Paper
7 April 1995 Glyphs for visualizing uncertainty in environmental vector fields
Author Affiliations +
Proceedings Volume 2410, Visual Data Exploration and Analysis II; (1995) https://doi.org/10.1117/12.205940
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1995, San Jose, CA, United States
Abstract
Environmental data have inherent uncertainty which is often ignored in visualization. For example, meteorological stations measure wind with good accuracy, but winds are often averaged over minutes or hours. As another example, Doppler radars (wind profilers and ocean current radars) take thousands of samples and average the possibly spurious returns. Others, including time series data, have a wealth of uncertainty information that the traditional vector visualization methods such as using wind barbs and arrow glyphs simply ignore. We have developed new vector glyphs to visualize uncertain winds and ocean currents. Our approach is to include uncertainty in direction and magnitude, as well as the mean direction and length, in vector glyph plots. Our glyphs show the variation in uncertainty, and provide fair comparisons of data from instruments, models, and time averages of varying certainty. We use both qualitative and quantitative methods to compare our glyphs to traditional ones. Subjective comparison tests with experts (meteorologists and oceanographers) are provided, as well as objective tests (data ink manximization), where the information density of our new glyphs and traditional glyphs are compared. We have shown that visualizing data together with their uncertainty information enhances the understanding of the continuous range of data quality in environmental vector fields.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Craig M. Wittenbrink, Elijah Saxon, Jeff J. Furman, Alex Pang, and Suresh Kumar Lodha "Glyphs for visualizing uncertainty in environmental vector fields", Proc. SPIE 2410, Visual Data Exploration and Analysis II, (7 April 1995); https://doi.org/10.1117/12.205940
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Cited by 30 scholarly publications.
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KEYWORDS
Visualization

Data modeling

Meteorology

Radar

Wind measurement

Atmospheric modeling

Doppler effect

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