| summary.controlledburn | R Documentation |
Compute a compact description of a burn() result in time proportional
to the number of records in its sparse tables, never to the number of
grid cells, so it is cheap even for a national grid of billions of cells.
## S3 method for class 'controlledburn'
summary(object, tiles = 256L, ...)
## S3 method for class 'summary.controlledburn'
print(x, ascii = FALSE, ...)
object |
A |
tiles |
Target number of tiles along the longer grid axis for the
overview, passed to |
... |
Ignored. |
x |
A |
ascii |
Also print the overview as text. |
The pieces are:
overviewa coarse matrix of mean coverage per tile, see
overview_burn(). When the grid is small enough that one tile is one
cell, this is the exact coverage.
row_marginal, col_marginalexact covered-cell totals per grid row and per grid column, at full resolution.
contentthe extent and 1-based row/col range of touched cells,
or NULL for an empty burn.
areaper-geometry totals in cells: interior (run cells),
boundary (summed edge fractions), lines (summed length, in
extent units), points (count).
fractionsquantiles of the edge coverage fractions.
recordsrow counts of the sparse tables.
Coverage here means: run cells 1 each, edge cells their fraction, line and point cells 1 each (presence).
The number of geometries is taken as the largest id present, so geometries after the last one that touched the grid are not counted.
A list of class summary.controlledburn.
if (requireNamespace("geos", quietly = TRUE)) {
poly <- geos::as_geos_geometry(c("POLYGON ((1 1, 9 1, 9 9, 1 9, 1 1))",
"POLYGON ((2 2, 4 3, 3 5, 2 2))"))
r <- burn(poly, extent = c(0, 10, 0, 10), dimension = c(100L, 100L))
s <- summary(r)
s
s$area
}
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