Figure 20A. We were aware that using a tapestry as a medium for data was not entirely original. However, our hope was to build upon the work of experts such as Sara Heitlinger, Lara Oehlberg, and Wesley Willet. Our goal was to create something tangible and specific in partnership with the community that could provide a view into the data.
Figures 20B-C. In order to weave a data visualisation, we first wanted to get some practice. So, a member of our team 3-D printed a hand-held hand loom for us to prepare. It not only helped us prepare but also served as an additional prop for visitors to engage with. I personally really struggled using this loom.
Figure D. Next, we considered the story we wanted to tell, and how we would do that using this medium. We sketched out some ideas for the pattern, quickly realising we were not skilled enough to do anything intricate. One thing I especially cared about was how this idea could scale to tell not just one story, but many. This would allow us to collect a wider dataset. So the pattern needed to be easy to replicate.
Figures E-F. I was reminded of Ada Lovelace and early computing systems based on binary codes. Even tapestry patterns (Figure F) can be reduced to a simple up/down, yes/no, on/off. Both coding and weaving are based on patterns.
Figure G. With limited expertise, we settled on using colour as the basis of our code. This digital mock-up is intended to represent how responses to a questionnaire would lead to a semi-persistent pattern. We wrote the questionnaire ahead of time and colour-coded the potential responses. From 8 questions we needed 15 different colours. As such, the colours utilised were determined by whatever was available at the shop.
Figure H. On the day of the event, I created this coding guide to help me remember all the different questions and which colour was associated with which response. Over the course of the day, I found myself referring to this less and less.
Figure I. While there were a lot of mistakes made as we perfected the loom, the end result was very successful. A pattern was definitely visible and visitors were highly engaged by the collective activity. Furthermore, it was an excellent way of capturing data. However, it could have been augmented through additional people there to record the conversations and meeting more people. A limitation we encountered was that we didn't always have a colour code for a particular response. In all, I thought this ended up being a great way to collect and represent the data.