Platte, SD
Platte wildfire risk explained
USFS scores Platte at the 49th national percentile for wildfire risk to structures (modestly above the national average for wildfire risk), a figure built from 915 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Platte at the 47th national percentile — 2 points below its risk-to-structures score, a gap driven by how much is actually built there.
Where Platte's buildings actually sit
Most of Platte's buildings (85.9%) 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.
Where Platte ranks
Inside South Dakota, Platte sits at just the 27th percentile even though it scores 49th 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, Platte ranks 16,151 for wildfire risk (1 is highest) and 12,599 by building count (1 is largest). Within South Dakota alone, it ranks 321 of 439 places by risk. See the full county-by-county picture for South Dakota on its state page.
Shopping for coverage in Platte
At the 49th national percentile, Platte rates elevated for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.
Lowering exposure, not just insuring around it
With ember exposure the dominant pattern in Platte (85.9% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.
Where Platte's figures come from
The methodology guide shows exactly how USFS turned 915 counted buildings into the percentiles shown above for Platte. The exposure-zones guide covers what Platte's dominant indirect exposure actually means, with real examples from across the dataset.