TY - GEN
T1 - Bayesian Modelling of Alluvial Diagram Complexity
AU - Arunkumar, Anjana
AU - Ginjpalli, Shashank
AU - Bryan, Chris
N1 - Funding Information: This research was supported by the U.S. National Science Foundation through grant OAC-1934766. Publisher Copyright: © 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Alluvial diagrams are a popular technique for visualizing flow and relational data. However, successfully reading and interpreting the data shown in an alluvial diagram is likely influenced by factors such as data volume, complexity, and chart layout. To understand how alluvial diagram consumption is impacted by its visual features, we conduct two crowdsourced user studies with a set of alluvial diagrams of varying complexity, and examine (i) participant performance on analysis tasks, and (ii) the perceived complexity of the charts. Using the study results, we employ Bayesian modelling to predict participant classification of diagram complexity. We find that, while multiple visual features are important in contributing to alluvial diagram complexity, interestingly the importance of features seems to depend on the type of complexity being modeled, i.e. task complexity vs. perceived complexity.
AB - Alluvial diagrams are a popular technique for visualizing flow and relational data. However, successfully reading and interpreting the data shown in an alluvial diagram is likely influenced by factors such as data volume, complexity, and chart layout. To understand how alluvial diagram consumption is impacted by its visual features, we conduct two crowdsourced user studies with a set of alluvial diagrams of varying complexity, and examine (i) participant performance on analysis tasks, and (ii) the perceived complexity of the charts. Using the study results, we employ Bayesian modelling to predict participant classification of diagram complexity. We find that, while multiple visual features are important in contributing to alluvial diagram complexity, interestingly the importance of features seems to depend on the type of complexity being modeled, i.e. task complexity vs. perceived complexity.
KW - Empirical studies in visualization
KW - Human-centered computing
KW - Visualization
KW - Visualization techniques
UR - http://www.scopus.com/inward/record.url?scp=85123771921&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85123771921&partnerID=8YFLogxK
U2 - 10.1109/VIS49827.2021.9623282
DO - 10.1109/VIS49827.2021.9623282
M3 - Conference contribution
T3 - Proceedings - 2021 IEEE Visualization Conference - Short Papers, VIS 2021
SP - 51
EP - 55
BT - Proceedings - 2021 IEEE Visualization Conference - Short Papers, VIS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Visualization Conference, VIS 2021
Y2 - 24 October 2021 through 29 October 2021
ER -