Review of Data Walking
This article is originally published at https://robertgrantstats.wordpress.com
The Data Walking project was organised and written up by David Hunter at Ravensbourne University London (which you might remember as an art school), lately located in the North Greenwich peninsula, that weird zone around the Millenium Dome (as I still like to call it), entirely remade over the centuries and now regenerated from ailing industries to residential and commercial space. The booklet is what I’ll review here.
Participants walked and used tech, each in their own way but given some inspiration by Hunter at the outset and by visitors like Valentina d’Efilippo in workshops, to collect data on their environs. Light, noise, images, pollution all featured in continuous data collection, as well as concepts that have to be counted by humans, such as numbers of people, signage, security and surveillance infrastructure
The book says (p.12) that the aim is “to expose designers or any participants to data gathering processes …” and so the tech has to be accessible in terms of skills and know how. Coding and PCs / laptops are kept to a minimum and admitted grudgingly.
Everyone should do this sort of thing. In particular, collecting data in the real world, with all the problems, compromises and leaps of faith that entails, is a valuable part of learning about analysis and quantitative thinking. It should be part of all stats teaching at an early stage. It should be in high school maths curricula too. cf “You Should Get Out More“.
It is an opportunity for participants to learn about data literacy, understand the fallibility of sensors, technology and processes, the ambiguity of results, and principles like ‘correlation does not mean causation’, in the hope they are better equipped to deal with data in other aspects of their lives.
When I say they used “tech”, the project was all about affordable DIY tools like Arduino. There is an intersection between creativity, collection as education, and citizen science here.
It might also be the case that certain types of data are not available for a specific aspect or place, or in high enough fidelity. In which case, it is time to roll up our sleeves and gather the data ourselves.
[cf “We Were There When They Made Dear Data” and “Explanation And Inference With House Sparrows“]
The second half of the book collects the participants’ impressive and varied outputs. Each participant chose how to create the output from their investigation, using all that nice kit that one finds in an art school: laser cutters, 3D printers, robotic routers, embroidery machines, and so on. But there were also hands-on processes that do collection and output together, like taking charcoal rubbings of pavements. It seems important to me to spend some time as lo-tech as possible, so you counteract the urge to be drawn deeper and deeper into tinkering with the tech to get it to do exactly what you had in mind.
Personally, I’d like to see more lo-tech, hands-on versions of stats / data science outputs. If we made flip books (p. 73) rather than GIFs of animated graphs, we might have more impact, as well as learning from the unavoidable confrontation with choices in curating data into experience.
It’s also vital to talk through what choices you are making with others in the same position:
Workshops have been a huge positive of this project as a framework for quickly learning new skills, evaluating techniques, gathering snapshots of data and prototyping visualisations. […] Initially workshops were technology driven but have evolved to explore other methods, moving from quantitative to qualitative techniques and analysis, gaining insight by examining the characteristics and nuances of collections, recognising the importance of discussion in learning and raising issues like data and visual literacy.
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This article is originally published at https://robertgrantstats.wordpress.com
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