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Generalization

By Center of Excellence for Geospatial Information Science (CEGIS)

Geospatial data are accessed, displayed, and used at a variety of scales and resolutions. Traditional cartographic generalization has focused on retaining positional accuracy, integrated content, and shape characteristics for displaying a map that legibly depicts important physical conditions, which may vary depending on the intended use of the map.

Little effort has focused on the usability of resulting data. Given greater storage, display, and processing capabilities, and more detailed data collection, this research task considers flexible legibility constraints that can better preserve sinuosity of surface water features and other shape characteristics to better support hydrologic modeling and geospatial analysis at smaller scales. The goal is to develop automated methods that can generalize data from the most detailed version to the least detailed scale needed for mapping or modeling, with the generalization process tailored to best meet the need.