Water area and Expected yearly hurricane loss
Where Water area is higher, Expected yearly hurricane loss tends to be higher. The link is weak: +0.31 across 3,043 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 29 states, the middle value of the link is +0.12.
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
Water area
The percentage of the county's total area that is water: lakes, rivers, bays and coastal sea. A county with a big lake or a long coast scores high. About two in three counties are under 2%.
USDA ERS Natural Amenities Scale · USDA ERS natural amenities scale, percent water area
Expected yearly hurricane loss
The damage hurricanes are expected to do to buildings in an average year, in dollars for every $100,000 that the buildings are worth. FEMA works it out from how often hurricanes have struck since 1851 (1949 in the Pacific) and how much damage they did. Where FEMA says hurricanes cannot reach, it is 0.
FEMA National Risk Index · FEMA National Risk Index v1.20, NRI_Table_Counties / NRI_Table_States, HRCN_ALRB
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 water area
| Water area | Expected yearly hurricane loss | |
|---|---|---|
| Highest | ||
| Dukes County, MA | 75 | 29.9 |
| Nantucket County, MA | 75 | 234 |
| Alger County, MI | 75 | 0 |
| Keweenaw County, MI | 75 | 0 |
| Leelanau County, MI | 75 | 0.0218 |
| Lowest | ||
| Cheyenne County, CO | 0 | 0 |
| Highland County, VA | 0 | 2.52 |
| Floyd County, VA | 0 | 2.51 |
| Craig County, VA | 0 | 1.3 |
| Buchanan County, VA | 0 | 1.24 |
By expected yearly hurricane loss
| Water area | Expected yearly hurricane loss | |
|---|---|---|
| Highest | ||
| Hyde County, NC | 57 | 621 |
| Carteret County, NC | 60.7 | 537 |
| Pamlico County, NC | 40.5 | 488 |
| Onslow County, NC | 15.6 | 465 |
| Tyrrell County, NC | 35 | 461 |
| Lowest | ||
| Yuma County, CO | 0.13 | 0 |
| Weld County, CO | 0.72 | 0 |
| Washington County, CO | 0.12 | 0 |
| Teller County, CO | 0.34 | 0 |
| Summit County, CO | 1.79 | 0 |
States — Water area is not published for states
By expected yearly hurricane loss
| Water area | Expected yearly hurricane loss | |
|---|---|---|
| Highest | ||
| Louisiana | — | 154 |
| Florida | — | 107 |
| South Carolina | — | 97.2 |
| North Carolina | — | 49.9 |
| Mississippi | — | 43.6 |
| Lowest | ||
| Colorado | — | 0 |
| Alaska | — | 0 |
| Wyoming | — | 0 |
| Washington | — | 0 |
| Utah | — | 0 |
Counties furthest from the pattern
Compared with the straight line that best fits all 3,062 counties: the five with far more expected yearly hurricane loss than their water area would suggest, and the five with far less.
| Water area | Expected yearly hurricane loss | |
|---|---|---|
| Above the line | ||
| Hyde County, NC | 57 | 621 |
| Carteret County, NC | 60.7 | 537 |
| Onslow County, NC | 15.6 | 465 |
| Craven County, NC | 8.7 | 442 |
| Pamlico County, NC | 40.5 | 488 |
| Below the line | ||
| San Francisco County, CA | 75 | 0 |
| Door County, WI | 75 | 0 |
| Keweenaw County, MI | 75 | 0 |
| Alger County, MI | 75 | 0 |
| Ozaukee County, WI | 75 | 0.0029 |
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 | +0.312 | 3,043 counties reporting both |
| After adjusting for density, income and education | +0.311 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.313 | size taken out as well as density |
| Also comparing only within each state | +0.326 | each county is compared only with others in its own state, so this is no longer a comparison between states |
| Giving bigger counties more weight | +0.262 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 277 equally weighted counties |
| Middle value inside single states | +0.124 | the link worked out inside each of 29 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.123 | 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 = 4.9e-69. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.039. The site tested 29,403 pairs, and testing so many turns up some flukes. The q-value allows for that too. For this pair it is 3.9e-68.
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 +0.70 (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
- Winter nightly low and Expected yearly hurricane loss — +0.38
- Average temperature and Expected yearly hurricane loss — +0.35
- Vacancy rate and Water area — +0.34
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 Water area and Expected yearly hurricane loss