Cloud cover and Burglary rate
Where Cloud cover is higher, Burglary rate tends to be lower. The link is weak: -0.33 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 -0.04.
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
Cloud cover
The share of the sky covered by cloud, in percent, averaged over the whole year, night as well as day. NASA estimates it from weather satellites for squares of land about 60 miles across, so nearby counties often share a value. It starts in 2001, since the older satellites measure cloud differently. Cloud cover, clear days and sunlight come from the same satellite data, so they are not separate evidence about each other.
NASA POWER (Prediction Of Worldwide Energy Resources) · NASA POWER daily regional API, community RE, CLOUD_AMT
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 cloud cover
| Cloud cover | Burglary rate | |
|---|---|---|
| Highest | ||
| Juneau City and Borough, AK | 82.5% | 341 |
| Sitka City and Borough, AK | 81.8% | 108 |
| Bristol Bay Borough, AK | 80.7% | 113 |
| Petersburg Borough, AK | 80% | 240 |
| Wrangell City and Borough, AK | 80% | 0 |
| Lowest | ||
| Yuma County, AZ | 23.8% | 254 |
| La Paz County, AZ | 25.4% | 402 |
| Imperial County, CA | 26.2% | 212 |
| Maricopa County, AZ | 30% | 255 |
| Mohave County, AZ | 31% | 290 |
By burglary rate
| Cloud cover | Burglary rate | |
|---|---|---|
| Highest | ||
| Glasscock County, TX | 46.7% | 1,597 |
| Robeson County, NC | 52.7% | 1,199 |
| Richmond County, NC | 52.7% | 1,058 |
| Tunica County, MS | 51% | 951 |
| Tillman County, OK | 44.8% | 914 |
| Lowest | ||
| San Juan County, CO | 46% | 0 |
| Wrangell City and Borough, AK | 80% | 0 |
| Essex County, VT | 71.4% | 0 |
| Roberts County, TX | 43.1% | 0 |
| Presidio County, TX | 38.3% | 0 |
States
By cloud cover
| Cloud cover | Burglary rate | |
|---|---|---|
| Highest | ||
| Alaska | 74.3% | 252 |
| Vermont | 69.3% | 143 |
| Maine | 64.9% | 112 |
| Michigan | 64.4% | 207 |
| Ohio | 63.9% | 237 |
| Lowest | ||
| Arizona | 30.7% | 253 |
| New Mexico | 39.2% | 518 |
| California | 41.7% | 347 |
| Nevada | 42.9% | 407 |
| Oklahoma | 47.6% | 460 |
By burglary rate
| Cloud cover | Burglary rate | |
|---|---|---|
| Highest | ||
| New Mexico | 39.2% | 518 |
| Washington | 61.5% | 481 |
| Oklahoma | 47.6% | 460 |
| Louisiana | 51.7% | 441 |
| Arkansas | 50.8% | 410 |
| Lowest | ||
| New Hampshire | 63.4% | 58.4 |
| Maine | 64.9% | 112 |
| Rhode Island | 61.6% | 118 |
| Virginia | 55.7% | 122 |
| Connecticut | 60.6% | 129 |
Metro areas
By cloud cover
| Cloud cover | Burglary rate | |
|---|---|---|
| Highest | ||
| Juneau, AK | 82.5% | 341 |
| Rochester, NY | 74.6% | 213 |
| Syracuse, NY | 74.5% | 264 |
| Houghton, MI | 74% | 53.7 |
| Anchorage, AK | 74% | 334 |
| Lowest | ||
| Yuma, AZ | 23.8% | 254 |
| El Centro, CA | 26.2% | 212 |
| Phoenix-Mesa-Chandler, AZ | 30.2% | 243 |
| Lake Havasu City-Kingman, AZ | 31% | 290 |
| Tucson, AZ | 31.4% | 293 |
By burglary rate
| Cloud cover | Burglary rate | |
|---|---|---|
| Highest | ||
| Lumberton, NC | 52.7% | 1,199 |
| Rockingham, NC | 52.7% | 1,058 |
| Hot Springs, AR | 51.7% | 911 |
| Springfield, IL | 54.1% | 857 |
| Jonesboro, AR | 51.2% | 813 |
| Lowest | ||
| Lexington, NE | 50.9% | 0 |
| Petoskey, MI | 68.3% | 23.8 |
| Quincy, IL-MO | 52.9% | 31 |
| Norfolk, NE | 53.9% | 33.8 |
| Jackson, WY-ID | 57.6% | 36.4 |
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 cloud cover would suggest, and the five with far less.
| Cloud cover | Burglary rate | |
|---|---|---|
| Above the line | ||
| Glasscock County, TX | 46.7% | 1,597 |
| Robeson County, NC | 52.7% | 1,199 |
| Richmond County, NC | 52.7% | 1,058 |
| Tunica County, MS | 51% | 951 |
| Garland County, AR | 51.7% | 911 |
| Below the line | ||
| Presidio County, TX | 38.3% | 0 |
| King County, TX | 41.1% | 0 |
| Roberts County, TX | 43.1% | 0 |
| San Juan County, CO | 46% | 0 |
| San Juan County, UT | 43.6% | 18.8 |
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 | -0.292 | 2,281 counties reporting both |
| After adjusting for density, income and education | -0.331 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | -0.306 | size taken out as well as density |
| Also comparing only within each state | -0.039 | each 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 | -0.300 | the 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 | -0.043 | the link worked out inside each of 36 states with at least 20 counties reporting |
| Using ranks, after adjusting | -0.386 | 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 = 2.5e-59. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 1.9e-7. 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 1.8e-58.
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 +0.28 (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 cloud cover 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.
| Period | Counties | Change together | Within states | q |
|---|---|---|---|---|
| 2014–2019 the main test: each Census survey figure averages five years, and these two do not overlap; before the pandemic | 2,020 | +0.06 | +0.07 | 0.069 |
| 2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years | 1,883 | -0.01 | -0.04 | 0.408 |
Over the main period, any link between the changes could easily be chance. How change is measured.
Related pairs
- Summer daily high and Cloud cover — -0.77
- Days of 90°F or hotter and Cloud cover — -0.69
- Average temperature and Cloud cover — -0.65
- Heating degree days and Cloud cover — +0.64
- Cooling degree days and Cloud cover — -0.63
- Freezing days and Cloud cover — +0.55
- Share Hispanic or Latino, any race and Cloud cover — -0.51
- Wet days and Cloud cover — +0.51
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 Cloud cover and Burglary rate