Modern science and industry rely on computational models for simulation, prediction, and data analysis. Spatial blind source separation (SBSS) is a model used to analyze spatial data. Designed explicitly for spatial data analysis, it is superior to popular non-spatial methods, like PCA. However, a challenge to its practical use is setting two complex tuning parameters, which requires parameter space analysis. In this paper, we focus on sensitivity analysis (SA). SBSS parameters and outputs are spatial data, which makes SA difficult as few SA approaches in the literature assume such complex data on both sides of the model. Based on the requirements in our design study with statistics experts, we developed a visual analytics prototype for data type agnostic visual sensitivity analysis that fits SBSS and other contexts. The main advantage of our approach is that it requires only dissimilarity measures for parameter settings and outputs. We evaluated the prototype heuristically with visualization experts and through interviews with two SBSS experts. In addition, we show the transferability of our approach by applying it to microclimate simulations. Study participants could confirm suspected and known parameter-output relations, find surprising associations, and identify parameter subspaces to examine in the future. During our design study and evaluation, we identified challenging future research opportunities.

翻译：现代科学与工业依赖于计算模型进行模拟、预测和数据分析。空间盲源分离（SBSS）是一种用于分析空间数据的模型。该模型专为空间数据分析设计，优于主成分分析（PCA）等流行的非空间方法。然而，其实际应用面临一个挑战：需要设置两个复杂的调优参数，这要求进行参数空间分析。本文聚焦于敏感性分析（SA）。SBSS的参数和输出均为空间数据，这使得SA实施困难，因为现有文献中很少有SA方法能处理模型两侧均存在如此复杂数据的情况。基于与统计学专家合作的设计研究中提出的需求，我们开发了一款可视化分析原型系统，用于实现数据类型无关的视觉敏感性分析，可适用于SBSS及其他场景。该方法的核心优势在于仅需参数设置与输出的相异性度量。我们通过可视化专家的启发式评估以及与两位SBSS专家的访谈对该原型进行了验证。此外，通过将其应用于微气候模拟，我们证明了该方法的可迁移性。研究参与者能够验证已知及推测的参数-输出关系，发现意外的关联，并识别出未来需要深入研究的参数子空间。在设计研究与评估过程中，我们也发现了具有挑战性的未来研究方向。