- Analyze the advantages and limitations of CA geospatial representations
- Explain how the use of CA to represent a geographical region relates to how places in a region are interconnected
- Describe how CA might represent a geographical region
- Describe how local and global transitional rules are handled in CA
- Describe how the rules of the Game of Life typically result in a continuously evolving pattern
- Explain two geographical processes that could be effectively represented using CA
- Explain two geographical processes that could not be effectively represented using CA
- Describe classic CA transition rules
- Describe the challenges of calibrating CA models
- Explain how temporal concepts are implemented in CA models
- Describe error sources of CA 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 linked directly to either their original (2006) or revised entries; forthcoming, future topics are italicized.