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Traversal and distance

library(a5R)

Selecting cells around a point

Pick an origin cell and use a5_spherical_cap() to grab every cell whose centre falls within a given great-circle radius. Like a5_grid_disk(), it returns an a5_covering: a compacted set that records its resolution. Pass it through a5_uncompact() for the cells.

origin <- a5_lonlat_to_cell(35, 55, resolution = 14)

a5_spherical_cap(origin, radius = 5, units = "km")
#> <a5_covering[1]>
#> [1] res 14: 621 cells (105 stored)

disk <- a5_uncompact(a5_grid_disk(origin, k = 20))
disk_vertex <- a5_uncompact(a5_grid_disk_vertex(origin, k = 20))
cap <- a5_uncompact(a5_spherical_cap(origin, radius = 5, units = "km"))

a5_grid_disk() selects cells by hop count rather than metric distance, stepping only between edge-sharing neighbours. a5_grid_disk_vertex() also steps between corner-sharing neighbours, so it reaches more cells for the same k: 1,679 against 1,121 here. Neither footprint is round, as the cap’s is.

Distance heatmap

Compute the distance from the origin to every cell in the cap (or disk) and visualise it as a colour gradient. Each panel has its own colour scale.

dcap <- a5_cell_distance(origin, cap, units = "km")
ddisk <- a5_cell_distance(origin, disk, units = "km")
ddisk_vertex <- a5_cell_distance(origin, disk_vertex, units = "km")

pal <- hcl.colors(256, "Inferno")

for (info in list(
  list(s = disk, d = ddisk, lab = "Grid disk (edges)"),
  list(s = disk_vertex, d = ddisk_vertex, lab = "Grid disk (vertices)"),
  list(s = cap, d = dcap, lab = "Spherical cap")
)) {
  brk <- seq(0, max(as.numeric(info$d)), length.out = 257)
  cols <- pal[findInterval(as.numeric(info$d), brk, all.inside = TRUE)]

  plot(a5_cell_to_boundary(info$s), col = cols, border = NA,
       asp = 1 / cos(55 * pi / 180), main = info$lab)
  plot(a5_cell_to_boundary(origin), border = "#ffffffff", add = TRUE)
}

Distance radiates smoothly from the centre. Cells near the origin are dark, cells at the rim are bright. The origin cell is shown with a white border for reference.

Distance methods

a5R supports three distance methods: haversine (great-circle), geodesic (WGS84 ellipsoid via Karney 2013), and rhumb (loxodrome / constant-bearing). For short distances they are nearly identical; differences grow with distance. The figure compares geodesic with haversine.

wide_origin <- a5_lonlat_to_cell(35, 55, resolution = 8)
wide_cap <- a5_uncompact(a5_spherical_cap(wide_origin, radius = 500, units = "km"))

hav <- a5_cell_distance(wide_origin, wide_cap, units = "m", method = "haversine")
geo <- a5_cell_distance(wide_origin, wide_cap, units = "m", method = "geodesic")

diff_m <- as.numeric(geo - hav)
brk <- seq(min(diff_m), max(diff_m), length.out = 257)
cols <- pal[findInterval(diff_m, brk, all.inside = TRUE)]

layout(matrix(c(1, 2), nrow = 1), widths = c(4, 1))
par(mar = c(2, 2, 2, 1))
plot(a5_cell_to_boundary(wide_cap), col = cols, border = NA,
     asp = 1 / cos(55 * pi / 180), main = "Geodesic \u2212 Haversine (m)")

par(mar = c(9, 0, 9, 9))
image(1, seq(min(diff_m), max(diff_m), length.out = 256),
      t(seq_along(pal)), col = pal, axes = FALSE, xlab = "", ylab = "")
axis(4, las = 1)

The difference is near zero at the centre and grows to about 1,675 m at the east and west rim, 500 km away; along the north-south axis it stays much smaller.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.