Black-white residential segregation and Firearm share of suicides, standardised
Where Black-white residential segregation is higher, Firearm share of suicides, standardised tends to be lower. The link is weak: -0.32 across 1,785 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 -0.02.
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
Black-white residential segregation
How unevenly Black and white residents are spread across the county's neighborhoods, from 0 (evenly mixed) to 100 (completely separate). It is the share of either group who would have to move for the two groups to be spread the same way. It is only calculated where both groups live in large enough numbers.
County Health Rankings · County Health Rankings v141
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
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 black-white residential segregation
| Black-white residential segregation | Firearm share of suicides, standardised | |
|---|---|---|
| Highest | ||
| Starr County, TX | 92.4 | 167 |
| Manistee County, MI | 90.2 | 102 |
| Umatilla County, OR | 89.6 | 96.5 |
| Pike County, KY | 89.3 | 141 |
| Cass County, IN | 88.9 | 116 |
| Lowest | ||
| Appomattox County, VA | 8.27 | 99.4 |
| Rappahannock County, VA | 8.37 | 102 |
| Clarke County, VA | 8.58 | 116 |
| Manassas city, VA | 9.12 | 81.8 |
| Greene County, VA | 10.5 | 108 |
By firearm share of suicides, standardised
| Black-white residential segregation | Firearm share of suicides, standardised | |
|---|---|---|
| Highest | ||
| Gates County, NC | 17.4 | 181 |
| Hale County, AL | 25.8 | 178 |
| Conecuh County, AL | 15.6 | 175 |
| Bolivar County, MS | 58.6 | 173 |
| Pendleton County, WV | 51.7 | 171 |
| Lowest | ||
| New York County, NY | 69 | 12.9 |
| Queens County, NY | 79.6 | 22.5 |
| Kings County, NY | 74.6 | 24.6 |
| Bronx County, NY | 53.5 | 26.6 |
| Norfolk County, MA | 56.7 | 29.7 |
States
By black-white residential segregation
| Black-white residential segregation | Firearm share of suicides, standardised | |
|---|---|---|
| Highest | ||
| Montana | 80.4 | 117 |
| Idaho | 77 | 119 |
| Wisconsin | 76.7 | 96 |
| New York | 75.1 | 52 |
| Maine | 74.9 | 101 |
| Lowest | ||
| Delaware | 44.8 | 93.2 |
| South Carolina | 48.2 | 118 |
| Nevada | 50.7 | 114 |
| Mississippi | 51.1 | 129 |
| Virginia | 51.2 | 109 |
By firearm share of suicides, standardised
| Black-white residential segregation | Firearm share of suicides, standardised | |
|---|---|---|
| Highest | ||
| Wyoming | 70.5 | 130 |
| Mississippi | 51.1 | 129 |
| Alabama | 57.7 | 129 |
| Louisiana | 55.9 | 121 |
| Georgia | 55.9 | 120 |
| Lowest | ||
| Massachusetts | 62.9 | 41.3 |
| Hawaii | 61.6 | 45 |
| District of Columbia | 66.6 | 46.9 |
| Rhode Island | 62.1 | 50.3 |
| New Jersey | 65 | 51.7 |
Metro areas — Black-white residential segregation is not published for metro areas
By firearm share of suicides, standardised
| Black-white residential segregation | Firearm share of suicides, standardised | |
|---|---|---|
| Highest | ||
| Cleveland, MS | — | 173 |
| Rio Grande City-Roma, TX | — | 167 |
| Winnemucca, NV | — | 161 |
| Snyder, TX | — | 160 |
| Brenham, TX | — | 159 |
| Lowest | ||
| New York-Newark-Jersey City, NY-NJ | — | 35.2 |
| Barnstable Town, MA | — | 38.8 |
| Boston-Cambridge-Newton, MA-NH | — | 39.2 |
| Pittsfield, MA | — | 40.7 |
| Urban Honolulu, HI | — | 40.9 |
Counties furthest from the pattern
Compared with the straight line that best fits all 1,785 counties: the five with far more firearm share of suicides, standardised than their black-white residential segregation would suggest, and the five with far less.
| Black-white residential segregation | Firearm share of suicides, standardised | |
|---|---|---|
| Above the line | ||
| Starr County, TX | 92.4 | 167 |
| Bolivar County, MS | 58.6 | 173 |
| Scurry County, TX | 77.7 | 160 |
| Pendleton County, WV | 51.7 | 171 |
| Gaines County, TX | 67.5 | 163 |
| Below the line | ||
| New York County, NY | 69 | 12.9 |
| Bronx County, NY | 53.5 | 26.6 |
| Norfolk County, MA | 56.7 | 29.7 |
| Middlesex County, MA | 53.9 | 32 |
| Hudson County, NJ | 55.8 | 31.5 |
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 | -0.295 | 1,785 counties reporting both |
| After adjusting for density, income and education | -0.317 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | -0.291 | size taken out as well as density |
| Also comparing only within each state | -0.090 | 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 | -0.390 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 257 equally weighted counties |
| Middle value inside single states | -0.025 | the link worked out inside each of 31 states with at least 20 counties reporting |
| Using ranks, after adjusting | -0.316 | 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 = 5.2e-43. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 6.4e-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 2.6e-42.
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 +0.36 (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
- Share Black, not Hispanic and Black-white residential segregation — -0.45
- High blood pressure and Black-white residential segregation — -0.45
- Median property tax bill and Firearm share of suicides, standardised — -0.43
- Taking blood pressure medication and Black-white residential segregation — -0.42
- Black-white residential segregation and Mobility score — +0.41
- Diagnosed diabetes and Black-white residential segregation — -0.41
- Firearm share of suicides, standardised and Returns reporting unemployment compensation — -0.39
- Stroke and Black-white residential segregation — -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 Black-white residential segregation and Firearm share of suicides, standardised