Scaling Up Parallel Coordinate Plot with Color-coded Stacked Histograms
Jinwook Bok, Bohyoung Kim, and Jinwook Seo / 2018
- Jinwook Bok, Seoul National University, Seoul, Korea
- Bohyoung Kim, Hankuk University of Foreign Studies, Yongin-si, Republic of Korea
- Jinwook Seo, Seoul National University, Seoul, Republic of Korea
The original visual encoding of parallel coordinates plot (PCP) backfires as limitations when the number of items and/or attributes increases. Polylines for individual items clutter with each other and the linear ordering of vertical PCP axes makes it difficult to interpret relationship between physically distant attributes. In this paper, we introduce a novel technique that overcomes the innate limitations of PCP by attaching stacked-bar histograms with discrete color schemes to PCP. The color-coded histograms enable users to grasp an overview of the whole data without cluttering or scalability issues. Each rectangle in the histograms is color-coded according to the ranking of data by a user-selected attribute. The color-coding scheme allows users to perceptually examine relationships between attributes, even between the ones displayed far apart, without repositioning or reordering axes. We adopt the Visual Information Seeking Mantra so that the polylines of the original PCP can be used to show details of a small number of selected items when the cluttering problem subsides.