发表机构
Northeastern University（东北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对小型多时序可视化中当前值与历史数据的整合问题，提出设计空间，并通过实证研究发现整合设计优于分离设计，尺寸编码可提升响应时间28%，颜色阈值编码优于阴影带编码。
AI 中文摘要
小型多时序可视化常用于医疗和制造等高风险领域的实时数据监控任务。有效的设计至关重要，因为用户依赖这些可视化来监控来自许多实体（如患者或机器）的数据，且常常在分心状态下进行。用户可能需要快速评估每个实体的当前值，监控那些超出可接受范围的值，同时观察时间趋势。然而，目前尚无设计指南用于在小型多图表示的历史时序中视觉强调当前值。通过一个由相关文献综述和关于视觉通道与强调的理论所指导的迭代设计过程，我们提出了一个用于可一瞥即得的小型多时序显示的设计空间。我们通过两项在线实证研究评估了这一空间，针对非阈值和阈值快速评估任务进行测试。我们的结果为在时序监控中快速评估任务中整合当前值与历史数据可视化提供了见解。对于非阈值任务，我们发现将当前值的尺寸编码空间整合到折线图中可能提供良好的折衷方案，在涉及寻找较大当前值的任务中响应时间提高了28%，且对趋势查找任务的干扰最小。更普遍地，整合设计优于分离设计（其中当前值表示在空间上与历史趋势线分离）。对于阈值任务，颜色阈值编码显著优于阴影带编码。所有补充材料可在以下网址获取。
英文摘要
Small multiple time series visualizations are often used for real-time data monitoring tasks in high-impact domains such as healthcare and manufacturing. Effective design is critical because users rely on these visualizations to monitor data from many entities, such as patients or machines, often while distracted. Users may need to rapidly appraise current values for each entity, monitoring for those that go outside an acceptable range, while also watching temporal trends. However, no design guidelines currently exist for visually emphasizing current values in historical time series represented by small multiples. Via an iterative design process informed by a review of related literature and theory on visual channels and emphasis, we present a design space for glanceable time series small multiple displays. We evaluate this space through two online empirical studies, testing against non-threshold and threshold rapid appraisal tasks. Our results provide insights into merging current value and historical data visualizations for rapid appraisal tasks in time series monitoring. For non-threshold tasks, we found that size encodings on the current value, spatially integrated into the line chart, may provide a good compromise, with 28% response time improvement for tasks involving finding large current values and minimal interference with trend lookup tasks. More generally, integrated designs outperformed separated designs (in which the current value representation is spatially separated from the historical trend line). For threshold tasks, color threshold encodings significantly outperformed shaded band encodings. All supplemental materials are available at https://osf.io/wzf6a.
Comments16 pages, 9 figures, to be published in IEEE TVCG
