Firearm share of suicides, standardised and Mobility score
Where Firearm share of suicides, standardised is higher, Mobility score tends to be lower. The link is weak: -0.30 across 1,825 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 31 states, the middle value of the link is -0.01.
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 share of suicides, standardised
The gun share of suicides compared with what you would expect if each sex, age and race group here used guns at its national rate. 100 means it matches what the local population mix would predict. Above 100 means more gun suicide than that. It is the best county measure of gun access available, and it closely tracks RAND's state estimates of gun ownership.
CDC WONDER mortality; RAND firearm ownership database · CDC WONDER underlying cause of death, suicide by intent, firearm by mechanism, 2018-2023
Mobility score
What happened to children raised poor in this county: their income rank as adults, and their chance of being locked up (flipped). Both describe children born around 1980. So this tells you about the county of forty years ago as much as the county today.
· derived from 2 measures (OA)
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 share of suicides, standardised
| Firearm share of suicides, standardised | Mobility score | |
|---|---|---|
| Highest | ||
| Hale County, AL | 178 | -0.0211 |
| Conecuh County, AL | 175 | -0.471 |
| Bolivar County, MS | 173 | -0.38 |
| Pemiscot County, MO | 170 | -1.7 |
| Starr County, TX | 167 | 0.642 |
| Lowest | ||
| New York County, NY | 12.9 | 0.494 |
| Queens County, NY | 22.5 | 1.09 |
| Kings County, NY | 24.6 | 0.425 |
| Bronx County, NY | 26.6 | 0.0803 |
| Norfolk County, MA | 29.7 | 1.37 |
By mobility score
| Firearm share of suicides, standardised | Mobility score | |
|---|---|---|
| Highest | ||
| Stark County, ND | 88.3 | 2.32 |
| Brown County, MN | 113 | 1.72 |
| Todd County, MN | 107 | 1.64 |
| Stearns County, MN | 89.5 | 1.62 |
| Carver County, MN | 95.2 | 1.56 |
| Lowest | ||
| Richmond city, VA | 107 | -2.57 |
| Portsmouth city, VA | 105 | -2.39 |
| Petersburg city, VA | 106 | -2.38 |
| Upson County, GA | 108 | -2.14 |
| Chatham County, GA | 104 | -2.08 |
States — Mobility score is not published for states
By firearm share of suicides, standardised
| Firearm share of suicides, standardised | Mobility score | |
|---|---|---|
| Highest | ||
| Wyoming | 130 | — |
| Mississippi | 129 | — |
| Alabama | 129 | — |
| Louisiana | 121 | — |
| Georgia | 120 | — |
| Lowest | ||
| Massachusetts | 41.3 | — |
| Hawaii | 45 | — |
| District of Columbia | 46.9 | — |
| Rhode Island | 50.3 | — |
| New Jersey | 51.7 | — |
Metro areas
By firearm share of suicides, standardised
| Firearm share of suicides, standardised | Mobility score | |
|---|---|---|
| Highest | ||
| Cleveland, MS | 173 | -0.5 |
| Rio Grande City-Roma, TX | 167 | 0.966 |
| Snyder, TX | 160 | 0.657 |
| Brenham, TX | 159 | -0.891 |
| Vernon, TX | 159 | -0.319 |
| Lowest | ||
| New York-Newark-Jersey City, NY-NJ | 35.2 | 0.781 |
| Barnstable Town, MA | 38.8 | 1.05 |
| Boston-Cambridge-Newton, MA-NH | 39.2 | 1.09 |
| Pittsfield, MA | 40.7 | 0.3 |
| Urban Honolulu, HI | 40.9 | 1.09 |
By mobility score
| Firearm share of suicides, standardised | Mobility score | |
|---|---|---|
| Highest | ||
| Dickinson, ND | 97 | 3.41 |
| New Ulm, MN | 113 | 2.43 |
| St. Cloud, MN | 93.7 | 2.19 |
| Alexandria, MN | 80.8 | 2.08 |
| Fergus Falls, MN | 114 | 2.06 |
| Lowest | ||
| Thomaston, GA | 108 | -2.62 |
| Savannah, GA | 112 | -2.19 |
| Arcadia, FL | 97.6 | -2.12 |
| Summerville, GA | 105 | -2.12 |
| Cordele, GA | 131 | -2.09 |
Counties furthest from the pattern
Compared with the straight line that best fits all 1,825 counties: the five with far more mobility score than their firearm share of suicides, standardised would suggest, and the five with far less.
| Firearm share of suicides, standardised | Mobility score | |
|---|---|---|
| Above the line | ||
| Stark County, ND | 88.3 | 2.32 |
| Brown County, MN | 113 | 1.72 |
| Gaines County, TX | 163 | 1.27 |
| LaGrange County, IN | 130 | 1.52 |
| Todd County, MN | 107 | 1.64 |
| Below the line | ||
| Richmond city, VA | 107 | -2.57 |
| Portsmouth city, VA | 105 | -2.39 |
| Petersburg city, VA | 106 | -2.38 |
| Baltimore city, MD | 78.3 | -1.96 |
| Upson County, GA | 108 | -2.14 |
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 | -0.277 | 1,825 counties reporting both |
| After adjusting for density, income and education | -0.305 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | -0.297 | size taken out as well as density |
| Also comparing only within each state | -0.038 | 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 | -0.457 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 262 equally weighted counties |
| Middle value inside single states | -0.014 | the link worked out inside each of 31 states with at least 20 counties reporting |
| Using ranks, after adjusting | -0.280 | 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-40. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 4.6e-7. 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 8.0e-40.
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 +0.40 (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
- Economic connectedness and Mobility score — +0.48
- Share Black, not Hispanic and Mobility score — -0.44
- Child mortality and Mobility score — -0.43
- Life expectancy and Mobility score — +0.43
- Premature mortality and Mobility score — -0.43
- Median property tax bill and Firearm share of suicides, standardised — -0.43
- Years of potential life lost and Mobility score — -0.43
- Children in single-parent households and Mobility score — -0.42
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 share of suicides, standardised and Mobility score