placestats

Share Asian, not Hispanic and Population (firearm measures)

Where Share Asian, not Hispanic is higher, Population (firearm measures) tends to be higher. The link is weak: +0.31 across 1,653 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 32 states, the middle value of the link is +0.60.

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

Share Asian, not Hispanic

The percentage of residents who are Asian alone and not Hispanic.

Census ACS 5-year · ACS table B03002_006, divided by total population

Population (firearm measures)

How many people live here, from the 2017-2021 American Community Survey. The gun measures are adjusted using this population.

CDC WONDER mortality; RAND firearm ownership database · ACS 2017-2021 five-year, B01001_001

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 share Asian, not Hispanic

Share Asian, not HispanicPopulation (firearm measures), per 1,000 residents
Highest
Honolulu County, HI42%948.86
Santa Clara County, CA39.3%956.76
San Francisco County, CA34.7%989.70
Alameda County, CA32%955.90
San Mateo County, CA30.5%967.36
Lowest
Polk County, AR0%947.64
Platte County, WY0%954.45
Converse County, WY0%929.43
Summers County, WV0%985.63
Monroe County, WV0%960.41

By population (firearm measures)

Share Asian, not HispanicPopulation (firearm measures), per 1,000 residents
Highest
Lassen County, CA1.59%1004.97
Hampshire County, MA5%999.58
San Francisco County, CA34.7%989.70
Tompkins County, NY9.51%988.35
Summers County, WV0%985.63
Lowest
Kaufman County, TX1.66%810.11
Rockwall County, TX3.35%845.11
Comal County, TX1.29%845.13
Liberty County, TX0.533%851.58
Jackson County, GA2.08%857.90
States

By share Asian, not Hispanic

Share Asian, not HispanicPopulation (firearm measures), per 1,000 residents
Highest
Hawaii36.7%944.70
California15.1%945.52
New Jersey9.83%939.20
Washington9.31%925.96
New York8.83%954.09
Lowest
West Virginia0.747%957.04
Wyoming0.798%935.52
Montana0.809%920.85
Mississippi0.965%943.57
Maine1.1%938.80

By population (firearm measures)

Share Asian, not HispanicPopulation (firearm measures), per 1,000 residents
Highest
West Virginia0.747%957.04
New York8.83%954.09
District of Columbia4.01%952.57
Illinois5.75%951.41
Connecticut4.71%951.32
Lowest
Idaho1.28%896.11
Utah2.32%896.29
Texas5.25%907.65
South Dakota1.39%914.43
Nevada8.54%916.21
Metro areas

By share Asian, not Hispanic

Share Asian, not HispanicPopulation (firearm measures), per 1,000 residents
Highest
Urban Honolulu, HI42%962.54
San Jose-Sunnyvale-Santa Clara, CA38.1%966.30
Kapaa, HI29%935.45
Kahului-Wailuku, HI27.8%944.58
San Francisco-Oakland-Fremont, CA27.4%979.97
Lowest
Mitchell, SD0%731.04
Las Vegas, NM0%849.79
Iron Mountain, MI-WI0%802.83
Hailey, ID0%718.09
Fort Payne, AL0.193%928.01

By population (firearm measures)

Share Asian, not HispanicPopulation (firearm measures), per 1,000 residents
Highest
Susanville, CA1.59%1085.62
Big Spring, TX1.01%1063.69
Selma, AL0.602%1017.45
Greenville, MS0.801%1011.44
Cleveland, MS0.952%1009.01
Lowest
Mount Pleasant, TX1.15%513.02
Mount Sterling, KY0.374%552.19
Paducah, KY-IL0.935%619.02
Cadillac, MI0.487%638.04
Clewiston, FL1.05%651.81
Cities and towns — Population (firearm measures) is not published for cities and towns

By share Asian, not Hispanic

Share Asian, not HispanicPopulation (firearm measures), per 1,000 residents
Highest
Cupertino city, CA71.7%—
Milpitas city, CA71.5%—
South San Gabriel CDP, CA69.5%—
San Marino city, CA68.9%—
Waipahu CDP, HI67.1%—
Lowest
Terryville CDP, CT0%—
Woodland Park city, CO0%—
Lochbuie town, CO0%—
Eagle town, CO0%—
Mecca CDP, CA0%—
ZIP codes — Population (firearm measures) is not published for ZIP codes

By share Asian, not Hispanic

Share Asian, not HispanicPopulation (firearm measures), per 1,000 residents
Highest
ZIP 94539 (Fremont city, CA)78.4%—
ZIP 11355 (New York city, NY)74.5%—
ZIP 94555 (Fremont city, CA)73.2%—
ZIP 95035 (Milpitas city, CA)71.3%—
ZIP 95014 (Santa Clara County, CA)71.3%—
Lowest
ZIP 08734 (Ocean County, NJ)0%—
ZIP 08533 (Ocean County, NJ)0%—
ZIP 08059 (Mount Ephraim borough, NJ)0%—
ZIP 07524 (Paterson city, NJ)0%—
ZIP 06331 (Windham County, CT)0%—

Counties furthest from the pattern

Compared with the straight line that best fits all 1,653 counties: the five with far more population (firearm measures) than their share Asian, not Hispanic would suggest, and the five with far less.

Share Asian, not HispanicPopulation (firearm measures), per 1,000 residents
Above the line
Lassen County, CA1.59%1004.97
Hampshire County, MA5%999.58
San Francisco County, CA34.7%989.70
Tompkins County, NY9.51%988.35
Summers County, WV0%985.63
Below the line
Kaufman County, TX1.66%810.11
Comal County, TX1.29%845.13
Rockwall County, TX3.35%845.11
Liberty County, TX0.533%851.58
Jackson County, GA2.08%857.90

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+0.4761,653 counties reporting both
After adjusting for density, income and education+0.309the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size+0.245size taken out as well as density
Also comparing only within each state+0.246each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight+0.282the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 254 equally weighted counties
Middle value inside single states+0.605the link worked out inside each of 32 states with at least 20 counties reporting
Using ranks, after adjusting+0.265uses 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-38. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.000. The site tested 20,910 pairs, and testing so many turns up some flukes. The q-value allows for that too. For this pair it is 3.8e-37.

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 +0.22 (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 Share Asian, not Hispanic and Population (firearm measures)