UC San Diego

Visual analytics is an interdisciplinary field that facilitates the analysis of the large volume of data through interactive visual interface. This dissertation focuses on the development of visual analytics techniques in scalable visualization environments. These scalable visualization environments offer a high-resolution, integrated virtual space, as well as a wide-open physical space that affords collaborative user interaction. At the same time, the sheer scale of these environments poses a number of challenges, including data management, visualization techniques, and interaction paradigms that support large-scale, interactive visual exploratory analysis. This dissertation addresses these challenges with the special attention on the large volume of very high-resolution image data sets. The presented core visualization approach can immediately address tens of terapixel worth of information by employing view-dependent, adaptive, out-of-core visualization techniques. Building on this approach, two domain-specific challenges are addressed. One is interactive image fusion, facilitating the visualization and analysis of high-resolution satellite imagery. The other is interactive visual exploratory analysis of the large volume of cultural data sets, in order to support the development and refinement of new insights and hypotheses into the data sets. Finally, a method towards creating a co-located, collaborative user interaction paradigm in scalable visualization environments is presented. This method provides a multiuser, user-centric graphical user interface (GUI) for these environments, controlled by multitouch mobile devices