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(data.table)#> #>#>#> #>library(sp) library(raster) library(quickPlot) set.seed(1642) # 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) cirsSP <- SpatialPoints(coords = cirs[, c("x", "y")]) 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)