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To identify what is not there: A definition of missingness patterns and evaluation of missing value visualization

机译：识别不存在的内容：缺失模式的定义和缺失值可视化评估

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摘要

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.
机译：尽管丢失数据是许多领域中普遍存在的问题，但它是可视化科学家已经大大忽略的一个话题。缺少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，但是它们的适当性通常取决于缺失的模式。对缺失模式和数据中缺失值的分布的更多了解可以极大地提高可靠性，并为深入了解数据收集和分析过程中的潜在问题提供有价值的见解，并更好地理解整个数据。可视化方法具有独特的可能性，可以通过显示缺失值及其与记录值的关系来支持对缺失模式的调查和理解。本文概述了缺失数据值的可视化，并定义了三个相关的缺失模式，用于理解数据的缺失。它还有助于进行可用性评估，该评估将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进行比较。结果表明，取决于可视化方法和缺失模式的性能差异。根据研究结果，提供了有关丢失数据可视化的未来设计的建议。

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