This site is notes and drafts that may someday become a Handbook of Data Visualization with Textiles.
I have taught a class by that name at Stanford every spring since 2023. It began as a funder mandate: in exchange for internal grant money to pay for student staff and supplies, we were encouraged to offer a hands-on making course. “Intro to Textile Crafts” didn’t sound like the kind of thing that Stanford students could easily get past their parents, but I thought “Data Visualization with Textiles” offered good-enough cover for essentially the same thing.
I expected maybe 5 students who wanted a few low-stress credits for messing around in the Textile Makerspace I ran. Instead, without any advertising, I ended up with 19 students who actually wanted to do data visualization using textiles. So that’s what we did, and what I’ve continued doing every year since.
Why textiles?
Textiles and text share an etymological root, and these two technologies have a long history of intertwining (e.g. through the stitching techniques used in creating textiles and binding codices.) For much of human history, textile creation was a significant, time-consuming, and visible activity. In our present moment of globalization and fast fashion, most of the textiles we encounter day to day are cheap and treated as disposable — and the human labor that still goes into them is invisible.
Textile labor is also coded feminine, and correspondingly devalued — much like computer programming when it was a female-dominated field in the early 20th century. Textiles and computing have had other points of historical intersection, including the same data-storage format (punched cards for computers as well as many different textile tools, including the jacquard loom and knitting machines.)
Why data?
We live in a world of data. We shed it constantly, into systems we have no visibility into or control over. This data shapes the ads we see, the prices we pay, and decision-making within the institutions and governments we’re subject to. “Data sets” are things we can download for free, and that AI companies pour money into creating or acquiring. Our tools and our wearables generate data, and most things can be transformed into data.
We don’t usually spend a lot of time thinking about data, even when we have it and are doing things with it. Modern computer software makes it fast and easy to analyze and visualize data in simple and complex ways with very little experience or skill. Generative AI makes it even faster to summon up data visualizations, whether or not the result accurately reflects the source data. In either case, the creator is not challenged to actually look at the data closely, or think hard about the choices they’re making in the visualization, and whether those choices work to tell the intended story well.
Why textile data visualization?
Some designers have pushed back on fast, thoughtless data by applying a personal and artistic lens to data visualization. Most notably, Giorgia Lupi and Stefanie Posavec’s Dear Data project and Observe, Collect, Draw! workbook offer a compelling, different vision for what data visualization can be.
One shortcoming of Lupi and Posavec’s work is the way it blurs the lines between what one can do specifically when drawing data visualizations, and the broader principles they lay out for thoughtful, creative, personal data visualization. Drawing comes with a particular set of affordances and limitations that differs from other media.
This handbook aims to expand on Dear Data by looking closely at the affordances of many different textile media. What are the properties of textiles (e.g. material, texture, thickness) that you can play with when doing a textile data visualization? What are the forms, shapes, and patterns that exist in the craft tradition of different textiles that you can adapt for data visualization (e.g. specific crochet granny square patterns or quilt blocks)? While most textile crafts can plausibly be used to create “standard issue” data visualizations like pie charts and bar charts, the approach to textile data visualization laid out here prioritizes adapting the visual language that has been developed and refined for centuries within textile craft communities.
Where do I begin?
Ultimately, you’ll need to engage with both the textile side and the data side. If you already have a textile craft practice of your own that you want to build on, start with Data. If you feel pretty good about data but textiles seem intimidating, you might want to start off thinking about Textiles and Choosing a Textile Craft. The Data questions might also be able to offer you something if you haven’t worked with personal data before, or thought about how you might visualize an arbitrary data set without computer assistance.