Visualizations are perceived by humans like any other type of images, thus it is crucial to select appropriate display parameters for faithfully transporting the underlying data in a visualization. In this project we want to analyse to what extent methods known from computer graphics, vision and perception can be applied to judge and appropriately create information visualization results.
In their previous works the applicants have shown that, e.g., selecting the right aspect ratio can fundamentally alter the perception of projected high-dimensional data and determine which visualization method is best for showing it. Choosing an appropriate aspect ratio is also important for rendering line graphs and other visualizations. In both works discrete visualizations were represented by density maps that were created by using Kernel Density Estimation (KDE) with Gaussian kernels. In the proposed project this approach will be extended and investigated for which visualization types which kind of continuous representations can be used and how to analyse them by automatic means in order to find optimal display parameters.
The applicants will work on several aspects: besides selecting appropriate aspect ratios and other graphical attributes based on continuous representations, they will examine functional plots, common statistical graphics (such as lines charts or bar charts) and study how decorations such as tick marks and background grids alter their perception. A good assignment of such elements might help users to focus on important or interesting aspects of the data. Another aspect is selecting the right colors. Color contrasts and balances help to optimize selection and classification tasks, in a preliminary study the applicants demonstrate that the right color assignment helps in visually distinguishing clusters in a multi-cluster visualization.
On both, the discrete data of a visualization and its continuous representation, the applicants want to apply perceptual laws for judging expressiveness. One of the applicants (Deussen) has shown in his previous works that Gestalt-based laws can be used for analyzing object patterns in images and 3d geometry. This will now be applied to the elements of visualizations. Furthermore, a number of aesthetics laws exist for the analysis of images that have been used in computer graphics and vision. The applicants will used this to optimize visual parameters of visualizations and at the same time they will use the underlying (discrete) data to optimize aspects that cannot
be seen in the continuous representation, such as outliers, very sparsely populated areas. By combining aspects of both representations perceptually optimal visualization parameters will be estimated. Thus the applicants want to lay the foundations of perceptually-driven information visualization by jointly investigating continuous and discrete factors that influence how visualizations are perceived.
Collaboration: This research will be conducted as collaborative effort between the partners University of Konstanz, represented by principal investigator Prof. Dr. Oliver Deussen (awarded by the 1000 talents program of the Chinese government), and Shandong University, represented by principal investigator Prof. Dr. Yunhai Wang. The collaborative research and software development will happen on both sides through extensive exchange of scientific knowledge and insights in forms of electronic communication, research visits, and software repositories accessible to both partners.
- FB Informatik und Informationswissenschaft
|Period:||05.12.2018 – 04.12.2021|