CV2-2 - Data abstraction: classification, selection, and generalization

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Author and Citation Info: 

DiBiase, D., DeMers, M., Johnson, A., Kemp, K., Luck, A. T., Plewe, B., and Wentz, E. (2006). Data abstraction: classification, selection, and generalization. The Geographic Information Science & Technology Body of Knowledge. Washington, DC: Association of American Geographers. (2nd Quarter 2016, first digital).

Learning Objectives: 
  • Discuss advantages and disadvantages of various data classification methods for choropleth mapping, including equal interval, quantiles, mean-standard deviation, natural breaks, and “optimal” methods
  • Explain why the reduction of map scale sometimes results in the need for mapped features to be reduced in size and moved
  • Identify mapping tasks that require each of the following: smoothing, aggregation, simplification, and displacement
  • Illustrate specific examples of feature elimination and simplification suited to mapping at smaller scales
  • Demonstrate how different classification schemes produce very different maps from a single set of interval- or ratio-level data
  • Apply appropriate selection criteria to change the display of map data to a smaller scale
  • Write algorithms to perform equal interval, quantiles, mean-standard deviation, natural breaks, and “optimal” classification for choropleth mapping
  • Discuss the limitations of current technological approaches to generalization for mapping purposes
  • Explain how generalization of one data theme can and must be reflected across multiple themes (e.g., if the river moves, the boundary, roads and towns also need to move)
  • Explain how the decisions for selection and generalization are made with regard to symbolization in mapping