dataphysicalization: first thoughts and research

Drip by Tweet - Domestic Data Streamers

Drip by Tweet – Domestic Data Streamers

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 2015, Jansen, Dragicevic & Fekete 2013, Stusak, Tabard & Butz 2013, Houben 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.  

Source unknown

Source unknown

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 (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.

Rearrangable bar chart by Yvonne Jansen and Pierre Dragicevic (2013)

Rearrangable bar chart by Yvonne Jansen and Pierre Dragicevic (2013)

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 2016, Perin 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 2014).

Jose Duarte’s Handmade Visualizations

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 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.

Stan’s Cafe - Of All the People of All the World

Stan’s Cafe – Of All the People of All the World

Why now?

  • Digital fabrication (numerically controlled machines) and datascience are growing fields of research and practice, and are increasingly surrounded by techno-optimism as well as societal & privacy concerns and issues around access (Dieter & Lovink 2012, Bohman 2015). A special interest goes to the scalability allowed by parametric design methods (modeling based on parameters, describing the relationships and ratios between objects rather than only the objects themselves) for 2D and 3D modelling rather than the easier but less flexible solids modeling.
  • Shape-changing interfaces, actuating physical properties of materials is growing area of research, providing lots of possibilities for design & expression (Jansen 2015).
Student work at workshop week Mind to Reality - Politecnico di Milano, May 2016

Student work at workshop week Mind to Reality – Politecnico di Milano, May 2016

  • Technoliteracy and creativity are often viewed as the ultimate 21st century skills to shape our future world. Also: you cannot understand a tool if you can never see or manipulate the core components and processes shaping it: we get highlevel blackbox toolkits, but we need untoolkits (Mellis 2013).
Author's work with students at Amsterdam University of Applied Sciences

Author’s work with students at Amsterdam University of Applied Sciences

  • Data is getting more complex, more ubiquitous: think of eg. IoT, automatization and linking of personal dataprofiles in health, gov and non-gov services (Schep 2016). It is becoming harder to grasp or imagine its impact. Embodied understanding and visceral perspectives on data can give people some grip.
  • Increasing complexity requires a multitude of perspectives and practices surrounding it: critical co-creation & discussion between citizens, government, arts, sciences, industry & design is necessary.
Michael Knuepfel - Keyboard Frequency Sculpture

Michael Knuepfel – Keyboard Frequency Sculpture

Suggested research agenda

Jansen (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:

  • Leverage perceptual exploration skills
  • Make data accessible
  • Provide cognitive benefits, because enables embodied cognition through physical manipulation
  • Bring data into the Real World of personal and public space, museum spaces etc.
  • Foster strong(er) public engagement

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:

  • Define a principled way of encoding physical variables (design methodology & pedagogy)
  • Understand perceptual effectiveness (Gestalt principles of perception extended to physical)

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.

  • ‘Doing’ dataphysicalization in co-creation settings as pedagogy for data literacy
  • Explore potential for more public engagement and memorability, eg. in public space

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.  (2014, June). Constructive visualization. In Proceedings of the 2014 conference on Designing interactive systems (pp. 433-442). ACM.

Jansen, Y. (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., (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.