
Identifies the cell numbers of all cells within a ring defined by minimum
and maximum distances from focal cells.
Uses spread
under the hood, with specific values set.
Under many situations, this will be faster than using rgeos::gBuffer
twice (once for smaller ring and once for larger ring, then removing the
smaller ring cells).
rings(landscape, loci = NA_real_, id = FALSE, minRadius = 2,
maxRadius = 5, allowOverlap = FALSE, returnIndices = FALSE,
returnDistances = TRUE, ...)# S4 method for RasterLayer
rings(landscape, loci = NA_real_, id = FALSE,
minRadius = 2, maxRadius = 5, allowOverlap = FALSE,
returnIndices = FALSE, returnDistances = TRUE, ...)
A RasterLayer
object. This defines the possible locations
for spreading events to start and spread into. This can also
be used as part of stopRule
. Require input.
A vector of locations in landscape
. These should be cell indexes.
If user has x and y coordinates, these can be converted with
cellFromXY
.
Logical. If TRUE, returns a raster of events ids.
If FALSE, returns a raster of iteration numbers,
i.e., the spread history of one or more events. NOTE:
this is overridden if returnIndices
is TRUE
.
Numeric. Minimum radius to be included in the ring. Note: this is inclusive, i.e., >=
Numeric. Maximum radius to be included in the ring. Note: this is inclusive, i.e., <=
Logical. If TRUE
, then individual events can overlap with one
another, i.e., they do not interact. Currently, this is slower than
if allowOverlap
is FALSE
. Default is FALSE
.
Logical. Should the function return a data.table with indices and values of successful spread events, or return a raster with values. See Details.
Logical. Should the function inclue a column with the individual cell distances from the locus where that event started. Default is FALSE. See Details.
Any other argument passed to spread
This will return a data.table
with columns
as described in spread
when returnIndices = TRUE
.
cir
which uses a different algorithm.
cir
tends to be faster when there are few starting points, rings
tends to be faster when there are many starting points. Another difference
between the two functions is that rings
takes the centre of the pixel
as the centre of a circle, whereas cir
takes the exact coordinates.
See example.
rgeos::gBuffer
# NOT RUN {
library(raster)
# Make random forest cover map
emptyRas <- raster(extent(0,1e2,0,1e2), res = 1)
# start from two cells near middle
loci <- (ncell(emptyRas)/2 - ncol(emptyRas))/2 + c(-3, 3)
# Allow overlap
emptyRas[] <- 0
Rings <- rings(emptyRas, loci = loci, allowOverlap = TRUE, returnIndices = TRUE)
# Make a raster that adds together all id in a cell
wOverlap <- Rings[,list(sumEventID=sum(id)),by="indices"]
emptyRas[wOverlap$indices] <- wOverlap$sumEventID
if (interactive()) {
clearPlot()
Plot(emptyRas)
}
# No overlap is default, occurs randomly
emptyRas[] <- 0
Rings <- rings(emptyRas, loci = loci, minRadius = 7, maxRadius = 9, returnIndices = TRUE)
emptyRas[Rings$indices] <- Rings$id
if (interactive()) {
clearPlot()
Plot(emptyRas)
}
# Variable ring widths, including centre cell for smaller one
emptyRas[] <- 0
Rings <- rings(emptyRas, loci = loci, minRadius = c(0,7), maxRadius = c(8, 18),
returnIndices = TRUE)
emptyRas[Rings$indices] <- Rings$id
if (interactive()) {
clearPlot()
Plot(emptyRas)
}
# }
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