Brendan and I wrote a chapter for an O’Reilly book called Beautiful Data. We took a lot of the analysis from earlier blog posts and distilled it into a longer book chapter about exploring a large data set and turning the messy data into beautiful, compelling graphs. We tried to highlight the tools and techniques that don’t make it into textbooks and are instead passed along by word-of-mouth among people in the field.

You can check out a version of our chapter, and if you like it, we recommend you buy the book which is full of authors I admire: Jeff Hammerbacher, Toby Segaran, Aaron Koblin, Nathan Yau, Mike Migurski, Peter Norvig, Andrew Gelman and many more.

Lots of people have been making great new visualizations of our color names data. Here are 4 more that folks have sent us.

Chris Harrison, a Ph.D. student at CMU HCI, combined our data with results from his own previous experiment, and created beautiful flower and spiral images. Unlike my and Martin’s color wheels, hue is scaled along the radius, creating a striking effect.

Next: network and cluster diagrams from David Sparks, Ph.D. student at Duke PoliSci. The layout below was computed from a similarity metric on color names. (I’m unclear whether it’s on labels or colors.) Size of node corresponds to the label’s frequency.

All of the visualizations so far have had to map three-dimensional color points into a 2D space. But Jeff Clark in Toronto went ahead and wrote a 3D explorer — you fly around a space of the color labels. He built it with the excellent Processing framework.

Finally, yet another tack: instead of creating a picture with all the labels, why not fit labels to a picture? Kristina Durivage, Chris Burg, and Scott Olson did that for an undergrad CS project at Winona State University. Their software takes any image and overlays color names. An example:

Four new visualizations in a month — whew!

To look at all our color posts, check out blog.doloreslabs.com/topics/colors.

-Brendan

Martin Wattenberg at IBM Research took our color names data and made a cool new cloud view:

Instead of plotting each individual color name like in the original, he grouped together identical names, took an average position, and sized the word by frequency. That’s why the more common names like “red” and “green” are large. This really helps readability (and, I’ll admit, the black background works a bit better :))

Thanks to Martin for sending this on!

I just packaged and released the data set for our color names experiment. It has 10,000 color/label pairs.

This is the download link. Read on for more details:

I tried to generate the color patches in a way to get interesting colors. This of course is incredibly subjective. My main concern was to eliminate muddy dark grays, which are very common when uniformly sampling over standard RGB values. (Perhaps I went too far — see the big donut hole in the color wheel plots.) So the color patches were sampled from HSV with uniform sampling over hue, but saturation and value biased high (normal distribution). The exact code and parameters for this is included in the download.

The plots in the post and the explorer look like a color wheel with hue as the angle. But actually they’re from running PCA over the RGB values, using the first two principal components as x and y. This was a very arbitrary decision, but seemed to make a nice visual effect. There are many other reasonable ways to plot the data.

The data includes anonymized identity on the workers. (The Mechanical Turk service makes all workers anonymous, but we anonymized yet again for releasing the data set.) You can see that certain workers did a large number of annotations. We have no demographic information for this one, sorry.

The files are:

• data.csv, which contains the color/label pairs, also with rgb and hsv representations.
• R.R, which has some routines that were used to generate and plot the data. It has examples of how to read and use the data, if you like to use R.
• html.rb, which with write_html() creates the explorer.
• sample-hit.html, one of the web forms used for data collection. There were 1000 forms with 10 colors each. For each single form (“HIT”), exactly one annotator filled it out. Individual annotators sometimes did multiple forms if they wanted to.

Let us know if this is useful, if you have any questions, or find something wrong with the download — either email or leave a comment here. And if you do anything cool with this data, we’d really love to hear about it.

-Brendan

What would you call these colors?

We showed thousands of random colors like this to people on Mechanical Turk and asked what they would call them. Here’s what they said:

The above picture contains about 1,300 colors and the names for them that Turkers gave.  Each is printed in its color and positioned on a color wheel.  Just looking around, there sure seem to be different regions for different names.  But there are also rich sets of modifiers (“light”, “dark”, “sea”), multiword names (“army green”), and fun obscure ones (“cerulean”). To help look at all this, we also made a color label explorer, so you can search for different terms and see different parts of the space. If the link doesn’t work for you, here are a few examples:

This study is basically the same design as the famous World Color Survey, where anthropologists showed color patches to speakers of many different languages and asked for names, to test the universality of language.  Of course, we have mostly native English speakers. However, we can get much more data.  (The above picture and links use only a small percentage of all the colors and names we collected.)  There’s tons more that can be done. Want to make a better visualizer?  Statistical analysis of colors to name terms?  Let us know and we should be able to get this data set online.

UPDATE 3/18: I posted the data set.

-Brendan