GRACE: A Visual Comparison Framework for Integrated Spatial and Non-Spatial Geriatric Data

Adrian Maries, Nathan Mays, Megan Olson Hunt, Kim F. Wong
William Layton, Robert Boudreau, Caterina Rosano, G. Elisabeta Marai

We present the design of a novel framework for the visual integration, comparison, and exploration of correlations in spatial and non-spatial geriatric research data. These data are in general high-dimensional and span both the spatial, volumetric domain ミ through magnetic resonance imaging volumes ミ and the non-spatial domain, through variables such as age, gender, or walking speed. The visual analysis framework blends medical imaging, mathematical analysis and interactive visualization techniques, and includes the adaptation of Sparse Partial Least Squares and iterated Tikhonov Regularization algorithms to quantify potential neurologymobility connections. A linked-view design geared specifically at interactive visual comparison integrates spatial and abstract visual representations to enable the users to effectively generate and refine hypotheses in a large, multidimensional, and fragmented space. In addition to the domain analysis and design description, we demonstrate the usefulness of this approach on two case studies. Last, we report the lessons learned through the iterative design and evaluation of our approach, in particular those relevant to the design of comparative visualization of spatial and non-spatial data.