- 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 linked directly to either their original (2006) or revised entries; forthcoming, future topics are italicized.