Using Digital Humanities to Demonstrate Income Inequality


Brian Foo made a sonic representation of the income inequality in New York City on his blog DataDrivenDJ. He successfully emulated a train ride on the 2 Train on the NYC Subway by composing a song that changed in volume and force as the train moved through wealthier parts of NYC. The song would also include different types of instruments depending on the income levels in the area (i.e. more expensive instruments for wealthier districts). Foo incorporated a handful of digital humanities tools to make this project:

  • List of train stations along the 2 Train and their geographic coordinates via NYC Open Data
  • List of instruments used in the song and their prices from various internet sources
  • Individual instrument samples from local NYC musicians or songs
  • Median income of NYC neighborhoods via Census Bureau American Community Survey 2011 Release
  • Processing programming language to develop the visualization
  • Python programming language for calculation of distances between stations and assignment of instruments based on income information
  • ChucK programming language for real time sound synthesis

Foo used a computer algorithm to actually compose the song. The algorithm was dependent on five different components:

  1. The track of the 2 Train itself
  2. U.S. Census Data for median income in NYC
  3. The NYC subway chime (the sound the door makes as it opens)
  4. “New York Counterpoint” Steve Reich’s (local NYC musician) minimalistic composition
  5. Phase shifting, a compositional approach where two identical melodies are repeated with slightly variable tempos

The combination of data access from resources such as the U.S. Census Bureau and NYC Open Data and computational platforms such as the Python programming language helped Foo construct his sonic representation. Without free access to these resources, Foo would have had to map out the distances between each station and record their geographic coordinates by hand. He would not have been able to easily extract the records of the U.S. Census Bureau into his Python program if they weren’t digitally stored on their servers.


ChucK is an interesting tool as it was created for the sole purpose of creating music. It was developed by Ge Wang a graduate student working with Perry R. Cook a computer science and music professor at Princeton University. ChucK allows for real-time synthesis and composition making it easy to change the sounds on the fly. This is great tool geared toward digital artists like Foo.

The income inequality in New York is a huge problem as the gap between Manhattan’s rich and poor is the largest in the U.S. The top five percent of households in Manhattan earns about 88 times more than the poorest twenty percent. Foo brings this information to us in a much more palatable form than raw statistics and graphs.

This song and visualization is a more accessible way to see how New York City has a large problem with income inequality. Foo did not do anything in this project but simply change the way the existing data on the Census Bureau is represented. Foo created an exciting and pleasing song that demonstrated the immense contrast of income in NYC through the contrast of sound, something we can all understand.

View Brian Foo’s post here

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