placestats

Homicide rate and Theft rate

Where Homicide rate is higher, Theft rate tends to be higher. The link is weak: +0.31 across 921 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.

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

Homicide rate

Deaths recorded as homicide per 100,000 people. It comes from death certificates, not police reports.

County Health Rankings · County Health Rankings v015

Theft rate

Thefts in the year, per 100,000 residents, from the FBI. This covers shoplifting, pickpocketing and stealing from cars or bikes, but not the theft of a vehicle itself. 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 larceny; 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 homicide rate

By theft rate

States

By homicide rate

Homicide rateTheft rate
Highest
District of Columbia232,959
Mississippi161,091
Louisiana15.91,780
Alabama12.71,352
Missouri111,410
Lowest
New Hampshire1.52789
Maine1.59937
Vermont2.041,582
Idaho2.19599
Rhode Island2.29858

By theft rate

Homicide rateTheft rate
Highest
District of Columbia232,959
Colorado5.221,899
Oregon3.541,844
New Mexico10.81,839
Louisiana15.91,780
Lowest
Idaho2.19599
New Hampshire1.52789
Rhode Island2.29858
Massachusetts2.3866
West Virginia5.85916
Metro areas

By homicide rate

Homicide rateTheft rate
Highest
Selma, AL41.51,070
Pine Bluff, AR37.32,212
Blytheville, AR28.71,793
Lumberton, NC27.72,805
Forrest City, AR26.71,362
Lowest
Portland-South Portland, ME1.07862
Provo-Orem-Lehi, UT1.22802
Iowa City, IA1.411,162
Barnstable Town, MA1.44558
Wausau, WI1.47570

By theft rate

Homicide rateTheft rate
Highest
Memphis, TN-MS-AR23.53,077
Great Falls, MT4.333,039
Wichita, KS8.212,952
Lumberton, NC27.72,805
Cordele, GA15.12,696
Lowest
Columbus, IN3.19.66
Las Vegas, NM13.690.3
Pikeville, KY5.94162
Clarksburg, WV5.98242
Hinesville, GA9.92271

Counties furthest from the pattern

Compared with the straight line that best fits all 921 counties: the five with far more theft rate than their homicide rate would suggest, and the five with far less.

Homicide rateTheft rate
Above the line
San Francisco County, CA4.994,195
Multnomah County, OR5.973,454
Chittenden County, VT1.653,101
Denver County, CO8.523,304
Alamosa County, CO12.13,414
Below the line
Macon County, AL44.8541
Madison Parish, LA30.7221
Montgomery County, AL26.2370
East Feliciana Parish, LA19.3126
West Feliciana Parish, LA13.941.1

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+0.359921 counties reporting both
After adjusting for density, income and education+0.310the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size+0.336size taken out as well as density
Also comparing only within each state+0.242each county is compared only with others in its own state, so this is no longer a comparison between states
Also holding winter sunshine equal+0.309compares 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+0.379the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 178 equally weighted counties
Middle value inside single states+0.471the link worked out inside each of 19 states with at least 20 counties reporting
Using ranks, after adjusting+0.306uses 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 = 6.7e-22. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.000. 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 2.4e-21.

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 +0.24 (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?

The figures above compare counties at one moment. This asks a different question: where homicide rate rose most, did theft ratemove too? Each county’s change is compared with the average change in its own state. That takes out ups and downs that hit a whole region at once. Each change is the gap between two estimates that both contain some error. That error pulls these links towards zero, so a weak one is weak partly for that reason. The q column shows whether a link could be a fluke, allowing for the many pairs tested: 0.05 or less means it is unlikely to be chance.

PeriodCountiesChange togetherWithin statesq
2014–2019 the main test: each Census survey figure averages five years, and these two do not overlap; before the pandemic613+0.05+0.040.826
2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years662-0.07+0.000.993

Measured over the nearest years published: Homicide rate: 2013–2019 for the 2014–2019 row; Homicide rate: 2019–2022 for the 2019–2023 row.

Over the main period, any link between the changes could easily be chance. How change is measured.

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 Homicide rate and Theft rate