Population (firearm measures) and Robbery rate
Where Population (firearm measures) is higher, Robbery rate tends to be higher. The link is weak: +0.30 across 1,757 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 32 states, the middle value of the link is +0.65.
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.
Loading every place…
What each measure is
Population (firearm measures)
How many people live here, from the 2017-2021 American Community Survey. The gun measures are adjusted using this population.
CDC WONDER mortality; RAND firearm ownership database · ACS 2017-2021 five-year, B01001_001
Robbery rate
Robberies in the year, per 100,000 residents, from the FBI. A robbery is taking something from a person by force or by threat, such as a mugging. It counts only crimes reported to police or found by them, so crimes nobody reports are missing. For a state, the figure is the FBI's estimate, which fills in for police agencies that did not report. For a county, it is only what the agencies reported, and it is shown only where agencies that reported for all 12 months serve at least 90% of the people who live there.
FBI Uniform Crime Reporting program (Crime Data Explorer) · FBI Crime Data Explorer: states, estimated_crimes robbery; counties, Return A master file, actual offenses
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 population (firearm measures)
| Population (firearm measures), per 1,000 residents | Robbery rate | |
|---|---|---|
| Highest | ||
| Lassen County, CA | 1004.97 | 38.1 |
| Hampshire County, MA | 999.58 | 4.1 |
| Buchanan County, VA | 996.68 | 10.6 |
| White Pine County, NV | 991.87 | 0 |
| San Francisco County, CA | 989.70 | 361 |
| Lowest | ||
| Kaufman County, TX | 810.11 | 20.4 |
| Rockwall County, TX | 845.11 | 13.5 |
| Comal County, TX | 845.13 | 11.7 |
| Liberty County, TX | 851.58 | 21.4 |
| Jackson County, GA | 857.90 | 6.35 |
By robbery rate
| Population (firearm measures), per 1,000 residents | Robbery rate | |
|---|---|---|
| Highest | ||
| District of Columbia, DC | 952.57 | 625 |
| Baltimore city, MD | 962.76 | 584 |
| Alameda County, CA | 955.90 | 371 |
| San Francisco County, CA | 989.70 | 361 |
| Philadelphia County, PA | 944.43 | 339 |
| Lowest | ||
| Moffat County, CO | 933.55 | 0 |
| Kit Carson County, CO | 940.84 | 0 |
| Chaffee County, CO | 940.33 | 0 |
| Stone County, AR | 941.80 | 0 |
| Sevier County, AR | 939.17 | 0 |
States
By population (firearm measures)
| Population (firearm measures), per 1,000 residents | Robbery rate | |
|---|---|---|
| Highest | ||
| West Virginia | 957.04 | 11.4 |
| New York | 954.09 | 105 |
| District of Columbia | 952.57 | 607 |
| Illinois | 951.41 | 110 |
| Connecticut | 951.32 | 43.6 |
| Lowest | ||
| Idaho | 896.11 | 9.39 |
| Utah | 896.29 | 25.9 |
| Texas | 907.65 | 68.6 |
| South Dakota | 914.43 | 20.6 |
| Nevada | 916.21 | 75.4 |
By robbery rate
| Population (firearm measures), per 1,000 residents | Robbery rate | |
|---|---|---|
| Highest | ||
| District of Columbia | 952.57 | 607 |
| Maryland | 937.51 | 131 |
| California | 945.52 | 128 |
| Illinois | 951.41 | 110 |
| New York | 954.09 | 105 |
| Lowest | ||
| Idaho | 896.11 | 9.39 |
| West Virginia | 957.04 | 11.4 |
| Wyoming | 935.52 | 11.6 |
| Maine | 938.80 | 11.7 |
| Vermont | 949.19 | 14.6 |
Metro areas
By population (firearm measures)
| Population (firearm measures), per 1,000 residents | Robbery rate | |
|---|---|---|
| Highest | ||
| Susanville, CA | 1085.62 | 38.1 |
| Big Spring, TX | 1063.69 | 27.1 |
| Selma, AL | 1017.45 | 49.8 |
| Middlesborough, KY | 997.47 | 12.8 |
| Roanoke Rapids, NC | 995.61 | 102 |
| Lowest | ||
| Mount Pleasant, TX | 513.02 | 13.6 |
| Mount Sterling, KY | 552.19 | 2.09 |
| Alamosa, CO | 553.23 | 34.5 |
| Paducah, KY-IL | 619.02 | 13.7 |
| Cadillac, MI | 638.04 | 5.62 |
By robbery rate
| Population (firearm measures), per 1,000 residents | Robbery rate | |
|---|---|---|
| Highest | ||
| San Francisco-Oakland-Fremont, CA | 979.97 | 235 |
| Memphis, TN-MS-AR | 926.65 | 234 |
| Stockton-Lodi, CA | 897.01 | 194 |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 938.64 | 178 |
| Pueblo, CO | 932.94 | 166 |
| Lowest | ||
| Waterloo-Cedar Falls, IA | 872.19 | 0 |
| Warren, PA | 978.55 | 0 |
| Urbana, OH | 942.95 | 0 |
| Stephenville, TX | 899.79 | 0 |
| Spencer, IA | 936.65 | 0 |
Counties furthest from the pattern
Compared with the straight line that best fits all 1,757 counties: the five with far more robbery rate than their population (firearm measures) would suggest, and the five with far less.
| Population (firearm measures), per 1,000 residents | Robbery rate | |
|---|---|---|
| Above the line | ||
| District of Columbia, DC | 952.57 | 625 |
| Baltimore city, MD | 962.76 | 584 |
| Alameda County, CA | 955.90 | 371 |
| San Francisco County, CA | 989.70 | 361 |
| Philadelphia County, PA | 944.43 | 339 |
| Below the line | ||
| White Pine County, NV | 991.87 | 0 |
| DeKalb County, MO | 986.67 | 0 |
| Doddridge County, WV | 984.94 | 0 |
| Boone County, WV | 984.28 | 0 |
| Wayne County, MO | 982.82 | 0 |
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.
Loading…
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 | +0.452 | 1,757 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 | +0.211 | size taken out as well as density |
| Also comparing only within each state | +0.236 | each county is compared only with others in its own state, so this is no longer a comparison between states |
| Also holding winter sunshine equal | +0.296 | compares counties that get the same sunlight in winter as well. If this is much weaker, part of the link is a difference between sunny and grey regions (more) |
| Giving bigger counties more weight | +0.324 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 219 equally weighted counties |
| Middle value inside single states | +0.652 | the link worked out inside each of 32 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.434 | 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 = 4.0e-39. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 5.6e-6. The site tested 32,131 pairs, and testing so many turns up some flukes. The q-value allows for that too. For this pair it is 2.1e-38.
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 +0.21 (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
- Local income tax collected and Robbery rate — +0.63
- Share of days with unhealthy air and Population (firearm measures) — +0.49
- Homicide rate and Robbery rate — +0.48
- Robbery rate and Homicide rate, annual — +0.45
- Share White, not Hispanic and Robbery rate — -0.42
- Homeownership rate and Robbery rate — -0.41
- Share foreign-born and Population (firearm measures) — +0.41
- Cancer other than skin cancer and Robbery rate — -0.40
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 Population (firearm measures) and Robbery rate