Team KeyStoners

From ComputationalReporting Wiki
Jump to: navigation, search

Team KeyStoners

Our Goal is to create an interactive infographic that tells the story of the what causes a tipping point on one issue in the House of Reps, The Keystone XL Pipeline, with the hopes that we can create a roadmap for telling similar stories in the future. We intend to answer the questions: What are the different ways you can influence a vote? How much is money a factor vs. ideology and constituency desire? The hackathon is the first step towards creating a model for analyzing and visualizing how votes may be influenced in the House of Representatives.

Team Members:

Sheba Najmi, Code for America

Danny Willis, Bay Area News Group

Somya Mathur, QPC

La Toya Tooles, Center for Investigative Reporting

Saahil Shenoy, Stanford University

Shakila Hameed, BlackBerry

Thomas Peele, Bay Area News Group

Joerge Imbaquingo, Knight Fellow at Stanford. Managing Editor HOY Newspaper-Ecuador

Melanie Swan, Bay Area Blogger

It's generally accepted that money affects politics. Our goal is to figure out how as precisely as possible. We want to figure out the rules of the game.

Using the Keystone Pipeline as our case study, we're analyzing the effects of a variety of factors on the vote in the House of Representatives, focusing on Representatives who crossed party lines. We aim to figure out why certain members of Congress are more likely to flip their vote, and what an interested party would need to contribute to that legislator in order to engender a vote flip. To that end we've assembled the Partisan Voter Index for each Congressional district and state, a measure of the ideological slant of the Representative and how often the Representative votes against party, as well as various statistics specific to this vote, such as whether or not the pipeline crosses through their state. This should determine which Representatives should be targeted to change their vote.

In addition we began collecting contribution information about key influential donors and will, in the next stage of the project, correlate that with people who changed their vote. This should tell us, once a potential flipped vote is identified, how much money it will take to actually flip it depending on the other factors in their decision.

Our multi-step process so far has been:

1. Researching Keystone Pipeline history

2. Narrowing the field, for sake of time, to researching House of Reps voting history. There was a test vote and an actual vote for the pipeline, that included 41 people who changed their votes.

3. Translating that information into 1s and 0s, which can be found in our master spreadsheet.

4. Gathering the money data on who received how much from whom.

5. Visualizing said information.

Some of our sources:

First vote on Keystone XL Pipeline

Second vote on Keystone XL Pipeline

Party voting record of Representatives

Lots of data from Influence Explorer

Lots of data from the House of Representatives site

Our Presentation (Please view our interactive visualization on Shakila's Windows laptop!).

Awards competing for: Best Insight, Best Visualization, Best Overall Project

Personal tools