TY - GEN
T1 - Revisiting common pitfalls in graphical representations utilizing a case-based learning approach
AU - Nguyen, Vinh T.
AU - Jung, Kwanghee
AU - Dang, Tommy
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/12/8
Y1 - 2020/12/8
N2 - Data visualization blends art and science to convey stories from data via graphical representations. Considering different problems, applications, requirements, and design goals, it is challenging to combine these two components at their full force. While the art component involves creating visually appealing and easily interpreted graphics for users, the science component requires accurate representations of a large amount of input data. With a lack of the science component, visualization cannot serve its role of creating correct representations of the actual data, thus leading to wrong perception, interpretation, and decision. It might be even worse if incorrect visual representations were intentionally produced to deceive the viewers. This work-in-progress paper focuses on misinformation in graphical representations utilizing a case-based learning approach. The misleading data visualization examples are surveyed and projected onto fundamental units of visual communication, such as size, value, shape, size, and position. This work aims at helping viewers understand the root causes of the misuse, as well as provide basic principles for making more effective visualizations.
AB - Data visualization blends art and science to convey stories from data via graphical representations. Considering different problems, applications, requirements, and design goals, it is challenging to combine these two components at their full force. While the art component involves creating visually appealing and easily interpreted graphics for users, the science component requires accurate representations of a large amount of input data. With a lack of the science component, visualization cannot serve its role of creating correct representations of the actual data, thus leading to wrong perception, interpretation, and decision. It might be even worse if incorrect visual representations were intentionally produced to deceive the viewers. This work-in-progress paper focuses on misinformation in graphical representations utilizing a case-based learning approach. The misleading data visualization examples are surveyed and projected onto fundamental units of visual communication, such as size, value, shape, size, and position. This work aims at helping viewers understand the root causes of the misuse, as well as provide basic principles for making more effective visualizations.
KW - effective visualization
KW - fundamental units of visual communication
KW - lie factor
KW - misinformation
KW - visual encodings
UR - http://www.scopus.com/inward/record.url?scp=85098454251&partnerID=8YFLogxK
U2 - 10.1145/3430036.3430071
DO - 10.1145/3430036.3430071
M3 - Conference contribution
AN - SCOPUS:85098454251
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 13th International Symposium on Visual Information Communication and Interaction, VINCI 2020
A2 - Nguyen, Quang Vinh
A2 - Zhao, Ying
A2 - Burch, Michael
A2 - Westenberg, Michel
PB - Association for Computing Machinery
T2 - 13th International Symposium on Visual Information Communication and Interaction, VINCI 2020
Y2 - 8 December 2020 through 10 December 2020
ER -