PieceStack: Toward Better Understanding of Stacked Graphs

Tongshuang Wu, Yingcai Wu, Conglei Shi, Huamin Qu, and Weiwei Cui

Overviews of unemployment rates of 14 industries from 2000 to 2010 with (a) PieceStack, with six extracted clusters marked as A-F; (b) traditional stacked graphs for comparison.

Abstract

Stacked graphs have been widely adopted in various fields, because they are capable of hierarchically visualizing a set of temporal sequences as well as their aggregation. However, because of visual illusion issues, connections between overly-detailed individual layers and overly-generalized aggregation are intercepted. Consequently, information in this area has yet to be fully excavated. Thus, we present PieceStack in this paper, to reveal the relevance of stacked graphs in understanding intrinsic details of their displayed shapes. This new visual analytic design interprets the ways through which aggregations are generated with individual layers by interactively splitting and re-constructing the stacked graphs. A clustering algorithm is designed to partition stacked graphs into sub-aggregated pieces based on trend similarities of layers. We then visualize the pieces with augmented encoding to help analysts decompose and explore the graphs with respect to their interests. Case studies and a user study are conducted to demonstrate the usefulness of our technique in understanding the formation of stacked graphs.

Materials

Citation