Contour Boxplots: A Method for Characterizing Uncertainty in Feature Sets from Simulation Ensembles

• Published in: IEEE transactions on visualization and computer graphics 2013-12, Vol.19 (12), p.2713-2722
• Main Authors: Whitaker, Ross T., Mirzargar, Mahsa, Kirby, Robert M.
• Format: Article
• Language: English
• Subjects: Algorithms; band depth; Boxplots; Computational modeling; Computer Graphics; Computer Simulation; Confidence intervals; Construction; Data Interpretation, Statistical; Data visualization; Density; ensemble visualization; Fluid dynamics; Mathematical analysis; Mathematical models; Models, Statistical; Numerical models; order statistics; Shape; Shape analysis; Simulation; Statistical analysis; Studies; Uncertainty; Uncertainty visualization; User-Computer Interface; Visualization; Weather forecasting
• ISSN: 1077-2626; 1941-0506
• DOI: 10.1109/TVCG.2013.143

Ensembles of numerical simulations are used in a variety of applications, such as meteorology or computational solid mechanics, in order to quantify the uncertainty or possible error in a model or simulation. Deriving robust statistics and visualizing the variability of an ensemble is a challenging task and is usually accomplished through direct visualization of ensemble members or by providing aggregate representations such as an average or pointwise probabilities. In many cases, the interesting quantities in a simulation are not dense fields, but are sets of features that are often represented as thresholds on physical or derived quantities. In this paper, we introduce a generalization of boxplots, called contour boxplots, for visualization and exploration of ensembles of contours or level sets of functions. Conventional boxplots have been widely used as an exploratory or communicative tool for data analysis, and they typically show the median, mean, confidence intervals, and outliers of a population. The proposed contour boxplots are a generalization of functional boxplots, which build on the notion of data depth. Data depth approximates the extent to which a particular sample is centrally located within its density function. This produces a center-outward ordering that gives rise to the statistical quantities that are essential to boxplots. Here we present a generalization of functional data depth to contours and demonstrate methods for displaying the resulting boxplots for two-dimensional simulation data in weather forecasting and computational fluid dynamics.