Kwigillingok, AK
Kwigillingok, AK's wildfire risk, in USFS's own numbers
USFS scores Kwigillingok at the 55th national percentile for wildfire risk to structures (modestly above the national average for wildfire risk), a figure built from 171 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Kwigillingok at the 63rd percentile, close to its 55th-percentile risk score.
What "at risk" means for the buildings here
62% of Kwigillingok's 171 buildings sit in USFS's Direct exposure zone, roughly 106 structures close enough to burnable vegetation for flame contact, not just embers — 38% fall in the Indirect, ember-only zone and 0% are Minimal.
How Kwigillingok compares
Kwigillingok's risk sits at a similar level relative to Alaska (44th percentile statewide) as it does nationally (55th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Kwigillingok ranks 14,203 for wildfire risk (1 is highest) and 25,971 by building count (1 is largest). Within Alaska alone, it ranks 180 of 322 places by risk. See the full county-by-county picture for Alaska on its state page.
What this risk score means for insurance
Kwigillingok's elevated rating (55th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
What would actually reduce this score
With 62% of Kwigillingok in Direct exposure, defensible-space clearing and ember-resistant construction (vents, Class-A roofing) address the exposure this page's own numbers describe, not a generic checklist. See the home-hardening guide.
Where Kwigillingok's figures come from
The methodology guide shows exactly how USFS turned 171 counted buildings into the percentiles shown above for Kwigillingok. The exposure-zones guide covers what Kwigillingok's dominant direct exposure actually means, with real examples from across the dataset.