What does tax lobbying look like in the 112th Congress?
Our visualization of the vast network of tax lobbying clearly shows clusters emerging around different sectors of the economy. We detect at least 15 distinct lobbying clusters. The densest thickets of activity center around: electricity generation; renewable energy; finance; and the high-tech industry.
Because of some the work we’ve done before on last minute negotiations and divided government, Sunlight prepared the following graphic that visualizes the recent history of US House votes on the debt ceiling, based on public voting records and a CRS report.
We’ll have more commentary forthcoming, but here are a few initial thoughts on what this graphic makes clear:
- Opposition to raising the debt ceiling is often partisan, with opposition coming from either party, based on who is in the White House. Many House Republicans have voted for raising the ceiling, just as President Obama voted against it when he was a Senator.
- Divided government has necessitated support from both parties to raise the limit.
- There is a significant untold story about the Gephardt Rule, a House Rule which enabled the limit to be raised with little public record. The role this rule played in setting up the current showdowns has been insufficiently examined.
- Good access to congressional data and reports enables this kind of analysis; it could be improved.
- Each of these votes was a predictable consequence of budgets that were passed before them, demonstrating another facet of political hypocrisy.
In or near DC? You should come to Sunlight Foundation’s Open House!
Wednesday, Dec. 5th
Doors open at 6PM
‘Bad data visualizations just dazzle; good ones illuminate; great ones allow for discovery.’ Great Percolate blog post by @thelesserdies
Just starting out in visualising your data? Looking for something simple to use? This is where we go
The Sunlight Foundation, that’s us, is offering a year-long data visualization fellowship for grad-level social scientist. Just a heads up.
Mike Kneupfel, a student at NYU’s Interactive Technology Program, made a 3D model showing the keys he presses most frequently when typing, composed of raised keys on a keyboard. It’s a fun and eye-catching way of visualizing data by using the thing whose data you’re analyzing.
See where the data came from and how the sculpture was made on Mike’s site.
Visualizing all bike accidents in the San Francisco Bay area. From Information Aesthetics:
Who is mostly to blame for bicycle accidents: car drivers or bicyclists? The Bay Citizen’s Bike Accident Tracker 2.0 [baycitizen.org] gives access to 5 years worth of bicycle accident and collision data, which even includes information about the lighting and road conditions, the designated party at fault, or the type of parties involved (e.g. auto, bicyclist, etc.)
Great work from the Bay Citizen. The interactive offers many ways to explore the data.