@phdthesis{trautner2022thesis,
title = {Visualization Hybridization with Spatialization Cues},
author = {Thomas Bernhard Trautner},
year = 2022,
month = {November},
isbn = 9788230855515,
url = {https://hdl.handle.net/11250/3031041},
school = {Department of Informatics, University of Bergen, Norway},
abstract = {
Visualization as a tool for visual processing of any underlying data has proven to be an accepted and legitimate part of the scientific reasoning process. Many different techniques help gaining new insights from captured phenomena, support the development or evaluation of hypotheses about collected data, reveal potential misconceptions or false assumptions, simplify communicating knowledge and novel findings, and enable a multitude of additional opportunities. The reason for this effectiveness is that the human visual system is ideally suited to capture and process visually encoded data. The development of visualization from a niche to an established scientific field has made a significant contribution to this success story. A large number of journals, conferences, seminars, and workshops regularly publish new results, evaluate presented approaches, and help making knowledge globally accessible. However, this large number of contributions tailored to variable user groups, the underlying data, and the wide variety of tasks that could be performed with them, emphasizes the plethora of available techniques and the resulting difficulty in choosing the most suitable visualizations.
Therefore, we investigated common data sets and analyzed typical tasks normally performed with them. Based on this, we selected well-established and most effective visualization techniques, combining them to form a hybrid representation. The goal of such a visualization hybridization was to merge advantages of individual techniques and, thereby, simultaneously eliminate their limitations. We present so-called hybrid vigors that make the underlying visualizations more widely applicable instead of either having to change required techniques sequentially, or not being able to perform certain tasks at all. Our contributions are intended to simplify the process of finding suitable visualizations for already established data sets. During our research, we focused on two-dimensional point data, depicted on the one hand as scatter plots and, on the other hand, as relationships between consecutive point such as in line charts. Our techniques can be used especially when data sets are so large, dense, and overplotted that conventional techniques reach their limits. We show that hybrid representations are well suited for combining discrete, continuous, or aggregated forms of visual representation. Our hybridizations additionally exploit spatialization cues. Such visual cues emphasize spatiality of the underlying data through shading, without having to embed the data in 3D space including its potential disadvantages. We chose this method of encoding as we consider it the most appropriate choice, given that visualization users interact naturally and preattentively with a spatial world on a daily basis.
},
pdf = {pdfs/Trautner-PhD-Thesis-2022.pdf},
images = {images/Trautner-2022-PhD.png},
thumbnails = {images/Trautner-2022-PhD.png},
project = {MetaVis}
}