Glasco, NY
Glasco, NY's wildfire risk, in USFS's own numbers
Out of every US place USFS scores, Glasco lands at the 34th percentile for wildfire risk to structures — close to the middle of USFS's national wildfire-risk range — a figure built from its 725 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Glasco at the 33rd percentile, close to its 34th-percentile risk score.
Where Glasco's buildings actually sit
Most of Glasco's buildings (59%) 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.
How Glasco compares
Within New York, Glasco ranks higher (74th percentile) than it does nationally (34th) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, Glasco ranks 20,770 for wildfire risk (1 is highest) and 14,394 by building count (1 is largest). Within New York alone, it ranks 339 of 1,289 places by risk. See the full county-by-county picture for New York on its state page.
Shopping for coverage in Glasco
At the 34th national percentile, Glasco rates moderate 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 59% of Glasco'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 Glasco's figures come from
Every one of the two percentiles behind Glasco's 20,770-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Glasco's dominant indirect exposure actually means, with real examples from across the dataset.