High-dimensional glyph-based visualization and interactive techniques.

The advancement of modern technology and scientific measurements has led to datasets growing in both size and complexity, exposing the need for more efficient and effective ways of visualizing and analysing data. Despite the amount of progress in visualization methods, high-dimensional data still poses a number of significant challenges in terms of the technical ability of realising such a mapping, and how accurate they are actually interpreted. The different data sources and characteristics which arise from a wide range of scientific domains as well as specific design requirements constantly create new special challenges for visualization research. This thesis presents several contributions to the field of glyph-based visualization. Glyphs are parametrised objects which encode one or more data values to its appearance (also referred to as visual channels) such as their size, colour, shape, and position. They have been widely used to convey information visually, and are especially well suited for displaying complex, multi-faceted datasets. Its major strength is the ability to depict patterns of data in the context of a spatial relationship, where multi-dimensional trends can often be perceived more easily. Our research is set in the broad scope of multi-dimensional visualization, addressing several aspects of glyph-based techniques, including visual design, perception, placement, interaction, and applications. In particular, this thesis presents a comprehensive study on one interaction technique, namely sorting, for supporting various analytical tasks. We have outlined the concepts of glyph- based sorting, identified a set of design criteria for sorting interactions, designed and prototyped a user interface for sorting multivariate glyphs, developed a visual analytics technique to support sorting, conducted an empirical study on perceptual orderability of visual channels used in glyph design, and applied glyph-based sorting to event visualization in sports applications. The content of this thesis is organised into two parts. Part I provides an overview of the basic concepts of glyph-based visualization, before describing the state-of-the-art in this field. We then present a collection of novel glyph-based approaches to address challenges created from real-world applications. These are detailed in Part II. Our first approach involves designing glyphs to depict the composition of multiple error-sensitivity fields. This work addresses the problem of single camera positioning, using both 2D and 3D methods to support camera configuration based on various constraints in the context of a real-world environment. Our second approach present glyphs to visualize actions and events "at a glance". We discuss the relative merits of using metaphoric glyphs in comparison to other types of glyph designs to the particular problem of real-time sports analysis. As a result of this research, we delivered a visualization software, MatchPad, on a tablet computer. It successfully helped coaching staff and team analysts to examine actions and events in detail whilst maintaining a clear overview of the match, and assisted in their decision making during the matches. Abstract shortened by ProQuest

Surface Perception of Planar Abstractions

Various algorithms have been proposed to create planar abstractions of 3D models, but there has been no systematic effort to evaluate the effectiveness of such abstractions in terms of perception of the abstracted surfaces. In this work, we perform a large crowd-sourced study involving approximately 70k samples to evaluate how well users can orient gauges on planar abstractions of commonly occurring models. We test four styles of planar abstractions against ground truth surface representations, and analyze the data to discover a wide variety of correlations between task error and measurements relating to surface-specific properties such as curvature, local thickness and medial axis distance, and abstraction-specific properties. We use these discovered correlations to create linear models to predict error in surface understanding at a given point, for both surface representations and planar abstractions. Our predictive models reveal the geometric causes most responsible for error, and we demonstrate their potential use to build upon existing planar abstraction techniques in order to improve perception of the abstracted surface