Assessing 2D and 3D Heatmaps for Comparative Analysis : An Empirical Study

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2020
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CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. New York, NY: ACM, 2020, 546. ISBN 978-1-4503-6708-0. Available under: doi: 10.1145/3313831.3376675
Zusammenfassung

Heatmaps are a popular visualization technique that encode 2D density distributions using color or brightness. Experimental studies have shown though that both of these visual variables are inaccurate when reading and comparing numeric data values. A potential remedy might be to use 3D heatmaps by introducing height as a third dimension to encode the data. Encoding abstract data in 3D, however, poses many problems, too. To better understand this tradeoff, we conducted an empirical study (N=48) to evaluate the user performance of 2D and 3D heatmaps for comparative analysis tasks. We test our conditions on a conventional 2D screen, but also in a virtual reality environment to allow for real stereoscopic vision. Our main results show that 3D heatmaps are superior in terms of error rate when reading and comparing single data items. However, for overview tasks, the well-established 2D heatmap performs better.

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004 Informatik
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virtual reality; visual analytics; heatmaps
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2020 CHI Conference on Human Factors in Computing Systems : CHI '20, 25. Apr. 2020 - 30. Apr. 2020, Honolulu
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ISO 690KRAUS, Matthias, Katrin ANGERBAUER, Juri F. BUCHMÜLLER, Daniel SCHWEITZER, Daniel A. KEIM, Michael SEDLMAIR, Johannes FUCHS, 2020. Assessing 2D and 3D Heatmaps for Comparative Analysis : An Empirical Study. 2020 CHI Conference on Human Factors in Computing Systems : CHI '20. Honolulu, 25. Apr. 2020 - 30. Apr. 2020. In: CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. New York, NY: ACM, 2020, 546. ISBN 978-1-4503-6708-0. Available under: doi: 10.1145/3313831.3376675
BibTex
@inproceedings{Kraus2020Asses-49555,
  year={2020},
  doi={10.1145/3313831.3376675},
  title={Assessing 2D and 3D Heatmaps for Comparative Analysis : An Empirical Study},
  isbn={978-1-4503-6708-0},
  publisher={ACM},
  address={New York, NY},
  booktitle={CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems},
  author={Kraus, Matthias and Angerbauer, Katrin and Buchmüller, Juri F. and Schweitzer, Daniel and Keim, Daniel A. and Sedlmair, Michael and Fuchs, Johannes},
  note={Article Number: 546}
}
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