- 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
This knowledge area embodies a variety of data driven analytics, geocomputational methods, simulation and model driven approaches designed to study complex spatial-temporal problems, develop insights into characteristics of geospatial data sets, create and test geospatial process models, and construct knowledge of the behavior of geographically-explicit and dynamic processes and their patterns.
Topics in this Knowledge Area are listed thematically below. Existing topics are in regular font and linked directly to their original entries (published in 2006; these contain only Learning Objectives). Entries that have been updated and expanded are in bold. Forthcoming, future topics are italicized.