This online tool lets you *hear* income inequality along the NYC subway

When it comes to income inequality, there is a lot of data. To understand it better, or at least in a different way, data visualization artist Brian Foo created a song out of income equality along the number 2 subway line in New York City, which runs through Brooklyn, all the way through some of the busiest places in Manhattan, all the way to the Bronx. To make the song, Foo took data points and coded them to correspond with sound tones. It’s called “data sonification.” Foo, who works at the American Museum of Natural History, told Mic, “The data set itself is the composition, or the thing that drives the sound. I kind of just define the rules in which the data gets mapped in sound. I don’t manipulate the data.”

It’s basically making art with numbers, though we probably shouldn’t say that to Foo, because it’s much more complicated than that. If you’re wondering why the “music” is so repetitive, it was a conscious choice. He took the data set and wrote scripts (using Python, if you’re up with the lingo) to process it. And then ran the scripts through a music programming language (Chuck) to create the sounds. But Foo didn’t want to assign certain sounds to income levels or neighborhoods, because that could get judgy. Foo said he wanted to stay away from that “icky” territory and keep it all “agnostic.”

So he used volume to to certain values instead. If you know New York City well, you can sort of make some assumptions about the data, but it’s not easy. Foo is super serious about his work. He said:

"That’s kind of the gray area between being a data scientist and being an artist. A data scientist shouldn’t bias the audience whereas an artist, they kind of want to do that. I want to be faithful to the data, but the medium of music is kind of inherently emotive."

The guy has a point. If you listen to it even once,  you’ll have it stuck in your head all day.

Two Trains – Sonification of Income Inequality on the NYC Subway from brian foo on Vimeo.

Sorry, not sorry.