Scalp Level, PA
Scalp Level wildfire risk explained
Scalp Level sits at the 44th percentile nationally for wildfire risk to structures — modestly above the national average for wildfire risk — per USFS's Wildfire Risk to Communities model, built from its 440 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Scalp Level's burn probability — fire likelihood with no building count factored in — sits at the 44th percentile nationally.
What "at risk" means for the buildings here
69.6% of Scalp Level's 440 buildings fall in USFS's Indirect exposure zone — ember cast rather than direct flame — against 30.5% Direct and 0% Minimal. Vent screens and roofing material matter more here than lot clearing alone.
Where Scalp Level ranks
Compare Scalp Level's two percentiles: 70th within Pennsylvania, only 44th nationally — a gap of 26 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, Scalp Level ranks 17,818 for wildfire risk (1 is highest) and 18,467 by building count (1 is largest). Within Pennsylvania alone, it ranks 603 of 1,991 places by risk. See the full county-by-county picture for Pennsylvania on its state page.
What this risk score means for insurance
At the 44th national percentile, Scalp Level 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
Because 69.6% of Scalp Level'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 Scalp Level's figures come from
Every one of the two percentiles behind Scalp Level's 17,818-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Scalp Level's dominant indirect exposure actually means, with real examples from across the dataset.