Precinct town-wide — 2020 election results
Sedgwick, Maine
How this precinct voted
| Office | Dem | Rep | Other | Total | Dem share | Rep share |
|---|---|---|---|---|---|---|
| President | 516 | 322 | 11 | 849 | 61.6% | 38.4% |
| U.S. Senate | 417 | 372 | 56 | 845 | 52.9% | 47.1% |
| U.S. House | 550 | 291 | 0 | 841 | 65.4% | 34.6% |
See this precinct on the live map →
Why the model expects this result
A typical precinct in this model sits at 52.3% Democratic on the two-party vote. This precinct's neighbourhood traits move that to 55.0%.
What pushes it toward the Democrats: Businesses per 1k residents, % without HS diploma and Population density.
What pushes it toward the Republicans: % in 10+ unit buildings, % uninsured and % non-Hispanic White.
It actually came out at 61.6% — 7 points more Democratic than its traits alone would suggest.
See these numbers as a table
| Fact | Running guess | Change (points) |
|---|---|---|
| Starting point: a typical area | 52.3% | — |
| Businesses per 1k residents | 57.6% | +5.4 |
| % in 10+ unit buildings | 53.7% | -3.9 |
| % without HS diploma | 57.4% | +3.6 |
| % uninsured | 53.8% | -3.6 |
| % non-Hispanic White | 50.7% | -3.0 |
| Population density | 53.3% | +2.6 |
| All other facts together | 55.0% | +1.7 |
| What the model expects here | 55.0% | — |
- Businesses per 1k residents — Local economy — businesses per 1,000 residents.
- % in 10+ unit buildings — Housing type — large (10+ unit) apartment buildings (urban marker).
- % without HS diploma — Education — adults without a high-school diploma.
- % uninsured — Health-insurance coverage gap.
- % non-Hispanic White — Racial makeup — white, non-Hispanic residents.
- Population density — Urban density — people per km², urban vs rural.
Across all precincts this model explains about 73% of the variation in results, and is typically off by 8 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 |
|---|---|---|---|
| Businesses per 1k residents | 42.9 | +5.4 points | Democrats |
| % in 10+ unit buildings | 2.9% | -3.9 points | Republicans |
| % without HS diploma | 4.9% | +3.6 points | Democrats |
| % uninsured | 10.2% | -3.6 points | Republicans |
| % non-Hispanic White | — | -3.0 points | Republicans |
| Population density | — | +2.6 points | Democrats |
Every other dot is one of the 561 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.