New US House Voting Patterns Network

Anyone who knows me well is aware of my general lack of enthusiasm for politics and politicians, so my latest network graph may come as a bit of a surprise. While I can’t express a lot of support for how my tax dollars are spent by the folks in DC, I can still make use of some of the data patterns they generate. Using data provided by govtrack.us (a non-government site), my latest Gephi project looks at the US House of Representatives votes over the last 4 months of 2014, specifically the ‘aye’ (yes) votes for each house vote.

The resulting graph lets us take a look at some general patterns, such as many cases where there is strong bi-partisan support for a bill. We can also see votes that failed, primarily in cases where the Democratic minority was unable to generate enough Republican support to pass a measure. Here are a few screenshots from the Gephi project; after that I’ll send you over to the interactive web version where you can search, zoom, pan, and otherwise interact with the data to your heart’s content.

First up is an overall view of the network, created using the Force Atlas 2 layout:

screenshot_082109
Here we can see the stereotypical view of Congress, with the blue Democrats on the left and red Republicans on the right. In the center are some very large nodes that depict near unanimous votes (nodes are sized by the number of ‘aye’ votes) with bi-partisan support. Darker gray nodes represent failed votes; note how many of these are at the far left, indicating support from only the Democrats in most cases. To the far right are bills that passed with primarily Republican support, as noted by their smaller size.

Our next view used node sizing to show only those representatives who cast 45 or fewer aye votes (of the more than 80 votes cast in this period). These voters are shown as oversized nodes relative to their colleagues. While missed votes may contribute to this classification, we also note the predominance of Democrats in this view. Given the Republican majority, it is hardly surprising that more Democrats would be likely to refrain from casting aye votes that are likely to reflect the Republican influence.

screenshot_45_vote_max

Next we take a look at those who cast at least 60 aye votes and are unsurprised to see that this one swings toward the Republican side of the graph. This view was achieved using some Gephi filters to hide individuals not meeting the selected criteria. Clearly, the most enthusiastic ‘aye’ voters in this period are primarily Republican.

screenshot_60_plus

Our final view for now (we could do dozens more) focuses on national security – generally considered to be a bi-partisan subject where both parties want to appear patriotic, regardless of whether the legislation actually advances security. To focus quickly on this topic, I have used Gephi to recolor all security-related nodes to yellow. Notice how these votes are almost uniformly bi-partisan, with overwhelming support from both parties.

screenshot_security

These are just a few examples for how Gephi can help dissect a reasonably complex network and provide quick visual insights. There are of course many other methods available in Gephi that would take this analysis much deeper.

Now that we’ve done a brief examination of this data, time to move on to the interactive example on the web, where you can do your own clicking, searching, zooming, and panning to uncover patterns in the data. This functionality all comes courtesy of Sigma.js, an oustanding Gephi plugin. You can find the network here: http://visual-baseball.com/gephi/us_house/network/index.html#.

At some point, I may attempt to link back to the actual voting data at govtrack.us, but for now I hope you find this to be a useful (and fun) way to examine voting patterns. Enjoy!

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kc2519

kc2519

Ken Cherven is the Founder and Curator of the Visual-Baseball.com website. He loves to merge baseball data with all sorts of visualization methods - charts, network graphs, maps, etc. to provide greater insight into underlying data patterns. Ken also authors books about baseball and visualization, and loves to listen to jazz while drinking some wine, craft beer, or bourbon.

2 thoughts to “New US House Voting Patterns Network”

  1. Dear Sir,
    First of all, you did a really great job with US House Voting Patterns Network! I recently start working with SNA and find your research very exciting. Secondly, I would kindly ask you to give me a hint how you did such beatiful interactive network (http://visual-baseball.com/gephi/us_house/network/index.html#). I mean, what kind of software you used and so on.
    I try to do such analysis for Ukraine’s parliament and your help will be more than useful.
    Sincerely,
    Dima
    VoxUkraine.

    1. Dima –

      Thanks for you kind words. Sorry for my slow response. This work, and many of my other works were created using Gephi. Gephi also has a Sigma.js plugin that creates the interactive aspect.

      Regards,

      Ken

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