701-0104-00L  Statistical Modelling of Spatial Data

SemesterSpring Semester 2020
LecturersA. J. Papritz
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
701-0104-00 GStatistical Modelling of Spatial Data2 hrs
Wed08:15-10:00CHN F 46 »
A. J. Papritz

Catalogue data

AbstractIn 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.
ObjectiveThe 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.
ContentAfter 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 notesSlides, descriptions of the problems for the data analyses and solutions to them will be provided.
LiteratureP.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 / NoticeFamiliarity 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)
Performance assessment as a semester course
ECTS credits3 credits
ExaminersA. J. Papritz
Typeend-of-semester examination
Language of examinationEnglish
RepetitionThe performance assessment is only offered at the end after the course unit. Repetition only possible after re-enrolling.
Additional information on mode of examinationWritten 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.

Learning materials

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Only public learning materials are listed.

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Offered in

ProgrammeSectionType
Civil Engineering MasterMajor in Construction and Maintenance ManagementWInformation
Data Science MasterCore ElectivesWInformation
Spatial Development and Infrastructure Systems MasterRecommended Electives of Master Degree ProgrammeWInformation
Statistics MasterStatistical and Mathematical CoursesWInformation
Environmental Sciences BachelorMethodes of Statistical Data AnalysisWInformation
Environmental Sciences BachelorMethodes of Statistical Data AnalysisWInformation