To Explore What Isn't There -- Glyph-based Visualization for Analysis of Missing Values
This work addresses the problem of effectively visualizing missing data patterns for data analysts, offering an incremental improvement over existing visualization methods.
This paper introduces Missingness Glyph, a new visualization method designed to help analyze and explore missing values in datasets. The method was evaluated against two other visualization techniques and demonstrated superior performance in identifying various missingness patterns.
This paper contributes a novel visualization method, Missingness Glyph, for analysis and exploration of missing values in data. Missing values are a common challenge in most data generating domains and may cause a range of analysis issues. Missingness in data may indicate potential problems in data collection and pre-processing, or highlight important data characteristics. While the development and improvement of statistical methods for dealing with missing data is a research area in its own right, mainly focussing on replacing missing values with estimated values, considerably less focus has been put on visualization of missing values. Nonetheless, visualization and explorative analysis has great potential to support understanding of missingness in data, and to enable gaining of novel insights into patterns of missingness in a way that statistical methods are unable to. The Missingness Glyph supports identification of relevant missingness patterns in data, and is evaluated and compared to two other visualization methods in context of the missingness patterns. The results are promising and confirms that the Missingness Glyph in several cases perform better than the alternative visualization methods.