Yang Fumeng, Cai Mandi, Mortenson Chloe, Fakhari Hoda, Lokmanoglu Ayse D, Diakopoulos Nicholas, Nisbet Erik C, Kay Matthew
IEEE Trans Vis Comput Graph. 2025 Jan;31(1):426-436. doi: 10.1109/TVCG.2024.3456366. Epub 2024 Nov 25.
A year ago, we submitted an IEEE VIS paper entitled "Swaying the Public? Impacts of Election Forecast Visualizations on Emotion, Trust, and Intention in the 2022 U.S. Midterms" [50], which was later bestowed with the honor of a best paper award. Yet, studying such a complex phenomenon required us to explore many more design paths than we could count, and certainly more than we could document in a single paper. This paper, then, is the unwritten prequel-the backstory. It chronicles our journey from a simple idea-to study visualizations for election forecasts-through obstacles such as developing meaningfully different, easy-to-understand forecast visualizations, crafting professional-looking forecasts, and grappling with how to study perceptions of the forecasts before, during, and after the 2022 U.S. midterm elections. This journey yielded a rich set of original knowledge. We formalized a design space for two-party election forecasts, navigating through dimensions like data transformations, visual channels, and types of animated narratives. Through qualitative evaluation of ten representative prototypes with 13 participants, we then identified six core insights into the interpretation of uncertainty visualizations in a U.S. election context. These insights informed our revisions to remove ambiguity in our visual encodings and to prepare a professional-looking forecasting website. As part of this story, we also distilled challenges faced and design lessons learned to inform both designers and practitioners. Ultimately, we hope our methodical approach could inspire others in the community to tackle the hard problems inherent to designing and evaluating visualizations for the general public.
一年前，我们提交了一篇题为《 swaying the Public? Impacts of Election Forecast Visualizations on Emotion, Trust, and Intention in the 2022 U.S. Midterms》的IEEE VIS论文[50]，该论文后来荣获最佳论文奖。然而，研究这样一个复杂的现象需要我们探索无数条设计路径，当然远远超过我们在一篇论文中所能记录的。因此，本文就是那篇未成文的前传——背后的故事。它记录了我们从一个简单的想法——研究选举预测可视化——开始的旅程，途中遇到了诸多障碍，比如开发有显著差异且易于理解的预测可视化、制作专业的预测以及应对如何在2022年美国中期选举之前、期间和之后研究对预测的看法等问题。这段旅程产生了丰富的原创知识。我们为两党选举预测形式化了一个设计空间，在数据转换、视觉通道和动画叙事类型等维度中进行探索。通过对10个代表性原型与13名参与者进行定性评估，我们确定了在美国选举背景下对不确定性可视化解读的六个核心见解。这些见解为我们的修订提供了依据，以消除视觉编码中的模糊性，并打造一个专业的预测网站。作为这个故事的一部分，我们还总结了所面临的挑战和学到的设计经验，为设计师和从业者提供参考。最终，我们希望我们的方法能够激励社区中的其他人解决为普通大众设计和评估可视化所固有的难题。
