Overview

This R package provides easy access to Antarctic geographic place name information, and tools for working with those names.

The authoritative source of place names in Antarctica is the Composite Gazetteer of Antarctica (CGA), which is produced by the Scientific Committee on Antarctic Research (SCAR). The CGA consists of approximately 37,000 names corresponding to 19,000 distinct features. It covers features south of 60 °S, including terrestrial and undersea or under-ice features.

There is no single naming authority responsible for place names in Antarctica because it does not fall under the sovereignty of any one nation. In general, individual countries have administrative bodies that are responsible for their national policy on, and authorisation and use of, Antarctic names. The CGA is a compilation of place names that have been submitted by representatives of national names committees from 22 countries.

The composite nature of the CGA means that there may be multiple names associated with a given feature. Consider using the an_preferred() function for resolving a single name per feature.

For more information, see the CGA home page. The CGA was begun in 1992. Since 2008, Italy and Australia have jointly managed the CGA, the former taking care of the editing, the latter maintaining the database and website. The SCAR Standing Committee on Antarctic Geographic Information (SCAGI) coordinates the project. This R package is a product of the SCAR Expert Group on Antarctic Biodiversity Informatics and SCAGI.

Citing

The SCAR Composite Gazetteer of Antarctica is made available under a CC-BY license. If you use it, please cite it:

Composite Gazetteer of Antarctica, Scientific Committee on Antarctic Research. GCMD Metadata (http://gcmd.nasa.gov/records/SCAR_Gazetteer.html)

Installing

install.packages("remotes")
remotes::install_github("ropensci/antanym")

Usage

Start by fetching the names data from the host server. Here we use a temporary cache so that we can re-load it later in the session without needing to re-download it:

library(antanym)
g <- an_read(cache = "session")

If you want to be able to work offline, you can cache the data to a directory that will persist between R sessions. The location of this directory is determined by rappdirs::user_cache_dir() (see an_cache_directory("persistent") if you want to know what it is):

g <- an_read(cache = "persistent")

If you prefer working with sp objects, then this will return a SpatialPointsDataFrame:

gsp <- an_read(sp = TRUE, cache = "session")

Data summary

How many names do we have in total?

nrow(g)
## [1] 38840

Corresponding to how many distinct features?

length(unique(g$scar_common_id))
## [1] 19961

We can get a list of the feature types in the data:

##  [1] "Aiguilles"           "Anchorage"           "Archipelago"        
##  [4] "Arm"                 "AWS"                 "Bank"               
##  [7] "Basin"               "Bay"                 "Beach"              
## [10] "Bedrock break point"

Data structure

A description of the data structure can be found in the an_load function help, (the same information is available via an_cga_metadata). By default, the gazetteer data consists of these columns:

field description
gaz_id The unique identifier of each gazetteer entry. Note that the same feature (e.g. ‘Browns Glacier’) might have multiple gazetteer entries, each with their own gaz_id, because the feature has been named multiple times by different naming authorities. The scar_common_id value for these entries will be identical, because scar_common_id identifies the feature itself
scar_common_id The unique identifier (in the Composite Gazetteer of Antarctica) of the feature. A single feature may have multiple names, given by different naming authorities
place_name The name of the feature
place_name_transliterated The name of the feature transliterated to simple ASCII characters (e.g. with diacritical marks removed)
longitude The longitude of the feature (negative values indicate degrees west). Note that many features are not point features (e.g. mountains, lakes), in which case the longitude and latitude values are indicative only, generally of the centroid of the feature
latitude The latitude of the feature (negative values indicate degrees south). Note that many features are not point features (e.g. mountains, lakes), in which case the longitude and latitude values are indicative only, generally of the centroid of the feature
altitude The altitude of the feature, in metres relative to sea level. Negative values indicate features below sea level
feature_type_name The feature type (e.g. ‘Archipelago’, ‘Channel’, ‘Mountain’). See an_feature_types for a full list
date_named The date on which the feature was named
narrative A text description of the feature; may include a synopsis of the history of its name
named_for The person after whom the feature was named, or other reason for its naming. For historical reasons the distinction between ‘narrative’ and ‘named for’ is not always obvious
origin The naming authority that provided the name. This is a country name, or organisation name for names that did not come from a national source
relic If TRUE, this name is associated with a feature that no longer exists (e.g. an ice shelf feature that has disappeared)
gazetteer The gazetteer from which this information came (currently only ‘CGA’)

A few additional (but probably less useful) columns can be included by calling an_read(..., simplified = FALSE). Consult the an_read documentation for more information.

Finding names

The an_filter function provides a range of options to find the names you are interested in.

Basic searching

A simple search for any place name containing the word ‘William’:

an_filter(g, query = "William")
## # A tibble: 75 x 14
##    gaz_id scar_common_id place_name place_name_tran… longitude latitude altitude
##     <dbl>          <dbl> <chr>      <chr>                <dbl>    <dbl>    <dbl>
##  1      8          16073 William S… William Scoresb…      59.8    -67.3       NA
##  2     75          16074 William S… William Scoresb…      59.6    -67.4       NA
##  3    559          16090 Williamso… Williamson Glac…     114.     -66.6       NA
##  4    643          16092 Williamso… Williamson Head      158.     -69.2       NA
##  5   1240          16083 Williams … Williams Lake         78.2    -68.5        3
##  6   1428          16096 Mount Wil… Mount Williams        50.8    -66.8       NA
##  7   1825          16084 Williams … Williams Nunatak     111.     -66.4       50
##  8   2189          16097 Point Wil… Point Williams        67.6    -67.8       NA
##  9   2430          16088 Williams … Williams Rocks        62.8    -67.4        5
## 10 100917           2288 Fort Will… Fort William, p…     -59.6    -62.4       NA
## # … with 65 more rows, and 7 more variables: feature_type_name <chr>,
## #   date_named <dttm>, narrative <chr>, named_for <chr>, origin <chr>,
## #   relic <lgl>, gazetteer <chr>

We can filter according to the country or organisation that issued the name. Which bodies (countries or organisations) provided the names in our data?

##  [1] "Argentina"                "Australia"               
##  [3] "Austria"                  "Belgium"                 
##  [5] "Bulgaria"                 "Canada"                  
##  [7] "Chile"                    "China"                   
##  [9] "Ecuador"                  "France"                  
## [11] "GEBCO"                    "Germany"                 
## [13] "India"                    "Italy"                   
## [15] "Japan"                    "Korea, Republic of"      
## [17] "New Zealand"              "Norway"                  
## [19] "Poland"                   "Russia"                  
## [21] "South Africa"             "Spain"                   
## [23] "United Kingdom"           "United States of America"
## [25] "Uruguay"

Find names containing “William” and originating from Australia or the USA:

an_filter(g, query = "William", origin = "Australia|United States of America")
## # A tibble: 39 x 14
##    gaz_id scar_common_id place_name place_name_tran… longitude latitude altitude
##     <dbl>          <dbl> <chr>      <chr>                <dbl>    <dbl>    <dbl>
##  1      8          16073 William S… William Scoresb…      59.8    -67.3       NA
##  2     75          16074 William S… William Scoresb…      59.6    -67.4       NA
##  3    559          16090 Williamso… Williamson Glac…     114.     -66.6       NA
##  4    643          16092 Williamso… Williamson Head      158.     -69.2       NA
##  5   1240          16083 Williams … Williams Lake         78.2    -68.5        3
##  6   1428          16096 Mount Wil… Mount Williams        50.8    -66.8       NA
##  7   1825          16084 Williams … Williams Nunatak     111.     -66.4       50
##  8   2189          16097 Point Wil… Point Williams        67.6    -67.8       NA
##  9   2430          16088 Williams … Williams Rocks        62.8    -67.4        5
## 10 125297           4839 Fort Will… Fort William         -59.7    -62.4      100
## # … with 29 more rows, and 7 more variables: feature_type_name <chr>,
## #   date_named <dttm>, narrative <chr>, named_for <chr>, origin <chr>,
## #   relic <lgl>, gazetteer <chr>

Compound names can be slightly trickier. This search will return no matches, because the actual place name is ‘William Scoresby Archipelago’:

an_filter(g, query = "William Archipelago")
## # A tibble: 0 x 14
## # … with 14 variables: gaz_id <dbl>, scar_common_id <dbl>, place_name <chr>,
## #   place_name_transliterated <chr>, longitude <dbl>, latitude <dbl>,
## #   altitude <dbl>, feature_type_name <chr>, date_named <dttm>,
## #   narrative <chr>, named_for <chr>, origin <chr>, relic <lgl>,
## #   gazetteer <chr>

To get around this, we can split the search terms so that each is matched separately (only names matching both “William” and “Archipelago” will be returned):

an_filter(g, query = c("William", "Archipelago"))
## # A tibble: 2 x 14
##   gaz_id scar_common_id place_name place_name_tran… longitude latitude altitude
##    <dbl>          <dbl> <chr>      <chr>                <dbl>    <dbl>    <dbl>
## 1      8          16073 William S… William Scoresb…      59.8    -67.3       NA
## 2 133723          16073 William S… William Scoresb…      59.8    -67.3       NA
## # … with 7 more variables: feature_type_name <chr>, date_named <dttm>,
## #   narrative <chr>, named_for <chr>, origin <chr>, relic <lgl>,
## #   gazetteer <chr>

Or a simple text query but additionally constrained by feature type:

an_filter(g, query = "William", feature_type = "Archipelago")
## # A tibble: 2 x 14
##   gaz_id scar_common_id place_name place_name_tran… longitude latitude altitude
##    <dbl>          <dbl> <chr>      <chr>                <dbl>    <dbl>    <dbl>
## 1      8          16073 William S… William Scoresb…      59.8    -67.3       NA
## 2 133723          16073 William S… William Scoresb…      59.8    -67.3       NA
## # … with 7 more variables: feature_type_name <chr>, date_named <dttm>,
## #   narrative <chr>, named_for <chr>, origin <chr>, relic <lgl>,
## #   gazetteer <chr>

Or we can use a regular expression:

an_filter(g, query = "William .* Archipelago")
## # A tibble: 2 x 14
##   gaz_id scar_common_id place_name place_name_tran… longitude latitude altitude
##    <dbl>          <dbl> <chr>      <chr>                <dbl>    <dbl>    <dbl>
## 1      8          16073 William S… William Scoresb…      59.8    -67.3       NA
## 2 133723          16073 William S… William Scoresb…      59.8    -67.3       NA
## # … with 7 more variables: feature_type_name <chr>, date_named <dttm>,
## #   narrative <chr>, named_for <chr>, origin <chr>, relic <lgl>,
## #   gazetteer <chr>

Regular expressions

Regular expressions also allow more complex searches. For example, names matching “West” or “East”:

an_filter(g, query = "West|East")
## # A tibble: 138 x 14
##    gaz_id scar_common_id place_name place_name_tran… longitude latitude altitude
##     <dbl>          <dbl> <chr>      <chr>                <dbl>    <dbl>    <dbl>
##  1     12          15899 West Arm   West Arm              62.9    -67.6       18
##  2     13           4025 East Arm   East Arm              62.9    -67.6        8
##  3    556          14345 Sylwester… Sylwester Glaci…     160.     -84.2       NA
##  4    738          15908 West Ice … West Ice Shelf        85      -67         NA
##  5    870           4028 East Budd… East Budd Island      62.8    -67.6       12
##  6    895          15904 West Budd… West Budd Island      62.8    -67.6       13
##  7    976           4041 Easther I… Easther Island        76.2    -69.4       65
##  8   1169           4032 East Lake  East Lake            143.     -67.0       27
##  9   1337           4030 East Eger… East Egerton         158.     -80.8       NA
## 10   1848          15923 Westhaven… Westhaven Nunat…     154.     -79.8       NA
## # … with 128 more rows, and 7 more variables: feature_type_name <chr>,
## #   date_named <dttm>, narrative <chr>, named_for <chr>, origin <chr>,
## #   relic <lgl>, gazetteer <chr>

Names starting with “West” or “East”:

an_filter(g, query = "^(West|East)")
## # A tibble: 92 x 14
##    gaz_id scar_common_id place_name place_name_tran… longitude latitude altitude
##     <dbl>          <dbl> <chr>      <chr>                <dbl>    <dbl>    <dbl>
##  1     12          15899 West Arm   West Arm              62.9    -67.6     18  
##  2     13           4025 East Arm   East Arm              62.9    -67.6      8  
##  3    738          15908 West Ice … West Ice Shelf        85      -67       NA  
##  4    870           4028 East Budd… East Budd Island      62.8    -67.6     12  
##  5    895          15904 West Budd… West Budd Island      62.8    -67.6     13  
##  6    976           4041 Easther I… Easther Island        76.2    -69.4     65  
##  7   1169           4032 East Lake  East Lake            143.     -67.0     27  
##  8   1337           4030 East Eger… East Egerton         158.     -80.8     NA  
##  9   1848          15923 Westhaven… Westhaven Nunat…     154.     -79.8     NA  
## 10   2195          15927 Westwood … Westwood Point        77.9    -68.6      8.5
## # … with 82 more rows, and 7 more variables: feature_type_name <chr>,
## #   date_named <dttm>, narrative <chr>, named_for <chr>, origin <chr>,
## #   relic <lgl>, gazetteer <chr>

Names with “West” or “East” appearing as complete words in the name (“\b” matches a word boundary; see help("regex")):

an_filter(g, query = "\\b(West|East)\\b")
## # A tibble: 93 x 14
##    gaz_id scar_common_id place_name place_name_tran… longitude latitude altitude
##     <dbl>          <dbl> <chr>      <chr>                <dbl>    <dbl>    <dbl>
##  1     12          15899 West Arm   West Arm              62.9    -67.6       18
##  2     13           4025 East Arm   East Arm              62.9    -67.6        8
##  3    738          15908 West Ice … West Ice Shelf        85      -67         NA
##  4    870           4028 East Budd… East Budd Island      62.8    -67.6       12
##  5    895          15904 West Budd… West Budd Island      62.8    -67.6       13
##  6   1169           4032 East Lake  East Lake            143.     -67.0       27
##  7   1337           4030 East Eger… East Egerton         158.     -80.8       NA
##  8   2489          15915 West Stack West Stack            58.1    -67.0       NA
##  9   2490           4038 East Stack East Stack            58.3    -67.1       NA
## 10 105006           1690 Bowles We… Bowles West Peak     -60.2    -62.6       NA
## # … with 83 more rows, and 7 more variables: feature_type_name <chr>,
## #   date_named <dttm>, narrative <chr>, named_for <chr>, origin <chr>,
## #   relic <lgl>, gazetteer <chr>

Spatial searching

We can filter by spatial extent:

an_filter(g, extent = c(100, 120, -70, -65))
## # A tibble: 751 x 14
##    gaz_id scar_common_id place_name place_name_tran… longitude latitude altitude
##     <dbl>          <dbl> <chr>      <chr>                <dbl>    <dbl>    <dbl>
##  1      7           6373 Highjump … Highjump Archip…      101.    -66.0       NA
##  2     15           8581 Loewe Mas… Loewe Massif Au…      112.    -68.4       NA
##  3     17           8210 Law Dome … Law Dome Summit       113.    -66.7     1395
##  4     28          11156 Petersen … Petersen Bank         111.    -65.8       NA
##  5     31           8143 Larsen Ba… Larsen Bank           111.    -66.3        0
##  6     41           8700 Luchistaj… Luchistaja Bay        101.    -66.1       NA
##  7     42           8881 Majachnaj… Majachnaja Bay        101.    -66.2       NA
##  8     44           8339 Leonova B… Leonova Bay           101.    -66.2       NA
##  9     45          15468 Vetvistaj… Vetvistaja Bay        101.    -66.1       NA
## 10     46          10722 Ostrovnaj… Ostrovnaja Bay        101.    -66.2       NA
## # … with 741 more rows, and 7 more variables: feature_type_name <chr>,
## #   date_named <dttm>, narrative <chr>, named_for <chr>, origin <chr>,
## #   relic <lgl>, gazetteer <chr>

The extent can also be passed as Spatial or Raster object, in which case its extent will be used:

my_sp <- sp::SpatialPoints(cbind(c(100, 120), c(-70, -65)))
an_filter(g, extent = my_sp)
## # A tibble: 751 x 14
##    gaz_id scar_common_id place_name place_name_tran… longitude latitude altitude
##     <dbl>          <dbl> <chr>      <chr>                <dbl>    <dbl>    <dbl>
##  1      7           6373 Highjump … Highjump Archip…      101.    -66.0       NA
##  2     15           8581 Loewe Mas… Loewe Massif Au…      112.    -68.4       NA
##  3     17           8210 Law Dome … Law Dome Summit       113.    -66.7     1395
##  4     28          11156 Petersen … Petersen Bank         111.    -65.8       NA
##  5     31           8143 Larsen Ba… Larsen Bank           111.    -66.3        0
##  6     41           8700 Luchistaj… Luchistaja Bay        101.    -66.1       NA
##  7     42           8881 Majachnaj… Majachnaja Bay        101.    -66.2       NA
##  8     44           8339 Leonova B… Leonova Bay           101.    -66.2       NA
##  9     45          15468 Vetvistaj… Vetvistaja Bay        101.    -66.1       NA
## 10     46          10722 Ostrovnaj… Ostrovnaja Bay        101.    -66.2       NA
## # … with 741 more rows, and 7 more variables: feature_type_name <chr>,
## #   date_named <dttm>, narrative <chr>, named_for <chr>, origin <chr>,
## #   relic <lgl>, gazetteer <chr>

Searching by proximity to a given point, for example islands within 20km of 100 °E, 66 °S:

an_near(an_filter(g, feature_type = "Island"), loc = c(100, -66), max_distance = 20)
## # A tibble: 3 x 14
##   gaz_id scar_common_id place_name place_name_tran… longitude latitude altitude
##    <dbl>          <dbl> <chr>      <chr>                <dbl>    <dbl>    <dbl>
## 1   1027           4856 Foster Is… Foster Island         100.    -66.1       NA
## 2 120508           4856 Severnyj … Severnyj holm         100.    -66.1       NA
## 3 125310           4856 Foster Is… Foster Island         100.    -66.1       NA
## # … with 7 more variables: feature_type_name <chr>, date_named <dttm>,
## #   narrative <chr>, named_for <chr>, origin <chr>, relic <lgl>,
## #   gazetteer <chr>

Resolving multiple names

As noted above, the CGA is a composite gazetteer and so there may be multiple names associated with a given feature.

Find all names associated with feature 1589 (Booth Island) and show the country of origin of each name:

an_filter(g, feature_ids = 1589)[, c("place_name", "origin")]
## # A tibble: 7 x 2
##   place_name   origin                  
##   <chr>        <chr>                   
## 1 Booth, isla  Argentina               
## 2 Wandel, Ile  Belgium                 
## 3 Booth, Isla  Chile                   
## 4 Boothinsel   Germany                 
## 5 Booth Island United Kingdom          
## 6 Booth Island Russia                  
## 7 Booth Island United States of America

The an_preferred function can help with finding one name per feature. It takes an origin parameter that specifies one or more preferred naming authorities (countries or organisations). For features that have multiple names (e.g. have been named by multiple countries) a single name will be chosen, preferring names from the specified naming authorities where possible.

We start with 38840 names in the full CGA, corresponding to 19961 distinct features. Choose one name per feature, preferring the Polish name where there is one, and the German name as a second preference:

g <- an_preferred(g, origin = c("Poland", "Germany"))

Now we have 19961 names in our data frame, corresponding to the same 19961 distinct features.

Features that have not been named by either of our preferred countries will have a name chosen from another country, with those countries in random order of preference.

Using the pipe operator

All of the above functionality can be achieved in a piped workflow, if that’s your preference, e.g.:

nms <- g %>% an_filter(feature_type = "Island") %>% an_near(loc = c(100, -66), max_distance = 20)
nms[, c("place_name", "longitude", "latitude")]
## # A tibble: 1 x 3
##   place_name    longitude latitude
##   <chr>             <dbl>    <dbl>
## 1 Foster Island      100.    -66.1

Working with sp

If you prefer to work with Spatial objects, the gazetteer data can be converted to a SpatialPointsDataFrame when loaded:

gsp <- an_read(cache = "session", sp = TRUE)

And the above functions work in the same way, for example:

my_sp <- sp::SpatialPoints(cbind(c(100, 120), c(-70, -65)))
an_filter(gsp, extent = my_sp)
## class       : SpatialPointsDataFrame 
## features    : 751 
## extent      : 100, 120, -69, -65  (xmin, xmax, ymin, ymax)
## crs         : +proj=longlat +datum=WGS84 +no_defs 
## variables   : 12
## names       : gaz_id, scar_common_id,       place_name, place_name_transliterated, altitude, feature_type_name, date_named,                                                                                                                                                                        narrative,                                                                                                                                                                                                                                                                                                                                                                                                                                                                   named_for,                   origin, relic, gazetteer 
## min values  :      7,             54,    Adams Glacier,             Adams Glacier,        0,       Archipelago, -725846400,                                                                                                                                      "Bunger Hills.  Charted by the SAE in 1956.,                                                                                                                                                                                                                                                               A distinct feature that provides access through the ASPA without trampling vegetation.  Moss biologists describe Casey as the Daintree of Antarctica to educate people about the importance of the vegetation,                Australia,     0,       CGA 
## max values  : 139194,          20125, Zimmerman Island,          Zimmerman Island,     1395,       Watercourse, 1575849600, Valley near Polish Antarctic A. B. Dobrowolski Station, with a small lake, Burger Hills area. Named in honour of Professor Stefan Manczarski (1899-1979) - see Manczarski Point., Winning name recommended by Dr Andie Smithies, Convenor, Advisory Committee on Names Competition in September 2004.This was a public competition for the naming of a new runway in August 2004. The reason given by Joe Weiley (12 years), of Broken Head NSW, student at St Finbars School in Byron Bay and the winner of the competition was:  "It would have to be named after Sir Hubert Wilkins, the amazing Australian adventurer and pioneer of Antarctic aviation!", United States of America,     0,       CGA

Selecting names for plotting

Let’s say we are preparing a figure of the greater Prydz Bay region (60-90 °E, 65-70 °S), to be shown at 80mm x 80mm in size (this is approximately a 1:10M scale map). Let’s plot all of the place names in this region:

my_longitude <- c(60, 90)
my_latitude <- c(-70, -65)

this_names <- an_filter(g, extent = c(my_longitude, my_latitude))

if (!requireNamespace("rworldmap", quietly = TRUE)) {
  message("Skipping map figure - install the rworldmap package to see it.")
} else {
  library(rworldmap)
  map <- getMap(resolution = "low")
  plot(map, xlim = my_longitude + c(-7, 4), ylim = my_latitude, col = "grey50")
  ## allow extra xlim space for labels
  points(this_names$longitude, this_names$latitude, pch = 21, bg = "green", cex = 2)
  ## alternate the positions of labels to reduce overlap
  pos <- rep(c(1, 2, 3, 4), ceiling(nrow(this_names)/4))
  pos[order(this_names$longitude)] <- pos[1:nrow(this_names)]
  text(this_names$longitude, this_names$latitude, labels = this_names$place_name, pos = pos)
}

Oooooo-kay. That’s not ideal.

Antanym includes an experimental function that will suggest which features might be best to add names to on a given map. These suggestions are based on maps prepared by expert cartographers, and the features that were explicitly named on those maps. We can ask for suggested names to show on our example map:

suggested <- an_suggest(g, map_extent = c(my_longitude, my_latitude), map_dimensions = c(80, 80))

Plot the top ten suggested names purely by score:

this_names <- head(suggested, 10)

if (!requireNamespace("rworldmap", quietly = TRUE)) {
  message("Skipping map figure - install the rworldmap package to see it.")
} else {
  plot(map, xlim = my_longitude + c(-7, 4), ylim = my_latitude, col = "grey50")
  points(this_names$longitude, this_names$latitude, pch = 21, bg = "green", cex = 2)
  pos <- rep(c(1, 2, 3, 4), ceiling(nrow(this_names)/4))
  pos[order(this_names$longitude)] <- pos[1:nrow(this_names)]
  text(this_names$longitude, this_names$latitude, labels = this_names$place_name, pos = pos)
}

Or the ten best suggested names considering both score and spatial coverage:

this_names <- an_thin(suggested, n = 10)

if (!requireNamespace("rworldmap", quietly = TRUE)) {
  message("Skipping map figure - install the rworldmap package to see it.")
} else {
  plot(map, xlim = my_longitude + c(-7, 4), ylim = my_latitude, col = "grey50")
  points(this_names$longitude, this_names$latitude, pch = 21, bg = "green", cex = 2)
  pos <- rep(c(1, 2, 3, 4), ceiling(nrow(this_names)/4))
  pos[order(this_names$longitude)] <- pos[1:nrow(this_names)]
  text(this_names$longitude, this_names$latitude, labels = this_names$place_name, pos = pos)
}

Other map examples

A leaflet app using Mercator projection and clustered markers for place names.

And a similar example using a polar stereographic projection.

See the antanym-demo repository for the source code of these examples.

Future directions

Antanym currently only provides information from the SCAR CGA. This does not cover other features that may be of interest to Antarctic researchers, such as those on subantarctic islands or features that have informal names not registered in the CGA. Antanym may be expanded to cover extra gazetteers containing such information, at a later date.

Other packages

The geonames package also provides access to geographic place names, including from the SCAR Composite Gazetteer. If you need global place name coverage, geonames may be a better option. However, the composite nature of the CGA is not particularly well suited to geonames, and at the time of writing the geonames database did not include the most current version of the CGA. The geonames package requires a login for some functionality, and because it makes calls to api.geonames.org it isn’t easily used while offline.