Proportional Aggregation in Hierarchical Data Visualization

Abstract

In the visual analysis of hierarchical data, a main challenge is comparing data attributes both within and between different levels of the hierarchy. Such tasks are typically addressed using aggregated data, where the attribute of a parent node is calculated from the attributes of its children. Our review of existing literature on visualization methods that encode the hierarchy implicitly (e.g., icicle plots, treemaps) shows that most approaches rely on additive aggregation. Proportional aggregation, where a parent is assigned a weighted average of the values of its children, remains unexplored although relevant in practice. We introduce stalactite plots, a visualization technique that displays proportional aggregation and supports visual value comparison. Our empirical evaluation (N=148, N=50) shows that, with some explanation, stalactite plots are as easily understood as established visualization techniques for hierarchical data. Furthermore, for large datasets, participants are faster and more accurate using our approach.