Due to the uncertain nature of weather prediction, climate simulations are usually performed multiple times with different spatial resolutions. The outputs of simulations are multi-resolution spatial temporal ensemble...
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Due to the uncertain nature of weather prediction, climate simulations are usually performed multiple times with different spatial resolutions. The outputs of simulations are multi-resolution spatial temporal ensembles. Each simulation run uses a unique set of values for multiple convective parameters. Distinct parameter settings from different simulation runs in different resolutions constitute a multi-resolution high-dimensional parameter space. Understanding the correlation between the different convective parameters, and establishing a connection between the parameter settings and the ensemble outputs are crucial to domain scientists. The multi-resolution high-dimensional parameter space, however, presents a unique challenge to the existing correlation visualization techniques. We present Nested parallelcoordinates Plot (NPCP), a new type of parallel coordinates plots that enables visualization of intra-resolution and inter-resolution parameter correlations. With flexible user control, NPCP integrates superimposition, juxtaposition and explicit encodings in a single view for comparative data visualization and analysis. We develop an integrated visual analytics system to help domain scientists understand the connection between multi-resolution convective parameters and the large spatial temporal ensembles. Our system presents intricate climate ensembles with a comprehensive overview and on-demand geographic details. We demonstrate NPCP, along with the climate ensemble visualization system, based on real-world use-cases from our collaborators in computational and predictive science.
We introduce parallel Histogram Plot (PHP), a technique that overcomes the innate limitations of parallelcoordinates plot (PCP) by attaching stacked-bar histograms with discrete color schemes to PCP. The color-coded ...
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We introduce parallel Histogram Plot (PHP), a technique that overcomes the innate limitations of parallelcoordinates plot (PCP) by attaching stacked-bar histograms with discrete color schemes to PCP. The color-coded histograms enable users to see an overview of the whole data without cluttering or scalability issues. Each rectangle in the PHP histograms is color coded according to the data ranking by a selected attribute. This color-coding scheme allows users to visually examine relationships between attributes, even between those that are displayed far apart, without repositioning or reordering axes. We adopt the Visual Information Seeking Mantra so that the polylines of the original PCP can be used to show details of a small number of selected items when the cluttering problem subsides. We also design interactions, such as a focus+context technique, to help users investigate small regions of interest in a space-efficient manner. We provide a real-world example in which PHP is effectively utilized compared with other visualizations, and we perform a controlled user study to evaluate the performance of PHP in helping users estimate the correlation between attributes. The results demonstrate that the performance of PHP was consistent in the estimation of correlations between two attributes regardless of the distance between them.
This paper presents a multitouch vocabulary for interacting with parallel coordinates plots on wall-sized displays. The gesture set relies on principles such as two-finger range definition, a functional distinction of...
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When working with raw data for multiple environmental indicators, it can be difficult to assess quality or 'health' because of the large number of indicators and inconsistencies among the indicators. By groupi...
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When working with raw data for multiple environmental indicators, it can be difficult to assess quality or 'health' because of the large number of indicators and inconsistencies among the indicators. By grouping the raw data into rankings, the data become more manageable and more comprehensible. We do not, however, want to lose information as a result of the groupings. It is possible to assess the quality of grouping options graphically by seeing if the resulting assessments of 'health' are concordant with the raw data. This can be done through the use of CDF-index values, cumulative distribution function plots, parallel coordinates plots, and scatterplots. A major purpose of this paper is to present approaches and the graphics for comparison and prioritization based on quintiles used, in this case, for ecological assessment of a large region.
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