Principal component analysis (PCA) condenses hundreds of votes into a few axes. Members who vote alike appear close together; the first axis (PC1) captures the biggest dividing line in Town Meeting voting.
| Component | Variance % | Cumulative % |
|---|
Residual is an aggregated remainder from components after PC5 and is reported as non-directional magnitude.
Click a PC column header to sort by absolute value (descending).
| Session | Vote Title | Vote Description | Vote Category | PC1 | PC2 | PC3 | PC4 | PC5 |
|---|
Member scores are projections on PC1-PC5. Click a PC column header to sort.
| Member | Precinct | Sessions | PC1 | PC2 | PC3 | PC4 | PC5 |
|---|
Each dot is a Town Meeting member, placed by their scores on the first two principal components. Nearby dots vote alike. Hover or tap a dot to see the member; for exact values, switch to the Member Scores view.
These two axes capture only part of the variation in voting (see the percentages in the axis titles). They describe voting patterns, not political or ideological labels.