placestats

Daily sunlight and Burglary rate

Where Daily sunlight is higher, Burglary rate tends to be higher. The link is weak: +0.31 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 +0.02.

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

Daily sunlight

The average amount of sunlight reaching the ground each day over the year, in kilowatt-hours per square metre. NASA estimates it from weather satellites: the satellites see the clouds, and a model of the air works out how much sunlight gets through them. The figures are for squares of land about 60 miles across, so nearby counties often share a value. Each county gets the square where its people live, around its center of population. Years before 2001 come from older satellites, which NASA adjusted to match the newer ones.

NASA POWER (Prediction Of Worldwide Energy Resources) · NASA POWER daily regional API, community RE, ALLSKY_SFC_SW_DWN

Burglary rate

Burglaries in the year, per 100,000 residents, from the FBI. A burglary is entering a home or other building without permission to steal or commit another crime. 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 burglary; 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 daily sunlight

By burglary rate

States

By daily sunlight

Daily sunlightBurglary rate
Highest
Arizona5.81253
Hawaii5.69243
New Mexico5.54518
Nevada5.3407
California5.13347
Lowest
Alaska2.46252
Vermont3.43143
Maine3.53112
New Hampshire3.6558.4
Michigan3.69207

By burglary rate

Daily sunlightBurglary rate
Highest
New Mexico5.54518
Washington3.72481
Oklahoma4.71460
Louisiana4.77441
Arkansas4.53410
Lowest
New Hampshire3.6558.4
Maine3.53112
Rhode Island3.99118
Virginia4.25122
Connecticut3.84129
Metro areas

By daily sunlight

Daily sunlightBurglary rate
Highest
Yuma, AZ5.97254
El Paso, TX5.92151
El Centro, CA5.91212
Nogales, AZ5.89330
Sierra Vista-Douglas, AZ5.89199
Lowest
Juneau, AK2.42341
Anchorage, AK2.48334
Barre, VT3.3387
Burlington-South Burlington, VT3.38188
Houghton, MI3.4153.7

By burglary rate

Daily sunlightBurglary rate
Highest
Lumberton, NC4.541,199
Rockingham, NC4.541,058
Hot Springs, AR4.47911
Springfield, IL4.28857
Jonesboro, AR4.53813
Lowest
Lexington, NE4.580
Petoskey, MI3.5623.8
Quincy, IL-MO4.3231
Norfolk, NE4.2933.8
Jackson, WY-ID4.3736.4

Counties furthest from the pattern

Compared with the straight line that best fits all 2,290 counties: the five with far more burglary rate than their daily sunlight would suggest, and the five with far less.

Daily sunlightBurglary rate
Above the line
Glasscock County, TX5.481,597
Robeson County, NC4.541,199
Richmond County, NC4.541,058
Tunica County, MS4.57951
Garland County, AR4.47911
Below the line
Presidio County, TX5.870
King County, TX5.280
San Juan County, UT5.3918.8
Roberts County, TX5.140
Crane County, TX5.6444.5

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+0.2832,281 counties reporting both
After adjusting for density, income and education+0.307the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size+0.278size taken out as well as density
Also comparing only within each state+0.055each county is compared only with others in its own state, so this is no longer a comparison between states
Also holding winter sunshine equal—compares 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+0.265the 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+0.016the link worked out inside each of 36 states with at least 20 counties reporting
Using ranks, after adjusting+0.365uses 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 = 8.3e-51. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 2.2e-6. 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 5.3e-50.

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 +0.29 (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 daily sunlight rose most, did burglary 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 pandemic2,020-0.10-0.050.196
2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years1,883+0.00+0.010.771

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 Daily sunlight and Burglary rate