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    This study introduces a new objective function for colormap adjustment, improving the visualization of spatial variations in scientific data. The method offers an additional option for exploring data with diverse dynamic ranges.

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    Area of Science:

    • Scientific Visualization
    • Data Analysis
    • Computer Graphics

    Background:

    • Colormapping is crucial for analyzing scalar fields, but manual adjustment is inefficient.
    • Automated methods based on data properties often fail to capture spatial variations.
    • Existing techniques lack direct correlation with spatial dynamics, limiting pattern discovery.

    Purpose of the Study:

    • To develop an automated colormap adjustment method addressing limitations of previous approaches.
    • To incorporate domain expert requirements into the colormap adjustment process.
    • To enhance the exploration of data with a dynamic range of spatial variations.

    Main Methods:

    • Conducted a pilot study with domain experts to define requirements for colormap adjustment.
    • Formulated colormap adjustment as an objective function with boundary and fidelity terms.
    • Compared the proposed method against alternatives using quantitative measures and a user study (25 participants).

    Main Results:

    • The proposed objective function supports interactive functionalities for colormap adjustment.
    • Evaluated through a user study and case studies with domain experts, demonstrating its utility.
    • The method provides a flexible option for exploring data with diverse spatial variations.

    Conclusions:

    • The developed colormap adjustment approach offers a novel way to visualize spatial variations.
    • It serves as a valuable addition to existing methods for scientific data exploration.
    • The objective function effectively balances data properties with spatial characteristics for improved visualization.