placestats

Loneliness and Mobility score

Where Loneliness is higher, Mobility score tends to be lower. The link is weak: -0.34 across 1,462 counties, on a scale from −1 (opposite) to +1 (in step). That figure compares places with the same population density, income and education, so those differences can't explain it. It holds inside states too. Measured separately within each of 24 states, the middle value of the link is -0.28.

Holds after adjusting

Every place, plotted

One dot for each place reporting both measures. Hover over or tap a dot to see its name and values. The thin line is the straight line that best fits the dots. The bold line, shown for counties, is the link after adjusting for density, income and education. A dot far from the lines is a place that does not follow the pattern.

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What each measure is

Loneliness

The estimated percentage of adults who always or usually feel lonely. It is adjusted so places with older or younger populations can be compared fairly. It is an estimate from a statistical model, not a direct count from a local survey.

CDC PLACES · PLACES LONELINESS, age-adjusted

Mobility score

What happened to children raised poor in this county: their income rank as adults, and their chance of being locked up (flipped). Both describe children born around 1980. So this tells you about the county of forty years ago as much as the county today.

· derived from 2 measures (OA)

Highest and lowest

Where each measure is highest and lowest among the places reporting both (2023), with the other measure beside it. This is shown for every size of place where both are published. Metro-area values are added up from their counties. A measure that cannot be added up across counties, such as a middle value (median), has no metro figure. Counts are shown per 1,000 residents, since raw totals would only list the largest places. Cities and towns and ZIP codes are shown where the Census survey or the CDC publishes them; most other sources stop at the county. They are shown for places of 5,000 residents or more, and only where the survey’s margin of error is small enough for the figure to be usable. The CDC publishes its health estimates for cities in a version adjusted for age, so places with older or younger people can be compared fairly, but not for ZIP codes.

Counties

By loneliness

LonelinessMobility score
Highest
Holmes County, MS43.8%-0.365
Jefferson County, GA43.4%-0.927
Clayton County, GA43.2%-1.42
Laurens County, GA42.6%-1.02
Hancock County, GA42.2%-1.03
Lowest
Ozaukee County, WI27.8%0.927
Beaufort County, SC28.2%-0.8
Olmsted County, MN28.2%0.607
Johnson County, KS28.5%0.581
Payette County, ID28.5%0.412

By mobility score

LonelinessMobility score
Highest
Stark County, ND28.6%2.32
O'Brien County, IA33.3%1.81
Brown County, MN29.9%1.72
Todd County, MN33.4%1.64
Stearns County, MN31.2%1.62
Lowest
Menominee County, WI36.2%-4.31
Pulaski County, GA40.4%-3.03
Charlottesville city, VA33.6%-2.7
Richmond city, VA37.3%-2.57
Portsmouth city, VA38.4%-2.39
Metro areas

By loneliness

LonelinessMobility score
Highest
Dublin, GA42.3%-1.2
Cordele, GA41%-2.09
Moultrie, GA40.8%-1.38
Bainbridge, GA40.7%-1.14
Fitzgerald, GA40.5%-1.83
Lowest
Ontario, OR-ID28.5%0.405
Sandpoint, ID28.8%1.09
Detroit Lakes, MN28.8%1.44
Dickinson, ND28.8%3.41
Lawrence, KS28.9%0.312

By mobility score

LonelinessMobility score
Highest
Dickinson, ND28.8%3.41
New Ulm, MN29.9%2.43
St. Cloud, MN31.1%2.19
Alexandria, MN30%2.08
Fergus Falls, MN33.1%2.06
Lowest
Thomaston, GA39.1%-2.62
Savannah, GA36.9%-2.19
Summerville, GA40%-2.12
Cordele, GA41%-2.09
Statesboro, GA36.5%-2.07
Cities and towns — Mobility score is not published for cities and towns

By loneliness

LonelinessMobility score
Highest
Westmont CDP, CA45.5%—
Rosemead city, CA45.3%—
Florence-Graham CDP, CA45.2%—
Compton city, CA45%—
East Rancho Dominguez CDP, CA44.9%—
Lowest
Midway city, UT26.4%—
Little Silver borough, NJ26.5%—
Fair Haven borough, NJ26.8%—
Mequon city, WI26.9%—
Rumson borough, NJ26.9%—

Counties furthest from the pattern

Compared with the straight line that best fits all 1,462 counties: the five with far more mobility score than their loneliness would suggest, and the five with far less.

LonelinessMobility score
Above the line
LaGrange County, IN37.3%1.52
San Francisco County, CA38.4%1.35
Queens County, NY39.4%1.09
O'Brien County, IA33.3%1.81
Stark County, ND28.6%2.32
Below the line
Menominee County, WI36.2%-4.31
Charlottesville city, VA33.6%-2.7
Pulaski County, GA40.4%-3.03
Williamsburg city, VA33.6%-2.16
Richmond city, VA37.3%-2.57

Two series, one county

Both measures over time for a county you pick. The two charts are stacked, so they share only the years along the bottom. You can pick from the thirty largest counties reporting both. This panel covers 2010–2024, and a measure published only once shows as a single dot.

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Every version of the link

Each row measures the same link, from −1 to +1, in a different way. If the rows mostly agree, the link does not depend on one choice of method.

Before adjusting for anything-0.4731,462 counties reporting both
After adjusting for density, income and education-0.343the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size-0.341size taken out as well as density
Also comparing only within each state-0.208each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight+0.017the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 177 equally weighted counties
Middle value inside single states-0.276the link worked out inside each of 24 states with at least 20 counties reporting
Using ranks, after adjusting-0.294uses each county’s rank, not its value, so a few extreme counties cannot create the link

How much to believe it

Could it be chance?

The p-value is the chance of seeing a link this strong if there were really none. A value of 0.05 or less means the link is unlikely to be chance. Very small values are written in short form: 3.1e-16 means 0.00000000000000031, and the bigger the number after “e-”, the smaller the value. Treating every county as a separate case gives p = 1.4e-41. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.002. The site tested 20,910 pairs, and testing so many turns up some flukes. The q-value allows for that too. For this pair it is 6.8e-41.

Do nearby places look alike?

Moran’s I measures how much nearby places look alike: 0 means no map pattern, and higher means neighbours are more similar. For the part of each county that the adjustment leaves unexplained, it is +0.44 (p = 0.001). That unexplained part still clusters on the map. So some regional factor shared by both measures is not accounted for.

Did they change together?

Not measurable: at least one of these is published for too few of the years a change needs (2014–2019 or 2019–2023), or for too few counties in both.

Related pairs

Association only. Nothing here shows that either measure changes the other. This describes counties, not the people in them. Two measures going together across places does not mean the same people have both. How the US county data works · Explore Loneliness and Mobility score