  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.
