Population, decennial census and Robbery rate
Where Population, decennial census is higher, Robbery rate tends to be higher. The link is weak: +0.33 across 2,281 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 36 states, the middle value of the link is +0.69.
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
Population, decennial census
The census count for that decade (2010 or 2020), which the Religion Census divides by. It is kept separate from the American Community Survey estimate so the religion rates have their own exact base.
US Religion Census (ASARB) · Religion Census 2020 files; 2010 from the ARDA, RCMSCY10 and RCMSST10
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, decennial census
| Population, decennial census, per 1,000 residents | Robbery rate | |
|---|---|---|
| Highest | ||
| King County, TX | 1402.12 | 0 |
| Garfield County, MT | 1250.53 | 0 |
| Petroleum County, MT | 1236.91 | 0 |
| Sierra County, CA | 1184.91 | 0 |
| Briscoe County, TX | 1173.34 | 0 |
| Lowest | ||
| Luce County, MI | 844.11 | 0 |
| Kaufman County, TX | 904.13 | 20.4 |
| Throckmorton County, TX | 906.23 | 0 |
| Rockwall County, TX | 922.08 | 13.5 |
| Long County, GA | 923.25 | 5.64 |
By robbery rate
| Population, decennial census, per 1,000 residents | Robbery rate | |
|---|---|---|
| Highest | ||
| District of Columbia, DC | 1025.98 | 625 |
| Baltimore city, MD | 1014.76 | 584 |
| Alameda County, CA | 1018.43 | 371 |
| San Francisco County, CA | 1045.01 | 361 |
| Philadelphia County, PA | 1013.50 | 339 |
| Lowest | ||
| Washington County, CO | 995.45 | 0 |
| San Miguel County, CO | 1005.73 | 0 |
| San Juan County, CO | 1021.74 | 0 |
| Saguache County, CO | 978.04 | 0 |
| Phillips County, CO | 1008.68 | 0 |
States
By population, decennial census
| Population, decennial census, per 1,000 residents | Robbery rate | |
|---|---|---|
| Highest | ||
| District of Columbia | 1025.98 | 607 |
| New York | 1016.54 | 105 |
| Illinois | 1009.48 | 110 |
| Louisiana | 1007.96 | 55.7 |
| California | 1007.52 | 128 |
| Lowest | ||
| Idaho | 971.37 | 9.39 |
| Montana | 981.11 | 24.2 |
| South Carolina | 981.90 | 39.8 |
| Utah | 982.11 | 25.9 |
| Florida | 982.17 | 42.5 |
By robbery rate
| Population, decennial census, per 1,000 residents | Robbery rate | |
|---|---|---|
| Highest | ||
| District of Columbia | 1025.98 | 607 |
| Maryland | 1001.05 | 131 |
| California | 1007.52 | 128 |
| Illinois | 1009.48 | 110 |
| New York | 1016.54 | 105 |
| Lowest | ||
| Idaho | 971.37 | 9.39 |
| West Virginia | 1005.18 | 11.4 |
| Wyoming | 994.98 | 11.6 |
| Maine | 989.11 | 11.7 |
| Vermont | 996.63 | 14.6 |
Metro areas
By population, decennial census
| Population, decennial census, per 1,000 residents | Robbery rate | |
|---|---|---|
| Highest | ||
| Town of Pecos, TX | 1253.02 | 8.26 |
| Big Spring, TX | 1140.93 | 27.1 |
| Susanville, CA | 1134.06 | 38.1 |
| Selma, AL | 1063.51 | 49.8 |
| Lake Charles, LA | 1060.68 | 42.5 |
| Lowest | ||
| Wildwood-The Villages, FL | 856.07 | 5.1 |
| Jefferson, GA | 856.59 | 6.35 |
| Myrtle Beach-Conway-North Myrtle Beach, SC | 883.14 | 36.3 |
| Lakeland-Winter Haven, FL | 886.01 | 23.1 |
| St. George, UT | 890.48 | 11.7 |
By robbery rate
| Population, decennial census, per 1,000 residents | Robbery rate | |
|---|---|---|
| Highest | ||
| San Francisco-Oakland-Fremont, CA | 1039.86 | 235 |
| Memphis, TN-MS-AR | 1007.28 | 234 |
| Stockton-Lodi, CA | 972.86 | 194 |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 999.83 | 178 |
| Pueblo, CO | 992.55 | 166 |
| Lowest | ||
| Zapata, TX | 1011.14 | 0 |
| Yankton, SD | 991.20 | 0 |
| Waterloo-Cedar Falls, IA | 1001.77 | 0 |
| Warren, PA | 1027.01 | 0 |
| Urbana, OH | 996.63 | 0 |
Counties furthest from the pattern
Compared with the straight line that best fits all 2,289 counties: the five with far more robbery rate than their population, decennial census would suggest, and the five with far less.
| Population, decennial census, per 1,000 residents | Robbery rate | |
|---|---|---|
| Above the line | ||
| District of Columbia, DC | 1025.98 | 625 |
| Baltimore city, MD | 1014.76 | 584 |
| Alameda County, CA | 1018.43 | 371 |
| San Francisco County, CA | 1045.01 | 361 |
| Philadelphia County, PA | 1013.50 | 339 |
| Below the line | ||
| King County, TX | 1402.12 | 0 |
| Garfield County, MT | 1250.53 | 0 |
| Petroleum County, MT | 1236.91 | 0 |
| Sierra County, CA | 1184.91 | 0 |
| Briscoe County, TX | 1173.34 | 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.
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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 | +0.460 | 2,281 counties reporting both |
| After adjusting for density, income and education | +0.328 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.270 | size taken out as well as density |
| Also comparing only within each state | +0.270 | 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 | +0.331 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 227 equally weighted counties |
| Middle value inside single states | +0.691 | the link worked out inside each of 36 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.402 | 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 = 4.2e-58. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 4.4e-6. The site tested 29,403 pairs, and testing so many turns up some flukes. The q-value allows for that too. For this pair it is 3.0e-57.
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, decennial census — +0.48
- 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
- Cancer other than skin cancer and Robbery rate — -0.40
- Average test score and Robbery rate — -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 Population, decennial census and Robbery rate