Metaphorical Visualization: Mapping Data to Familiar Concepts

Gleb Tkachev, Rene Cutura, Michael Sedlmair, Steffen Frey, Thomas Ertl

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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We present a new approach to visualizing data that is well-suited
for personal and casual applications. The idea is to map the data to
another dataset that is already familiar to the user, and then rely
on their existing knowledge to illustrate relationships in the data.
We construct the map by preserving pairwise distances or by maintaining relative values of specific data attributes. This metaphorical
mapping is very flexible and allows us to adapt the visualization to
its application and target audience. We present several examples
where we map data to different domains and representations. This
includes mapping data to cat images, encoding research interests
with neural style transfer and representing movies as stars in the
night sky. Overall, we find that although metaphors are not as accurate as the traditional techniques, they can help design engaging
and personalized visualizations.
Original languageEnglish
Title of host publicationCHI Conference on Human Factors in Computing Systems Extended Abstracts
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery
Number of pages10
Publication statusPublished - 28-Apr-2022
EventCHI '22: CHI Conference on Human Factors in Computing Systems - New Orleans, LA, United States
Duration: 29-Apr-20225-May-2022


ConferenceCHI '22
Country/TerritoryUnited States
CityNew Orleans, LA

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