Information Density Comparison

Information Density Comparison is a visualization evaluation metric that quantifies the amount of relevant information conveyed per unit of visual space across different visualization designs, distinguishing itself by focusing on the efficiency of information transmission relative to visual complexity.

27

Publications

1.3K

Citations

71

Authors

34

Institutions

Publications per year

2017–2026

9

Authors

71

  1. CM
    Craig M. Wittenbrink

    University of California, Santa Cruz, United States

    2 publications · 283 citations

    2

    h-index

  2. SK
    Suresh K. Lodha

    University of California, Santa Cruz, United States

    2 publications · 283 citations

    2

    h-index

  3. AS
    Anselm Spoerri

    Rutgers, The State University of New Jersey, United States

    2 publications · 40 citations

    2

    h-index

  4. AP
    Alex Pang

    University of California, Santa Cruz, United States

    2 publications · 283 citations

    2

    h-index

  5. JZ
    Jimmy Zambrano R.

    Open University of the Netherlands, Netherlands

    1 publications · 72 citations

    1

    h-index

1–5 of 71

Venues

  1. Perspectives

    2 publications · 50 citations

    2

    h-index

  2. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 2 publications · 274 citations

    2

    h-index

  3. Neurocomputing

    1 publications · 12 citations

    1

    h-index

  4. IEEE Symposium on Information Visualization

    Conference · 1 publications · 17 citations

    1

    h-index

  5. Bioinformatics

    1 publications · 124 citations

    1

    h-index

1–5