Mr. Qingguang Cui Profile

Qingguang Cui

at Worcester Polytechnic Institute

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Proceedings Article | 16 January 2006 Paper

Enhancing scatterplot matrices for data with ordering or spatial attributes

Qingguang Cui, Matthew Ward, Elke Rundensteiner

Proceedings Volume 6060, 60600R (2006) https://doi.org/10.1117/12.650409

KEYWORDS: Visualization, Raster graphics, Data analysis, Magnetism, Matrices, Information visualization, Potassium, Computer science, Associative arrays, Scientific visualization

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The scatterplot matrix is one of the most common methods used to project multivariate data onto two dimensions for display. While each off-diagonal plot maps a pair of non-identical dimensions, there is no prescribed mapping for the diagonal plots. In this paper, histograms, 1D plots and 2D plots are drawn in the diagonal plots of the scatterplots matrix. In 1D plots, the data are assumed to have order, and they are projected in this order. In 2D plots, the data are assumed to have spatial information, and they are projected onto locations based on these spatial attributes using color to represent the dimension value. The plots and the scatterplots are linked together by brushing. Brushing on these alternate visualizations will affect the selected data in the regular scatterplots, and vice versa. Users can also navigate to other visualizations, such as parallel coordinates and glyphs, which are also linked with the scatterplot matrix by brushing. Ordering and spatial attributes can also be used as methods of indexing and organizing data. Users can select an ordering span or a spatial region by interacting with 1D plots or with 2D plots, and then observe the characteristics of the selected data subset. 1D plots and 2D plots provide the ability to explore the ordering and spatial attributes, while other views are for viewing the abstract data. In a sense, we are linking what are traditionally seen as scientific visualization methods with methods from the information visualization and statistical graphics fields. We validate the usefulness of this integration by providing two case studies, time series data analysis and spatial data analysis.

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