placestats

Economic connectedness and Safety score

Across 2,076 counties, Economic connectedness and Safety score have a link of +0.56, on a scale from −1 (opposite) to +1 (in step). But compare places with the same population density, income and education, and a weak link of +0.27 remains. Most of the first figure came from both measures following how urban, rich and educated a place is, not from a link with each other.

Mostly gone 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

Safety score

Deaths from violence and injury (guns, crashes and overdoses), flipped so higher is safer. Homicide, suicide, drunk-driving and total injury deaths are left out, since they would count the same deaths twice. Gun deaths already include most homicides and suicides, and injury deaths include all four.

· derived from 4 measures (BRFSS, CHR)

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 connectednessSafety score
Highest
Rockingham County, NH1.360.8
Douglas County, CO1.351.05
San Francisco County, CA1.310.113
Madison County, ID1.281.38
Arlington County, VA1.271.4
Lowest
Todd County, SD0.295-4.82
Oglala Lakota County, SD0.33-4.61
Bullock County, AL0.355-2.73
Allendale County, SC0.361-2.61
Robeson County, NC0.363-2.96

By safety score

Economic connectednessSafety score
Highest
Arlington County, VA1.271.4
Loudoun County, VA1.221.39
Madison County, ID1.281.38
Story County, IA1.181.29
Fairfax County, VA1.191.29
Lowest
Big Horn County, MT0.484-5.15
Phillips County, AR0.495-4.96
Todd County, SD0.295-4.82
Oglala Lakota County, SD0.33-4.61
Wilcox County, AL0.422-4.61
States — Economic connectedness is not published for states

By safety score

Economic connectednessSafety score
Highest
Hawaii—0.788
Washington—0.756
Minnesota—0.747
California—0.68
Oregon—0.628
Lowest
Mississippi—-1.82
Kentucky—-1.52
West Virginia—-1.48
Missouri—-1.37
Montana—-1.23
Metro areas

By economic connectedness

Economic connectednessSafety score
Highest
Provo-Orem-Lehi, UT1.221.28
Alexandria, MN1.180.838
Laramie, WY1.170.694
Ames, IA1.151.47
Red Wing, MN1.150.552
Lowest
Lumberton, NC0.363-3.33
Cordele, GA0.388-0.606
Laurinburg, NC0.396-1.76
Eufaula, AL-GA0.414-1.52
Gallup, NM0.416-1.53

By safety score

Economic connectednessSafety score
Highest
Ames, IA1.151.47
Iowa City, IA1.071.41
State College, PA1.081.38
McAllen-Edinburg-Mission, TX0.5221.37
Urban Honolulu, HI1.121.37
Lowest
Greenwood, MS0.513-4.26
Greenville, MS0.463-3.93
Lumberton, NC0.363-3.33
Española, NM0.447-2.61
Selma, AL0.455-2.61

Counties furthest from the pattern

Compared with the straight line that best fits all 2,076 counties: the five with far more safety score than their economic connectedness would suggest, and the five with far less.

Economic connectednessSafety score
Above the line
Cameron County, TX0.4861.12
Hidalgo County, TX0.5221.19
Starr County, TX0.5080.886
Webb County, TX0.5860.901
Western Connecticut Planning Region, CT0.7171.26
Below the line
Big Horn County, MT0.484-5.15
Phillips County, AR0.495-4.96
Estill County, KY0.768-3.94
McDowell County, WV0.618-4.3
Wilcox County, AL0.422-4.61

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+0.5612,076 counties reporting both
After adjusting for density, income and education+0.269the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size+0.297size taken out as well as density
Also comparing only within each state+0.218each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight+0.134the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 271 equally weighted counties
Middle value inside single states+0.527the link worked out inside each of 33 states with at least 20 counties reporting
Using ranks, after adjusting+0.250uses 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 = 8.2e-36. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.001. 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 3.6e-35.

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 +0.39 (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

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 Safety score