This project visualizes the voting patterns of the US congress for Taiwan-related bills. Each congress member is mapped to a location on a 2D map according to his or her voting history. Congress members having similar voting history are mapped to nearby locations on the map, although their absolute locations are arbitrary. We focus on bill votes related to Taiwan only, that is, we restrict to bills containing keywords 'Taiwan' or 'China'. The goal is to provide a quick overview of the distribution of congress members based on their Taiwan-related bill votes.
In the above screenshot, each congress member is mapped to a cell based on his/her voting history for Taiwan-related bills. Congress members who are mapped to nearby cells have generally similar voting history. The background colors indicate parties: red for Republicant, blue for Democrats, and green for other parties. A number marked in a cell indicates that multiple congress members are mapped to the same cell, due to identical or very similar voting history.
To see who are mapped in a cell, as well as some details about their voting history, simply mouseover the cell. Voting history is visualized as a color bar (see the picture above), where green indicates that the congress member voted 'yes' to a bill, red indicates a 'no' vote, gray indicates a present vote, and white indicates no data, mostly likely due to ineligibility to vote.
To allow easy comparison, two cells can be selected (left-click) at the same time. One can also use mouse drag and wheel to zoom and pan.
The dropdown menu at the top lists available datasets.
The visualizations are made using self-organizing maps (SOMs), a class of neural network that learns to project high-dimensional data onto a 2D surface in a nonlinear and topology-preserving fashion.
- Data source: https://www.govtrack.us/developers
- Data format: https://github.com/unitedstates/congress/wiki/votes
- Glossary: https://www.senate.gov/reference/glossary.htm