Identify the pixels and coordinates that are at a (set of) buffer distance(s)
of the objects passed into coords.
This is similar to rgeos::gBuffer but much faster and without
the geo referencing information.
In other words, it can be used for similar problems, but where speed is important.
This code is substantially adapted from PlotRegionHighlighter::createCircle.
cir(
landscape,
coords,
loci,
maxRadius = ncol(landscape)/4,
minRadius = maxRadius,
allowOverlap = TRUE,
allowDuplicates = FALSE,
includeBehavior = "includePixels",
returnDistances = FALSE,
angles = NA_real_,
returnAngles = FALSE,
returnIndices = TRUE,
closest = FALSE,
simplify = TRUE
)Raster on which the circles are built.
Either a matrix with 2 (or 3) columns, x and y (and id), representing the
coordinates (and an associated id, like cell index),
or a SpatialPoints* object around which to make circles. Must be same
coordinate system as the landscape argument. Default is missing,
meaning it uses the default to loci
Numeric. An alternative to coords. These are the indices on
landscape to initiate this function. See coords. Default is one
point in centre of landscape..
Numeric vector of length 1 or same length as coords
Numeric vector of length 1 or same length as coords. Default is
maxRadius, meaning return all cells that are touched
by the narrow ring at that exact radius. If smaller than maxRadius,
then this will create a buffer or donut or ring.
Logical. Should duplicates across id be removed or kept. Default TRUE.
Logical. Should duplicates within id be removed or kept. Default FALSE. This is useful if the actual x, y coordinates are desired, rather than the cell indices. This will increase the size of the returned object.
Character string. Currently accepts only "includePixels", the default, and "excludePixels". See details.
Logical. If TRUE, then a column will be added to the returned
data.table that reports the distance from coords to every
point that was in the circle/donut surrounding coords. Default
FALSE, which is faster.
Numeric. Optional vector of angles, in radians, to use. This will create "spokes" outward from coords. Default is NA, meaning, use internally derived angles that will "fill" the circle.
Logical. If TRUE, then a column will be added to the returned
data.table that reports the angle from coords to every
point that was in the circle/donut surrounding coords. Default
FALSE.
Logical or numeric. If 1 or TRUE, will
return a data.table with indices and values of
successful spread events.
If 2, it will simply return a vector of pixel indices of
all cells that were touched. This will be the fastest option. If
FALSE, then it will return a raster with
values. See Details.
Logical. When determining non-overlapping circles, should the function
give preference to the closest loci or the first one (much faster).
Default is FALSE, meaning the faster, though maybe not desired behaviour.
logical. If TRUE, then all duplicate pixels are removed. This means that some x, y combinations will disappear.
A matrix with 4 columns, id, indices,
x, y. The x and y indicate the exact coordinates of
the indices (i.e., cell number) of the landscape
associated with the ring or circle being identified by this function.
This function identifies all the pixels as defined by a donut
with inner radius minRadius and outer radius of maxRadius.
The includeBehavior defines whether the cells that intersect the radii
but whose centres are not inside the donut are included includePixels
or not excludePixels in the returned pixels identified.
If this is excludePixels, and if a minRadius and
maxRadius are equal, this will return no pixels.
rings() which uses spread internally.
cir tends to be faster when there are few starting points, rings
tends to be faster when there are many starting points. cir scales with
maxRadius ^ 2 and coords. 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. For the specific case of creating distance surfaces from specific
points, see distanceFromEachPoint(), which is often faster.
For the more general GIS buffering, see rgeos::gBuffer.
library(data.table)
#>
#> Attaching package: ‘data.table’
#> The following object is masked from ‘package:raster’:
#>
#> shift
library(sp)
library(raster)
library(quickPlot)
set.seed(1462)
# circle centred
ras <- raster(extent(0, 15, 0, 15), res = 1, val = 0)
middleCircle <- cir(ras)
ras[middleCircle[, "indices"]] <- 1
circlePoints <- SpatialPoints(middleCircle[, c("x", "y")])
if (interactive()) {
clearPlot()
Plot(ras)
Plot(circlePoints, addTo = "ras")
}
# circles non centred
ras <- randomPolygons(ras, numTypes = 4)
n <- 2
agent <- SpatialPoints(coords = cbind(x = stats::runif(n, xmin(ras), xmax(ras)),
y = stats::runif(n, xmin(ras), xmax(ras))))
cirs <- cir(ras, agent, maxRadius = 15, simplify = TRUE) ## TODO: empty with some seeds! e.g. 1642
cirsSP <- SpatialPoints(coords = cirs[, c("x", "y")]) ## TODO: error with some seeds! e.g. 1642
cirsRas <- raster(ras)
cirsRas[] <- 0
cirsRas[cirs[, "indices"]] <- 1
if (interactive()) {
clearPlot()
Plot(ras)
Plot(cirsRas, addTo = "ras", cols = c("transparent", "#00000055"))
Plot(agent, addTo = "ras")
Plot(cirsSP, addTo = "ras")
}
# Example comparing rings and cir
hab <- raster(system.file("extdata", "hab1.tif", package = "SpaDES.tools"))
radius <- 4
n <- 2
coords <- SpatialPoints(coords = cbind(x = stats::runif(n, xmin(hab), xmax(hab)),
y = stats::runif(n, xmin(hab), xmax(hab))))
# cirs
cirs <- cir(hab, coords, maxRadius = rep(radius, length(coords)), simplify = TRUE)
# rings
loci <- cellFromXY(hab, coordinates(coords))
cirs2 <- rings(hab, loci, maxRadius = radius, minRadius = radius - 1, returnIndices = TRUE)
# Plot both
ras1 <- raster(hab)
ras1[] <- 0
ras1[cirs[, "indices"]] <- cirs[, "id"]
ras2 <- raster(hab)
ras2[] <- 0
ras2[cirs2$indices] <- cirs2$id
if (interactive()) {
clearPlot()
Plot(ras1, ras2)
}
hab <- raster(system.file("extdata", "hab2.tif", package = "SpaDES.tools"))
cirs <- cir(hab, coords, maxRadius = 44, minRadius = 0)
ras1 <- raster(hab)
ras1[] <- 0
cirsOverlap <- data.table(cirs)[, list(sumIDs = sum(id)), by = indices]
ras1[cirsOverlap$indices] <- cirsOverlap$sumIDs
if (interactive()) {
clearPlot()
Plot(ras1)
}
# Provide a specific set of angles
ras <- raster(extent(0, 330, 0, 330), res = 1)
ras[] <- 0
n <- 2
coords <- cbind(x = stats::runif(n, xmin(ras), xmax(ras)),
y = stats::runif(n, xmin(ras), xmax(ras)))
circ <- cir(ras, coords, angles = seq(0, 2 * pi, length.out = 21),
maxRadius = 200, minRadius = 0, returnIndices = FALSE,
allowOverlap = TRUE, returnAngles = TRUE)