Kernel smoothing uses stats::ksmooth() to smooth out existing vertices using Gaussian kernel regression. Kernel smoothing is applied to the x and y coordinates are independently. Prior to smoothing, smooth_densify() is called to generate additional vertices, and the smoothing is applied to this densified set of vertices.

smooth_ksmooth(
  x,
  wrap = FALSE,
  smoothness = 1,
  bandwidth,
  n = 10L,
  max_distance
)

Arguments

x

numeric matrix; 2-column matrix of coordinates.

wrap

logical; whether the coordinates should be wrapped at the ends, as for polygons and closed lines, to ensure a smooth edge.

smoothness

numeric; a parameter controlling the bandwidth of the Gaussian kernel, and therefore the smoothness and level of generalization. By default, the bandwidth is chosen as the mean distance between adjacent points. The smoothness parameter is a multiplier of this chosen bandwidth, with values greater than 1 yielding more highly smoothed and generalized features and values less than 1 yielding less smoothed and generalized features.

bandwidth

numeric; the bandwidth of the Guassian kernel. If this argument is supplied, then smoothness is ignored and an optimal bandwidth is not estimated.

n

integer; number of times to split each line segment for smooth_densify(). Ignored if max_distance is specified.

max_distance

numeric; the maximum distance between vertices for smooth_densify(). This is the Euclidean distance and not the great circle distance.

Value

A matrix with the coordinates of the smoothed curve.

Details

Kernel smoothing both smooths and generalizes curves, and the extent of these effects is dependent on the bandwidth of the smoothing kernel. Therefore, choosing a sensible bandwidth is critical when using this method. The choice of bandwidth will be dependent on the projection, scale, and desired amount of smoothing and generalization. The are two methods of adjusting the bandwidth. By default, the bandwidth will be set to the average distances between adjacent vertices. The smoothness factor can then be used to adjust this calculated bandwidth, values greater than 1 will lead to more smoothing, values less than 1 will lead to less smoothing. Alternatively, the bandwidth can be chosen manually with the bandwidth argument. Typically, users will need to explore a range of bandwidths to determine which yields the best results for their situation.

This function works on matrices of points and is generally not called directly. Instead, use smooth() with method = "ksmooth" to apply this smoothing algorithm to spatial features.

References

The kernel smoothing method was inspired by the following StackExchange answers:

See also

Examples

# smooth_ksmooth works on matrices of coordinates # use the matrix of coordinates defining a polygon as an example m <- jagged_polygons$geometry[[2]][[1]] m_smooth <- smooth_ksmooth(m, wrap = TRUE) class(m)
#> [1] "matrix" "array"
class(m_smooth)
#> [1] "matrix" "array"
plot(m, type = "l", col = "black", lwd = 3, axes = FALSE, xlab = NA, ylab = NA)
lines(m_smooth, lwd = 3, col = "red")
# lines can also be smoothed l <- jagged_lines$geometry[[2]][] l_smooth <- smooth_ksmooth(l, wrap = FALSE, max_distance = 0.05) plot(l, type = "l", col = "black", lwd = 3, axes = FALSE, xlab = NA, ylab = NA)
lines(l_smooth, lwd = 3, col = "red")
# explore different levels of smoothness p <- jagged_polygons$geometry[[2]][[1]] ps1 <- smooth_ksmooth(p, wrap = TRUE, max_distance = 0.01, smoothness = 0.5) ps2 <- smooth_ksmooth(p, wrap = TRUE, max_distance = 0.01, smoothness = 1) ps3 <- smooth_ksmooth(p, wrap = TRUE, max_distance = 0.01, smoothness = 2) # plot par(mar = c(0, 0, 0, 0), oma = c(10, 0, 0, 0)) plot(p, type = "l", col = "black", lwd = 3, axes = FALSE, xlab = NA, ylab = NA)
lines(ps1, lwd = 3, col = "#E41A1C")
lines(ps2, lwd = 3, col = "#4DAF4A")
lines(ps3, lwd = 3, col = "#377EB8")
par(fig = c(0, 1, 0, 1), oma = c(0, 0, 0, 0), new = TRUE) plot(0, 0, type = "n", bty = "n", xaxt = "n", yaxt = "n", axes = FALSE)
legend("bottom", legend = c("0.5", "1", "2"), col = c("#E41A1C", "#4DAF4A", "#377EB8"), lwd = 3, cex = 2, box.lwd = 0, inset = 0, horiz = TRUE)
library(sf) p <- jagged_polygons$geometry[[2]] p_smooth <- smooth(p, method = "ksmooth") class(p)
#> [1] "XY" "POLYGON" "sfg"
class(p_smooth)
#> [1] "XY" "POLYGON" "sfg"
plot(p_smooth, border = "red")
plot(p, add = TRUE)