Andreas Jürg Papritz: Catalogue data in Spring Semester 2020 |
Name | Dr. Andreas Jürg Papritz |
Address | Inst. f. Terrestrische Oekosysteme ETH Zürich, CHN E 35.2 Universitätstrasse 16 8092 Zürich SWITZERLAND |
andreas.papritz@env.ethz.ch | |
Department | Environmental Systems Science |
Relationship | Lecturer |
Number | Title | ECTS | Hours | Lecturers | |
---|---|---|---|---|---|
701-0104-00L | Statistical Modelling of Spatial Data | 3 credits | 2G | A. J. Papritz | |
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. | ||||
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. 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. |