Doylestown, OH
Doylestown wildfire risk explained
USFS scores Doylestown at the 30th national percentile for wildfire risk to structures (close to the middle of USFS's national wildfire-risk range), a figure built from 1,461 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Doylestown's burn probability — fire likelihood with no building count factored in — sits at the 31st percentile nationally.
Doylestown's building exposure, zone by zone
Most of Doylestown's buildings (46.4%) sit in the Indirect zone, where wind-blown embers rather than flame front are the mechanism USFS is scoring — ember-resistant vents and non-combustible roofing are the interventions this pattern favors, more than defensible space alone.
Doylestown against the rest of the country
Compare Doylestown's two percentiles: 65th within Ohio, only 30th nationally — a gap of 35 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, Doylestown ranks 22,099 for wildfire risk (1 is highest) and 9,348 by building count (1 is largest). Within Ohio alone, it ranks 443 of 1,264 places by risk. See the full county-by-county picture for Ohio on its state page.
Shopping for coverage in Doylestown
Doylestown's moderate rating (30th 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
Because 46.4% of Doylestown's buildings sit in the Indirect zone, sealing the ember pathway (vents, roofing, gutters) is the intervention this page's data actually supports — see the home-hardening guide.
Where Doylestown's figures come from
Every one of the two percentiles behind Doylestown's 22,099-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Doylestown's dominant indirect exposure actually means, with real examples from across the dataset.