Graphical Tools for Visualization of Missing Data in Large Longitudinal Phenomena

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dc.contributor.authorJiménez, Edgaren_US
dc.contributor.authorMacías, Rodrigoen_US
dc.contributor.editorHauser, Helwig and Alliez, Pierreen_US
dc.date.accessioned2022-03-25T12:31:07Z
dc.date.available2022-03-25T12:31:07Z
dc.date.issued2022
dc.description.abstractThe analysis of large quantities of longitudinal data requires quick decision tools to ensure data quality and to find useful patterns for analysis in exploratory stages. We propose algorithms based on ordering, sampling and grouping applied to lasagna plots, a special kind of matrix plot, which are heat maps created to visualize longitudinal studies. These algorithms can be applied to large data sets to find patterns of interest, monotone and intermittent, in the missing data with low computational cost compared to previous alternatives. Visualization with these algorithms addresses a trade‐off in visualization design: reducing visual clutter versus increasing the information content in a visualization. The method enables the visualization of missing data in a clear and concise way. We apply our techniques to four real‐world data sets of different origins and sizes that share analysis and visualization tasks and discuss the patterns found within them.en_US
dc.description.number1
dc.description.sectionheadersArticles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume41
dc.identifier.doi10.1111/cgf.14445
dc.identifier.issn1467-8659
dc.identifier.pages438-452
dc.identifier.urihttps://doi.org/10.1111/cgf.14445
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14445
dc.publisher© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltden_US
dc.subjectvisualization
dc.subjectmissing data
dc.subjectalgorithms
dc.titleGraphical Tools for Visualization of Missing Data in Large Longitudinal Phenomenaen_US
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