Revisiting common pitfalls in graphical representations utilizing a case-based learning approach

Vinh T. Nguyen, Kwanghee Jung, Tommy Dang

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

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 13th International Symposium on Visual Information Communication and Interaction, VINCI 2020
EditorsQuang Vinh Nguyen, Ying Zhao, Michael Burch, Michel Westenberg
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450387507
DOIs
StatePublished - Dec 8 2020
Event13th International Symposium on Visual Information Communication and Interaction, VINCI 2020 - Virtual, Online, Netherlands
Duration: Dec 8 2020Dec 10 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference13th International Symposium on Visual Information Communication and Interaction, VINCI 2020
CountryNetherlands
CityVirtual, Online
Period12/8/2012/10/20

Keywords

  • effective visualization
  • fundamental units of visual communication
  • lie factor
  • misinformation
  • visual encodings

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