Saket Bahador, Endert Alex, Demiralp Cagatay
IEEE Trans Vis Comput Graph. 2019 Jul;25(7):2505-2512. doi: 10.1109/TVCG.2018.2829750. Epub 2018 May 4.
Visualizations of tabular data are widely used; understanding their effectiveness in different task and data contexts is fundamental to scaling their impact. However, little is known about how basic tabular data visualizations perform across varying data analysis tasks. In this paper, we report results from a crowdsourced experiment to evaluate the effectiveness of five small scale (5-34 data points) two-dimensional visualization types-Table, Line Chart, Bar Chart, Scatterplot, and Pie Chart-across ten common data analysis tasks using two datasets. We find the effectiveness of these visualization types significantly varies across task, suggesting that visualization design would benefit from considering context-dependent effectiveness. Based on our findings, we derive recommendations on which visualizations to choose based on different tasks. We finally train a decision tree on the data we collected to drive a recommender, showcasing how to effectively engineer experimental user data into practical visualization systems.
表格数据可视化应用广泛；了解其在不同任务和数据环境中的有效性对于扩大其影响至关重要。然而，对于基本表格数据可视化在各种数据分析任务中的表现，我们知之甚少。在本文中，我们报告了一项众包实验的结果，该实验使用两个数据集，在十种常见数据分析任务中评估了五种小规模（5 - 34个数据点）二维可视化类型——表格、折线图、柱状图、散点图和饼图——的有效性。我们发现这些可视化类型的有效性在不同任务中差异显著，这表明可视化设计若能考虑与上下文相关的有效性将更有益处。基于我们的研究结果，我们得出了根据不同任务选择何种可视化的建议。最后，我们基于收集到的数据训练了一个决策树来驱动一个推荐器，展示了如何有效地将实验用户数据转化为实用的可视化系统。
