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Color is one of the most widespread index for capturing the meaning of data in a textile. You can map color onto individual discrete data values, or use it for ranges of numerical data.
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Different textiles can do different things with negative space. For Embroidery an unembroidered area can represent negative space. For Sewing, a solid color can represent negative space if the piece otherwise uses colorful prints. Yarn Crafts and Needle Felting are capable of creating physical holes of different sizes.
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Organization is one of the properties offered in Observe, Collect, Draw! for representing data through drawing. Most textile crafts require more planning ahead than drawing, and you have less flexibility to go back and add things after the fact.
Still, the spatial arrangement of the other properties in your textile can hold meaning. One of the most common ways to use organization is for it to represent time, by presenting data chronologically. If you’re visualizing a text, it’s natural to present the data in the order in which it occurred in the text (e.g. if visualizing properties of words, the first word comes first, then the second, etc.)
Properties are aspects of the data that you can map onto different textile techniques to represent the data in a textile.
When data can’t be mapped one-to-one onto a textile technique (even using scaling, where a single item can represent tens or hundreds of something in the data), you can often represent different values proportionally, or as a percentage of the total amount of data.
Quantity represents (generally) quantitative data by having some amount of a repeated element — whether it’s a knitting motif, a pick of weaving (e.g. a weft yarn), or an embroidered shape.
If you’re working with very large data sets, it may not be feasible to have one thing in the textile represent one of whatever the data was measuring. In such a case, you can decide that one repetition represents some larger quantity (10? 100? 100,000,000?) of things.
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Some textile data visualizations recreate the form and conventions of “standard” data visualization (e.g. the kinds of visualizations you can easily produce on a computer). This can be done carefully and deliberately to make a point, but this handbook focuses on using the visual vocabulary of the textile crafts themselves rather than immediately jumping to familiar shapes like pie charts and bar charts.
Shape is one of the properties for conveying data values that is shared across textile crafts, along with many other artistic media.
Different textile crafts have their own traditions around the kinds of shapes the textile craft tends to use, and how it creates those shapes.
In most cases, you can use textile crafts to create shapes drawn from standard data visualization (e.g. bar graphs, pie charts), but this handbook of textile data visualization focuses on adapting the shapes and patterns that are part of each craft’s traditional repertoire.
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Size is one of the properties for conveying data that is shared with many other crafts.
Typically, the final project (or piece) is intended to be a particular size overall. Rather than adapting one data point as one textile unit (stitch, row, etc.), it’s generally better to convert the data to percentages and then map that onto the textile units. For instance, if each row of the back of a knit sweater pattern is 150 stitches, and you have three data values that are 33%, 50%, and 17%, you could represent it by 50, 75, and 25 stitches in the colors representing those data values, respectively. Or there are 50 rows that make up the back of the sweater, you could do rows (all 150 stitches) in a single solid color, and have 16 rows in the first color, 25 rows in the second color, and 9 in the third.
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Some textile data visualizations use text to convey information. This is in keeping with the visual vocabulary of some textile crafts, including embroidery.
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