Dimock, SD
Dimock, SD's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts Dimock at the 38th national percentile for risk to structures, close to the middle of USFS's national wildfire-risk range — a score built from 119 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Dimock at the 36th percentile, close to its 38th-percentile risk score.
Dimock's building exposure, zone by zone
USFS classifies 75.6% of Dimock's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 24.4% Direct and 0% Indirect — a landscape-level risk rather than a building-by-building one.
Dimock against the rest of the country
Dimock's 38th national percentile looks worse in isolation than its 11th ranking inside South Dakota does — this place is on the milder end for its own state, by 26 points. Among the 31,521 US communities USFS scores, Dimock ranks 19,680 for wildfire risk (1 is highest) and 28,216 by building count (1 is largest). Within South Dakota alone, it ranks 387 of 439 places by risk. See the full county-by-county picture for South Dakota on its state page.
Dimock and the insurance market
Dimock's moderate wildfire rating (38th 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.
What would actually reduce this score
Even with 75.6% of Dimock outside USFS's Direct and Indirect zones, basic hardening — gutter maintenance, ember-resistant vents — is inexpensive relative to the regional burn-probability risk noted above. Details in the home-hardening guide.
Where Dimock's figures come from
Dimock's 38th-percentile score and its burn-probability figure both come from the same USFS workbook, documented in the methodology guide. The exposure-zones guide covers what Dimock's dominant minimal exposure actually means, with real examples from across the dataset.