moodle course repository.
No public learning materials available.
Only public learning materials are listed.
No information on groups available.
There are no additional restrictions for the registration.