Doctor of Philosophy, The Ohio State University, 2009, Computer Science and Engineering

Visualization of time-varying scientific and medical datatraditionally has been done through animation or a series of still frame renders. Animation and still frame comparison is only minimally sufficient, due to limitations, such as short term visual memory and the lack of analytical feedback, to effectively find and compare temporal trends. To improve time-varying analysis, several different visualization methods are described. For direct visual comparison of individual time steps, we introduce a rendering technique that fuses multiple time steps into single data, by projection and composition methods. This can be achieved through projection along time, and further generalized to high dimensional space-time projection. Furthermore, time volumes (or multivariate data) can be compared through composition and set operations. To aid in the understanding of comparative time volumes, focus+context animation is used to reveal features in the data, by utilizing human motion perceptual capabilities. In addition to comparative and highlighting techniques, we also provide the quantitative analysis of time-varying data via time behavior classification. We allow a user to visualize and explore their time-varying data as classes of multi-scale temporal trends. Also through the analysis of the time activity, we can also semi-automatically generate classifications (transfer functions) to be used in the visualization pipeline.

Committee: Han-Wei Shen PhD (Advisor); Roger Crawfis PhD (Committee Member); Rick Parent PhD (Committee Member) Subjects: Computer Science