2013 journal article
SUMMARY VISUALIZATIONS FOR COASTAL SPATIAL-TEMPORAL DYNAMICS
INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION, 3(3), 241–253.
author keywords: uncertainty; visualization; geovisualization; glyph-based visualization; spatial-temporal analysis; space-time cube; coastal terrain; geomorphology; GRASS GIS; Outer Banks
TL;DR:
A set of techniques for visually summarizing the dynamics of coastal dunes is presented, and summary statistics of important data attributes and risk or vulnerability indices are visualize as functions of both spatial and temporal dimensions in the authors' data and represent uncertainty in the data set.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
14. Life Below Water
(OpenAlex)
Source: Web Of Science
Added: August 6, 2018
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.