Congregations per 10,000 people and Friending clustering
Across 3,076 counties, Congregations per 10,000 people and Friending clustering have a link of +0.55, on a scale from −1 (opposite) to +1 (in step). But compare places with the same population density, income and education, and a weak link of +0.23 remains. Most of the first figure came from both measures following how urban, rich and educated a place is, not from a link with each other.
Mostly gone 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
Congregations per 10,000 people
How many separate congregations a place has per ten thousand residents. That is a different question from how many people belong. Rural counties have many small churches, and cities have fewer large ones.
· Religion Census 2020 files; 2010 from the ARDA, RCMSCY10 and RCMSST10
Friending clustering
How often a person's friends are also friends with each other. It shows how tight-knit the local network is, which is separate from how far it reaches across income groups.
Social Capital Atlas · Opportunity Insights clustering_county
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 congregations per 10,000 people
| Congregations per 10,000 people | Friending clustering | |
|---|---|---|
| Highest | ||
| Grant County, NE | 115 | 0.0994 |
| Sheridan County, ND | 103 | 0.109 |
| Boyd County, NE | 99.4 | 0.138 |
| San Juan County, CO | 99.3 | 0.0825 |
| Highland County, VA | 98.6 | 0.152 |
| Lowest | ||
| Alpine County, CA | 0 | 0.0916 |
| Ocean County, NJ | 3.63 | 0.103 |
| Kendall County, IL | 3.64 | 0.0921 |
| Bronx County, NY | 3.96 | 0.0716 |
| Broomfield County, CO | 4.05 | 0.0857 |
By friending clustering
| Congregations per 10,000 people | Friending clustering | |
|---|---|---|
| Highest | ||
| Kusilvak Census Area, AK | 28.7 | 0.261 |
| Nome Census Area, AK | 32.8 | 0.235 |
| Northwest Arctic Borough, AK | 30.8 | 0.228 |
| East Carroll Parish, LA | 24.1 | 0.222 |
| Bethel Census Area, AK | 33.2 | 0.221 |
| Lowest | ||
| Bronx County, NY | 3.96 | 0.0716 |
| Osceola County, FL | 6.61 | 0.0734 |
| Utah County, UT | 21.1 | 0.0758 |
| Kings County, NY | 4.35 | 0.0759 |
| Bernalillo County, NM | 6.45 | 0.0761 |
States — Friending clustering is not published for states
By congregations per 10,000 people
| Congregations per 10,000 people | Friending clustering | |
|---|---|---|
| Highest | ||
| Arkansas | 24.7 | — |
| Mississippi | 24.3 | — |
| West Virginia | 22.7 | — |
| South Dakota | 21.9 | — |
| Alabama | 21.4 | — |
| Lowest | ||
| Nevada | 4.96 | — |
| California | 5.96 | — |
| Massachusetts | 6.2 | — |
| Rhode Island | 6.45 | — |
| New Jersey | 6.54 | — |
Metro areas
By congregations per 10,000 people
| Congregations per 10,000 people | Friending clustering | |
|---|---|---|
| Highest | ||
| Camden, AR | 66.1 | 0.136 |
| Arkadelphia, AR | 46.2 | 0.125 |
| Magnolia, AR | 43.4 | 0.144 |
| El Dorado, AR | 41.5 | 0.144 |
| Forrest City, AR | 39 | 0.152 |
| Lowest | ||
| Las Vegas-Henderson-North Las Vegas, NV | 4.2 | 0.0763 |
| San Jose-Sunnyvale-Santa Clara, CA | 4.89 | 0.0898 |
| Reno, NV | 5.15 | 0.0853 |
| San Diego-Chula Vista-Carlsbad, CA | 5.3 | 0.0842 |
| Riverside-San Bernardino-Ontario, CA | 5.31 | 0.0804 |
By friending clustering
| Congregations per 10,000 people | Friending clustering | |
|---|---|---|
| Highest | ||
| Zapata, TX | 15.8 | 0.182 |
| Natchez, MS-LA | 26.9 | 0.176 |
| Greenwood, MS | 33.4 | 0.166 |
| Cleveland, MS | 31 | 0.165 |
| Clarksdale, MS | 24.3 | 0.163 |
| Lowest | ||
| Las Vegas-Henderson-North Las Vegas, NV | 4.2 | 0.0763 |
| Provo-Orem-Lehi, UT | 21.1 | 0.077 |
| El Paso, TX | 5.96 | 0.0772 |
| Colorado Springs, CO | 6.42 | 0.0784 |
| Orlando-Kissimmee-Sanford, FL | 7.27 | 0.0786 |
Counties furthest from the pattern
Compared with the straight line that best fits all 3,091 counties: the five with far more friending clustering than their congregations per 10,000 people would suggest, and the five with far less.
| Congregations per 10,000 people | Friending clustering | |
|---|---|---|
| Above the line | ||
| Kusilvak Census Area, AK | 28.7 | 0.261 |
| Nome Census Area, AK | 32.8 | 0.235 |
| Northwest Arctic Borough, AK | 30.8 | 0.228 |
| East Carroll Parish, LA | 24.1 | 0.222 |
| Bethel Census Area, AK | 33.2 | 0.221 |
| Below the line | ||
| San Juan County, CO | 99.3 | 0.0825 |
| Grant County, NE | 115 | 0.0994 |
| Sheridan County, ND | 103 | 0.109 |
| Thomas County, NE | 89.7 | 0.101 |
| Dickens County, TX | 84.7 | 0.105 |
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 | +0.554 | 3,076 counties reporting both |
| After adjusting for density, income and education | +0.226 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.124 | size taken out as well as density |
| Also comparing only within each state | +0.127 | each county is compared only with others in its own state, so this is no longer a comparison between states |
| Giving bigger counties more weight | +0.541 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 286 equally weighted counties |
| Middle value inside single states | +0.654 | the link worked out inside each of 40 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.326 | 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 = 6.5e-37. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.000. 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 2.9e-36.
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 +0.42 (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
- Congregations per 10,000 people and Homicide rate, annual — +0.49
- Friending clustering and Homicide rate, annual — +0.48
- Share Black, not Hispanic and Friending clustering — +0.43
- Routine checkup in the past year and Friending clustering — +0.42
- Homicide rate and Friending clustering — +0.41
- Taking blood pressure medication and Friending clustering — +0.41
- High blood pressure and Friending clustering — +0.39
- Child mortality and Congregations per 10,000 people — +0.39
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 Congregations per 10,000 people and Friending clustering