Jinhan Choi
ABSTRACT
Visualizations are now widely adopted across disciplines, providing effective means to understand and communicate data. However, people still frequently create misleading visualizations that distort the underlying data and ultimately misinform the audience. While design guidelines exist, they are currently scattered across different sources and devised by different people, often missing design trade-offs in different contexts and providing inconsistent and conflicting design knowledge to visualization practitioners. Our goal in this work is to investigate the ontology of visualization design guidelines and derive a unified framework for structuring the guidelines. We collected existing guidelines on the web and analyzed them using the grounded theory approach. We describe the current landscape of the available guidelines and propose a structured template for describing visualization design guidelines.
Supplemental Material
Available for Download
- Accessed: February 25, 2021. Data To Viz. https://www.data-to-viz.com/.Google Scholar
- Accessed: February 25, 2021. The Data Visualisation Catalogue. https://datavizcatalogue.com/.Google Scholar
- Accessed: February 25, 2021. Data Viz Project. https://datavizproject.com/.Google Scholar
- Accessed: February 25, 2021. DataJournalism.com. https://datajournalism.com/.Google Scholar
- Accessed: February 25, 2021. Datawrapper. https://www.datawrapper.de/.Google Scholar
- Accessed: February 25, 2021. Financial Times Visual Vocabulary. https://ft-interactive.github.io/visual-vocabulary/.Google Scholar
- Accessed: February 25, 2021. Perceptual Edge. https://www.perceptualedge.com/.Google Scholar
- Basak Alper, Nathalie Henry Riche, Fanny Chevalier, Jeremy Boy, and Metin Sezgin. 2017. Visualization literacy at elementary school. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. 5485–5497.Google ScholarDigital Library
- Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N Bennett, Kori Inkpen, 2019. Guidelines for human-AI interaction. In Proceedings of the 2019 chi conference on human factors in computing systems. 1–13.Google ScholarDigital Library
- Katy Börner, Andreas Bueckle, and Michael Ginda. 2019. Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments. Proceedings of the National Academy of Sciences 116, 6 (2019), 1857–1864.Google ScholarCross Ref
- Jeremy Boy, Ronald A Rensink, Enrico Bertini, and Jean-Daniel Fekete. 2014. A principled way of assessing visualization literacy. IEEE transactions on visualization and computer graphics 20, 12(2014), 1963–1972.Google ScholarCross Ref
- Matthew Brehmer and Tamara Munzner. 2013. A multi-level typology of abstract visualization tasks. IEEE transactions on visualization and computer graphics 19, 12(2013), 2376–2385.Google ScholarDigital Library
- Kathy Charmaz and Linda Liska Belgrave. 2007. Grounded theory. The Blackwell encyclopedia of sociology(2007).Google Scholar
- William S Cleveland and Robert McGill. 1984. Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American statistical association 79, 387(1984), 531–554.Google ScholarCross Ref
- Alexandra Diehl, Alfie Abdul-Rahman, Mennatallah El-Assady, Benjamin Bach, Daniel A Keim, and Min Chen. 2018. VisGuides: A Forum for Discussing Visualization Guidelines. In EuroVis (Short Papers). 61–65.Google Scholar
- Barney G Glaser and Anselm L Strauss. 2010. Grounded theory: strategien qualitativer forschung. Huber.Google Scholar
- Leo A Goodman. 1961. Snowball sampling. The annals of mathematical statistics(1961), 148–170.Google Scholar
- Jeffrey Heer and Michael Bostock. 2010. Crowdsourcing graphical perception: using mechanical turk to assess visualization design. In Proceedings of the SIGCHI conference on human factors in computing systems. ACM, 203–212.Google ScholarDigital Library
- Younghoon Kim and Jeffrey Heer. 2018. Assessing effects of task and data distribution on the effectiveness of visual encodings. In Computer Graphics Forum, Vol. 37. Wiley Online Library, 157–167.Google Scholar
- Sukwon Lee, Sung-Hee Kim, and Bum Chul Kwon. 2016. Vlat: Development of a visualization literacy assessment test. IEEE transactions on visualization and computer graphics 23, 1(2016), 551–560.Google Scholar
- Miriah Meyer, Michael Sedlmair, P Samuel Quinan, and Tamara Munzner. 2015. The nested blocks and guidelines model. Information Visualization 14, 3 (2015), 234–249.Google ScholarCross Ref
- Tamara Munzner. 2014. Visualization analysis and design. CRC press.Google Scholar
- Bahador Saket, Alex Endert, and Çağatay Demiralp. 2018. Task-based effectiveness of basic visualizations. IEEE transactions on visualization and computer graphics 25, 7(2018), 2505–2512.Google Scholar
- James Scott-Brown. 2018. Presenting Visualization Guideline Collections. 2nd IEEE VIS 2018 Workshop on the Creation, Curation, Critique and Conditioning of Principles and Guidelines in Visualization (2018). https://c4pgv.dbvis.de/Scott-Brown_2018.pdfGoogle Scholar
- Zezhong Wang, Lovisa Sundin, Dave Murray-Rust, and Benjamin Bach. 2020. Cheat sheets for data visualization techniques. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–13.Google ScholarDigital Library
Index Terms
- Toward a Unified Framework for Visualization Design Guidelines
Recommendations
Using guidelines to assist in the visualisation design process
APVis '05: proceedings of the 2005 Asia-Pacific symposium on Information visualisation - Volume 45The design process of any visualisation involves many design decisions. One way to assist with these design decisions is to provide guidelines. This paper introduces the MS-Guidelines and the rationale behind them. The MS-Guidelines form a group of ...
Beyond Guidelines: What Can We Learn from the Visual Information Seeking Mantra?
IV '05: Proceedings of the Ninth International Conference on Information VisualisationThe field of information visualization offers little methodological guidance to practitioners who seek to design novel systems. Though many sources describe the foundations of the domain, few discuss practical methods for solving visualization problems. ...
A Model and Framework for Visualization Exploration
Visualization exploration is the process of extracting insight from data via interaction with visual depictions of that data. Visualization exploration is more than presentation; the interaction with both the data and its depiction is as important as ...
Comments