I'm trying to create a choropleth of world countries that also shows microstates - but the issue I have is that they're too small to be seen at the scale.

Wikimedia projects sometimes use this illustration for this purpose - microstates are displayed with a dot, which is not to scale but at least is visible.

I realise this is not really a geographically accurate map, but I was wondering if something like this was available in code form, so that I could use it to make maps programmatically (in my case, with R, sf and ggplot2):

dat |>
    left_join(ne_countries(scale = "small", returnclass = "sf")) |>
    ggplot() +
    geom_sf(aes(fill = yes_no, geometry = geometry))
  • I'm assuming you need a fixed size map and can't use something like SVG or Leaflet or just a really large map? Aug 22 at 12:04
  • Yes, fixed size map (illustration for a scientific article). I could edit the Wikipedia SVG I linked manually, but it would be very tedious, so I'd prefer to do it programmatically: merge a GIS dataset with my dataset and then create the plot that way. I'm not familiar with Leaflet.
    – Andrea M
    Aug 22 at 13:41
  • Leaflet lets you create zooming maps, so won't help. You could go the insane route of magnifying the map near microstates and then doing a continuous dropoff so the map remains smooth. If you have a high-resolution image, you can use an image manipulation tool to do this and then reduce back down to the size you need. Aug 22 at 13:45
  • Or you could use different "side maps" for microstates, like many US maps do for Alaska and Hawaii Aug 22 at 13:46
  • 1
    I'm not familiar with r mapping so not sure if doable in your case but an easy way would be to use a symbology with both a fill and a dot the same colour as the fill at the center of each country, for "small" country the dot will entirely cover the country making them visible
    – J.R
    Aug 22 at 13:56

1 Answer 1


see if this may help.

The idea is:

  1. To identify microstates by area. So you may need to compute the area of each country and add that as an additional variable. Based on a thresold (in my example, it is set to 1900000000 (m^2)
  2. Split your dataset based on micro vs non-micro states.
  3. For those countries identified as micro states, replace the shape for a polygon that is the centroid of the country buffered by a given distance (in my case 200 kms). This would determine the final size of the circle for each microstate
  4. Recreate the initial dataset with the non-micro states plus the modified microstates.

See how this can be done. You may want to play with the thresold for identifying microstates and the distance of the buffer.

#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>     filter, lag
#> The following objects are masked from 'package:base':
#>     intersect, setdiff, setequal, union
#> Linking to GEOS 3.9.1, GDAL 3.4.3, PROJ 7.2.1; sf_use_s2() is TRUE

cnt <- gisco_get_countries() %>%

mapborder <- st_bbox(gisco_get_countries(epsg = 4326)) %>%
  st_as_sfc() %>%
  st_segmentize(1000000) %>%
  st_transform(st_crs(cnt)) %>%

# Mock variable for plotting choropleth

cnt$mock <- sample(letters[1:4], size = nrow(cnt), replace = TRUE)

# Area of countries
cnt$area <- as.double(st_area(cnt))

cnt <- cnt %>% arrange(desc(area))

# Thresold for determining which countries are tiny (in terms of area)
thr <- 1900000000

notiny <- cnt %>% filter(area >= thr)
# Make tiny circles
tiny <- cnt %>%
  filter(area < thr) %>%
  mutate(tiny = TRUE) %>%
  # Create a circle with a buffer of 200 kms
  st_centroid(of_largest_polygon = TRUE) %>%
#> Warning in st_centroid.sf(., of_largest_polygon = TRUE): st_centroid assumes
#> attributes are constant over geometries of x

all <- notiny %>% bind_rows(tiny)

ggplot(all) +
  geom_sf(data = mapborder, size = 0.1) +
  geom_sf(aes(fill = mock), color = NA) +

Created on 2022-08-23 with reprex v2.0.2

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