Galestown, MD
Galestown, MD's wildfire risk, in USFS's own numbers
Galestown's 82 buildings earn a 69th-percentile wildfire-risk score nationally under USFS's model — well above the national norm for wildfire risk. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Galestown at the 69th national percentile — 0 points above its risk-to-structures score, a gap driven by how much is actually built there.
Where Galestown's buildings actually sit
Direct exposure dominates in Galestown: 63.4% of its 82 buildings, versus 36.6% Indirect and 0% Minimal. Clearing space around a structure changes the outcome here more than any single building-material swap.
Where Galestown ranks
Compare Galestown's two percentiles: 92nd within Maryland, only 69th nationally — a gap of 24 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, Galestown ranks 9,929 for wildfire risk (1 is highest) and 29,800 by building count (1 is largest). Within Maryland alone, it ranks 42 of 527 places by risk. See the full county-by-county picture for Maryland on its state page.
Galestown and the insurance market
Galestown's 69th-percentile, high rating is the kind of score behind a broader national pattern: insurers pulling back from the highest wildfire-risk markets, non-renewing or declining new policies outright, most visibly in California. Shopping around, not assuming automatic renewal, tends to matter here.
What would actually reduce this score
With 63.4% of Galestown 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 Galestown's figures come from
The methodology guide shows exactly how USFS turned 82 counted buildings into the percentiles shown above for Galestown. The exposure-zones guide covers what Galestown's dominant direct exposure actually means, with real examples from across the dataset.