One of the primary purposes of visualization is to assist users in discovering insights. While there has been much research in information visualization aiming at complex data transformation and novel presentation techniques, relatively little has been done to understand how users derive insights through interactive visualization of data. This paper presents a crowdsourced study with 158 participants investigating the relation between entity-based interaction (an action + its target entity) and the resulting insight. To this end, we generalized the interaction with an existing CO2 Explorer as entity-based interaction and enabled users to input notes and refer to relevant entities to assist their narratives. We logged interactions of users freely exploring the visualization and characterized their externalized insights about the data. Using entity-based interactions and references to infer insight characteristics (category, overview versus detail, and prior knowledge), we found evidence that compared with interactions, entity references improved insight characterization from slight/fair to fair/moderate agreements. To interpret prediction outcomes, feature importance and correlation analysis indicated that, e.g., detailed insights tended to have more mouse-overs in the chart area and cite the vertical reference lines in the line chart as evidence. We discuss study limitations and implications on knowledge-assisted visualization, e.g., insight recommendations based on user exploration.
翻译:可视化的主要目的之一是协助用户发现洞察力。虽然在信息可视化方面进行了大量研究,目的是实现复杂的数据转换和新的演示技术,但是在了解用户如何通过数据互动可视化获得洞察力方面却做得相对较少。本文介绍了由158名参与者组成的众源研究,其中调查了实体互动(行动+目标实体)与由此产生的洞察力之间的关系。为此,我们把与现有的二氧化碳探索者的互动作为实体互动,使用户能够提供注释,并能够向相关实体提供帮助其叙述。我们记录了用户自由探索可视化的相互作用,并说明了他们对数据的外部洞察力。我们利用基于实体的互动和引用来推断洞察力特征(类别、概览与细节,以及先前的知识),发现了一些证据,与互动相比,实体参考改善了从轻视/公允/公允到公平/多边协定的洞察力特征。我们将预测结果、特征和关联分析加以解释,例如,详细观察力往往在图表区域有更多的鼠标翻图,并引用线图中的纵向参考线条作为证据。我们讨论了关于探索数据的局限性和意义。我们根据对用户的观察力研究,分析了关于探索建议进行了研究。