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 )
| landscape | Raster on which the circles are built. |
|---|---|
| coords | 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 |
| loci | Numeric. An alternative to |
| maxRadius | Numeric vector of length 1 or same length as coords |
| minRadius | Numeric vector of length 1 or same length as |
| allowOverlap | Logical. Should duplicates across id be removed or kept. Default TRUE. |
| allowDuplicates | 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. |
| includeBehavior | Character string. Currently accepts only "includePixels", the default, and "excludePixels". See details. |
| returnDistances | Logical. If TRUE, then a column will be added to the returned
data.table that reports the distance from |
| angles | 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. |
| returnAngles | Logical. If TRUE, then a column will be added to the returned
data.table that reports the angle from |
| returnIndices | Logical or numeric. If |
| closest | Logical. When determining non-overlapping circles, should the function
give preference to the closest |
| simplify | 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(sp) library(raster) library(quickPlot) set.seed(1462) if (require(RandomFields)) { # 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 a <- raster(extent(0, 30, 0, 30), res = 1) hab <- gaussMap(a, speedup = 1) # if raster is large (>1e6 pixels) use speedup > 1 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) } a <- raster(extent(0, 100, 0, 100), res = 1) hab <- gaussMap(a, speedup = 1) 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) }#>#>#> #>#> #> #>