placestats

Firearm fatalities and Social connection score

Where Firearm fatalities is higher, Social connection score tends to be lower. The link is weak: -0.33 across 2,004 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 34 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

Firearm fatalities

Deaths involving a gun per 100,000 people. Most are suicides and homicides, counted together.

County Health Rankings · County Health Rankings v148

Social connection score

Social connection: friendships across income lines, friendships across income lines in high school, volunteering, community Facebook pages, membership groups, and loneliness and lack of support (flipped). Friending bias is left out, since it is built from the same numbers as friendships across income lines.

· derived from 7 measures (CDC, CHR, SC)

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 firearm fatalities

Firearm fatalitiesSocial connection score
Highest
Phillips County, AR79.5-1.15
Leflore County, MS70.5-1.17
Wilcox County, AL63.8-1.58
Kusilvak Census Area, AK62.5-1.61
Lowndes County, AL61.1-1.5
Lowest
Middlesex County, MA1.860.176
Bergen County, NJ2.10.0263
Norfolk County, MA2.180.32
Rockland County, NY2.29-0.024
Nassau County, NY2.32-0.0227

By social connection score

Firearm fatalitiesSocial connection score
Highest
Douglas County, MO28.33.01
Teton County, WY14.51.42
District of Columbia, DC22.31.31
Door County, WI11.81.3
McPherson County, KS9.611.25
Lowest
Madison Parish, LA38.4-1.84
Holmes County, MS51.2-1.84
Tunica County, MS33.2-1.81
Bullock County, AL29.6-1.74
Bronx County, NY6.19-1.7
States — Social connection score is not published for states

By firearm fatalities

Firearm fatalitiesSocial connection score
Highest
Mississippi27.1—
Louisiana24.9—
New Mexico24.1—
Wyoming23.8—
Alabama23.7—
Lowest
Massachusetts3.7—
Hawaii4.32—
Rhode Island4.47—
New Jersey4.81—
New York4.9—
Metro areas

By firearm fatalities

Firearm fatalitiesSocial connection score
Highest
Greenwood, MS61.5-1.38
Greenville, MS58.3-0.935
Selma, AL48.4-0.876
Pine Bluff, AR43.9-0.974
Payson, AZ40.4-0.592
Lowest
Boston-Cambridge-Newton, MA-NH3.480.0388
Worcester, MA3.49-0.18
Urban Honolulu, HI3.58-0.492
New York-Newark-Jersey City, NY-NJ3.65-0.796
Bridgeport-Stamford-Danbury, CT3.83-0.246

By social connection score

Firearm fatalitiesSocial connection score
Highest
Barre, VT12.22.07
McPherson, KS9.611.87
Somerset, PA10.91.67
Alexandria, MN8.791.64
Red Wing, MN11.51.54
Lowest
Rio Grande City-Roma, TX7.69-2.33
McAllen-Edinburg-Mission, TX5.89-1.96
Eagle Pass, TX6.86-1.92
Laredo, TX6.95-1.77
Gallup, NM19.4-1.76

Counties furthest from the pattern

Compared with the straight line that best fits all 2,005 counties: the five with far more social connection score than their firearm fatalities would suggest, and the five with far less.

Firearm fatalitiesSocial connection score
Above the line
Douglas County, MO28.33.01
Platte County, WY351.07
District of Columbia, DC22.31.31
Teton County, WY14.51.42
Richland County, MT25.31.14
Below the line
Bronx County, NY6.19-1.7
Starr County, TX7.69-1.66
Hidalgo County, TX5.89-1.41
Queens County, NY2.54-1.27
Maverick County, TX6.86-1.33

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-0.4022,004 counties reporting both
After adjusting for density, income and education-0.329the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size-0.347size taken out as well as density
Also comparing only within each state-0.178each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight-0.069the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 268 equally weighted counties
Middle value inside single states-0.296the link worked out inside each of 34 states with at least 20 counties reporting
Using ranks, after adjusting-0.287uses 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 = 9.0e-52. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 2.1e-6. 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 5.0e-51.

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 +0.53 (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 Firearm fatalities and Social connection score