Browsing by Author "Lehmann, Dirk J."

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    CatNetVis: Semantic Visual Exploration of Categorical High-Dimensional Data with Force-Directed Graph Layouts
    (The Eurographics Association, 2023) Thane, Michael; Blum, Kai M.; Lehmann, Dirk J.; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
    We introduce CatNetVis, a novel method of representing semantical relations in categorical high-dimensional data. Traditional methods provide insights into many aspects of visual exploration of data. However, most of them lack information on relations in between categories or even clusters of categories. The force-directed network layout utilized by CatNetVis enables a lightweight approach in order to explore such semantical relations. The connections within the network are perceived as an intuitive metaphor for clusters of connections/relations in categorical data denoted as communities. While the user interacts, visual encodings such as information about the entropy and frequencies allow a fast perception of relation between categories and its frequencies, respectively. We illustrate how CatNetVis performs as an effective addition to traditional methods by demonstrating the method on an example data sets and comparing it to conventional methods.
  • Thumbnail Image
    Item
    Correlated Point Sampling for Geospatial Scalar Field Visualization
    (The Eurographics Association, 2018) Roveri, Riccardo; Lehmann, Dirk J.; Gross, Markus; Günther, Tobias; Beck, Fabian and Dachsbacher, Carsten and Sadlo, Filip
    Multi-variate visualizations of geospatial data often use combinations of different visual cues, such as color and texture. For textures, different point distributions (blue noise, regular grids, etc.) can encode nominal data. In this paper, we study the suitability of point distribution interpolation to encode quantitative information. For the interpolation, we use a texture synthesis algorithm, which paves the path towards an encoding of quantitative data using points. First, we conduct a user study to perceptually linearize the transitions between uniform point distributions, including blue noise, regular grids and hexagonal grids. Based on the linearization models, we implement a point sampling-based visualization for geospatial scalar fields and we assess the accuracy of the user perception abilities by comparing the perceived transition with the transition expected from our linearized models. We illustrate our technique on several real geospatial data sets, in which users identify regions with a certain distribution. Point distributions work well in combination with color data, as they require little space and allow the user to see through to the underlying color maps. We found that interpolations between blue noise and regular grids worked perceptively best among the tested candidates.
  • Thumbnail Image
    Item
    Optimal Axes for Data Value Estimation in Star Coordinates and Radial Axes Plots
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Rubio-Sánchez, Manuel; Lehmann, Dirk J.; Sanchez, Alberto; Rojo-Álvarez, Jose Luis; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
    Radial axes plots are projection methods that represent high-dimensional data samples as points on a two-dimensional plane. These techniques define mappings through a set of axis vectors, each associated with a data variable, which users can manipulate interactively to create different plots and analyze data from multiple points of view. However, updating the direction and length of an axis vector is far from trivial. Users must consider the data analysis task, domain knowledge, the directions in which values should increase, the relative importance of each variable, or the correlations between variables, among other factors. Another issue is the difficulty to approximate high-dimensional data values in the two-dimensional visualizations, which can hamper searching for data with particular characteristics, analyzing the most common data values in clusters, inspecting outliers, etc. In this paper we present and analyze several optimization approaches for enhancing radial axes plots regarding their ability to represent high-dimensional data values. The techniques can be used not only to approximate data values with greater accuracy, but also to guide users when updating axis vectors or extending visualizations with new variables, since they can reveal poor choices of axis vectors. The optimal axes can also be included in nonlinear plots. In particular, we show how they can be used within RadViz to assess the quality of a variable ordering. The in-depth analysis carried out is useful for visualization designers developing radial axes techniques, or planning to incorporate axes into other visualization methods.