While missing data is a commonly occurring issue in many domains, it is a topic that has been greatly overlooked by visualization scientists. Missing data values reduce the reliability of analysis results. A range of methods exist to replace the missing values with estimated values, but their appropriateness often depend on the patterns of missingness. Increased understanding of the missingness patterns and the distribution of missing values in data may greatly improve reliability, as well as provide valuable insight into potential problems in data gathering and analyses processes, and better understanding of the data as a whole. Visualization methods have a unique possibility to support investigation and understanding of missingness patterns by making the missing values and their relationship to recorded values visible. This article provides an overview of visualization of missing data values and defines a set of three missingness patterns of relevance for understanding missingness in data. It also contributes a usability evaluation which compares visualization methods representing missing values and how well they help users identify missingness patterns. The results indicate differences in performance depending on the visualization method as well as missingness pattern. Recommendations for future design of missing data visualization are provided based on the outcome of the study.

中文翻译：

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识别不存在的东西：缺失模式的定义和缺失值可视化的评估

虽然缺失数据是许多领域中普遍存在的问题，但这是一个被可视化科学家严重忽视的话题。缺失数据值会降低分析结果的可靠性。存在一系列用估计值替换缺失值的方法，但它们的适当性通常取决于缺失的模式。增加对数据中缺失模式和缺失值分布的理解可以大大提高可靠性，并提供对数据收集和分析过程中潜在问题的宝贵见解，以及更好地理解整个数据。通过使缺失值及其与记录值的关系可见，可视化方法具有支持调查和理解缺失模式的独特可能性。本文概述了缺失数据值的可视化，并定义了一组与理解数据缺失相关的三种缺失模式。它还有助于可用性评估，比较表示缺失值的可视化方法以及它们如何帮助用户识别缺失模式。结果表明性能差异取决于可视化方法以及缺失模式。根据研究结果提供了对未来缺失数据可视化设计的建议。它还有助于可用性评估，比较表示缺失值的可视化方法以及它们如何帮助用户识别缺失模式。结果表明性能差异取决于可视化方法以及缺失模式。根据研究结果提供了对未来缺失数据可视化设计的建议。它还有助于可用性评估，比较表示缺失值的可视化方法以及它们如何帮助用户识别缺失模式。结果表明性能差异取决于可视化方法以及缺失模式。根据研究结果提供了对未来缺失数据可视化设计的建议。