Precinct 1460 — 2022 election results

Peru, Maine

Certified results

How this precinct voted

Office	Dem	Rep	Other	Total	Dem share	Rep share
U.S. House	287	385	100	772	42.7%	57.3%

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 44.7%.

What pushes it toward the Democrats: % aged 65+ and % housing cost-burdened.

What pushes it toward the Republicans: % mobile homes, % in 10+ unit buildings and Average annual pay.

It actually came out at 42.7%, close to what the traits suggest.

See these numbers as a table

Each fact and how far it moves the model's guess

Fact	Running guess	Change (points)
Starting point: a typical area	52.1%	—
% mobile homes	46.2%	-6.0
% in 10+ unit buildings	42.5%	-3.6
% aged 65+	46.0%	+3.5
Average annual pay	43.3%	-2.8
% housing cost-burdened	45.8%	+2.5
% without internet	43.5%	-2.3
All other facts together	44.7%	+1.2
What the model expects here	44.7%	—

• % mobile homes — Housing type — mobile/manufactured homes (rural marker).
• % in 10+ unit buildings — Housing type — large (10+ unit) apartment buildings (urban marker).
• % aged 65+ — Age profile — residents 65 and older.
• Average annual pay — Local wages — average annual pay.
• % housing cost-burdened — Housing stress — income spent on housing.
• % without internet — Digital access — households without internet.

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

Each fact, this area's value, and how far it moved the guess

Fact	Value here	Moves the guess by	Towards
% mobile homes	11.9%	-6.0 points	Republicans
% in 10+ unit buildings	2.3%	-3.6 points	Republicans
% aged 65+	22.9%	+3.5 points	Democrats
Average annual pay	$50,809	-2.8 points	Republicans
% housing cost-burdened	25.2%	+2.5 points	Democrats
% without internet	16.5%	-2.3 points	Republicans

this arealow highnot measured here

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.