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- Describe how data mining can be used for geospatial intelligence
- Explain how the analytical reasoning techniques, visual representations, and interaction techniques that make up the domain of visual analytics have a strong spatial component
- Demonstrate how cluster analysis can be used as a data mining tool
- Interpret patterns in space and time using Dorling and Openshaw’s geographical analysis machine (GAM) demonstration of disease incidence diffusion
- Differentiate between data mining approaches used for spatial and non-spatial applications
- Explain how spatial statistics techniques are used in spatial data mining
- Compare and contrast the primary types of data mining: summarization/characterization, clustering/categorization, feature extraction, and rule/relationships extraction