Visualizing Uncertainty in Spatio-temporal Data

Lydia Gerharz*, Edzer Pebesma, Harald Hecking Institute for Geoinformatics University of Munster Miinster, Germany

Abstract: Visualization methods to show uncertainties in geospatial data are important tools for communication. Methods have been mainly developed for marginal probability distribution functions (pdfs) describing uncertainties independently for each location in space and time. Often uncertainties can be described better by joint pdfs, including the spatio-temporal dependencies of uncertainties. In this paper, methods for visualization of marginal distributions for space-time grids or features were compared to the case where the full joint distribution needs to be considered in order to find typical or rare spatial or spatio-temporal patterns, such as in ensemble weather forecasts. A number of statistical methods to sample representative realizations from a collection of model ensembles based on the spatio-temporal dependencies such as Mahalanobis distance were investigated and compared. We conclude that taking the full joint probability into account by showing a set of selected ensembles besides visualization methods using marginal distributions is helpful to understand the spatio-temporal structure.

Keywords: uncertainty visualization; ensembles; Mahalanobis distance; similarity selection

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