Visualizing Changes of Hierarchical Data using Treemaps

Abstract

Abstract-While the treemap is a popular method for visualizing hierarchical data, it is often difficult for users to track layout and attribute changes when the data evolve over time. When viewing the treemaps side by side or back and forth, there exist several problems that can prevent viewers from performing effective comparisons. Those problems include abrupt layout changes, a lack of prominent visual patterns to represent layouts, and a lack of direct contrast to highlight differences. In this paper, we present strategies to visualize changes of hierarchical data using treemaps. A new treemap layout algorithm is presented to reduce abrupt layout changes and produce consistent visual patterns. Techniques are proposed to effectively visualize the difference and contrast between two treemap snapshots in terms of the map items' colors, sizes, and positions. Experimental data show that our algorithm can achieve a good balance in maintaining a treemap's stability, continuity, readability, and average aspect ratio. A software tool is created to compare treemaps and generate the visualizations. User studies show that the users can better understand the changes in the hierarchy and layout, and more quickly notice the color and size differences using our method.

Stable Treemaps via Local Moves

“…However, as the input data changes there are no guarantees on how close any two rectangles will stay even if they are neighbors in the order. There are several ordered treemap algorithms: the Pivot-by-(Middle, Size and Split-Size) algorithms by Shneiderman and Wattenberg [14], the Strip algorithm by Bederson, Shneiderman and Wattenberg [2], the Split algorithm by Engdahl [6], the Spiral algorithm by Tu and Shen [16], and the Hilbert and Moore algorithms by Tak and Cockburn [15].…”

“…The readability metric measures how often the motion of the reader's eye changes direction as the treemap is scanned in order. In addition, Tu and Shen [16] introduced the continuity metric which measures how often the next item in the order is not the neighbor of the current item. Both these metrics attempt to quantify how easy it is to visually scan an ordered treemap to find a particular item.…”

Improving Window Switching Interfaces

“…We therefore experimentally compared target acquisition performance using totally stable placement (the gold standard control condition) as well as morphing behaviour implemented with squarified [23] and spiral [24] treemaps. Treemaps recursively divide 2D spaces into rectangles of various sizes, with size representing an underlying quantitative data attribute.…”

“…Treemaps recursively divide 2D spaces into rectangles of various sizes, with size representing an underlying quantitative data attribute. Various algorithms for creating treemaps exist, and two main properties are the average aspect ratio and spatial stability (see [24] for a recent review). Squarified and spiral treemaps were used in this experiment because they respectively support low and high spatial stability.…”

A Survey of Multiple Tree Visualisation

“…Furnas & Zacks' Multitrees 19 allows two tree structures to be fused together in one node-link representation, in this instance the context is that of a family tree, with one tree showing ancestors and the other descendants. Using their own definition of Multitrees, it follows that the structures between these trees are always shared exactly, so a node in one tree always has the same set of edges in the other For more involved structures where parentage of nodes may change between trees, Tu & Shen 84 propose a structure known as a union tree in which nodes that have different parents between the two trees are cloned to appear under both parents simultaneously in a merged structure. This removes cycles from the merged structure, retaining a strict tree structure for subsequent visualisation while preserving the edge sets found in both component trees.…”