Creating maps with R is usually straightforward, but representations that cross the International Date Line or that use polar projections can be tricky.
Different spatial-data providers use different conventions: some break geometries at certain longitudes (for example, cutting the Chukchi Peninsula), while others omit portions of the data. These inconsistencies can produce awkward artifacts near the poles.
In this post I fix the GISCO (European Commission) shapefile for Antarctica and produce clean orthographic maps. I walk through the manual corrections and then create a few example maps.
# Libraries
library(tidyverse)
library(sf)
library(giscoR)
library(ggrepel)
library(rmapshaper)
Fixing the geometry
First, we obtain the GISCO Antarctica polygon and transform it to an orthographic projection centered on the South Pole.
antarct <- gisco_get_countries(year = 2024, resolution = 1, country = "ATA") %>%
select(NAME = NAME_ENGL) |>
# Orthographic projection centered on the South Pole
st_transform(crs = "+proj=ortho +lat_0=-90 +lon_0=0")
ggplot(antarct) +
geom_sf(fill = "lightblue")
The shapefile contains a visible "lollipop" cut that looks unnatural in an orthographic projection. I correct it manually with these steps:
- Identify the polygon that represents the main Antarctic landmass.
- Convert that polygon to a sequence of coordinates (points).
- Remove the small sequence of points that create the artifact.
- Rebuild the polygon from the cleaned coordinates and replace the broken geometry with the corrected one.
We convert polygons to point coordinates and inspect them to find the offending sequence:
# Identify the largest polygon
ant_explode <- antarct |>
st_cast("POLYGON")
nrow(ant_explode)
#> [1] 778
# Largest polygon
ant_max <- ant_explode[which.max(st_area(ant_explode)), ]
coords <- st_coordinates(ant_max) |>
as_tibble() |>
# Add an ID for the points
mutate(np = row_number())
ggplot(coords, aes(X, Y)) +
geom_point(size = 0.05, color = "darkblue") +
geom_text(aes(label = np), check_overlap = TRUE) +
coord_equal()
From the plotted indices, we can see the problematic points fall roughly in the range 8200–9200. We inspect that interval in detail to select the exact indices to remove.
test <- coords |>
filter(np %in% seq(8200, 9200))
test |>
ggplot(aes(X, Y)) +
geom_point(size = 0.05, color = "darkblue") +
geom_text(aes(label = np), check_overlap = TRUE)
# Final solution after some iterations...
test |>
filter(np %in% seq(8289, 9130)) |>
ggplot(aes(X, Y)) +
geom_point(color = "darkblue") +
labs(title = "To remove")
test |>
filter(!np %in% seq(8289, 9130)) |>
ggplot(aes(X, Y)) +
geom_point(color = "darkblue") +
labs(title = "To keep")
After removing the offending points, we rebuild the polygon and reconstitute the full Antarctica shape from the corrected piece plus the remaining polygons.
# From coordinates to polygon
newpol <- coords |>
as.data.frame() |>
filter(!np %in% seq(8289, 9130)) |> # Removing offending points
select(X, Y) |>
as.matrix() |>
list() |>
st_polygon() |>
st_sfc() |>
st_set_crs(st_crs(ant_max))
ant_max_fixed <- st_sf(st_drop_geometry(ant_max), geometry = newpol)
# Regenerate initial shape
antarctica_fixed <- bind_rows(
ant_max_fixed,
ant_explode[-which.max(st_area(ant_explode)), ]
) |>
group_by(NAME) |>
summarise(m = 1) |>
select(-m) |>
st_make_valid()
antarctica_fixed
#> Simple feature collection with 1 feature and 1 field
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: -2583099 ymin: -2458296 xmax: 2690846 ymax: 2233395
#> Projected CRS: +proj=ortho +lat_0=-90 +lon_0=0
#> # A tibble: 1 × 2
#> NAME geometry
#> * <chr> <MULTIPOLYGON [m]>
#> 1 Antarctica (((-2456385 1179033, -2456141 1178965, -2456464 1178341, -2456563 117…
ggplot(antarctica_fixed) +
geom_sf(fill = "lightblue")
Plotting examples
With the corrected shape we can produce maps. Below are a few examples based on proposed Antarctic flag designs.
Graham Bartram's proposal (1996)
A simple rendition of Bartram's original concept:
bbox <- st_bbox(antarctica_fixed) # For limits on the panel
antarctica_fixed |>
ggplot() +
geom_sf(fill = "white", color = NA) +
theme(
panel.background = element_rect(fill = "#009fdc"),
panel.grid = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank()
) +
labs(title = "Graham Bartram's proposal") +
coord_sf(
xlim = c(bbox[c(1, 3)]) * 1.8,
ylim = c(bbox[c(2, 4)]) * 1.4
)
Emblem of the Antarctic Treaty
This example uses graticules to create a concentric "bullseye" pattern around Antarctica. Generating such graticules and merging meridians requires a few extra steps to avoid small gaps near the pole.
# Need graticules
grats <- giscoR::gisco_get_countries() |>
st_transform(st_crs(antarctica_fixed)) |>
# Specify the cuts of the graticules
st_graticule(
lat = c(-80, -70, -60),
lon = seq(-180, 180, 30),
ndiscr = 10000,
margin = 0.000001
)
ggplot(grats) +
geom_sf(color = "darkblue")
We merge meridians so the area around the South Pole is filled. st_graticule() can leave a tiny hole at the pole, and we fix this by joining complementary meridians.
# Merge meridians
merid <- lapply(seq(-180, 0, 30), function(x) {
df <- grats |>
filter(type == "E") |>
filter(degree %in% c(x, x + 180))
df2 <- df |>
st_geometry() |>
st_cast("MULTIPOINT") |>
st_union() |>
st_cast("LINESTRING")
sf_x <- st_sf(
degree = x,
type = "E",
geometry = df2
)
}) |> bind_rows()
grats_end <- merid |>
bind_rows(grats |>
filter(type != "E"))
We then cut and color the resulting graticules so they form the emblem-like pattern.
# Cut since some grats should be colored differently
antarctica_simp <- rmapshaper::ms_simplify(antarctica_fixed, keep = 0.005)
grats_yes <- st_intersection(grats_end, antarctica_simp)
grats_no <- st_difference(grats_end, antarctica_simp)
antarctica_simp |>
ggplot() +
geom_sf(fill = "white", color = NA) +
theme(
panel.background = element_rect(fill = "#072b5f"),
panel.grid = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank()
) +
geom_sf(data = grats_yes, color = "#072b5f", linewidth = 1) +
geom_sf(data = grats_no, color = "white", linewidth = 1) +
coord_sf(
xlim = c(bbox[c(1, 3)]) * 1.8,
ylim = c(bbox[c(2, 4)]) * 1.4
) +
labs(title = "Emblem of the Antarctic Treaty")
Antarctica flag redesigned
In 2024, Graham Bartram revealed a new version of his original flag as part of a global campaign to raise awareness about the growing problem of microplastic pollution. The new design keeps the familiar white outline of Antarctica but swaps the plain blue background for one filled with countless tiny, colorful dots. These dots represent the microscopic bits of plastic that have been discovered even in the planet’s most untouched places, including the Antarctic ice and its surrounding oceans.
Because the design relies on randomness, we approximate it using the following procedure:
- Sample random points across the Antarctic polygon.
- Build Voronoi polygons from those points, then apply a small negative buffer to create gaps.
- Randomly sample the resulting polygons to increase visual noise.
- Color polygons so larger areas remain white while smaller polygons use magenta/pink tones.
# Maximum chunk of Antarctica, the one that we fixed
ant_max_fixed
#> Simple feature collection with 1 feature and 1 field
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: -2447764 ymin: -2125910 xmax: 2690846 ymax: 2233395
#> Projected CRS: +proj=ortho +lat_0=-90 +lon_0=0
#> NAME geometry
#> 1 Antarctica POLYGON ((-2423737 1557908,...
set.seed(2024)
# Sample, Voronoi and negative buffer
plastics <- st_sample(ant_max_fixed, 3000) |>
st_union() |>
st_voronoi(envelope = st_geometry(ant_max_fixed)) |>
st_collection_extract() |>
st_buffer(dist = -10000)
# Keep only those properly included in the outline
toinc <- st_contains_properly(ant_max_fixed, plastics, sparse = FALSE) |>
as.vector()
# Select random chunks
plastic_end <- plastics[toinc, ] |>
st_as_sf() |>
slice_sample(prop = 0.75)
ggplot(plastic_end) +
geom_sf(fill = "darkblue")
# Random coloring
plastic_end$area <- st_area(plastic_end) |> as.double()
plastic_end$fill <- sample(c("#ff00ec", "#9e00ec"), nrow(plastic_end), replace = TRUE)
plastic_end$fill <- ifelse(plastic_end$area > quantile(plastic_end$area, probs = 0.4),
"white",
plastic_end$fill
)
bbox2 <- st_bbox(plastic_end)
ggplot() +
geom_sf(data = plastic_end, aes(fill = fill), color = NA) +
scale_fill_identity() +
theme(
panel.background = element_rect(fill = "#009fdc"),
panel.grid = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank()
) +
labs(title = "New redesign") +
coord_sf(
xlim = c(bbox2[c(1, 3)] * 1.8),
ylim = c(bbox2[c(2, 4)]) * 1.4
)
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