Precinct 594 — 2022 election results
Franklin, Maine
How this precinct voted
| Office | Dem | Rep | Other | Total | Dem share | Rep share |
|---|---|---|---|---|---|---|
| U.S. House | 331 | 354 | 75 | 760 | 48.3% | 51.7% |
See this precinct on the live map →
Why the model expects this result
A typical precinct in this model sits at 52.1% Democratic on the two-party vote. This precinct's neighbourhood traits move that to 59.5%.
What pushes it toward the Democrats: % without HS diploma, % housing cost-burdened and % aged 65+.
What pushes it toward the Republicans: Average annual pay, % in 10+ unit buildings and % limited English.
It actually came out at 48.3% — 11 points more Republican than its traits alone would suggest.
See these numbers as a table
| Fact | Running guess | Change (points) |
|---|---|---|
| Starting point: a typical area | 52.1% | — |
| % without HS diploma | 59.7% | +7.5 |
| % housing cost-burdened | 64.4% | +4.8 |
| % aged 65+ | 67.6% | +3.2 |
| Average annual pay | 64.7% | -2.9 |
| % in 10+ unit buildings | 62.6% | -2.1 |
| % limited English | 61.0% | -1.6 |
| All other facts together | 59.5% | -1.5 |
| What the model expects here | 59.5% | — |
- % without HS diploma — Education — adults without a high-school diploma.
- % housing cost-burdened — Housing stress — income spent on housing.
- % aged 65+ — Age profile — residents 65 and older.
- Average annual pay — Local wages — average annual pay.
- % in 10+ unit buildings — Housing type — large (10+ unit) apartment buildings (urban marker).
- % limited English — Language — households with limited English.
Across all precincts this model explains about 29% of the variation in results, and is typically off by 16 points for any one precinct. The model only knows facts about the area, such as age, pay and housing. It knows nothing about the people standing for election, what they said, what mattered locally, or who turned out to vote. That is why the real result can be a long way from its guess. These are patterns across whole areas. They do not tell you how any one person voted, and they do not show that one thing causes another. When two things go together, such as low pay and low rents, the model cannot tell them apart, so it shares the credit between them.
How does this area compare with the rest of the state?
One row for each fact. Every dot is an area in this state, placed by how far that fact moved the guess there. This area is the ringed dot.
See this area's numbers as a table
| Fact | Value here | Moves the guess by | Towards |
|---|---|---|---|
| % without HS diploma | 4.9% | +7.5 points | Democrats |
| % housing cost-burdened | 25.1% | +4.8 points | Democrats |
| % aged 65+ | 25.7% | +3.2 points | Democrats |
| Average annual pay | $53,780 | -2.9 points | Republicans |
| % in 10+ unit buildings | 2.9% | -2.1 points | Republicans |
| % limited English | 0.2% | -1.6 points | Republicans |
Every other dot is one of the 481 areas in Maine that the same computer looked at. For each fact we only show the areas where it was one of the six that mattered most, so you are seeing that fact where it counted, not everywhere.
How the model was built
We give a computer about 20 facts about each area and ask it to guess how the area voted. It is never shown any votes. The facts are things like age, race, pay, jobs, health cover and the kind of homes people live in. They come from the 2020 Census and from other government figures.
To check the guesses are fair, we train the computer on some states and then test it on states it has never seen. That way it cannot simply remember the answer.
Each bar shows how much one fact moved the guess up or down for this area. Some facts are measured for the whole county, so they are the same for every area in that county. Where two neighbouring areas differ, it is their own 2020 Census figures that pull them apart.
For the technically minded: the model is gradient-boosted trees (XGBoost), the target is the two-party Democratic share, the bars are SHAP values, and it is validated by holding out whole states. County-level inputs include the Census/CDC Social Vulnerability Index 2018–2022, County Business Patterns and wage data.