Mapping Antarctica

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")

South Pole-centered map of Antarctica with a narrow lollipop-shaped gap extending from the coast toward the pole, caused by a polygon artifact.

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()

Coordinate plot of the Antarctic coastline with numbered vertices. A line and loop extending into the interior identify the polygon artifact.

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)

Close-up coordinate plot of Antarctic vertices 8200 through 9200. The points form a loop and stem corresponding to the lollipop artifact.

Note: This cleaning is tailored to this specific shapefile and may need to be repeated for other shapefiles. The approach is straightforward but depends on the particular geometry and projection.

# 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")

Coordinate plot of vertices 8289 through 9130 selected for removal. They form the unwanted loop and stem.

Coordinate plot of the vertices retained from the inspected interval. They trace a section of the Antarctic coastline.

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")

South Pole-centered map of Antarctica after removing the polygon artifact. The landmass is continuous across the previously affected area.

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
  )

Antarctic flag design with a white silhouette of the continent on a blue background.

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")

Polar coordinate grid centered on the South Pole, with concentric latitude circles and radial longitude lines.

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")

Antarctic Treaty emblem with a white Antarctic silhouette and polar coordinate grid on a dark blue background.

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")

Map of Antarctica filled with randomly distributed blue points inside the continent boundary.

# 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
  )

Antarctic flag variation with the continental silhouette formed by scattered white points on a blue background.