placestats

Mobility score and Safety score

Where Mobility score is higher, Safety score tends to be higher. The link is weak: +0.37 across 1,834 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 30 states, the middle value of the link is +0.34.

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

Mobility score

What happened to children raised poor in this county: their income rank as adults, and their chance of being locked up (flipped). Both describe children born around 1980. So this tells you about the county of forty years ago as much as the county today.

· derived from 2 measures (OA)

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 mobility score

Mobility scoreSafety score
Highest
Stark County, ND2.320.306
Brown County, MN1.720.882
Todd County, MN1.640.557
Stearns County, MN1.620.865
Carver County, MN1.561.2
Lowest
Oglala Lakota County, SD-4.13-4.61
Charlottesville city, VA-2.70.539
Richmond city, VA-2.57-1.6
Portsmouth city, VA-2.39-1.89
Petersburg city, VA-2.38-4.06

By safety score

States — Mobility score is not published for states

By safety score

Mobility scoreSafety 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 mobility score

Mobility scoreSafety score
Highest
Dickinson, ND3.410.377
New Ulm, MN2.430.953
St. Cloud, MN2.191.01
Alexandria, MN2.080.838
Fergus Falls, MN2.060.806
Lowest
Thomaston, GA-2.62-0.644
Savannah, GA-2.190.0896
Arcadia, FL-2.12-0.522
Summerville, GA-2.12-1.02
Cordele, GA-2.09-0.606

By safety score

Mobility scoreSafety score
Highest
Ames, IA1.611.47
Iowa City, IA1.141.41
State College, PA0.8081.38
McAllen-Edinburg-Mission, TX0.8371.37
Urban Honolulu, HI1.091.37
Lowest
Greenwood, MS-1.2-4.26
Greenville, MS-1.51-3.93
Lumberton, NC-1.02-3.33
Española, NM-0.969-2.61
Selma, AL-0.491-2.61

Counties furthest from the pattern

Compared with the straight line that best fits all 1,834 counties: the five with far more safety score than their mobility score would suggest, and the five with far less.

Mobility scoreSafety score
Above the line
Charlottesville city, VA-2.70.539
Brazos County, TX-1.230.935
McLennan County, TX-1.870.45
Bulloch County, GA-1.640.528
Western Connecticut Planning Region, CT-0.1551.26
Below the line
Phillips County, AR-0.945-4.96
Holmes County, MS-0.365-3.82
Leflore County, MS-1.03-4.14
Lowndes County, AL-0.47-3.81
Logan County, WV0.119-3.21

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+0.4351,834 counties reporting both
After adjusting for density, income and education+0.368the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size+0.358size taken out as well as density
Also comparing only within each state+0.281each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight+0.488the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 262 equally weighted counties
Middle value inside single states+0.337the link worked out inside each of 30 states with at least 20 counties reporting
Using ranks, after adjusting+0.405uses 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 = 6.5e-60. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 2.4e-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 3.9e-59.

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 +0.38 (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 Mobility score and Safety score