Mapping IGOs

Maps are a powerful tool to show data. As the scope of igoR are the InterGovermental Organizations, mapping and IGOs are a perfect match.

This vignette provides some geospatial visualizations using the IGO datasets (Pevehouse et al. 2020) included in this package. Specific packages used for geospatial data:

Also countrycode is a very handy package for translating between coding schemes (CoW, ISO3, NUTS, FIPS) and country names.

Evolution of the composition of UN

The following maps shows the evolution of countries that are members of the United Nations.

First we should extract the data:

# Extract shapes

world <- gisco_get_countries(year = "2010")

un <- igo_search("UN", exact = TRUE)

# Extract three dates - some errors given that ISO doesnt have every COW Code
# Also join with world sf
UN1950 <-
  igo_members("UN", 1950) %>%
  mutate(ISO3_CODE = countrycode(ccode,
    "cown",
    "iso3c",
    warn = FALSE
  )) %>%
  left_join(world, .) %>%
  mutate(year = 1950) %>%
  select(year, orgname)

UN1980 <-
  igo_members("UN", 1980) %>%
  mutate(ISO3_CODE = countrycode(ccode,
    "cown",
    "iso3c",
    warn = FALSE
  )) %>%
  left_join(world, .) %>%
  mutate(year = 1980) %>%
  select(year, orgname)

UN2010 <-
  igo_members("UN", 2010) %>%
  mutate(ISO3_CODE = countrycode(ccode,
    "cown",
    "iso3c",
    warn = FALSE
  )) %>%
  left_join(world, .) %>%
  mutate(year = 2010) %>%
  select(year, orgname)

# Join all

UN_all <- bind_rows(UN1950, UN1980, UN2010)

Note that the map is not completely accurate, as the base shapefile contains the countries that exists on 2016. Some countries, as Czechoslovakia, East or West Germany are not included.

Now we are ready to plot with ggplot2:


ggplot(UN_all) +
  geom_sf(aes(fill = orgname), color = NA, show.legend = FALSE) +
  # Robinson
  coord_sf(crs = "ESRI:54030") +
  facet_wrap(vars(year),
    ncol = 1,
    strip.position = "left"
  ) +
  scale_fill_manual(
    values = "#74A9CF",
    na.value = "#E0E0E0"
  ) +
  theme_void() +
  labs(
    title = "UN Members",
    caption = gisco_attributions()
  ) +
  theme(plot.caption = element_text(face = "italic"))

plot of chunk UNMaps

Number of shared memberships

Shared memberships are useful for identifying regional patterns.

The following code produces a map showing the number of full memberships shared with Australia for each country on the world:


## Number of igos shared - 2014

# Countries alive in 2014

states2014 <- states2016 %>%
  filter(styear <= 2014 & endyear >= 2014)


# Shared memberships with Australia

shared <- igo_dyadic("AUL",
  as.character(states2014$statenme),
  year = 2014
)
shared$shared <- rowSums(shared == 1)

# ISO3 Code
shared$ISO3_CODE <- countrycode(shared$ccode2,
  "cown",
  "iso3c",
  warn = FALSE
)


# Merge with map
sharedmap <-
  world %>%
  left_join(shared) %>%
  select(ISO3_CODE, shared)

# Plot with custom palette

pal <- hcl.colors(10, palette = "Lajolla")

# Plot

ggplot(sharedmap) +
  geom_sf(aes(fill = shared), color = NA) +
  # Australia
  geom_sf(
    data = sharedmap %>% filter(ISO3_CODE == "AUS"),
    fill = "black",
    color = NA,
  ) +
  # Robinson
  coord_sf(crs = "ESRI:54030") +
  scale_fill_gradientn(
    colours = pal,
    n.breaks = 10,
    guide = "legend"
  ) +
  guides(fill = guide_legend(
    direction = "horizontal",
    title.position = "top",
    label.position = "bottom",
    nrow = 1,
    keyheight = 0.5,
    keywidth = 1.5
  )) +
  labs(
    title = "Shared Full Memberships with Australia (2014)",
    fill = "Number of IGOs shared",
    caption = gisco_attributions()
  ) +
  theme_void() +
  theme(
    legend.position = "bottom",
    plot.title = element_text(face = "bold", hjust = 0.5),
    plot.caption = element_text(
      face = "italic",
      size = 8,
      hjust = 0.05
    ),
    legend.title = element_text(size = 7),
    legend.text = element_text(size = 8)
  )

plot of chunk AustShared

Cross-shared memberships

The following map shows how the relationships between the countries of North America has flourished on the last 90 years, using a year as representative of each decade.


# Get shapes
countries.sf <- gisco_get_countries(country = c("USA", "MEX", "CAN"))

# Select years
years <- seq(1930, 2010, 10)

# Shared memberships
USA <- igo_dyadic("USA", c("MEX", "CAN"), years)
CAN <- igo_dyadic("CAN", c("USA", "MEX"), years)
MEX <- igo_dyadic("MEX", c("CAN", "USA"), years)

USA$value <- rowSums(USA == 1)
CAN$value <- rowSums(CAN == 1)
MEX$value <- rowSums(MEX == 1)

# Long data
Final <- USA %>%
  rbind(CAN) %>%
  rbind(MEX) %>%
  select(ccode1, year, value) %>%
  mutate(ISO3_CODE = countrycode(ccode1, "cown", "iso3c"))

# Create map
map <- left_join(countries.sf, Final)


# Map
ggplot(map) +
  geom_sf(aes(fill = value), color = NA) +
  coord_sf(
    crs = 2163,
    xlim = c(-3200000, 3333018)
  ) +
  facet_wrap(vars(year),
    ncol = 3
  ) +
  scale_fill_gradientn(
    colors = hcl.colors(10, "YlGn", rev = TRUE),
    breaks = seq(0, 100, 10),
    guide = "legend"
  ) +
  guides(fill = guide_legend(keyheight = 1.5)) +
  labs(
    title = "Shared Full Memberships on North America",
    subtitle = "(1930-2010)",
    fill = "shared IGOs",
    caption = gisco_attributions(),
  ) +
  theme_void() +
  theme(
    plot.title = element_text(face = "bold"),
    plot.subtitle = element_text(margin = margin(t = 3, b = 10)),
    plot.caption = element_text(
      face = "italic",
      hjust = 0.05
    ),
    legend.box.margin = margin(l = 20),
    legend.title = element_text(size = 8),
    strip.background = element_rect(fill = "grey90", colour = NA)
  )

plot of chunk NAShared

Mapping IGOs

Diego Hernangómez

Evolution of the composition of UN

Number of shared memberships

Cross-shared memberships

References