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.
Lernziel
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.
Inhalt
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.
Skript
Slides, descriptions of the problems for the data analyses and solutions to them will be provided.
Literatur
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.
Voraussetzungen / Besonderes
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.
Leistungskontrolle
Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Die Leistungskontrolle wird nur am Semesterende nach der Lerneinheit angeboten. Die Repetition ist nur nach erneuter Belegung möglich.
Zusatzinformation zum Prüfungsmodus
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.
Lernmaterialien
Keine öffentlichen Lernmaterialien verfügbar.
Es werden nur die öffentlichen Lernmaterialien aufgeführt.
Gruppen
Keine Informationen zu Gruppen vorhanden.
Einschränkungen
Keine zusätzlichen Belegungseinschränkungen vorhanden.
Leistungskontrolle als Semesterkurs