Loneliness and Cooling degree days
Where Loneliness is higher, Cooling degree days tends to be higher. The link is moderate: +0.40 across 2,255 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 29 states, the middle value of the link is +0.13.
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
Cooling degree days
A measure of how hot the year was, as a total over its days. A day whose average temperature is 80°F counts 15 cooling degree days, the degrees it rose above 65°F. A day at 65°F or cooler counts none. More cooling degree days mean more weather for air conditioning. NOAA estimates them from its monthly temperatures for each county. A state's figure is the average of its counties, each weighted by its land area. Alaska and Hawaii are not covered.
NOAA NCEI nClimDiv · NOAA NCEI nClimDiv climdiv-cddccy, sum of the 12 months; states, counties averaged by land area
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
| Loneliness | Cooling degree days | |
|---|---|---|
| Highest | ||
| Holmes County, MS | 43.8% | 2,648 |
| Jefferson County, GA | 43.4% | 1,973 |
| Clayton County, GA | 43.2% | 1,862 |
| Laurens County, GA | 42.6% | 2,267 |
| Hancock County, GA | 42.2% | 1,986 |
| Lowest | ||
| Wasatch County, UT | 27.3% | 83 |
| Teton County, ID | 27.4% | 100 |
| Blaine County, ID | 27.7% | 215 |
| Ozaukee County, WI | 27.8% | 557 |
| Ramsey County, ND | 27.9% | 537 |
By cooling degree days
| Loneliness | Cooling degree days | |
|---|---|---|
| Highest | ||
| Imperial County, CA | 38.2% | 4,386 |
| Yuma County, AZ | 33.6% | 4,107 |
| La Paz County, AZ | 34.9% | 4,024 |
| Maricopa County, AZ | 32.5% | 3,793 |
| Terrebonne Parish, LA | 36.5% | 3,569 |
| Lowest | ||
| Summit County, UT | 28.7% | 32 |
| Deer Lodge County, MT | 30.8% | 35 |
| Beaverhead County, MT | 30.8% | 45 |
| Custer County, ID | 29.3% | 52 |
| Granite County, MT | 31.5% | 57 |
States — Loneliness is not published for states
By cooling degree days
| Loneliness | Cooling degree days | |
|---|---|---|
| Highest | ||
| Florida | — | 3,667 |
| Louisiana | — | 3,100 |
| Texas | — | 3,063 |
| Mississippi | — | 2,553 |
| Alabama | — | 2,172 |
| Lowest | ||
| Wyoming | — | 240 |
| Maine | — | 243 |
| Vermont | — | 257 |
| New Hampshire | — | 298 |
| Idaho | — | 300 |
Metro areas
By loneliness
| Loneliness | Cooling degree days | |
|---|---|---|
| Highest | ||
| Dublin, GA | 42.3% | 2,233 |
| Cordele, GA | 41% | 2,466 |
| Moultrie, GA | 40.8% | 2,393 |
| Bainbridge, GA | 40.7% | 2,716 |
| Fitzgerald, GA | 40.5% | 2,416 |
| Lowest | ||
| Jackson, WY-ID | 27.4% | 17.3 |
| Heber, UT | 28.1% | 51.7 |
| Ontario, OR-ID | 28.5% | 564 |
| Hailey, ID | 28.5% | 299 |
| Sandpoint, ID | 28.8% | 255 |
By cooling degree days
| Loneliness | Cooling degree days | |
|---|---|---|
| Highest | ||
| El Centro, CA | 38.2% | 4,386 |
| Yuma, AZ | 33.6% | 4,107 |
| Phoenix-Mesa-Chandler, AZ | 32.7% | 3,685 |
| Houma-Bayou Cane-Thibodaux, LA | 36.3% | 3,557 |
| New Orleans-Metairie, LA | 38.6% | 3,515 |
| Lowest | ||
| Jackson, WY-ID | 27.4% | 17.3 |
| Heber, UT | 28.1% | 51.7 |
| Butte-Silver Bow, MT | 31% | 81 |
| Evanston, WY-UT | 31.7% | 89.9 |
| Bozeman, MT | 30% | 92 |
Cities and towns — Cooling degree days is not published for cities and towns
By loneliness
| Loneliness | Cooling degree days | |
|---|---|---|
| Highest | ||
| Westmont CDP, CA | 45.5% | — |
| Rosemead city, CA | 45.3% | — |
| Florence-Graham CDP, CA | 45.2% | — |
| Compton city, CA | 45% | — |
| East Rancho Dominguez CDP, CA | 44.9% | — |
| Lowest | ||
| Midway city, UT | 26.4% | — |
| Little Silver borough, NJ | 26.5% | — |
| Fair Haven borough, NJ | 26.8% | — |
| Mequon city, WI | 26.9% | — |
| Rumson borough, NJ | 26.9% | — |
Counties furthest from the pattern
Compared with the straight line that best fits all 2,263 counties: the five with far more cooling degree days than their loneliness would suggest, and the five with far less.
| Loneliness | Cooling degree days | |
|---|---|---|
| Above the line | ||
| Yuma County, AZ | 33.6% | 4,107 |
| Maricopa County, AZ | 32.5% | 3,793 |
| La Paz County, AZ | 34.9% | 4,024 |
| Imperial County, CA | 38.2% | 4,386 |
| Pinal County, AZ | 34.1% | 3,500 |
| Below the line | ||
| San Francisco County, CA | 38.4% | 110 |
| Washington County, ME | 37.4% | 234 |
| Franklin County, ME | 36.8% | 155 |
| Del Norte County, CA | 37.1% | 242 |
| Somerset County, ME | 36.5% | 177 |
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 | +0.525 | 2,255 counties reporting both |
| After adjusting for density, income and education | +0.403 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.403 | size taken out as well as density |
| Also comparing only within each state | +0.150 | each county is compared only with others in its own state, so this is no longer a comparison between states |
| Also holding winter sunshine equal | +0.194 | compares counties that get the same sunlight in winter as well. If this is much weaker, part of the link is a difference between sunny and grey regions (more) |
| Giving bigger counties more weight | +0.172 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 189 equally weighted counties |
| Middle value inside single states | +0.127 | the link worked out inside each of 29 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.283 | uses 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 = 1.6e-88. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.000. The site tested 32,131 pairs, and testing so many turns up some flukes. The q-value allows for that too. For this pair it is 1.5e-87.
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 +0.81 (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
- Days of 90°F or hotter and Cooling degree days — +0.95
- Cooling degree days and Summer daily high — +0.92
- Freezing days and Cooling degree days — -0.81
- Cooling degree days and Winter daily sunlight — +0.79
- Cooling degree days and Winter nightly low — +0.79
- Cooling degree days and Daily sunlight — +0.70
- Loneliness and Material security score — -0.66
- Loneliness and Returns claiming the earned income credit — +0.63
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 Cooling degree days