Tak Susanne, Cockburn Andy
IEEE Trans Vis Comput Graph. 2013 Jan;19(1):141-8. doi: 10.1109/TVCG.2012.108. Epub 2012 Apr 17.
Treemaps are a well known and powerful space-filling visualisation method for displaying hierarchical data. Many alternative treemap algorithms have been proposed, often with the aim being to optimise performance across several criteria, including spatial stability to assist users in locating and monitoring items of interest. In this paper, we demonstrate that spatial stability is not fully captured by the commonly used "distance change" (DC) metric, and we introduce a new "location drift" (LD) metric to more fully capture spatial stability. An empirical study examines the validity and usefulness of the location drift metric, showing that it explains some effects on user performance that distance change does not. Next, we introduce "Hilbert" and "Moore" treemap algorithms, which are designed to achieve high spatial stability. We assess their performance in comparison to other treemaps, showing that Hilbert and Moore treemaps perform well across all stability metrics.
树形图是一种广为人知且强大的用于显示层次数据的空间填充可视化方法。已经提出了许多替代的树形图算法，其目标通常是在多个标准上优化性能，包括空间稳定性，以帮助用户定位和监控感兴趣的项目。在本文中，我们证明常用的“距离变化”（DC）度量不能完全捕捉空间稳定性，并且我们引入了一种新的“位置漂移”（LD）度量来更全面地捕捉空间稳定性。一项实证研究检验了位置漂移度量的有效性和实用性，表明它解释了一些距离变化无法解释的对用户性能的影响。接下来，我们介绍“希尔伯特”和“摩尔”树形图算法，它们旨在实现高空间稳定性。我们将它们与其他树形图的性能进行评估比较，结果表明希尔伯特和摩尔树形图在所有稳定性度量上都表现良好。
