Visualization of missing data: a state-of-the-art survey

TL;DR

This paper surveys current visualization techniques for missing data, highlighting their importance in statistical analysis and identifying challenges in selecting appropriate visualizations for understanding missing data patterns.

Contribution

It provides the first comprehensive survey of missing data visualization techniques, aiming to motivate further research in this area.

Findings

Overview of existing visualization methods for missing data

Identification of challenges in visualization selection

Encouragement for increased research involvement

Abstract

Missing data, the data value that is not recorded for a variable, occurs in almost all statistical analyses and may be caused by many reasons, such as lack of collection or a lack of documentation. Researchers need to adequately deal with this issue to provide a valid analysis. The visualization of missing values plays an important role in supporting the investigation and understanding of the missing data patterns. While some techniques and tools for visualization of missing values are available, it is still a challenge to select the right visualization that will fulfil the user requirements for visualizing missing data. This paper provides an overview and state-of-the-art report (STAR) of research literature focusing on missing values visualization. To the best of our knowledge, this is the first survey paper with a focus on missing data visualization. The goal of this paper is to encourage visualization researchers to increase their involvement with Missing data visualization.