CV2-2 - Data abstraction: classification, selection, and generalization
You are currently viewing an archived version of Topic Scale and Generalization.
If updates or revisions have been published you can find them at Scale and Generalization.
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
You are currently viewing an archived version of Topic Scale and Generalization. If updates or revisions have been published you can find them at Scale and Generalization.
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).