Zhang Mingdong, Chen Li, Li Quan, Yuan Xiaoru, Yong Junhai
IEEE Trans Vis Comput Graph. 2021 Feb;27(2):1808-1818. doi: 10.1109/TVCG.2020.3030377. Epub 2021 Jan 28.
As an important method of handling potential uncertainties in numerical simulations, ensemble simulation has been widely applied in many disciplines. Visualization is a promising and powerful ensemble simulation analysis method. However, conventional visualization methods mainly aim at data simplification and highlighting important information based on domain expertise instead of providing a flexible data exploration and intervention mechanism. Trial-and-error procedures have to be repeatedly conducted by such approaches. To resolve this issue, we propose a new perspective of ensemble data analysis using the attribute variable dimension as the primary analysis dimension. Particularly, we propose a variable uncertainty calculation method based on variable spatial spreading. Based on this method, we design an interactive ensemble analysis framework that provides a flexible interactive exploration of the ensemble data. Particularly, the proposed spreading curve view, the region stability heat map view, and the temporal analysis view, together with the commonly used 2D map view, jointly support uncertainty distribution perception, region selection, and temporal analysis, as well as other analysis requirements. We verify our approach by analyzing a real-world ensemble simulation dataset. Feedback collected from domain experts confirms the efficacy of our framework.
作为处理数值模拟中潜在不确定性的一种重要方法，系综模拟已在许多学科中得到广泛应用。可视化是一种很有前景且强大的系综模拟分析方法。然而，传统的可视化方法主要旨在基于领域专业知识进行数据简化和突出重要信息，而不是提供灵活的数据探索和干预机制。通过这些方法必须反复进行试错过程。为了解决这个问题，我们提出了一种以属性变量维度作为主要分析维度的系综数据分析新视角。特别地，我们提出了一种基于变量空间扩展的变量不确定性计算方法。基于此方法，我们设计了一个交互式系综分析框架，该框架提供了对系综数据的灵活交互式探索。特别地，所提出的扩展曲线视图、区域稳定性热图视图和时间分析视图，与常用的二维地图视图一起，共同支持不确定性分布感知、区域选择和时间分析以及其他分析需求。我们通过分析一个实际的系综模拟数据集来验证我们的方法。从领域专家那里收集的反馈证实了我们框架的有效性。
