Sicangu Village, SD
Sicangu Village wildfire risk explained
Sicangu Village's 93 buildings earn a 94th-percentile wildfire-risk score nationally under USFS's model — in USFS's highest wildfire-risk band nationally. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Sicangu Village's burn probability — fire likelihood with no building count factored in — sits at the 92nd percentile nationally.
Where Sicangu Village's buildings actually sit
Most of Sicangu Village's buildings (79.6%) 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 Sicangu Village compares
Sicangu Village scores 94th nationally and 97th within South Dakota — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Sicangu Village ranks 1,838 for wildfire risk (1 is highest) and 29,378 by building count (1 is largest). Within South Dakota alone, it ranks 14 of 439 places by risk. See the full county-by-county picture for South Dakota on its state page.
What this risk score means for insurance
Sicangu Village's very high rating (94th percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.
What would actually reduce this score
Sicangu Village's 79.6% Indirect-exposure share points at embers, not flame contact, as the main pathway — ember-resistant vents and non-combustible roofing rank ahead of defensible space here. Detail in the home-hardening guide.
Where Sicangu Village's figures come from
Sicangu Village's 94th-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 Sicangu Village's dominant indirect exposure actually means, with real examples from across the dataset.