Tonkawa, OK
Tonkawa, OK's wildfire risk, in USFS's own numbers
USFS scores Tonkawa at the 44th national percentile for wildfire risk to structures (modestly above the national average for wildfire risk), a figure built from 1,892 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Tonkawa at the 43rd national percentile — 2 points below its risk-to-structures score, a gap driven by how much is actually built there.
Tonkawa's building exposure, zone by zone
Only 11.2% of Tonkawa's 1,892 buildings carry Direct exposure and 0% carry Indirect; the remaining 88.8% are Minimal, which shifts the risk driving this page's score toward the surrounding landscape rather than any one structure.
How Tonkawa compares
Tonkawa's 44th national percentile looks worse in isolation than its 4th ranking inside Oklahoma does — this place is on the milder end for its own state, by 41 points. Among the 31,521 US communities USFS scores, Tonkawa ranks 17,563 for wildfire risk (1 is highest) and 7,790 by building count (1 is largest). Within Oklahoma alone, it ranks 802 of 834 places by risk. See the full county-by-county picture for Oklahoma on its state page.
What this risk score means for insurance
At the 44th national percentile, Tonkawa rates elevated for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
What would actually reduce this score
Even with 88.8% of Tonkawa 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 Tonkawa's figures come from
Every one of the two percentiles behind Tonkawa's 17,563-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Tonkawa's dominant minimal exposure actually means, with real examples from across the dataset.