A Comparative Study Of The Perceptual Sensitivity Of Topological Visualizations To Feature Variations

Color maps are a commonly used visualization technique in which data are mapped to optical properties, e.g., color or opacity. Color maps, however, do not explicitly convey structures (e.g., positions and scale of features) within data. Topology-based visualizations reveal and explicitly communicate structures underlying data. Although our understanding of what types of features are captured by topological visualizations is good, our understanding of people’s perception of those features is not. This paper evaluates the sensitivity of topology-based isocontour, Reeb graph, and persistence diagram visualizations compared to a reference color map visualization for synthetically generated scalar fields on 2-manifold triangular meshes embedded in 3D. In particular, we built and ran a human-subject study that evaluated the perception of data features characterized by Gaussian signals and measured how effectively each visualization technique portrays variations of data features arising from the position and amplitude variation of a mixture of Gaussians. For positional feature variations, the results showed that only the Reeb graph visualization had high sensitivity. For amplitude feature variations, persistence diagrams and color maps demonstrated the highest sensitivity, whereas isocontours showed only weak sensitivity. These results take an important step toward understanding which topology-based tools are best for various data and task scenarios and their effectiveness in conveying topological variations as compared to conventional color mapping.

Tushar M. Athawale, Bryan Triana, Tanmay Kotha, David Pugmire, and Paul Rosen. A Comparative Study Of The Perceptual Sensitivity Of Topological Visualizations To Feature Variations. IEEE Transaction on Computer Graphics and Visualization (IEEE VIS), 2024.

@article{athawale2023reeb,
  title = {A Comparative Study of the Perceptual Sensitivity of Topological
    Visualizations to Feature Variations},
  author = {Athawale, Tushar M. and Triana, Bryan and Kotha, Tanmay and Pugmire, David
    and Rosen, Paul},
  journal = {IEEE Transaction on Computer Graphics and Visualization (IEEE VIS)},
  year = {2024},
  note = {textit{Presented at IEEE VIS 2023.}},
  abstract = {Color maps are a commonly used visualization technique in which data are
    mapped to optical properties, e.g., color or opacity. Color maps, however, do not
    explicitly convey structures (e.g., positions and scale of features) within data.
    Topology-based visualizations reveal and explicitly communicate structures underlying
    data. Although our understanding of what types of features are captured by topological
    visualizations is good, our understanding of people's perception of those features is
    not. This paper evaluates the sensitivity of topology-based isocontour, Reeb graph, and
    persistence diagram visualizations compared to a reference color map visualization for
    synthetically generated scalar fields on 2-manifold triangular meshes embedded in 3D. In
    particular, we built and ran a human-subject study that evaluated the perception of data
    features characterized by Gaussian signals and measured how effectively each
    visualization technique portrays variations of data features arising from the position
    and amplitude variation of a mixture of Gaussians. For positional feature variations,
    the results showed that only the Reeb graph visualization had high sensitivity. For
    amplitude feature variations, persistence diagrams and color maps demonstrated the
    highest sensitivity, whereas isocontours showed only weak sensitivity. These results
    take an important step toward understanding which topology-based tools are best for
    various data and task scenarios and their effectiveness in conveying topological
    variations as compared to conventional color mapping.}
}