Experimenting with Dataphys, a Physicalization Toolkit for Representing Spatio-Temporal Environmental Data
Article No.: 24, Pages 1 - 9
Abstract
Data physicalization offers an exciting opportunity for engaging people with physical representations of data that can be examined and manipulated to gain a mental model of a set of variables and their relationships. This is of paramount importance not only for researchers and professionals but also for improving citizens’ awareness in different domains, impacting civil society, and pushing behavior change. Accessibility of data is another important issue that can benefit from the adoption of data physicalization. Despite this potential, several pitfalls prevent the wide adoption of this paradigm, including the lack of a methodology for guiding the design of the representations and toolkits permitting to engage citizens in their creation without requiring them to start from scratch. This paper intends to contribute to the discussion by presenting and evaluating Dataphys, a toolkit for creating physical representations of data. The toolkit is modular, conceived for the application to different scenarios and domains, printed with cheap technologies, and open to modifications to ease its adoption. The evaluation was carried out in the context of a workshop with the participation of 17 master students in computer science, and focused on the gathering of environmental data collected along a path and the construction of a physical representation. The study’s results confirm the interest in the approach and offer insights for improving it.
References
[1]
Lorenzo Ambrosini and Miriah Meyer. 2022. Data Bricks Space Mission: Teaching Kids about Data with Physicalization. In 2022 IEEE Workshop on Visualization for Social Good (VIS4Good). 10–14. https://doi.org/10.1109/VIS4Good57762.2022.00007
[2]
S Sandra Bae, Danielle Albers Szafir, and Ellen Yi-Luen Do. 2022. Exploring the Benefits and Challenges of Data Physicalization. In Proceedings of ETIS 2022.
[3]
S Sandra Bae, Clement Zheng, Mary Etta West, Ellen Yi-Luen Do, Samuel Huron, and Danielle Albers Szafir. 2022. Making data tangible: A cross-disciplinary design space for data physicalization. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 1–18.
[4]
Jacques Bertin. 1983. Semiology of graphics. University of Wisconsin press.
[5]
Anna R. L. Carter, Marianna Obrist, Christopher Dawes, Alan Dix, Jennifer Pearson, Matt Jones, Dimitrios Zampelis, and Ceylan Beşevli. 2023. Scent InContext: Design and Development around Smell in Public and Private Spaces. In Companion Publication of the 2023 ACM Designing Interactive Systems Conference (Pittsburgh, PA, USA) (DIS ’23 Companion). Association for Computing Machinery, New York, NY, USA, 138–141. https://doi.org/10.1145/3563703.3591455
[6]
Jinho Choi, Sanghun Jung, Deok Gun Park, Jaegul Choo, and Niklas Elmqvist. 2019. Visualizing for the Non-Visual: Enabling the Visually Impaired to Use Visualization. Computer Graphics Forum (2019). https://doi.org/10.1111/cgf.13686
[7]
William S. Cleveland and Robert McGill. 1984. Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods. J. Amer. Statist. Assoc. 79, 387 (1984), 531–554. https://doi.org/10.1080/01621459.1984.10478080 arXiv:https://www.tandfonline.com/doi/pdf/10.1080/01621459.1984.10478080
[8]
DataPhys 2023. Data Physicalization Wiki. http://dataphys.org
[9]
Frank Elavsky, Cynthia Bennett, and Dominik Moritz. 2022. How accessible is my visualization? Evaluating visualization accessibility with Chartability. Computer Graphics Forum 41, 3 (2022), 57–70. https://doi.org/10.1111/cgf.14522
[10]
Mordechai (Muki) Haklay, Daniel Dörler, Florian Heigl, Marina Manzoni, Susanne Hecker, and Katrin Vohland. 2021. What Is Citizen Science? The Challenges of Definition. Springer International Publishing, Cham, 13–33. https://doi.org/10.1007/978-3-030-58278-4_2
[11]
Juho Hamari, David J. Shernoff, Elizabeth Rowe, Brianno Coller, Jodi Asbell-Clarke, and Teon Edwards. 2016. Challenging games help students learn: An empirical study on engagement, flow and immersion in game-based learning. Computers in Human Behavior 54 (2016), 170–179. https://doi.org/10.1016/j.chb.2015.07.045
[12]
David Hunter. 2020. Data walking. In Proceedings of the 16th Participatory Design Conference 2020-Participation (s) Otherwise-Volume 2. 188–191.
[13]
David Hunter. 2022. Data Waliking. Retrieved January 21, 2024 from https://www.datawalking.com
[14]
Samuel Huron, Sheelagh Carpendale, Alice Thudt, Anthony Tang, and Michael Mauerer. 2014. Constructive visualization. In Proceedings of the 2014 conference on Designing interactive systems. 433–442.
[15]
Yvonne Jansen, Pierre Dragicevic, Petra Isenberg, Jason Alexander, Abhijit Karnik, Johan Kildal, Sriram Subramanian, and Kasper Hornbæk. 2015. Opportunities and challenges for data physicalization. In proceedings of the 33rd annual acm conference on human factors in computing systems. 3227–3236.
[16]
Shaun K. Kane and Jeffrey P. Bigham. 2014. Tracking @stemxcomet: Teaching Programming to Blind Students via 3D Printing, Crisis Management, and Twitter. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education (Atlanta, Georgia, USA) (SIGCSE ’14). Association for Computing Machinery, New York, NY, USA, 247–252. https://doi.org/10.1145/2538862.2538975
[17]
Andy Kirk. 2019. Data visualisation: A handbook for data driven design. Data Visualisation (2019), 1–328.
[18]
Martin Valdemar Anker Lindrup, Arjun Rajendran Menon, and Aksel Biørn-Hansen. 2023. Carbon Scales: Collective Sense-Making of Carbon Emissions from Food Production through Physical Data Representation. In Proceedings of the 2023 ACM Designing Interactive Systems Conference (Pittsburgh, PA, USA) (DIS ’23). Association for Computing Machinery, New York, NY, USA, 1515–1530. https://doi.org/10.1145/3563657.3596043
[19]
Alan Lundgard, Crystal Lee, and Arvind Satyanarayan. 2019. Sociotechnical Considerations for Accessible Visualization Design. In 2019 IEEE Visualization Conference (VIS). 16–20. https://doi.org/10.1109/VISUAL.2019.8933762
[20]
Charles Perin. 2021. What Students Learn With Personal Data Physicalization. IEEE Computer Graphics and Applications 41, 6 (2021), 48–58. https://doi.org/10.1109/MCG.2021.3115417
[21]
Fabio Pittarello and Manuel Semenzato. 2024. Towards a Data Physicalization Toolkit for Non-Sighted Users. In 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC). 1–6. https://doi.org/10.1109/CCNC51664.2024.10454861
[22]
Kim Sauvé, Hans Brombacher, Rosa Van Koningsbruggen, Annemiek Veldhuis, Steven Houben, and Jason Alexander. 2023. Physicalization from Theory to Practice: Exploring Physicalization Design across Domains. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI EA ’23). Association for Computing Machinery, New York, NY, USA, Article 360, 7 pages. https://doi.org/10.1145/3544549.3573824
[23]
Kim Sauvé, Pierre Dragicevic, and Yvonne Jansen. 2023. Edo: A Participatory Data Physicalization on the Climate Impact of Dietary Choices. In Proceedings of the Seventeenth International Conference on Tangible, Embedded, and Embodied Interaction (Warsaw, Poland) (TEI ’23). Association for Computing Machinery, New York, NY, USA, Article 35, 13 pages. https://doi.org/10.1145/3569009.3572807
[24]
Denise Schmandt-Besserat. 2013. Tokens and writing: the cognitive development. In UT Faculty/Researcher Works. Ex Oriente.
[25]
Robert Spence. 2014. Information Visualization: An Introduction (3rd ed.). Springer Publishing Company Incorporated.
[26]
Adam Strantz. 2023. Print-and-Play: Data Physicalization Methods for Research Analysis in Technical Communication. In Proceedings of the 41st ACM International Conference on Design of Communication (Orlando, FL, USA) (SIGDOC ’23). Association for Computing Machinery, New York, NY, USA, 215–220. https://doi.org/10.1145/3615335.3623039
[27]
Simon Stusak, Andreas Butz, and Aurélien Tabard. 2018. Variables for Data Physicalization Units. Toward a Design Language for Data Physicalization, IEEE VIS. (2018).
[28]
W3C. 2024. Web Accessibility Initiative (WAI). Retrieved January 21, 2024 from https://www.w3.org/WAI/
[29]
Data Physicalization Wiki. 2022. Global Cities: Elevation Maps of City Population. Retrieved January 21, 2024 from http://dataphys.org/list/city-populations/
[30]
Data Physicalization Wiki. 2022. Summer in the City. Retrieved January 21, 2024 from http://dataphys.org/list/summer-in-the-city/
[31]
David Worrall. 2019. Sonification Design: From Data to Intelligible Soundfields (1st ed.). Springer Publishing Company, Incorporated.
[32]
Jack Zhao and Andrew Vande Moere. 2008. Embodiment in data sculpture: a model of the physical visualization of information. In Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts. 343–350.
Index Terms
- Experimenting with Dataphys, a Physicalization Toolkit for Representing Spatio-Temporal Environmental Data
Recommendations
DataChest: a Constructive Data Physicalization Toolkit
TEI '24: Proceedings of the Eighteenth International Conference on Tangible, Embedded, and Embodied InteractionIncreasingly individuals use devices, diaries, or applications to track aspects of their lives. Visualizing diverse sets of data and exploring their relationships poses challenges when data is fragmented across different devices or sources. Constructive ...
Self-Reflection and Personal Physicalization Construction
CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing SystemsSelf-reflection is a central goal of personal informatics systems, and constructing visualizations from physical tokens has been found to help people reflect on data. However, so far, constructive physicalization has only been studied in lab ...
SensorBricks: a Collaborative Tangible Sensor Toolkit to Support the Development of Data Literacy
TEI '24: Proceedings of the Eighteenth International Conference on Tangible, Embedded, and Embodied InteractionData is often inaccessible for non-expert users, leading to a lack of data transparency and speculation in the understanding and interpretation of data, not providing personal value to individuals. The SensorBricks toolkit was evaluated during 5 ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
June 2024
578 pages
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution-ShareAlike International 4.0 License.
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 03 June 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
AVI 2024
AVI 2024: International Conference on Advanced Visual Interfaces 2024
June 3 - 7, 2024
Arenzano, Genoa, Italy
Acceptance Rates
AVI '24 Paper Acceptance Rate 21 of 82 submissions, 26%;
Overall Acceptance Rate 128 of 490 submissions, 26%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 246Total Downloads
- Downloads (Last 12 months)246
- Downloads (Last 6 weeks)51
Reflects downloads up to 19 Nov 2024
Other Metrics
Citations
View Options
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML FormatLogin options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in