Patterns and Pace: Quantifying Diverse Exploration Behavior with Visualizations on the Web
Patterns and Pace: Quantifying Diverse Exploration Behavior with Visualizations on the Web
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模式和节奏：通过网络可视化量化多样化的探索行为
DOI：
10.1109/tvcg.2018.2865117
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发表时间：
2019
影响因子：
5.2
通讯作者：
Lane Harrison
中科院分区：
文献类型：
--
作者：
Mi Feng;Evan M. Peck;Lane Harrison
网络上交互式可视化的多样化和充满活力的生态系统为研究人员和从业者提供了观察和分析人们如何与数据可视化互动的机会。但是，研究中使用的可视化相互作用行为的现有指标并不能完全揭示人们对可视化的开放式探索的广度。应对这一挑战的一种可能方法是确定可视化交互指标的高级目标，并从用户交互数据中推断出相应的功能，这些功能表征了人们的可视化探索的不同方面。在本文中，我们确定了可视化行为测量的需求，并开发了可以从用户的交互数据中推断出的相应候选功能。然后，我们提出的指标捕捉了人们开放式探索的新颖方面，包括探索独特性和探索起搏。我们通过将它们应用于先前可视化研究的相互作用数据，评估这些指标以及最近在可视化文献中提出的其他四个指标。这些评估的结果表明这些新指标1）揭示了人们使用可视化的新特征，2）可用于评估可视化设计之间的统计差异，而3）在统计上与可视化研究中使用的先前指标无关。我们讨论了这些结果对未来研究的含义，包括将这些指标应用于可视化交互分析中的潜力，以及在开发和选择描述可视化探索的指标时面临的挑战。
The diverse and vibrant ecosystem of interactive visualizations on the web presents an opportunity for researchers and practitioners to observe and analyze how everyday people interact with data visualizations. However, existing metrics of visualization interaction behavior used in research do not fully reveal the breadth of peoples' open-ended explorations with visualizations. One possible way to address this challenge is to determine high-level goals for visualization interaction metrics, and infer corresponding features from user interaction data that characterize different aspects of peoples' explorations of visualizations. In this paper, we identify needs for visualization behavior measurement, and develop corresponding candidate features that can be inferred from users' interaction data. We then propose metrics that capture novel aspects of peoples' open-ended explorations, including exploration uniqueness and exploration pacing. We evaluate these metrics along with four other metrics recently proposed in visualization literature by applying them to interaction data from prior visualization studies. The results of these evaluations suggest that these new metrics 1) reveal new characteristics of peoples' use of visualizations, 2) can be used to evaluate statistical differences between visualization designs, and 3) are statistically independent of prior metrics used in visualization research. We discuss implications of these results for future studies, including the potential for applying these metrics in visualization interaction analysis, as well as emerging challenges in developing and selecting metrics depicting visualization explorations.
警告，可能会出现偏差：一种检测交互式视觉分析中认知偏差的提议方法
DOI：
10.1109/vast.2017.8585669
发表时间：
2017
期刊：
IEEE Visual Analytic Science and Technology (VAST
影响因子：
--
作者：
Wall, Emily;Blaha, Leslie M.;Franklin, Lyndsey;Endert, Alex
通讯作者：
Endert, Alex