Dynamic data visualizations can convey large amounts of information over time, such as using motion to depict changes in data values for multiple entities. Such dynamic displays put a demand on our visual processing capacities, yet our perception of motion is limited. Several techniques have been shown to improve the processing of dynamic displays. Staging the animation to sequentially show steps in a transition and tracing object movement by displaying trajectory histories can improve processing by reducing the cognitive load. In this paper, We examine the effectiveness of staging and tracing in dynamic displays. We showed participants animated line charts depicting the movements of lines and asked them to identify the line with the highest mean and variance. We manipulated the animation to display the lines with or without staging, tracing and history, and compared the results to a static chart as a control. Results showed that tracing and staging are preferred by participants, and improve their performance in mean and variance tasks respectively. They also preferred display time 3 times shorter when staging is used. Also, encoding animation speed with mean and variance in congruent tasks is associated with higher accuracy. These findings help inform real-world best practices for building dynamic displays. The supplementary materials can be found at https://osf.io/8c95v/

翻译：动态数据可视化能够随时间传递大量信息，例如通过运动呈现多个实体数据值的变化。此类动态显示对视觉处理能力提出了要求，然而人类对运动的感知存在局限。已有多种技术被证明能改善动态显示的处理效果：通过分阶段呈现过渡动画以顺序展示变化步骤，以及通过显示轨迹历史来追踪对象运动，均可降低认知负荷从而提升处理效率。本文研究了分阶段呈现与轨迹追踪在动态显示中的有效性。我们向参与者展示描绘线条运动的动态折线图，要求其识别均值与方差最高的线条。通过控制动画是否采用分阶段呈现、轨迹追踪及历史显示进行实验操作，并以静态图表作为对照。结果表明：参与者更倾向于使用轨迹追踪与分阶段呈现技术，二者分别提升了均值与方差任务的完成效果；使用分阶段呈现时，参与者更倾向于将显示时长缩短至原来的三分之一；此外，在一致性任务中将动画速度编码为均值与方差可获得更高准确率。这些发现为构建动态显示的实际应用提供了最佳实践指导。补充材料详见 https://osf.io/8c95v/