ColorMapND: A Data-Driven Approach and Tool for Mapping Multivariate Data to Color

Cheng, Shenghui; Xu, Wei; Mueller, Klaus

doi:10.1109/TVCG.2018.2808489

Title: ColorMap^ND: A Data-Driven Approach and Tool for Mapping Multivariate Data to Color

Abstract

A wide variety of color schemes have been devised for mapping scalar data to color. We address the challenge of color-mapping multivariate data. While a number of methods can map low-dimensional data to color, for example, using bilinear or barycentric interpolation for two or three variables, these methods do not scale to higher data dimensions. Likewise, schemes that take a more artistic approach through color mixing and the like also face limits when it comes to the number of variables they can encode. Our approach does not have these limitations. It is data driven in that it determines a proper and consistent color map from first embedding the data samples into a circular interactive multivariate color mapping display (ICD) and then fusing this display with a convex (CIE HCL) color space. The variables (data attributes) are arranged in terms of their similarity and mapped to the ICD’s boundary to control the embedding. Using this layout, the color of a multivariate data sample is then obtained via modified generalized barycentric coordinate interpolation of the map. In conclusion, the system we devised has facilities for contrast and feature enhancement, supports both regular and irregular grids, can deal with multi-field as well asmore »« less

Authors:

^[1]; Xu, Wei ^[1];

^[1]

Stony Brook Univ., Stony Brook, NY (United States)

Publication Date:: Tue Feb 27 00:00:00 EST 2018

Research Org.:: Brookhaven National Lab. (BNL), Upton, NY (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Advanced Scientific Computing Research (SC-21)

OSTI Identifier:: 1491695

Report Number(s):: BNL-210896-2019-JAAM
Journal ID: ISSN 1077-2626

Grant/Contract Number:: SC0012704

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Visualization and Computer Graphics

Additional Journal Information:: Journal Volume: 25; Journal Issue: 2; Journal ID: ISSN 1077-2626

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; multivariate data; color mapping; color space; high dimensional data; pseudo coloring

Citation Formats


                    Cheng, Shenghui, Xu, Wei, and Mueller, Klaus. ColorMapND: A Data-Driven Approach and Tool for Mapping Multivariate Data to Color.  United States: N. p., 2018. 
Web.  doi:10.1109/TVCG.2018.2808489.

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                    Cheng, Shenghui, Xu, Wei, & Mueller, Klaus. ColorMapND: A Data-Driven Approach and Tool for Mapping Multivariate Data to Color.  United States.  https://doi.org/10.1109/TVCG.2018.2808489

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                    Cheng, Shenghui, Xu, Wei, and Mueller, Klaus. Tue .  
"ColorMapND: A Data-Driven Approach and Tool for Mapping Multivariate Data to Color".  United States.  https://doi.org/10.1109/TVCG.2018.2808489.  https://www.osti.gov/servlets/purl/1491695.

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@article{osti_1491695,

  title        = {ColorMapND: A Data-Driven Approach and Tool for Mapping Multivariate Data to Color},

  author       = {Cheng, Shenghui and Xu, Wei and Mueller, Klaus},

  abstractNote = {A wide variety of color schemes have been devised for mapping scalar data to color. We address the challenge of color-mapping multivariate data. While a number of methods can map low-dimensional data to color, for example, using bilinear or barycentric interpolation for two or three variables, these methods do not scale to higher data dimensions. Likewise, schemes that take a more artistic approach through color mixing and the like also face limits when it comes to the number of variables they can encode. Our approach does not have these limitations. It is data driven in that it determines a proper and consistent color map from first embedding the data samples into a circular interactive multivariate color mapping display (ICD) and then fusing this display with a convex (CIE HCL) color space. The variables (data attributes) are arranged in terms of their similarity and mapped to the ICD’s boundary to control the embedding. Using this layout, the color of a multivariate data sample is then obtained via modified generalized barycentric coordinate interpolation of the map. In conclusion, the system we devised has facilities for contrast and feature enhancement, supports both regular and irregular grids, can deal with multi-field as well as multispectral data, and can produce heat maps, choropleth maps, and diagrams such as scatterplots.},

  doi          = {10.1109/TVCG.2018.2808489},

  journal      = {IEEE Transactions on Visualization and Computer Graphics},

  number       = 2,

  volume       = 25,

  place        = {United States},

  year         = {Tue Feb 27 00:00:00 EST 2018},

  month        = {Tue Feb 27 00:00:00 EST 2018}

}

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Journal Article:

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https://doi.org/10.1109/TVCG.2018.2808489

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Cited by: 10 works

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Figures / Tables:

Fig. 1: System interface with all major displays and components (using the battery data, see Section 6.2 for more detail). Users can select a multivariate data point in any of these displays via mouse click. The system responds by highlighting the selected data point with a small circle both inmore »

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