Introduction to mathematical and statistical tools for geospatial data analysis.
Learning objective
The goal is to familiarise students with the principles and tools of geospatial data analysis, and to enable them to apply those tools to practical tasks.
Content
The course introduces basic methods of geostatistics and geospatial data analysis. Topics include spatial correlation, auto-correlation and the variogram; surface interpolation (kernel-based, kriging, parametric surface models); spatially adaptive filtering (bilinear, guided filter); spatial stochastic processes and random fields; time series models and spatio-temporal analysis.
Prerequisites / Notice
Bachelor level mathematics: analysis, linear algebra, statistics and probability theory, parameter estimation. Basic knowledge of multivariate statistics and machine learning is recommended.
Competencies
Subject-specific Competencies
Concepts and Theories
assessed
Techniques and Technologies
assessed
Method-specific Competencies
Analytical Competencies
assessed
Decision-making
fostered
Problem-solving
assessed
Personal Competencies
Creative Thinking
fostered
Critical Thinking
fostered
Performance assessment
Performance assessment information (valid until the course unit is held again)
Repetition only possible after re-enrolling for the course unit.
Additional information on mode of examination
During the course, students will implement various computational analysis tasks and will hand in those assignments, and in some cases discuss them with the lab demonstrators. The grade will be determined from the submitted solutions.
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.
Performance assessment as a semester course