Satanta, KS
How exposed is Satanta to wildfire?
Satanta sits at the 66th percentile nationally for wildfire risk to structures — well above the national norm for wildfire risk — per USFS's Wildfire Risk to Communities model, built from its 751 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Satanta at the 64th percentile, close to its 66th-percentile risk score.
What "at risk" means for the buildings here
90.8% of Satanta's 751 buildings sit in USFS's Minimal exposure zone, with only 9.2% rated Direct — risk here comes from regional burn probability more than proximity to burnable vegetation at the structure itself.
Satanta against the rest of the country
There's little gap between Satanta's 66th national percentile and its 61st percentile inside Kansas, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Satanta ranks 10,619 for wildfire risk (1 is highest) and 14,113 by building count (1 is largest). Within Kansas alone, it ranks 282 of 721 places by risk. See the full county-by-county picture for Kansas on its state page.
Satanta and the insurance market
Satanta's high rating (66th percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.
Hardening a home in Satanta
Even with 90.8% of Satanta outside USFS's Direct and Indirect zones, basic hardening — gutter maintenance, ember-resistant vents — is inexpensive relative to the regional burn-probability risk noted above. Details in the home-hardening guide.
Where Satanta's figures come from
Satanta's 66th-percentile score and its burn-probability figure both come from the same USFS workbook, documented in the methodology guide. The exposure-zones guide covers what Satanta's dominant minimal exposure actually means, with real examples from across the dataset.