- Devise simple ways to represent probability information in GIS
- Describe the basic principles of randomness and probability
- Compute descriptive statistics and geostatistics of geographic data
- Interpret descriptive statistics and geostatistics of geographic data
- Recognize the assumptions underlying probability and geostatistics and the situations in which they are useful analytical tools
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.