Bifocal parallel coordinates plot for multivariate data visualization
Visualization of multivariate data using parallel coordinates plot (PCP) becomes overwhelming as the number of dimensions/variables increases beyond one dozen or so. Here we propose bifocal parallel coordinates plot (BPCP) based on the focus + context approach. BPCP splits vertically the overall rendering into the focus and context regions whose sizes can be adjusted to optimize the use of the available space. The focus area maps a few selected dimensions of interest, referred to as priority axes, at sufficiently wide spacing. The remaining dimensions are represented in the context area in a compact way so as to retain useful information and provide the data continuity. The focus display can be further enhanced with various options, such as axes overlays, scatterplot, and nested juxtaposed PCPs. In order to accommodate an arbitrarily large number of dimensions, the context display supports multi-level stacked view, each PCP level mapping a subset of the context axes. With flexible interactivity, BPCP can manage the priority axes and data rendering with respect to the corresponding dimensions to support exploratory visualization while providing useful context on the same visualization display.
Publication Source (Journal or Book title)
VISIGRAPP 2018 - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Kaur, G., & Karki, B. (2018). Bifocal parallel coordinates plot for multivariate data visualization. VISIGRAPP 2018 - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 3, 176-183. https://doi.org/10.5220/0006549901760183