Quantified Community: Visualizing the health and illness of Washington, DC through open data and art

My good friend MV Jantzen ( @mvs202 ) is not a physician or in public health. He doesn’t study social determinants of health. The first thing he said to me when I went to go see this unique visualization of Capital Bikeshare ( @bikeshare ) trips in Washington, DC, on its busiest day, March, 23, 2012, was “I was able to do this because of open data.”

Compare this piece with another piece on the other side of the room:

2012 DC Zeitgeist III 11805
2012 DC Zeitgeist III 11784

On the left is a visualization of crime in Washington, DC, with the biggest cutouts being homicide, the smallest assault. The right, the visualization of bike trips. There are bike stations throughout the city. Do you notice how one image is the mirror of the other? The part of the city with reduced bike trips and higher violence (Wards 7 and 8 ) also has the highest obesity rate, higher than the most obese state in the United States – 42 %. If you want to familiarize yourself with that map, see “Do national numbers inaccurately represent Washington, DC’s obesity condition? what electronic and personal health records can do to help

Think about it – every part of Washington, DC has access to this technology (cell service, bikeshare stations), and yet there is a dramatic and visible difference in health. These images teach that community wellness is more complex than creating the right app, even more complex than individual behavior change.

Art + data teaches valuable lessons (hmm…where I have learned that before :)).

Thanks, MV, and the DC Arts Center for making this real. The show that this is part of, “Zeitgeist III” runs through June 10. More photos below, as well as the video of the Capital Bikeshare visualization. You can learn more about that piece on MV’s blog.

How to interpret the above: Blue ripples are bike trips among regular riders (with memberships), green ripples are bike rips among casual riders (one time users). Notice what happens in the early morning (commuting), versus later in the day (riding for pleasure). More ripples equals more bike trips. This was the busiest day in the Bikeshare system to date, March 23, 2012. The data is made available for people to analyze.

“Rise up with me against the organisation of misery,” – For more information on social determinants of health, see my post on the Marmot review (“Now Reading: Why a focus on lifestyle behavior change may not improve health: The Marmot Review“)


What an amazing look at the city through the eye of the artist!  My experience of  #SDOH says that it has a huge impact in our world. So, I surmise that the number of bikeshare stations in the areas where the obesity rates are high are significantly fewer than those in the other areas. The unwillingness to use the existing bikeshare stations may also be prompted by any  number of factors: the distance to one’s place of employment and the time it would take to use that type of transportation, the safety of the neighborhoods one would have to ride through, the availability of bike lanes or safe roads, the ability to afford the fee to use bikeshare, the ability to ride a bike: first- the know how (not everyone learns it) and second the physical ability -(asthma or other medical reasons why cycling is not possible- which came first?)  there are so many reasons why… or why not  – and we haven’t even touched on cultural norms or other factors such as bike ownership vs bikeshare… I love it when you give me stuff to think about.

 @kaitbr Kait,
Likewise, about the thinking! There’s a lot there, isn’t there. The bikeshare map is here: http://capitalbikeshare.com/stations . I suppose the next step would be to walk the neighborhoods affected and see what’s going on. I’m up for it,

 @tedeytan  I’m game. I’m also in town most of this coming week. I have the boys to get to school by 9 and I have to be back by 5ish to pick up Isaac from afterschool care, but let’s do some walking.

Hey Kait, I was directed to this article about CaBi usage, which includes an interesting regression analysis: http://greatergreaterwashington.org/post/14368/what-makes-some-cabi-stations-more-used-than-others – there is some interesting commentary about lots of factors, including whether there are more hills in Anacostia. All of this says, a picture is worth a thousand words. Very interesting conversation and analysis!

Ellyn, Thank you for curating a thoughtful exhibit, I can tell it is stimulating conversation about big data, health and health care, and social determinants of health. Spirit of Washington, DC,

Ted Eytan, MD