Java, SD
How exposed is Java to wildfire?
Out of every US place USFS scores, Java lands at the 58th percentile for wildfire risk to structures — modestly above the national average for wildfire risk — a figure built from its 218 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 Java at the 53rd percentile, close to its 58th-percentile risk score.
Java's building exposure, zone by zone
Of Java's 218 counted buildings, 50.5% carry Direct exposure and only 0% carry Minimal — a lopsided split that puts defensible-space clearing ahead of vents or roofing as the intervention worth doing first.
Java against the rest of the country
Inside South Dakota, Java sits at just the 43rd percentile even though it scores 58th nationally — the state's overall wildfire exposure is high enough to make this a relatively quiet corner of it. Among the 31,521 US communities USFS scores, Java ranks 13,313 for wildfire risk (1 is highest) and 24,220 by building count (1 is largest). Within South Dakota alone, it ranks 252 of 439 places by risk. See the full county-by-county picture for South Dakota on its state page.
Java and the insurance market
Java's elevated wildfire rating (58th percentile) isn't the kind of score driving the carrier pullback making news in the highest-risk Western markets — but a standard homeowners quote is still worth comparing on its own terms.
Lowering exposure, not just insuring around it
With 50.5% of Java 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 Java's figures come from
The methodology guide shows exactly how USFS turned 218 counted buildings into the percentiles shown above for Java. The exposure-zones guide covers what Java's dominant direct exposure actually means, with real examples from across the dataset.