St. David, IL
St. David wildfire risk explained
Out of every US place USFS scores, St. David lands at the 26th percentile for wildfire risk to structures — close to the middle of USFS's national wildfire-risk range — a figure built from its 400 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 St. David at the 26th percentile, close to its 26th-percentile risk score.
St. David's building exposure, zone by zone
87.3% of St. David's 400 buildings fall in USFS's Indirect exposure zone — ember cast rather than direct flame — against 12.8% Direct and 0% Minimal. Vent screens and roofing material matter more here than lot clearing alone.
St. David against the rest of the country
St. David's 73rd-percentile standing inside Illinois outpaces its 26th national percentile — this is a hotter spot than most of its own state, even though the state as a whole runs cooler nationally. Among the 31,521 US communities USFS scores, St. David ranks 23,349 for wildfire risk (1 is highest) and 19,204 by building count (1 is largest). Within Illinois alone, it ranks 392 of 1,445 places by risk. See the full county-by-county picture for Illinois on its state page.
What this risk score means for insurance
At the 26th national percentile, St. David 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 87.3% of St. David'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 St. David's figures come from
The methodology guide shows exactly how USFS turned 400 counted buildings into the percentiles shown above for St. David. The exposure-zones guide covers what St. David's dominant indirect exposure actually means, with real examples from across the dataset.