A geographic bubble map where markers are sized proportionally to quantitative data values at each location. Unlike scatter maps where size is optional, bubble maps use marker size as the primary visual encoding to show data magnitude across geographic regions. This visualization makes it immediately apparent where high and low values occur spatially, enabling intuitive comparison of quantities across locations.
#' anyplot.ai
#' bubble-map-geographic: Bubble Map with Sized Geographic Markers
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 86/100 | Created: 2026-05-18
library(ggplot2)
library(dplyr)
library(scales)
library(ragg)
library(tibble)
# Theme tokens
THEME <- Sys.getenv("ANYPLOT_THEME", "light")
PAGE_BG <- if (THEME == "light") "#FAF8F1" else "#1A1A17"
ELEVATED_BG <- if (THEME == "light") "#FFFDF6" else "#242420"
INK <- if (THEME == "light") "#1A1A17" else "#F0EFE8"
INK_SOFT <- if (THEME == "light") "#4A4A44" else "#B8B7B0"
OCEAN_BG <- if (THEME == "light") "#D6E8F2" else "#182530"
GRID_COLOR <- if (THEME == "light") "#AACCDC" else "#2C4455"
IMPRINT <- c("#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD")
# Data: Major world cities with population (millions, 2023 estimates)
cities <- tibble(
city = c(
"Tokyo", "Delhi", "Shanghai", "São Paulo", "Mexico City",
"Cairo", "Mumbai", "Beijing", "New York", "Dhaka",
"Karachi", "Buenos Aires", "Kolkata", "Lagos", "Istanbul",
"Kinshasa", "Manila", "Rio de Janeiro", "Tianjin", "Guangzhou",
"Los Angeles", "Moscow", "Shenzhen", "Bangalore", "Paris",
"Jakarta", "Chennai", "Lima", "Chicago", "Lahore",
"London", "Tehran", "Seoul", "Bangkok", "Nairobi",
"Sydney", "Singapore", "Ho Chi Minh City", "Bogotá", "Johannesburg"
),
lat = c(
35.7, 28.6, 31.2, -23.5, 19.4,
30.1, 19.1, 39.9, 40.7, 23.8,
24.9, -34.6, 22.6, 6.5, 41.0,
-4.3, 14.6, -22.9, 39.1, 23.1,
34.1, 55.8, 22.5, 12.9, 48.9,
-6.2, 13.1, -12.1, 41.9, 31.6,
51.5, 35.7, 37.6, 13.8, -1.3,
-33.9, 1.3, 10.8, 4.7, -26.2
),
lon = c(
139.7, 77.2, 121.5, -46.6, -99.1,
31.2, 72.9, 116.4, -74.0, 90.4,
67.0, -58.4, 88.4, 3.4, 29.0,
15.3, 121.0, -43.2, 117.2, 113.3,
-118.2, 37.6, 114.1, 77.6, 2.3,
106.8, 80.3, -77.0, -87.6, 74.3,
-0.1, 51.4, 126.9, 100.5, 36.8,
151.2, 103.8, 106.7, -74.1, 28.0
),
population = c(
37.4, 32.9, 28.5, 22.4, 22.1,
21.8, 21.7, 21.5, 18.8, 22.5,
17.2, 15.5, 14.9, 14.9, 15.4,
17.1, 14.4, 13.7, 15.7, 16.1,
12.5, 12.4, 13.4, 12.8, 11.1,
11.2, 10.5, 11.0, 8.9, 14.0,
9.5, 9.6, 9.9, 11.1, 5.3,
5.4, 6.0, 9.3, 11.3, 6.1
),
continent = c(
"Asia", "Asia", "Asia", "S. America", "N. America",
"Africa", "Asia", "Asia", "N. America", "Asia",
"Asia", "S. America", "Asia", "Africa", "Europe",
"Africa", "Asia", "S. America", "Asia", "Asia",
"N. America", "Europe", "Asia", "Asia", "Europe",
"Asia", "Asia", "S. America", "N. America", "Asia",
"Europe", "Asia", "Asia", "Asia", "Africa",
"Oceania", "Asia", "Asia", "S. America", "Africa"
)
)
# Continent reference labels for geographic context
region_labels <- tibble(
label = c("NORTH\nAMERICA", "SOUTH\nAMERICA", "EUROPE", "AFRICA", "ASIA", "AUSTRALIA"),
lon = c(-100, -60, 10, 20, 95, 134),
lat = c(50, -20, 55, 4, 48, -27)
)
# Color mapping by continent (Okabe-Ito order, Asia first = #009E73)
continent_colors <- c(
"Asia" = IMPRINT[1],
"Africa" = IMPRINT[2],
"N. America" = IMPRINT[3],
"S. America" = IMPRINT[4],
"Europe" = IMPRINT[5],
"Oceania" = IMPRINT[6]
)
LABEL_COLOR <- if (THEME == "light") "#7A8C96" else "#4A6070"
# Plot
p <- ggplot(cities, aes(x = lon, y = lat)) +
geom_text(
data = region_labels,
aes(x = lon, y = lat, label = label),
color = LABEL_COLOR,
size = 4,
fontface = "bold",
lineheight = 0.85,
inherit.aes = FALSE
) +
geom_point(
aes(size = population, color = continent),
alpha = 0.70,
shape = 16
) +
scale_color_manual(
values = continent_colors,
name = "Continent"
) +
scale_size_area(
max_size = 24,
name = "Population",
breaks = c(5, 10, 20, 37),
labels = c("5M", "10M", "20M", "37M")
) +
scale_x_continuous(
breaks = seq(-150, 150, by = 30),
labels = function(x) paste0(abs(x), ifelse(x < 0, "°W", ifelse(x > 0, "°E", "°")))
) +
scale_y_continuous(
breaks = seq(-60, 80, by = 30),
labels = function(y) paste0(abs(y), ifelse(y < 0, "°S", ifelse(y > 0, "°N", "°")))
) +
coord_fixed(ratio = 1.3, xlim = c(-175, 175), ylim = c(-62, 82)) +
labs(
title = "World’s Largest Cities · bubble-map-geographic · r · ggplot2 · anyplot.ai",
subtitle = "Bubble size proportional to city population (millions), 2023 estimates",
x = "Longitude",
y = "Latitude"
) +
theme_minimal(base_size = 14) +
theme(
plot.background = element_rect(fill = PAGE_BG, color = PAGE_BG),
panel.background = element_rect(fill = OCEAN_BG, color = NA),
panel.grid.major = element_line(color = GRID_COLOR, linewidth = 0.4),
panel.grid.minor = element_blank(),
panel.border = element_rect(color = INK_SOFT, fill = NA, linewidth = 0.6),
axis.title = element_text(color = INK, size = 20),
axis.text = element_text(color = INK_SOFT, size = 14),
plot.title = element_text(color = INK, size = 22, face = "bold"),
plot.subtitle = element_text(color = INK_SOFT, size = 16),
legend.background = element_rect(fill = ELEVATED_BG, color = INK_SOFT, linewidth = 0.4),
legend.text = element_text(color = INK_SOFT, size = 14),
legend.title = element_text(color = INK, size = 16),
legend.key = element_rect(fill = NA, color = NA),
plot.margin = margin(20, 30, 20, 20)
)
# Save
ggsave(
filename = sprintf("plot-%s.png", THEME),
plot = p,
device = ragg::agg_png,
width = 16,
height = 9,
units = "in",
dpi = 300
)
Part of Bubble Map with Sized Geographic Markers on anyplot.ai.