3D Occlusion Management and Causality Visualization

This thesis is split into two parts: one part dealing with the management of occlusion in 3D environments, the other with the visualization of causal relations. Both of these parts fall within the general framework of visualization—the graphical representation of data (abstract or concrete) with the purpose of amplifying cognition—but they do so in different ways. 3D occlusion management, on the one hand, is a basic approach to augmenting three-dimensional visualizations with a set of techniques for reducing (or even eliminating) the effect of inter-object occlusion in the environment. We present four different such techniques in the thesis, each utilizing a different solution space to achieve the effect: image space for dynamic transparency, view space for view projection animation, object space for interactive 3D distortion, and temporal space for our approach to 3D navigation guidance. Each technique is orthogonal to the others, and each has been verified empirically through formal user experiments to promote significantly more efficiency and accuracy for users solving representative visual perception task in 3D environments than standard navigation controls such as flying and walking. Causality visualization, on the other hand, is a specific class of visualization techniques designed to make complex chains of causal dependencies, or causeand-effect relations, visible and understandable. A core information visualization problem, the techniques described in this part of the thesis are examples of growing geometry, a subset of methods based on mapping the time parameter to the size of geometrical primitives such as squares and polygons. Consequently, the techniques are called Growing Squares and Growing Polygons, respectively, and utilize color, texture, and animation to visualize causality from application areas such as distributed systems, social networks, and mathematics. These, too, have been empirically shown to be superior to traditional time-space diagrams for representing causal relations. The CiteWiz system serves as a concrete example of how to apply causality visualization to a real dataset, in this case scientific citation data.