## geostatistics

##### AM-27 - Principles of semi-variogram construction • 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
##### AM-29 - Kriging methods • 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
##### AM-28 - Semi-variogram modeling • 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
##### AM-27 - Principles of semi-variogram construction • 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
##### AM-29 - Kriging methods • 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
##### AM-28 - Semi-variogram modeling • 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
##### AM-27 - Principles of semi-variogram construction • 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
##### AM-29 - Kriging methods • 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
##### AM-28 - Semi-variogram modeling • 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
##### AM-27 - Principles of semi-variogram construction • 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