Plankinton, SD
Plankinton, SD's wildfire risk, in USFS's own numbers
USFS scores Plankinton at the 57th national percentile for wildfire risk to structures (modestly above the national average for wildfire risk), a figure built from 555 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Plankinton at the 56th national percentile — 1 points below its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
USFS classifies 71% of Plankinton's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 29% Direct and 0% Indirect — a landscape-level risk rather than a building-by-building one.
Plankinton against the rest of the country
Plankinton ranks lower within South Dakota (40th percentile statewide) than its 57th national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Plankinton ranks 13,688 for wildfire risk (1 is highest) and 16,507 by building count (1 is largest). Within South Dakota alone, it ranks 263 of 439 places by risk. See the full county-by-county picture for South Dakota on its state page.
Shopping for coverage in Plankinton
Plankinton's elevated wildfire rating (57th 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
Plankinton's 71% Minimal-exposure share means structure-level hardening matters less here than it would elsewhere — still worth the low-cost basics, per the home-hardening guide.
Where Plankinton's figures come from
The methodology guide shows exactly how USFS turned 555 counted buildings into the percentiles shown above for Plankinton. The exposure-zones guide covers what Plankinton's dominant minimal exposure actually means, with real examples from across the dataset.