[AM8-3] Variogram modeling

A variogram is a tool used to describe the spatial continuity of data points. Different kinds of variograms are used, such as experimental variogram and semi-variogram.

Introduction

External resources

• Cressie, N. 1991. Statistics for spatial data. Chichester, England: John Wiley.
  ISBN number: 9780471002550
• Deutsch, C.V. (2003) Geostatistics. In: Robert A. Meyers (eds) Encyclopedia of Physical Science and Technology (Third Edition), Academic Press, Pages 697-707. ISBN 9780122274107, https://doi.org/10.1016/B0-12-227410-5/00869-3.

Learning outcomes

• 262 - Identify and define the parameters of a semi-variogram range, sill, nugget
  Identify and define the parameters of a semi-variogram range, sill, nugget
• 263 - Describe the relationships between semi-variograms and correlograms, and Morans indices of spatial association
  Describe the relationships between semi-variograms and correlograms, and Morans indices of spatial association
• 264 - Demonstrate how semi-variograms react to spatial nonstationarity
  Demonstrate how semi-variograms react to spatial nonstationarity
• 265 - Construct a semi-variogram and illustrate with a semi-variogram cloud
  Construct a semi-variogram and illustrate with a semi-variogram cloud
• 266 - List the possible sources of error in a selected and fitted model of an experimental semi-variogram
  List the possible sources of error in a selected and fitted model of an experimental semi-variogram
• 267 - Describe some commonly used semi-variogram models
  Describe some commonly used semi-variogram models
• 268 - Describe the conditions under which each of the commonly used semi-variograms models would be most appropriate
  Describe the conditions under which each of the commonly used semi-variograms models would be most appropriate
• 269 - Explain the necessity of defining a semi-variogram model for geographic data
  Explain the necessity of defining a semi-variogram model for geographic data
• 270 - Apply the method of weighted least squares and maximum likelihood to fit semi-variogram models to datasets
  Apply the method of weighted least squares and maximum likelihood to fit semi-variogram models to datasets

Self assessment

Outgoing relations

• [AM8-3] Variogram modeling subclassof [AM8] Geostatistics

Contributors

• Rob Lemmens
  University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC)
  https://research.utwente.nl/en/persons/rob-lemmens