提升条形图:评估树状图与包裹条形图,以实现排序数字数据的密集可视化

M. A. Yalçın, N. Elmqvist, B. Bederson
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引用次数: 8

摘要

标准(单列)条形图可以有效地可视化数字记录的排序列表。但是,图表高度限制了可见记录的数量。为了显示更多的记录,可以将条形图做得更薄(这可能会阻碍单独识别记录),滚动需要交互才能查看概述。树映射已在非层次设置中用于数字数据的密集可视化。或者,我们考虑换行条形图,这是一种多列条形图,使用长度而不是面积来编码数值。我们根据树图和包装条的设计特征,以及使用众包实验进行比较、排名和概览任务的图形感知性能,对它们进行比较。我们的分析发现,对于非分层、排序的数字数据的密集可视化,包裹条在所有三项任务中的感知能力都优于树图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Raising the Bars: Evaluating Treemaps vs. Wrapped Bars for Dense Visualization of Sorted Numeric Data
A standard (single-column) bar chart can effectively visualize a sorted list of numeric records. However, the chart height limits the number of visible records. To show more records, the bars could be made thinner (which could hinder identifying records individually), and scrolling requires interaction to see the overview. Treemaps have been used in practice in non-hierarchical settings for dense visualization of numeric data. Alternatively, we consider wrapped bars, a multi-column bar chart that uses length instead of area to encode numeric values. We compare treemaps and wrapped bars based on their design characteristics, and graphical perception performance for comparison, ranking, and overview tasks using crowdsourced experiments. Our analysis found that wrapped bars perceptually outperform treemaps in all three tasks for dense visualization of non-hierarchical, sorted numeric data.
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CiteScore
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