To Explore What Isn't There -- Glyph-based Visualization for Analysis of Missing Values

TL;DR

This paper introduces Missingness Glyph, a new visualization technique for exploring and understanding patterns of missing data, outperforming existing methods in identifying relevant missingness patterns.

Contribution

The paper presents a novel glyph-based visualization method specifically designed for analyzing missing data patterns, filling a gap in data visualization tools.

Findings

Missingness Glyph effectively identifies missing data patterns.

It performs better than two existing visualization methods in tests.

The method aids in understanding data collection issues and data characteristics.

Abstract

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.