III: Medium: Bias Tracking and Reduction Methods for High-Dimensional Exploratory Visual Analysis and Selection

大型复杂数据集的探索性可视化和分析在一系列领域中越来越普遍。例如，在线公司跟踪用户以了解他们的产品，计算机安全日志捕获网络活动的详细痕迹，医疗保健系统捕获患者的详细纵向记录。在所有这些领域，正在创建大型和复杂的数据存储库，其目标是支持数据驱动的基于证据的决策。然而，今天的可视化工具-分析师工具箱的关键部分-在应用于高维数据集时往往不堪重负（即，具有大量变量的数据集）。真实世界的数据集通常有数千个变量;这与大多数可视化支持的维度数量少得多形成鲜明对比。这种维度上的差距使任何分析的有效性都面临极大的偏倚风险，可能导致严重的隐藏错误。该研究项目将开发一种新的高维探索性可视化方法，有助于检测和减少探索性高维数据可视化过程中的选择偏差和其他数据解释问题。该项目的成果，包括开放源码软件，将广泛适用于各个领域。此外，将在健康成果研究环境中与用户一起对该项目进行评估。这为改善世界各地的医疗保健提供了巨大的潜力。该项目开发了一套用于探索性数据分析的上下文可视化方法，旨在支持从高维数据中发现更强大和更普遍的见解。这些方法是建立在这样一种认识的基础上的，即使得许多视觉方法有效的非常概括也固有地模糊了高维数据集的方面，这对于准确解释用户的视觉发现可能是至关重要的。更具体地说，在可视化中积极考虑的数据子集（包括维度和记录）-数据焦点-必须在可视化中省略或未明确表示的许多维度和数据记录的上下文中进行解释-数据上下文。因此，该项目开发的方法旨在（1）明确建模和分析数据上下文，（2）传达数据焦点和上下文之间的关系，以便更好地告知用户隐藏的问题，如混淆变量和选择偏差。该项目的主要技术贡献包括：（1）用于视觉验证的内联复制;（2）用于高维可视化的基线选择方法;（3）用于代表性可视化的交互式重新平衡。此外，还将开发开放源码软件，并利用真实世界的数据和从业人员进行评估。这一研究项目的产品-包括新方法、软件产品和评价结果-将通过一个项目网站（https：//vaclab.web.unc.edu/contextual-visualization/）传播。

Exploratory visualization and analysis of large and complex datasets is growing increasingly common across a range of domains. For example, online companies track users to learn about their products, computer security logs capture detailed traces of network activity, and health care systems capture detailed longitudinal records for their patients. In all of these fields, large and complex data repositories are being created with the goal supporting data-driven, evidence-based decision making. However, today's visualization tools -- a critical part of an analyst's toolbox -- are often overwhelmed when applied to high-dimensional datasets (i.e., datasets with large numbers of variables). Real-world datasets can often have many thousands of variables; a stark contrast to the much smaller number of dimensions supported by most visualizations. This gap in dimensionality puts the validity of any analysis at great risk of bias, potentially leading to serious, hidden errors. This research project will develop a new approach to high-dimensional exploratory visualization that will help detect and reduce selection bias and other problems with data interpretation during exploratory high-dimensional data visualization. The project's results, including open-source software, will be broadly applicable across domains. In addition, the project will be evaluated with users in a health outcomes research setting. This offers significant potential to improve health care around the world. This project develops a set of Contextual Visualization Methods for exploratory data analysis which are designed to support the discovery of more robust and generalizable insights from high-dimensional data. These methods are built upon a recognition that the very summarization that makes many visual methods effective also inherently obscures aspects of a high-dimensional dataset that may be critical to accurate interpretation of a user's visual findings. More specifically, the subset of data (comprising both dimensions and records) that is actively accounted for within a visualization -- the data focus -- must be interpreted within the context of the many dimensions and data records that have been omitted or are not clearly represented within a visualization--the data context. The methods that this project develops, therefore, are designed to (1) explicitly model and analyze the data context, and (2) convey the relationship between the data focus and the context in order to better inform users about hidden problems such as confounding variables and selection bias. The primary technical contributions of the project include: (1) inline replication for visual validation; (2) baselined selection methods for high-dimensional visualization; (3) interactive rebalancing for representative visualization. In addition, open-source software will be developed and evaluated with real-world data and practitioners. The products of this research project -- including new methods, software products, and evaluation results -- will be disseminated through a project website (https://vaclab.web.unc.edu/contextual-visualization/).