placestats

Vision disability and Friending bias

Where Vision disability is higher, Friending bias tends to be higher. The link is weak: +0.31 across 2,820 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 38 states, the middle value of the link is +0.26.

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

Vision disability

The estimated percentage of adults who report serious difficulty seeing. 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 VISION, age-adjusted

Friending bias

How much less often low-income people make high-income friends than you would expect from how often they meet them. A high value means the chance to mix is there but is not taken.

Social Capital Atlas · Opportunity Insights bias_grp_mem_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 vision disability

Vision disabilityFriending bias
Highest
Oglala Lakota County, SD17.5%0.0874
Todd County, SD16.3%0.335
Dimmit County, TX15.5%0.0957
Zavala County, TX15.3%0.101
Starr County, TX14.7%0.00257
Lowest
Douglas County, CO2.7%-0.0305
Delaware County, OH2.7%0.0961
Los Alamos County, NM2.7%0.022
Hamilton County, IN2.8%0.0014
Pitkin County, CO2.9%0.00361

By friending bias

Vision disabilityFriending bias
Highest
Todd County, SD16.3%0.335
Nantucket County, MA2.9%0.33
DeSoto County, FL8.9%0.308
Corson County, SD11.6%0.273
Marin County, CA3.4%0.271
Lowest
Clark County, KS4.2%-0.108
Glascock County, GA5.6%-0.106
Armstrong County, TX4%-0.0962
Oldham County, TX5.4%-0.0928
Skagway Municipality, AK3.9%-0.0917
Metro areas

By vision disability

Vision disabilityFriending bias
Highest
Rio Grande City-Roma, TX14.7%0.00257
Zapata, TX14.2%0.0809
Gallup, NM12.9%0.198
Raymondville, TX12.6%0.0566
Eagle Pass, TX11.5%0.0598
Lowest
Los Alamos, NM2.7%0.022
Nantucket, MA2.9%0.33
Heber, UT3.18%0.0944
Vineyard Haven, MA3.2%0.218
Barnstable Town, MA3.2%0.0818

By friending bias

Vision disabilityFriending bias
Highest
Nantucket, MA2.9%0.33
Arcadia, FL8.9%0.308
Trenton-Princeton, NJ4.5%0.241
Napa, CA4.7%0.222
Bridgeport-Stamford-Danbury, CT4.45%0.219
Lowest
Harrison, AR5.08%-0.0249
Urban Honolulu, HI4.7%-0.0207
Cullman, AL5.1%-0.0194
Rexburg, ID5.82%-0.0125
Moscow, ID4.3%-0.0123
Cities and towns — Friending bias is not published for cities and towns

By vision disability

Vision disabilityFriending bias
Highest
Cameron Park CDP, TX19.2%—
Doffing CDP, TX19%—
Mila Doce CDP, TX17.9%—
East Bakersfield CDP, CA17.6%—
Lamont CDP, CA17.6%—
Lowest
Huntington Woods city, MI2%—
Winnetka village, IL2%—
Western Springs village, IL2%—
Glencoe village, IL2%—
Difficult Run CDP, VA2.1%—

Counties furthest from the pattern

Compared with the straight line that best fits all 2,828 counties: the five with far more friending bias than their vision disability would suggest, and the five with far less.

Vision disabilityFriending bias
Above the line
Nantucket County, MA2.9%0.33
Marin County, CA3.4%0.271
DeSoto County, FL8.9%0.308
Todd County, SD16.3%0.335
Mercer County, NJ4.5%0.241
Below the line
Glascock County, GA5.6%-0.106
Clark County, KS4.2%-0.108
Oldham County, TX5.4%-0.0928
Armstrong County, TX4%-0.0962
Cleveland County, AR5.8%-0.0816

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+0.2262,820 counties reporting both
After adjusting for density, income and education+0.309the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size+0.266size taken out as well as density
Also comparing only within each state+0.268each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight+0.171the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 263 equally weighted counties
Middle value inside single states+0.259the link worked out inside each of 38 states with at least 20 counties reporting
Using ranks, after adjusting+0.387uses 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 = 2.6e-63. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 9.0e-10. 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 1.6e-62.

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 +0.18 (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 Vision disability and Friending bias