Information
- Publication Type: Master Thesis
- Workgroup(s)/Project(s): not specified
- Date: 2024
- Second Supervisor: Johanna Schmidt
- Open Access: yes
- First Supervisor: Eduard Gröller
- Pages: 113
- Keywords: Parallel Coordinates, Aspect Ratio, Perception
Abstract
This thesis investigates the impact of different aspect ratios on the perception of angles and lines in parallel coordinates. Parallel coordinates are a visualization technique for representing multivariate data where each variable is drawn as a parallel axis, and data points are connected by lines across these axes. This method allows for the simultaneous visualization of more than two variables and enables the interpretation of correlation patterns within a given dataset.However, the reliability and accuracy of this interpretation can be significantly influenced by the aspect ratio of the plot. This thesis aims to explore how variations in aspect ratios affect the accuracy and confidence of users in perceiving correlations within parallel coordinates.The methodological approach comprises three components: the development of a web-based visualization tool, a statistical analysis of line and angle parameters, and an empirical user study. The visualization tool enables users to display parallel coordinates in various aspect ratios and analyze the geometric properties of the lines in the plot. The statistical analysis reveals that aspect ratios significantly correlate with the minimum and maximum angles in parallel coordinates, which in turn affects the visual perception and interpretation of the data. These findings are validated through a web-based user study, demonstrating that specific aspect ratios lead to more accurate and reliable correlation estimates. The results underscore considerate usage of flexible aspect ratios to minimize distortion and ensure the reliability of visual data interpretation in parallel coordinates.Additional Files and Images
Weblinks
BibTeX
@mastersthesis{meka-2024-lpi, title = "Line Perception in Parallel Coordinates under different Aspect Ratios", author = "Leon Meka", year = "2024", abstract = "This thesis investigates the impact of different aspect ratios on the perception of angles and lines in parallel coordinates. Parallel coordinates are a visualization technique for representing multivariate data where each variable is drawn as a parallel axis, and data points are connected by lines across these axes. This method allows for the simultaneous visualization of more than two variables and enables the interpretation of correlation patterns within a given dataset.However, the reliability and accuracy of this interpretation can be significantly influenced by the aspect ratio of the plot. This thesis aims to explore how variations in aspect ratios affect the accuracy and confidence of users in perceiving correlations within parallel coordinates.The methodological approach comprises three components: the development of a web-based visualization tool, a statistical analysis of line and angle parameters, and an empirical user study. The visualization tool enables users to display parallel coordinates in various aspect ratios and analyze the geometric properties of the lines in the plot. The statistical analysis reveals that aspect ratios significantly correlate with the minimum and maximum angles in parallel coordinates, which in turn affects the visual perception and interpretation of the data. These findings are validated through a web-based user study, demonstrating that specific aspect ratios lead to more accurate and reliable correlation estimates. The results underscore considerate usage of flexible aspect ratios to minimize distortion and ensure the reliability of visual data interpretation in parallel coordinates.", pages = "113", address = "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria", school = "Research Unit of Computer Graphics, Institute of Visual Computing and Human-Centered Technology, Faculty of Informatics, TU Wien", keywords = "Parallel Coordinates, Aspect Ratio, Perception", URL = "https://www.cg.tuwien.ac.at/research/publications/2024/meka-2024-lpi/", }