- Differentiate among machine learning, data mining, and pattern recognition
- Explain the principles of pattern recognition
- Apply a simple spatial mean filter to an image as a means of recognizing patterns
- Construct an edge-recognition filter
- Design a simple spatial mean filter
- Explain the outcome of an artificial intelligence analysis (e.g., edge recognition), including a discussion of what the human did not see that the computer identified and vice versa
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.