geostatistics | GIS&T Body of Knowledge

Identify and define the parameters of a semi-variogram (range, sill, nugget)
Demonstrate how semi-variograms react to spatial nonstationarity
Construct a semi-variogram and illustrate with a semi-variogram cloud
Describe the relationships between semi-variograms and correlograms, and Moran’s indices of spatial association

Describe the relationship between the semi-variogram and kriging
Explain why it is important to have a good model of the semi-variogram in kriging
Explain the concept of the kriging variance, and describe some of its shortcomings
Explain how block-kriging and its variants can be used to combine data sets with different spatial resolution (support)
Compare and contrast block-kriging with areal interpolation using proportional area weighting and dasymetric mapping
Outline the basic kriging equations in their matrix formulation
Conduct a spatial interpolation process using kriging from data description to final error map
Explain why kriging is more suitable as an interpolation method in some applications than others

List the possible sources of error in a selected and fitted model of an experimental semi-variogram
Describe the conditions under which each of the commonly used semi-variograms models would be most appropriate
Explain the necessity of defining a semi-variogram model for geographic data
Apply the method of weighted least squares and maximum likelihood to fit semi-variogram models to datasets
Describe some commonly used semi-variogram models

Identify and define the parameters of a semi-variogram (range, sill, nugget)
Demonstrate how semi-variograms react to spatial nonstationarity
Construct a semi-variogram and illustrate with a semi-variogram cloud
Describe the relationships between semi-variograms and correlograms, and Moran’s indices of spatial association

Describe the relationship between the semi-variogram and kriging
Explain why it is important to have a good model of the semi-variogram in kriging
Explain the concept of the kriging variance, and describe some of its shortcomings
Explain how block-kriging and its variants can be used to combine data sets with different spatial resolution (support)
Compare and contrast block-kriging with areal interpolation using proportional area weighting and dasymetric mapping
Outline the basic kriging equations in their matrix formulation
Conduct a spatial interpolation process using kriging from data description to final error map
Explain why kriging is more suitable as an interpolation method in some applications than others

List the possible sources of error in a selected and fitted model of an experimental semi-variogram
Describe the conditions under which each of the commonly used semi-variograms models would be most appropriate
Explain the necessity of defining a semi-variogram model for geographic data
Apply the method of weighted least squares and maximum likelihood to fit semi-variogram models to datasets
Describe some commonly used semi-variogram models

Identify and define the parameters of a semi-variogram (range, sill, nugget)
Demonstrate how semi-variograms react to spatial nonstationarity
Construct a semi-variogram and illustrate with a semi-variogram cloud
Describe the relationships between semi-variograms and correlograms, and Moran’s indices of spatial association

List the possible sources of error in a selected and fitted model of an experimental semi-variogram
Describe the conditions under which each of the commonly used semi-variograms models would be most appropriate
Explain the necessity of defining a semi-variogram model for geographic data
Apply the method of weighted least squares and maximum likelihood to fit semi-variogram models to datasets
Describe some commonly used semi-variogram models

Identify and define the parameters of a semi-variogram (range, sill, nugget)
Demonstrate how semi-variograms react to spatial nonstationarity
Construct a semi-variogram and illustrate with a semi-variogram cloud
Describe the relationships between semi-variograms and correlograms, and Moran’s indices of spatial association

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