GSA Annual Meeting in Denver, Colorado, USA - 2016

Paper No. 280-9
Presentation Time: 10:15 AM


ZASLAVSKY, Ilya, LI, Side, VALENTINE, David and WHITENACK, Thomas, San Diego Supercomputer Center, Univ of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0505,

Rock and mineral specimens are maintained by natural history museums, geological organizations and academic research collections; they are an invaluable resource for researchers and educators. As access to physical samples is often complicated, it becomes increasingly important to examine visual images of samples online, compare them, explore associated metadata, group samples by a variety of characteristics, and interrelate visual and statistical patterns discovered in the collections. Furthermore, online publishing and sharing of rock samples in a manner that supports their visual exploration and analysis, and knowledge sharing, enables collaboration among researchers working on related projects. These tasks can now be accomplished with a new tool called Survey Analysis via Visual Exploration (SuAVE,

SuAVE has been applied to explore surveys in several fields, from sociology and political science to environmental sustainability analysis, biodiversity and landscape ecology studies, art history and digital humanities, and archaeology. It presents a novel visual metaphor for joint analysis of digital image collections and image metadata, through faceted search, efficient subsetting, and Google maps-like navigation over an image gallery. In addition, SuAVE supports several types of data analysis (including interface with R statistical package) and allows exploration of distributions and outliers. In addition, the SuAVE system now includes the ability for users to publish online surveys and collections, annotate patterns they discover via visual and statistical analysis, and share these annotations. We report initial results of SuAVE development, its applications in research and teaching, and its use for online sharing and analysis of several rock collections. In particular, we focus on technical challenges of scalable publication and visualization of large image collections, and on cross-collection integration and analysis.