Prediction Uncertainty Visualization

Prediction Uncertainty Visualization is a visualization technique that specifically represents the uncertainty associated with predicted outcomes in spatial data, distinguishing it from other uncertainty visualizations by focusing on the predictive nature of the uncertainty rather than observed or measured variability.

13

Publications

1.1K

Citations

49

Authors

26

Institutions

Publications per year

2017–2026

7

Authors

49

  1. JH
    Jessica Hullman

    University of Washington, United States

    3 publications · 499 citations

    3

    h-index

  2. DH
    Donald H. House

    Texas A&M University, United States

    3 publications · 337 citations

    3

    h-index

  3. MK
    Matthew Kay

    University of Washington, United States

    3 publications · 499 citations

    3

    h-index

  4. AP
    Alexander P. Boone

    University of California, Santa Barbara, United States

    2 publications · 227 citations

    2

    h-index

  5. SH
    Sarah H. Creem-Regehr

    University of Utah, United States

    2 publications · 227 citations

    2

    h-index

1–5 of 49

Venues

  1. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 2 publications · 144 citations

    2

    h-index

  2. Journal of Materials Processing Technology

    1 publications · 27 citations

    1

    h-index

  3. Nature Methods

    1 publications · 33 citations

    1

    h-index

  4. 1

    h-index

  5. Spatial Cognition and Computation

    1 publications · 143 citations

    1

    h-index

1–5