Where's My Data? Evaluating Visualizations with Missing Data
Where's My Data? Evaluating Visualizations with Missing Data
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DOI：
10.1109/tvcg.2018.2864914
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发表时间：
2019-01
影响因子：
5.2
通讯作者：
Hayeong Song;D. Szafir
中科院分区：
文献类型：
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作者：
Hayeong Song;D. Szafir
由于数据收集失败或融合数据集之间的不对齐等因素，许多真实世界的数据集是不完整的。不完整数据集的可视化应该允许分析人员从数据中得出结论，同时有效地推理数据的质量和得出的结论。我们进行了一对众包研究，以衡量用于估算和可视化缺失数据的方法如何影响分析师对数据质量的看法以及他们对结论的信心。我们的实验使用不同的设计选择线图和条形图来估计不完整时间序列数据集的平均值和趋势。我们的研究结果提供了初步的指导，可视化设计师考虑在不同的领域和场景中使用不完整的数据时。
Many real-world datasets are incomplete due to factors such as data collection failures or misalignments between fused datasets. Visualizations of incomplete datasets should allow analysts to draw conclusions from their data while effectively reasoning about the quality of the data and resulting conclusions. We conducted a pair of crowdsourced studies to measure how the methods used to impute and visualize missing data may influence analysts' perceptions of data quality and their confidence in their conclusions. Our experiments used different design choices for line graphs and bar charts to estimate averages and trends in incomplete time series datasets. Our results provide preliminary guidance for visualization designers to consider when working with incomplete data in different domains and scenarios.