Data-Driven Mark Orientation for Trend Estimation in Scatterplots

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LIU, Tingting, Xiaotong LI, Chen BAO, Michael CORRELL, Changehe TU, Oliver DEUSSEN, Yunhai WANG, 2021. Data-Driven Mark Orientation for Trend Estimation in Scatterplots. 2021 CHI Conference on Human Factors in Computing Systems. Yokohama Japan, May 8, 2021 - May 13, 2021. In: KITAMURA, Yoshifumi, ed., Aaron QUIGLEY, ed.. CHI’21 : Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. New York, NY:ACM, 473. ISBN 978-1-4503-8096-6. Available under: doi: 10.1145/3411764.3445751

@inproceedings{Liu2021DataD-56523, title={Data-Driven Mark Orientation for Trend Estimation in Scatterplots}, year={2021}, doi={10.1145/3411764.3445751}, isbn={978-1-4503-8096-6}, address={New York, NY}, publisher={ACM}, booktitle={CHI’21 : Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems}, editor={Kitamura, Yoshifumi and Quigley, Aaron}, author={Liu, Tingting and Li, Xiaotong and Bao, Chen and Correll, Michael and Tu, Changehe and Deussen, Oliver and Wang, Yunhai}, note={Article Number: 473} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dc:contributor>Correll, Michael</dc:contributor> <dcterms:abstract xml:lang="eng">A common task for scatterplots is communicating trends in bivariate data. However, the ability of people to visually estimate these trends is under-explored, especially when the data violate assumptions required for common statistical models, or visual trend estimates are in conflict with statistical ones. In such cases, designers may need to intervene and de-bias these estimations, or otherwise inform viewers about differences between statistical and visual trend estimations. We propose data-driven mark orientation as a solution in such cases, where the directionality of marks in the scatterplot guide participants when visual estimation is otherwise unclear or ambiguous. Through a set of laboratory studies, we investigate trend estimation across a variety of data distributions and mark directionalities, and find that data-driven mark orientation can help resolve ambiguities in visual trend estimates.</dcterms:abstract> <dc:language>eng</dc:language> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:creator>Correll, Michael</dc:creator> <dcterms:title>Data-Driven Mark Orientation for Trend Estimation in Scatterplots</dcterms:title> <dc:date rdf:datatype="">2022-02-10T14:57:19Z</dc:date> <dcterms:isPartOf rdf:resource=""/> <dc:creator>Bao, Chen</dc:creator> <dc:contributor>Bao, Chen</dc:contributor> <dc:contributor>Tu, Changehe</dc:contributor> <dspace:isPartOfCollection rdf:resource=""/> <dc:contributor>Li, Xiaotong</dc:contributor> <dc:contributor>Wang, Yunhai</dc:contributor> <bibo:uri rdf:resource=""/> <dc:contributor>Deussen, Oliver</dc:contributor> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:creator>Li, Xiaotong</dc:creator> <dcterms:rights rdf:resource=""/> <dc:creator>Tu, Changehe</dc:creator> <dc:creator>Liu, Tingting</dc:creator> <dc:contributor>Liu, Tingting</dc:contributor> <dcterms:available rdf:datatype="">2022-02-10T14:57:19Z</dcterms:available> <dcterms:issued>2021</dcterms:issued> <dc:creator>Deussen, Oliver</dc:creator> <dc:rights>terms-of-use</dc:rights> <dc:creator>Wang, Yunhai</dc:creator> </rdf:Description> </rdf:RDF>

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