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International Journal for Uncertainty Quantification
インパクトファクター: 3.259 5年インパクトファクター: 2.547 SJR: 0.417 SNIP: 0.8 CiteScore™: 1.52

ISSN 印刷: 2152-5080
ISSN オンライン: 2152-5099

Open Access

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2012003969
pages 241-253

SUMMARY VISUALIZATIONS FOR COASTAL SPATIAL-TEMPORAL DYNAMICS

Sidharth Thakur
Renaissance Computing Institute, Raleigh, NC 27695
Laura Tateosian
North Caroline State University Center for Earth Observation, Raleigh, NC 27695
Helena Mitasova
North Caroline State University Department of Marine, Earth, and Atmospheric Sciences, Raleigh, NC 27695
Eric Hardin
North Caroline State University Department of Physics, Raleigh, NC 27695
Margery Overton
North Caroline State University Department of Civil, Construction, and Environmental Engineering, Raleigh, NC 27695

要約

Digital scans of dynamic terrains such as coastal regions are now being gathered at high spatial and temporal resolution. Although standard tools based on geographic information systems (GIS) are indispensable for analyzing geospatial data, they have limited support to display time-dependent changes in data and information such as statistical distributions and uncertainty in data. We present a set of techniques for visually summarizing the dynamics of coastal dunes. We visualize summary statistics of important data attributes and risk or vulnerability indices as functions of both spatial and temporal dimensions in our data and represent uncertainty in the data set. We apply standard techniques, the space time cube and clustering, in novel ways to the domain of geomorphology. We combine surface-mapping and imagery with summary visualizations to retain important geographical context in the visualizations and reduce clutter due to direct plotting of statistical data in displays of geospatial information. We also address some issues pertaining to visualization of summary statistics for geographical regions at varying scales. We demonstrate visualization tools on time series of elevation models from the Outer Banks of North Carolina and observe temporal-spatial trends therein.


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