Making sense of a visualization requires the reader to consider both the visualization design and the underlying data values. Existing work in the visualization community has largely considered affordances driven by visualization design elements, such as color or chart type, but how visual design interacts with data values to impact interpretation and reasoning has remained under-explored. Dot plots and bar graphs are commonly used to help users identify groups of points that form trends and clusters, but are liable to manifest groupings that are artifacts of spatial arrangement rather than inherent patterns in the data itself. These ``Data-induced Groups'' can drive suboptimal data comparisons and potentially lead the user to incorrect conclusions. We conduct two user studies using dot plots as a case study to understand the prevalence of data-induced groupings. We find that users rely on data-induced groupings in both conditions despite the fact that trend-based groupings are irrelevant in nominal data. Based on the study results, we build a model to predict whether users are likely to perceive a given set of dot plot points as a group. We discuss two use cases illustrating how the model can assist visualization designers by both diagnosing potential user-perceived groupings in dot plots and offering redesigns that better accentuate desired groupings through data rearrangement.

翻译：理解可视化结果需要读者同时考虑可视化设计及底层数据值。可视化领域现有研究主要关注由可视化设计元素（如颜色或图表类型）驱动的可供性，但视觉设计如何与数据值相互作用以影响解释与推理，这一问题仍未得到充分探索。点图与条形图常用于帮助用户识别形成趋势与聚类的点群，但容易显现出由空间排布而非数据固有模式所导致的分组。这些“数据诱导分组”可能导致次优的数据比较，并可能使用户得出错误结论。我们以点图为例开展了两项用户研究，以探究数据诱导分组的普遍性。研究发现，尽管基于趋势的分组在名义数据中并无意义，用户在两种实验条件下均会依赖数据诱导分组。基于研究结果，我们构建了一个模型，用于预测用户是否可能将给定的点图点集感知为一个分组。我们通过两个用例说明该模型如何协助可视化设计者：既可用于诊断点图中潜在的用户感知分组，又能通过数据重排提供优化设计，以更有效地突出期望的分组。