Comparative Uncertainty Visualization

Comparative Uncertainty Visualization is a type of uncertainty visualization that enables the systematic juxtaposition of uncertain data or models to highlight differences in uncertainty distributions, structures, or magnitudes across multiple scenarios, datasets, or conditions.

6

Publications

280

Citations

24

Authors

10

Institutions

Publications per year

2017–2026

0

Authors

24

  1. CM
    Craig M. Wittenbrink

    University of California, Santa Cruz, United States

    1 publications · 30 citations

    1

    h-index

  2. ČŠ
    Čeněk Šašinka

    Masaryk University, Czechia

    1 publications · 30 citations

    1

    h-index

  3. HG
    Hanqi Guo

    Argonne National Laboratory, United States

    1 publications · 10 citations

    1

    h-index

  4. ES
    Elijah Saxon

    University of California, Santa Cruz, United States

    1 publications · 30 citations

    1

    h-index

  5. XY
    Xiaoru Yuan

    Peking University, China

    1 publications · 10 citations

    1

    h-index

1–5 of 24

Venues

  1. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 2 publications · 195 citations

    2

    h-index

  2. 1

    h-index

  3. Annals of GIS

    1 publications · 30 citations

    1

    h-index

1–3