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

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