| Number | Name | Actions |
|---|---|---|
| 1 | Describe emerging geographical analysis techniques in geocomputation derived from artificial intelligence e.g., expert systems, artificial neural networks, genetic algorithms, and software agents | View |
| 2 | Describe difficulties in dealing with large spatial databases, especially those arising from spatial heterogeneity | View |
| 3 | Explain what is meant by the term contaminated data, suggesting how it can arise | View |
| 4 | Explain how to recognize contaminated data in large datasets | View |
| 5 | Outline the implications of complexity for the application of statistical ideas in geography | View |
| 6 | Describe how data mining can be used for geospatial intelligence | View |
| 7 | Differentiate between data mining approaches used for spatial and non-spatial applications | View |
| 8 | Compare and contrast the primary types of data mining: summarization/characterization, clustering/categorization, feature extraction, and rule/relationships extraction | View |
| 9 | Explain how spatial statistics techniques are used in spatial data mining | View |
| 10 | Explain how the analytical reasoning techniques, visual representations, and interaction techniques that make up the domain of visual analytics have a strong spatial component | View |
| 11 | Demonstrate how cluster analysis can be used as a data mining tool | View |
| 12 | Interpret patterns in space and time using Dorling and Openshaws Geographical Analysis Machine GAM demonstration of disease incidence diffusion | View |
| 13 | Explain how spatial data mining techniques can be used for knowledge discovery | View |
| 14 | Explain how visual data exploration can be combined with data mining techniques as a means of discovering research hypotheses in large spatial datasets | View |
| 15 | Explain how a Bayesian framework can incorporate expert knowledge in order to retrieve all relevant datasets given an initial user query | View |
| 16 | Differentiate among machine learning, data mining and pattern recognition | View |
| 17 | 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 | View |
| 18 | Explain the principles of pattern recognition | View |
| 19 | Apply a simple spatial mean filter to an image as a means of recognizing patterns | View |
| 20 | Construct an edge-recognition filter | View |
| 21 | Design a simple spatial mean filter | View |
| 22 | Define the following terms pertaining to a network: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle | View |
| 23 | Define different interpretations of cost in various routing applications | View |
| 24 | Describe networks that apply to specific applications or industries | View |
| 25 | Create a data set with network attributes and topology | View |