Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> Interpretability (G6) <s> The traditional approach to representing tree structures is as a rooted, directed graph with the root node at the top of the page and children nodes below the parent node with lines connecting them (Figure 1). Knuth (1968, p. 305-313) has a long discussion about this standard representation, especially why the root is at the top and he offers several alternatives including brief mention of a space-filling approach. However, the remainder of his presentation and most other discussions of trees focus on various node and edge representations. By contrast, this paper deals with a two-dimensional (2-d) space-filling approach in which each node is a rectangle whose area is proportional to some attribute such as node size. <s> BIB001 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> Interpretability (G6) <s> This paper introduces PivotGraph, a software tool that uses a new technique for visualizing and analyzing graph structures. The technique is designed specifically for graphs that are "multivariate," i.e., where each node is associated with several attributes. Unlike visualizations which emphasize global graph topology, PivotGraph uses a simple grid-based approach to focus on the relationship between node attributes and connections. The interaction technique is derived from an analogy with methods seen in spreadsheet pivot tables and in online analytical processing (OLAP). Finally, several examples are presented in which PivotGraph was applied to real-world data sets. <s> BIB002
Principle. Aggregate items only so much so that the aggregation is still correctly interpretable within the visual mapping. Discussion. Related to guideline G5, this guideline reinforces the basic message of this paper: aggregates convey meaning to the viewer, and if an aggregate is degenerate, it will cause the viewer to easily become misinformed. Items should not be aggregated merely for the sake of reducing visual complexity, but the aggregation should also be interpretable to the viewer in visual space. For example, aggregates in a space-filling visualization based on containment, such as treemaps BIB001 or pivot graphs BIB002 , carry high interpretability. However, an overlapping visualization such as a scatterplot, for example, will have low interpretability if the items are aggregated using dimensions not mapped to the visual attributes (i.e., the vertical and horizontal axes for a two-dimensional scatterplot). Also relevant in the interpretability discussion is the fact that visual aggregates should be designed so that they communicate to the viewer that there is more to see by expanding aggregated nodes (this goes hand in hand with guideline G4, which states that visual aggregates and visual data items should be distinguishable from each other).