CHS: Small: Developing a Probabilistic Grammar of Graphics for Flexible Uncertainty Visualization

该项目旨在建立对不确定信息的数据可视化的更系统的理解，并使数据可视化设计者更容易构建这种可视化。当报道从选举到自然灾害等高风险和不确定的话题时，记者们经常使用不确定的可视化手段，试图帮助公众回答一些重要的问题，比如“谁会赢得下一届总统选举？”或者“面对潜在的洪水，我应该撤离吗？”例如，记者使用“不确定性锥体”来说明飓风路径预测，尽管有证据表明，不确定性锥体可视化很难解释，而存在更好的不确定性可视化。在实践中采用更有效的不确定性可视化滞后于研究，部分原因是构建更复杂和有效的不确定性可视化比构建常见但效率较低的不确定性可视化(如置信度区间)更困难，而开发原型来探索不确定性可视化的设计空间是昂贵的。该项目将创建各种不确定可视化的正式描述，使用该描述的工具包使这种数据可视化的创建变得更容易，并评估该工具包是否使设计者更容易创建不确定数据的可视化。将数据可视化构造成代数或语法的形式试图降低用户探索一系列正确和有效的可视化所需的技术复杂性。虽然有几个流行的例子，但这些语法都没有将不确定性或概率的概念形式化，需要用户重新组合统计变换和几何(或者更糟糕的是，在可视化框架之外变换输入数据)，以便创建有效的不确定性可视化，包括像假设结果图这样的现代频率框架不确定性可视化。这项工作提出了一种直接结合概率分布的图形的概率语法，以便更容易地探索不确定性可视化的设计空间。这项工作将包括系统地对现有的不确定性可视化进行分类，开发一个一致的语法来描述它们，然后在现有的框架中实施该语法，并评估其潜力，使经验丰富的可视化设计师和可视化设计学生更容易构建高质量的不确定性可视化。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to build a more systematic understanding of data visualizations of uncertain information and to make it easier for data visualization designers to construct such visualizations. When reporting on high-stakes and uncertain topics ranging from elections to natural disasters, journalists routinely employ uncertainty visualizations in an attempt to help the public answer important questions, such as "Who will win the next presidential election?" or "Should I evacuate in the face of potential flooding?". For example, journalists use "cones of uncertainty" to illustrate hurricane path predictions, despite evidence that uncertainty cone visualizations are hard to interpret and better uncertainty visualizations exist. The adoption of more effective uncertainty visualizations in practice lags behind research, in part because the construction of more sophisticated and effective uncertainty visualizations is harder than the construction of common but less effective uncertainty visualizations like confidence intervals, while developing prototypes to explore the design space of uncertainty visualizations is costly. This project will create a formal description of a wide variety of uncertainty visualizations, a toolkit that uses this description to make the creation of such data visualizations easier, and evaluations of whether the toolkit makes it easier for designers to create visualizations of uncertain data.Formalizations of data visualization construction into algebras or grammars have sought to decrease the technical sophistication users need to explore a range of correct and effective visualizations. While several popular examples exist, none of these grammars formalize the notions of uncertainty or probability, requiring users to recombine statistical transformations and geometries (or worse, to transform input data outside of the visualization framework) in order to create effective uncertainty visualizations, including modern frequency-framing uncertainty visualizations like hypothetical outcome plots. This work proposes a probabilistic grammar of graphics that directly incorporates probability distributions to make it easier to explore the design space of uncertainty visualizations. The work will involve systematically cataloging existing uncertainty visualizations, developing a consistent grammar to describe them, then implementing that grammar in existing frameworks and evaluating its potential to make it easier for both experienced visualization designers and visualization design students to construct high quality uncertainty visualizations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.