Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Document formatted into pages; contains xvi, p. 116; also includes graphics. Includes bibliographical references (p. 108-116). Available online via OhioLINK's ETD Center

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.

Invasive alien plants have widespread ecological and socioeconomic impacts throughout many parts of the world, including Swaziland where the government declared them a national disaster. Control of these species requires knowledge on the invasion ecology of each species including how they interact with the invaded environment. Species distribution models are vital for providing solutions to such problems including the prediction of their niche and distribution. Various modelling approaches are used for species distribution modelling albeit with limitations resulting from statistical assumptions, implementation and interpretation of outputs. This study explores the usefulness of Bayesian networks (BNs) due their ability to model stochastic, nonlinear inter-causal relationships and uncertainty. Data-driven BNs were used to explore patterns and processes influencing the spatial distribution of 16 priority invasive alien plants in Swaziland. Various BN structure learning algorithms were applied within the Weka software to build models from a set of 170 variables incorporating climatic, anthropogenic, topo-edaphic and landscape factors. While all the BN models produced accurate predictions of alien plant invasion, the globally scored networks, particularly the hill climbing algorithms, performed relatively well. However, when considering the probabilistic outputs, the constraint-based Inferred Causation algorithm which attempts to generate a causal BN structure, performed relatively better. The learned BNs reveal that the main pathways of alien plants into new areas are ruderal areas such as road verges and riverbanks whilst humans and human activity are key driving factors and the main dispersal mechanism. However, the distribution of most of the species is constrained by climate particularly tolerance to very low temperatures and precipitation seasonality. Biotic interactions and/or associations among the species are also prevalent. The findings suggest that most of the species will proliferate by extending their range resulting in the whole country being at risk of further invasion. The ability of BNs to express uncertain, rather complex conditional and probabilistic dependencies and to combine multisource data makes them an attractive technique for species distribution modeling, especially as joint invasive species distribution models (JiSDM). Suggestions for further research are provided including the need for rigorous invasive species monitoring, data stewardship and testing more BN learning algorithms. / Environmental Sciences / D. Phil. (Environmental Science)