Quality Metrics for Categorical Data Visualizations

Theoretical (Analytical):

Practical (Implementation):

Literature Work:


Overview

Categorical data is ubiquitous in real-world datasets. There exist a plethora of visualizations for categorical data. However, it is difficult to quantify their quality or improve the visualization. Task: Design and implement quality metrics for a categorical data visualization.

Problem Statement

Screen-space quality metrics describe a set of metrics specifically designed metrics or features that measure the quality of visualizations and can be used to optimize them for readability or quantify the appearance of specific patterns. The goal of the project is to implement a set of quality metrics for a categorical data visualization.

Tasks

  • Implement a set of quality metrics for a catagorical data visualization.
  • Implement a framework to apply the quality metrics to the visualization interactively.
  • Plan an evaluation of the quality metrics using the developed framework.

Requirements

Good programming skills in Java/Python and TypeScript/JavaScript. Good familiarity with the D3 library.

Scope/Duration/Start

  • Scope: Bachelor or Master
  • 3 Month Project, 6 Month Thesis
  • Start: immediately

Contact

References

Frederik L. Dennig, Maximilian T. Fischer, Michael Blumenschein, Johannes Fuchs, Daniel A. Keim, Evanthia Dimara. ParSetgnostics: Quality Metrics for Parallel Sets. Computer Graphics Forum; 40(3):375-386 (2021).