placestats

Land surface form (flat to mountainous) and Days of 90°F or hotter

Where Land surface form (flat to mountainous) is higher, Days of 90°F or hotter tends to be lower. The link is weak: -0.38 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 39 states, the middle value of the link is -0.44.

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

Land surface form (flat to mountainous)

The shape of the land, as a code from 1 (flat plains) to 21 (high mountains), taken from the 1970 National Atlas. Codes in between cover plains with hills, tablelands (high flat land cut by valleys) and open hills. A higher code means more varied land.

USDA ERS Natural Amenities Scale · USDA ERS natural amenities scale, land surface form topography code

Days of 90°F or hotter

The number of days in the year when the high temperature reached 90°F or more. The high is the average over the county of NOAA's daily map of weather station readings, made of squares about 3 miles wide. So a county with hot valleys and cool mountains counts fewer days than its hottest town. A state's figure is the average of its counties, each weighted by its land area. Lower 48 states only, from 1951.

NOAA NCEI nClimGrid-Daily · NOAA NCEI nClimGrid-Daily v1.0.0 county averages (prcp/tmax/tmin, scaled), hot_days; states, counties averaged by land area

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 land surface form (flat to mountainous)

By days of 90°F or hotter

States — Land surface form (flat to mountainous) is not published for states

By days of 90°F or hotter

Land surface form (flat to mountainous)Days of 90°F or hotter
Highest
Texas—118
Louisiana—114
Florida—105
Arizona—90.2
Mississippi—89
Lowest
Rhode Island—0.396
Vermont—0.566
New York—0.947
Maine—1.16
New Hampshire—1.69
Metro areas — Land surface form (flat to mountainous) is not published for metro areas

By days of 90°F or hotter

Land surface form (flat to mountainous)Days of 90°F or hotter
Highest
Rio Grande City-Roma, TX—196
Zapata, TX—192
McAllen-Edinburg-Mission, TX—189
Alice, TX—178
Yuma, AZ—176
Lowest
Youngstown-Warren, OH—0
Wooster, OH—0
Wheeling, WV-OH—0
Waynesville, NC—0
Watertown-Fort Drum, NY—0

Counties furthest from the pattern

Compared with the straight line that best fits all 3,062 counties: the five with far more days of 90°F or hotter than their land surface form (flat to mountainous) would suggest, and the five with far less.

Land surface form (flat to mountainous)Days of 90°F or hotter
Above the line
Maricopa County, AZ17168
Starr County, TX4196
Zapata County, TX4192
Yuma County, AZ11176
Jim Hogg County, TX4186
Below the line
Sandusky County, OH10
Ottawa County, OH10
Koochiching County, MN10
Arenac County, MI10
Roseau County, MN11

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-0.3763,043 counties reporting both
After adjusting for density, income and education-0.382the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size-0.415size taken out as well as density
Also comparing only within each state-0.329each county is compared only with others in its own state, so this is no longer a comparison between states
Also holding winter sunshine equal-0.313compares 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-0.173the 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-0.439the link worked out inside each of 39 states with at least 20 counties reporting
Using ranks, after adjusting-0.420uses 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 = 3.5e-106. 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 3.7e-105.

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 +0.91 (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 Land surface form (flat to mountainous) and Days of 90°F or hotter