Economic connectedness and Aggravated assault rate
Where Economic connectedness is higher, Aggravated assault rate tends to be lower. The link is weak: -0.33 across 2,224 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 -0.33.
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
Economic connectedness
How many of a low-income person's friends are high-income, compared with an even mix. One means their friends match the local mix of rich and poor. Below one means their friendships are more divided by income than that.
Social Capital Atlas · Opportunity Insights ec_county
Aggravated assault rate
Aggravated assaults in the year, per 100,000 residents, from the FBI. An aggravated assault is an attack with a weapon, or one that leaves someone badly hurt. Fights without a weapon or serious injury are not counted. Agencies that report far fewer of these attacks than robberies are left out, since they did not report them in the usual way. 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 aggravated_assault; 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 economic connectedness
| Economic connectedness | Aggravated assault rate | |
|---|---|---|
| Highest | ||
| Rockingham County, NH | 1.36 | 36.1 |
| Douglas County, CO | 1.35 | 123 |
| San Francisco County, CA | 1.31 | 324 |
| Madison County, ID | 1.28 | 50.5 |
| Arlington County, VA | 1.27 | 148 |
| Lowest | ||
| Bullock County, AL | 0.355 | 383 |
| Robeson County, NC | 0.363 | 1,219 |
| Early County, GA | 0.38 | 760 |
| Marlboro County, SC | 0.385 | 753 |
| Crisp County, GA | 0.388 | 909 |
By aggravated assault rate
| Economic connectedness | Aggravated assault rate | |
|---|---|---|
| Highest | ||
| Crittenden County, AR | 0.562 | 1,500 |
| Shelby County, TN | 0.528 | 1,463 |
| Orangeburg County, SC | 0.472 | 1,274 |
| Robeson County, NC | 0.363 | 1,219 |
| St. Francis County, AR | 0.531 | 1,202 |
| Lowest | ||
| Conejos County, CO | 0.754 | 0 |
| Hot Springs County, WY | 1.01 | 0 |
| Menominee County, WI | 0.429 | 0 |
| Hemphill County, TX | 0.855 | 0 |
| Hansford County, TX | 0.76 | 0 |
States — Economic connectedness is not published for states
By aggravated assault rate
| Economic connectedness | Aggravated assault rate | |
|---|---|---|
| Highest | ||
| New Mexico | — | 608 |
| Alaska | — | 527 |
| Tennessee | — | 514 |
| Arkansas | — | 500 |
| District of Columbia | — | 455 |
| Lowest | ||
| Maine | — | 56.8 |
| New Hampshire | — | 59.5 |
| Connecticut | — | 83.8 |
| Rhode Island | — | 106 |
| Wyoming | — | 123 |
Metro areas
By economic connectedness
| Economic connectedness | Aggravated assault rate | |
|---|---|---|
| Highest | ||
| Provo-Orem-Lehi, UT | 1.22 | 63.3 |
| Alexandria, MN | 1.18 | 94.4 |
| Laramie, WY | 1.17 | 114 |
| Hutchinson, MN | 1.16 | 160 |
| Ames, IA | 1.15 | 92.1 |
| Lowest | ||
| Lumberton, NC | 0.363 | 1,219 |
| Cordele, GA | 0.388 | 909 |
| Laurinburg, NC | 0.396 | 918 |
| Eufaula, AL-GA | 0.414 | 327 |
| Gallup, NM | 0.416 | 373 |
By aggravated assault rate
| Economic connectedness | Aggravated assault rate | |
|---|---|---|
| Highest | ||
| Orangeburg, SC | 0.472 | 1,274 |
| Lumberton, NC | 0.363 | 1,219 |
| Forrest City, AR | 0.531 | 1,202 |
| Memphis, TN-MS-AR | 0.57 | 1,137 |
| Albuquerque, NM | 0.661 | 933 |
| Lowest | ||
| Quincy, IL-MO | 0.854 | 0 |
| Lexington, NE | 0.696 | 0 |
| Columbus, IN | 0.825 | 0.501 |
| Frankfort, IN | 0.702 | 15.3 |
| Angola, IN | 0.81 | 20.3 |
Counties furthest from the pattern
Compared with the straight line that best fits all 2,228 counties: the five with far more aggravated assault rate than their economic connectedness would suggest, and the five with far less.
| Economic connectedness | Aggravated assault rate | |
|---|---|---|
| Above the line | ||
| Crittenden County, AR | 0.562 | 1,500 |
| Shelby County, TN | 0.528 | 1,463 |
| Orangeburg County, SC | 0.472 | 1,274 |
| St. Francis County, AR | 0.531 | 1,202 |
| Pulaski County, AR | 0.682 | 1,123 |
| Below the line | ||
| Menominee County, WI | 0.429 | 0 |
| Dewey County, SD | 0.439 | 20.1 |
| Sioux County, ND | 0.448 | 27.4 |
| McCreary County, KY | 0.573 | 6 |
| Clay County, KY | 0.565 | 20.2 |
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 | -0.412 | 2,224 counties reporting both |
| After adjusting for density, income and education | -0.329 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | -0.290 | size taken out as well as density |
| Also comparing only within each state | -0.244 | 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 | -0.271 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 237 equally weighted counties |
| Middle value inside single states | -0.333 | the link worked out inside each of 36 states with at least 20 counties reporting |
| Using ranks, after adjusting | -0.385 | 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 = 3.4e-57. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 1.2e-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 2.4e-56.
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 +0.31 (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
- Housing insecurity and Economic connectedness — -0.65
- Food insecurity and Economic connectedness — -0.64
- Returns claiming the earned income credit and Economic connectedness — -0.64
- Adult income rank of children raised poor here and Economic connectedness — +0.63
- Share White, not Hispanic and Economic connectedness — +0.62
- Lack of reliable transportation and Economic connectedness — -0.61
- Vision disability and Economic connectedness — -0.60
- Child poverty rate, ages 0-17 and Economic connectedness — -0.58
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 Economic connectedness and Aggravated assault rate