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447-6233-00L 1 Credits MSC , WBZ D-MATH

Spatial Statistics

Special Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to . The Registrar's Office will then register you for the course.
VVZ CR n/a

Last Updated: 2026-06-03 00:07:32

Abstract

Many phenomena are spatial, hence geographical analysis of data is relevant to a wide range of domains like geology, soil science, ecology, hydrology, climatology, or epidemiology. The course provides an introduction to geostatistical methods like kriging and machine learning with a focus on spatial prediction and model evaluation.

Objective

In this course participants will learn: • basic handling of spatial data, • explaining spatial autocorrelation, • estimating and interpreting of variograms and spatial trends, • analyzing spatial data by means of classical geostatistical and machine learning models, • computing spatial predictions from these models with associated uncertainty maps, • evaluating performance of a predictive models, • summarize properties of different spatial prediction techniques, • being able to list further spatial techniques not detailed in the course.

Content

The course introduction will give an overview of the types of problems and objectives encountered while analyzing spatial data. It will be shown how to explore the spatial structure of data sets and the variogram is introduced to characterize the spatial autocorrelation. Spatial patterns over larger areas are identified as trends and modeled by regression. In addition, spatial prediction is demonstrated using machine learning models. For both classical geostatistical and machine learning models maps of uncertainty about the prediction are introduced. Moreover, validation approaches and metrics for spatial predictions will be discussed. Participants will apply the presented techniques to example datasets using R. An outlook will provide an overview of further elements in the spatial statistical toolbox not covered in the module.

Resources

Lecture Notes

Lecture slides website, problem descriptions for the practicals with worked-out solutions.

Literature

Diggle, P.J., Ribeiro, P.J. Jr. 2007. Model-based Geostatistics. Springer Lovelace, R., Nowosad, J., Muenchow, J. 2019. Geocomputation with R, CRC Press, https://r.geocompx.org Wikle, C.K., Zammit-Mangion, A., and Cressie, N. 2019. Spatio-Temporal Statistics with R. Chapman & Hall/CRC, Boca Raton, FL. https://spacetimewithr.org

General Information

Language
English
Levels
MSC , WBZ
Frequency
Every two years

Examination

Type
ungraded semester performance

Registration & Places

Priority: Registration for the course unit is only possible for the primary target group

Course Components

Type Title Time & Place Hours
lecture with exercise Spatial Statistics
Block course Mon 21.09.26 08:15 - 12:00 Mon 28.09.26 08:15 - 12:00 Mon 28.09.26 14:15 - 18:00 Mon 05.10.26 08:15 - 12:00 Final Examination: Mon 12.10.26
No time listed 14 h semesterly

Offered In