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, ...)

## Arguments

landscape 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. A vector of locations in landscape. These should be cell indices. 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 (this is slower than if allowOverlap = 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 include 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

## Value

This will return a data.table with columns as described in spread when returnIndices = TRUE.

## See also

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

## Examples

library(raster)
library(quickPlot)

# 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
rngs <- rings(emptyRas, loci = loci, allowOverlap = TRUE, returnIndices = TRUE)
# Make a raster that adds together all id in a cell
wOverlap <- rngs[, list(sumEventID = sum(id)), by = "indices"]
emptyRas[wOverlap$indices] <- wOverlap$sumEventID
if (interactive()) {
clearPlot()
Plot(emptyRas)
}

# No overlap is default, occurs randomly
emptyRas[] <- 0
rngs <- rings(emptyRas, loci = loci, minRadius = 7, maxRadius = 9, returnIndices = TRUE)
emptyRas[rngs$indices] <- rngs$id
if (interactive()) {
clearPlot()
Plot(emptyRas)
}

# Variable ring widths, including centre cell for smaller one
emptyRas[] <- 0
rngs <- rings(emptyRas, loci = loci, minRadius = c(0, 7), maxRadius = c(8, 18),
returnIndices = TRUE)
emptyRas[rngs$indices] <- rngs$id
if (interactive()) {
clearPlot()
Plot(emptyRas)
}