This blogpost is a summarized (maybe not so elegant) overview of readings and research I’ve been doing on the topic of dataphyiscialization. I will be using elements of this in a variety of activities at the Amsterdam University of Applied Science’s makerspaces where I’m investigating the potential added value of making activities for (design) researchers.
What is dataphysicalisation?
“Data expressed via physical modalities that can be experienced directly, through the eye […] or other human senses” – Bohrman 2015, p.22
“Infoexperiences” – Domestic Data Streamers
Ocularcentrism is dead, long live embodied, full-sensory experience. There are many definitions, but these two highlight the important point of what physical renderings of data do differently for the explorer of that data. By experiencing data, you get a better understanding of data, it can support analytical tasks, and the empathic, emotional impact and engagement with the data can be greater too (Jansen et.al 2015, Jansen, Dragicevic & Fekete 2013, Stusak, Tabard & Butz 2013, Houben et.al. 2016).
For this reason there could be a great potential role for dataphysicalization in debates and citizen empowerment around the themes of data and social change. Data remains rather invisible, ungraspable for non-specialists but has great impact on our lives, our bodies, work, health and education. We can trace back a long history from Mesopotamian clay tokens to shape-changing interactive displays of data: us humans have been doing this for a long time to understand the world, as shown on this historical list.
To manipulate is to understand
“Not hearing is not as good as hearing, hearing is not as good as seeing, seeing is not as good as knowing, knowing is not as good as acting; true learning continues until it is put into action.”
– Xunzi, confucian philosopher –
The embodied cognition thesis argues that we can understand and learn better if we can manipulate the thing we are dealing with in a tangible way. To explore this, dataphysicalization researchers like Jansen, Dragicevic & Fekete (2013) as well as Stusak, Tabard & Butz (2013) and Jansen et.al. (2015) are studying to what extent data physicalization can actually leverage cognitive skills in terms of information processing. The results so far are in favor of 3D, physical data objects when compared to their on-screen or 2D siblings.
Raw data however is not “accessible” without manipulating and visualizing it. It is through the manipulation process of correlating and rendering combinations of data that certain perspectives, possible stories and interrogations emerge (Houben et.al. 2016, Perin et.al. 2015, Bertin 1983). It is in the process of designing, rendering and formgiving that a data designer or analysts start to create and understanding of it. This is supported by constructivist paradigms of learning and understanding developed by Piaget, Fröbel and Papert (Huron et.al. 2014).
Democratization in terms of data can then happen in a number of ways: by providing access to tools and knowledge about how to 1) collect and publish data (citizen sensing toolkits). By 2) rendering data in more accessible ways for non-experts (Bohman 2015, Zhao & Vande Moere 2008). But ultimately, also to 3) enable non-experts to manipulate data. Opening up this area can democratize formerly exclusive specialist knowledge in such a way that people can start making sense of it on their own terms, based on their own needs, stories and context (Houben et.al. 2016). Democratization of data is providing ways for them to take action on the data, and create meaning and discussion about data perspectives on their own terms.
Suggested research agenda
Jansen et.al. (2015) delineate a research agenda for the emerging field of dataphysicalization, they list a number of potential benefits that make it worth to look into this topic, as it has potential to:
Based on my own interests, personal background and experiences I would propose the following two angles on dataphysicalization in the context of citizen empowerment & data:
1. Conveying data effectively
There’s a great body of work on the effectiveness and principles of visual perception of information (Bertin, Tufte, Gestalt school of psychology and others). There’s a need for similar understanding in the haptic and tactile perception of information in order to establish the fundamentals of dataphysicalization as a design practice. It is required to:
2. Applying dataphysicalization in real-life contexts
In relation to the costs and benefits of dataphysicalization, it is vital to apply this practice in contexts where it can have impact.
3. Developing evaluation methodologies
Define criteria for merits: eg. persuasion, pedagogical power, engagement, memorability.
References and further reading
Amar, R., Eagan, J., & Stasko, J. (2005, October). Low-level components of analytic activity in information visualization. In Information Visualization, 2005. INFOVIS 2005. IEEE Symposium on (pp. 111-117). IEEE.
Bertin, J. (1983). Semiology of graphics: diagrams, networks, maps.
Bohman, S. (2015, November). Civic Participation and Empowerment through Visualization. In Proceedings of SIGRAD 2015, June 1st and 2nd, Stockholm, Sweden (No. 120, pp. 20-23). Linköping University Electronic Press.
Houben, S. et. al. (2016, May). Physikit: Data Engagement Through Physical Ambient Visualizations in the Home. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 1608-1619). ACM.
Huron, S. et.al. (2014, June). Constructive visualization. In Proceedings of the 2014 conference on Designing interactive systems (pp. 433-442). ACM.
Jansen, Y. et.al. (2015, April). Opportunities and challenges for data physicalization. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 3227-3236). ACM.
Jansen, Y., Dragicevic, P., & Fekete, J. D. (2013, April). Evaluating the efficiency of physical visualizations. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 2593-2602). ACM.
Mellis, D. A., et.al.. (2013, February). Microcontrollers as material: crafting circuits with paper, conductive ink, electronic components, and an untoolkit. In Proceedings of the 7th International Conference on Tangible, Embedded and Embodied Interaction (pp. 83-90). ACM.
Perin, C. et. al. (2015, April). DIY Bertin Matrix. In Proceedings of the CHI Workshop on Exploring the Challenges of Making Data Physical.
Stusak, S., Tabard, A., & Butz, A. (2013). Can physical visualizations support analytical tasks.Posters of IEEE InfoVis.
Zhao, J., & Vande Moere, A. (2008, September). Embodiment in data sculpture: a model of the physical visualization of information. InProceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts(pp. 343-350). ACM.