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.
