Visual Analytics and Imaging Laboratory (VAI Lab)
Computer Science Department, Stony Brook University, NY

Improving the Fidelity of Contextual Data Layouts Using a Generalized Barycentric Coordinates Framework

Abstract: Contextual layouts preserve the context of the data with the associated attributes (variables). However, their linear mapping causes errors in the layout similar data points and variable nodes may not map to similar regions, and vice versa. In this paper, we first unify the various data layout schemes and choose the Generalized Barycentric Coordinates (GBC) plot as the standard way to describe them. Second, we propose three algorithms distance spaced layout, iterative error reduction, and force directed adjustment to reduce the layout error of variables to variables, data to variables and data to data, respectively. We find that the combination of these three algorithms can yield large improvements in the layout error and so achieve a more comprehensive layout. Third, we describe an interface, the GBC Error Explorer, which allows users to explore the error using a variety of visualization schemes combined with some interactions.

Teaser: The interface of our system with various facilities linked together.

The contectual layout is shown on the right, and a parallel coordinate display shows the raw data on the bottom. The control panel in the center allows users to control the layout's parameters. The two remaiining displays visualize different types of layout errors to inform the parameter settings.

Video: Watch it to get a quick overview:

Paper: S. Cheng, K. Mueller, "Improving the Fidelity of Contextual Data Layouts Using a Generalized Barycentric Coordinates Framework,"Proc. Pacific Vis, pp. 295-302, Hangzhou, China, April, 2015. ppt pdf

Funding: NSF grant IIS-1117132