Religious adherents and Clear days
Where Religious adherents is higher, Clear days tends to be higher. The link is weak: +0.31 across 3,121 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 40 states, the middle value of the link is +0.06.
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
Religious adherents
Members, their children, and others who attend regularly, as a share of the population. It is above 100% in thirty very small counties, where a congregation draws people from beyond the county it sits in.
US Religion Census (ASARB) · Religion Census 2020 files; 2010 from the ARDA, RCMSCY10 and RCMSST10
Clear days
The number of days in the year when clouds blocked less than 7% of the sunlight, so the ground got at least 93% of what a cloudless sky would give. It is the closest measure here to a count of sunny days. It is a NASA estimate from weather satellites for squares of land about 60 miles across, not a count by someone watching the sky, and nearby counties often share a value. The 93% line was chosen so that, on average, the count matches NOAA's counts of clear days at 64 weather stations. There, a clear day is one with three tenths of the sky or less covered by cloud in daylight. It starts in 2001, since the older satellites count clear days differently.
NASA POWER (Prediction Of Worldwide Energy Resources) · NASA POWER daily regional API, community RE, ALLSKY_SFC_SW_DWN / CLRSKY_SFC_SW_DWN
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 religious adherents
| Religious adherents | Clear days | |
|---|---|---|
| Highest | ||
| King County, TX | 452 | 194 |
| Stonewall County, TX | 167 | 194 |
| Harmon County, OK | 164 | 181 |
| Harding County, NM | 156 | 188 |
| Kiowa County, CO | 153 | 156 |
| Lowest | ||
| Alpine County, CA | 0 | 123 |
| Loving County, TX | 0 | 210 |
| Arthur County, NE | 0 | 147 |
| Gilpin County, CO | 2.03 | 97 |
| Loup County, NE | 3.46 | 149 |
By clear days
| Religious adherents | Clear days | |
|---|---|---|
| Highest | ||
| Yuma County, AZ | 35.1 | 273 |
| La Paz County, AZ | 30.6 | 272 |
| Imperial County, CA | 86.3 | 258 |
| Maricopa County, AZ | 47.2 | 254 |
| Gila County, AZ | 33.1 | 236 |
| Lowest | ||
| Yakutat City and Borough, AK | 61.3 | 3 |
| Juneau City and Borough, AK | 32.6 | 3 |
| Hoonah-Angoon Census Area, AK | 19.2 | 3 |
| Aleutians West Census Area, AK | 18.8 | 5 |
| Aleutians East Borough, AK | 41.7 | 6 |
States
By religious adherents
| Religious adherents | Clear days | |
|---|---|---|
| Highest | ||
| Utah | 76.1 | 112 |
| Alabama | 63.6 | 121 |
| Louisiana | 63.3 | 119 |
| Oklahoma | 61.2 | 158 |
| Mississippi | 59.4 | 128 |
| Lowest | ||
| New Hampshire | 27.2 | 84 |
| Maine | 30.8 | 81 |
| Oregon | 33.1 | 103 |
| Montana | 34.9 | 82 |
| Alaska | 35.1 | 17 |
By clear days
| Religious adherents | Clear days | |
|---|---|---|
| Highest | ||
| Arizona | 44 | 241 |
| Nevada | 41.6 | 186 |
| New Mexico | 52.5 | 168 |
| California | 44.8 | 168 |
| Oklahoma | 61.2 | 158 |
| Lowest | ||
| Alaska | 35.1 | 17 |
| Vermont | 37.6 | 62 |
| Michigan | 39.7 | 79 |
| Hawaii | 41.5 | 80 |
| Maine | 30.8 | 81 |
Metro areas
By religious adherents
| Religious adherents | Clear days | |
|---|---|---|
| Highest | ||
| Camden, AR | 105 | 131 |
| Mount Pleasant, TX | 98.4 | 135 |
| Brigham City, UT-ID | 89 | 96 |
| Provo-Orem-Lehi, UT | 88.7 | 103 |
| Nogales, AZ | 88.2 | 210 |
| Lowest | ||
| Crescent City, CA | 14.1 | 99 |
| Eureka-Arcata, CA | 19.2 | 85 |
| Athens, OH | 19.2 | 93 |
| Port Townsend, WA | 20.7 | 82 |
| Pahrump, NV | 21.6 | 227 |
By clear days
| Religious adherents | Clear days | |
|---|---|---|
| Highest | ||
| Yuma, AZ | 35.1 | 273 |
| El Centro, CA | 86.3 | 258 |
| Phoenix-Mesa-Chandler, AZ | 46.6 | 252 |
| Payson, AZ | 33.1 | 236 |
| Lake Havasu City-Kingman, AZ | 25.6 | 234 |
| Lowest | ||
| Juneau, AK | 32.6 | 3 |
| Anchorage, AK | 35.7 | 11 |
| Hilo-Kailua, HI | 41.8 | 14 |
| Fairbanks-College, AK | 30.2 | 29 |
| Ketchikan, AK | 31.4 | 33 |
Counties furthest from the pattern
Compared with the straight line that best fits all 3,144 counties: the five with far more clear days than their religious adherents would suggest, and the five with far less.
| Religious adherents | Clear days | |
|---|---|---|
| Above the line | ||
| La Paz County, AZ | 30.6 | 272 |
| Yuma County, AZ | 35.1 | 273 |
| Maricopa County, AZ | 47.2 | 254 |
| Mohave County, AZ | 25.6 | 234 |
| Gila County, AZ | 33.1 | 236 |
| Below the line | ||
| King County, TX | 452 | 194 |
| Yakutat City and Borough, AK | 61.3 | 3 |
| Aleutians East Borough, AK | 41.7 | 6 |
| Juneau City and Borough, AK | 32.6 | 3 |
| Kodiak Island Borough, AK | 33 | 8 |
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 | +0.328 | 3,121 counties reporting both |
| After adjusting for density, income and education | +0.314 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.323 | size taken out as well as density |
| Also comparing only within each state | +0.129 | 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 | — | 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 | +0.112 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 287 equally weighted counties |
| Middle value inside single states | +0.055 | the link worked out inside each of 40 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.344 | 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 = 1.7e-72. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 8.0e-5. 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 1.4e-71.
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 +0.82 (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
- Summer daily high and Clear days — +0.77
- Days of 90°F or hotter and Clear days — +0.64
- Average temperature and Clear days — +0.59
- Cooling degree days and Clear days — +0.58
- Wet days and Clear days — -0.57
- Heating degree days and Clear days — -0.56
- Share Hispanic or Latino, any race and Clear days — +0.49
- Freezing days and Clear days — -0.48
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 Religious adherents and Clear days