placestats

Share Black, not Hispanic and Friending clustering

Where Share Black, not Hispanic is higher, Friending clustering tends to be higher. The link is moderate: +0.43 across 2,320 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 36 states, the middle value of the link is -0.30.

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

Share Black, not Hispanic

The percentage of residents who are Black or African American alone and not Hispanic. The middle county is under 2%, and this group is one of the most concentrated in the data. A handful of counties in the Mississippi Delta and the Black Belt are above 80%.

Census ACS 5-year · ACS table B03002_004, divided by total population

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 share Black, not Hispanic

Share Black, not HispanicFriending clustering
Highest
Claiborne County, MS86.1%0.192
Holmes County, MS82.4%0.167
Greene County, AL81.5%0.176
Jefferson County, MS81%0.186
Tunica County, MS79.3%0.167
Lowest
San Juan County, CO0%0.0825
Phillips County, CO0%0.118
Kiowa County, CO0%0.131
Huerfano County, CO0%0.112
Dolores County, CO0%0.121

By friending clustering

Share Black, not HispanicFriending clustering
Highest
Nome Census Area, AK1.04%0.235
East Carroll Parish, LA64.1%0.222
Bethel Census Area, AK1.15%0.221
Sharkey County, MS73.7%0.215
Humphreys County, MS78.8%0.214
Lowest
Bronx County, NY29.1%0.0716
Osceola County, FL9.65%0.0734
Utah County, UT0.599%0.0758
Kings County, NY27.5%0.0759
Bernalillo County, NM2.55%0.0761
States — Friending clustering is not published for states

By share Black, not Hispanic

Share Black, not HispanicFriending clustering
Highest
District of Columbia42.5%—
Mississippi36.9%—
Georgia31%—
Louisiana30.8%—
Maryland29.2%—
Lowest
Puerto Rico0.12%—
Montana0.474%—
Idaho0.686%—
Wyoming0.815%—
Utah1.04%—
Metro areas

By share Black, not Hispanic

Share Black, not HispanicFriending clustering
Highest
Clarksdale, MS75.9%0.163
Greenville, MS72%0.157
Selma, AL70.3%0.163
Greenwood, MS63.9%0.166
Cleveland, MS63.2%0.165
Lowest
Pierre, SD0%0.117
Mitchell, SD0%0.115
Jackson, WY-ID0%0.0932
Hailey, ID0%0.104
Gardnerville Ranchos, NV-CA0%0.0925

By friending clustering

Share Black, not HispanicFriending clustering
Highest
Natchez, MS-LA53.2%0.176
Greenwood, MS63.9%0.166
Cleveland, MS63.2%0.165
Clarksdale, MS75.9%0.163
Selma, AL70.3%0.163
Lowest
Las Vegas-Henderson-North Las Vegas, NV11.7%0.0763
Provo-Orem-Lehi, UT0.599%0.077
El Paso, TX2.93%0.0772
Colorado Springs, CO5.38%0.0784
Orlando-Kissimmee-Sanford, FL15%0.0786
Cities and towns — Friending clustering is not published for cities and towns

By share Black, not Hispanic

Share Black, not HispanicFriending clustering
Highest
Merrydale CDP, LA97.3%—
East St. Louis city, IL94%—
Warrensville Heights city, OH92.4%—
Panthersville CDP, GA92.1%—
Silver Hill CDP, MD91.6%—
Lowest
Salida city, CO0%—
Roxborough Park CDP, CO0%—
Redlands CDP, CO0%—
Milliken town, CO0%—
Mead town, CO0%—
ZIP codes — Friending clustering is not published for ZIP codes

By share Black, not Hispanic

Share Black, not HispanicFriending clustering
Highest
ZIP 36610 (Prichard city, AL)97.2%—
ZIP 62207 (Centreville city, IL)97%—
ZIP 70812 (Merrydale CDP, LA)97%—
ZIP 36617 (Mobile city, AL)96.6%—
ZIP 62205 (East St. Louis city, IL)96.5%—
Lowest
ZIP 08867 (Hunterdon County, NJ)0%—
ZIP 08551 (Hunterdon County, NJ)0%—
ZIP 08251 (Villas CDP, NJ)0%—
ZIP 07739 (Little Silver borough, NJ)0%—
ZIP 07072 (Carlstadt borough, NJ)0%—

Counties furthest from the pattern

Compared with the straight line that best fits all 2,324 counties: the five with far more friending clustering than their share Black, not Hispanic would suggest, and the five with far less.

Share Black, not HispanicFriending clustering
Above the line
Nome Census Area, AK1.04%0.235
Bethel Census Area, AK1.15%0.221
North Slope Borough, AK1.61%0.198
East Carroll Parish, LA64.1%0.222
Owsley County, KY0%0.181
Below the line
Baltimore city, MD59.3%0.0771
Orleans Parish, LA54.7%0.0798
Clayton County, GA68.7%0.0881
Prince George's County, MD59%0.0833
District of Columbia, DC42.5%0.0777

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+0.3792,320 counties reporting both
After adjusting for density, income and education+0.428the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size+0.410size taken out as well as density
Also comparing only within each state+0.362each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight+0.198the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 271 equally weighted counties
Middle value inside single states-0.297the link worked out inside each of 36 states with at least 20 counties reporting
Using ranks, after adjusting+0.150uses 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 = 1.3e-103. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 6.3e-9. 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 1.1e-102.

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

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 Share Black, not Hispanic and Friending clustering