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: letsplot 4.9.0 | Python 3.13.13
Quality: 80/100 | Updated: 2026-05-18
"""
import numpy as np
import pandas as pd
from lets_plot import *
from lets_plot.export import ggsave as export_ggsave
LetsPlot.setup_html()
# Data: Major world cities with population (millions)
# Bubble size represents population magnitude - the primary visual encoding
np.random.seed(42)
cities_data = {
"city": [
"Tokyo",
"Delhi",
"Shanghai",
"Sao Paulo",
"Mexico City",
"Cairo",
"Mumbai",
"Beijing",
"Dhaka",
"Osaka",
"New York",
"Karachi",
"Buenos Aires",
"Istanbul",
"Lagos",
"Rio de Janeiro",
"Moscow",
"Paris",
"London",
"Los Angeles",
"Bangkok",
"Seoul",
"Lima",
"Sydney",
"Toronto",
"Singapore",
"Dubai",
"Madrid",
"Berlin",
"Rome",
],
"latitude": [
35.68,
28.61,
31.23,
-23.55,
19.43,
30.04,
19.08,
39.90,
23.81,
34.69,
40.71,
24.86,
-34.60,
41.01,
6.52,
-22.91,
55.76,
48.86,
51.51,
34.05,
13.76,
37.57,
-12.05,
-33.87,
43.65,
1.35,
25.20,
40.42,
52.52,
41.90,
],
"longitude": [
139.69,
77.21,
121.47,
-46.63,
-99.13,
31.24,
72.88,
116.41,
90.41,
135.50,
-74.01,
67.01,
-58.38,
28.98,
3.38,
-43.17,
37.62,
2.35,
-0.13,
-118.24,
100.50,
127.00,
-77.04,
151.21,
-79.38,
103.82,
55.27,
-3.70,
13.40,
12.50,
],
"population": [
37.4,
32.9,
29.2,
22.4,
21.8,
21.3,
21.0,
20.9,
22.5,
19.1,
18.8,
16.8,
15.4,
15.6,
15.3,
13.5,
12.5,
11.0,
9.5,
12.5,
10.7,
9.9,
11.0,
5.4,
6.2,
5.9,
3.5,
6.7,
3.6,
4.3,
],
"region": [
"Asia",
"Asia",
"Asia",
"S. America",
"N. America",
"Africa",
"Asia",
"Asia",
"Asia",
"Asia",
"N. America",
"Asia",
"S. America",
"Europe",
"Africa",
"S. America",
"Europe",
"Europe",
"Europe",
"N. America",
"Asia",
"Asia",
"S. America",
"Oceania",
"N. America",
"Asia",
"Asia",
"Europe",
"Europe",
"Europe",
],
}
df = pd.DataFrame(cities_data)
# Simplified continent outlines for basemap (closed polygons)
continents = []
# North America
na_lon = [
-170,
-168,
-140,
-125,
-124,
-117,
-105,
-97,
-82,
-77,
-68,
-55,
-52,
-80,
-87,
-97,
-105,
-125,
-145,
-165,
-170,
]
na_lat = [60, 65, 70, 55, 48, 33, 25, 26, 25, 35, 45, 48, 45, 27, 30, 20, 22, 50, 60, 55, 60]
for i in range(len(na_lon)):
continents.append({"continent": "N. America", "order": i, "lon": na_lon[i], "lat": na_lat[i]})
# South America
sa_lon = [-80, -68, -60, -50, -35, -40, -50, -55, -68, -72, -75, -80, -82, -80]
sa_lat = [10, 12, 5, 0, -5, -22, -35, -52, -55, -18, -5, 0, 8, 10]
for i in range(len(sa_lon)):
continents.append({"continent": "S. America", "order": i, "lon": sa_lon[i], "lat": sa_lat[i]})
# Europe
eu_lon = [-10, 0, 10, 20, 30, 40, 50, 60, 50, 35, 25, 20, 10, 0, -10, -10]
eu_lat = [35, 37, 36, 35, 35, 40, 45, 55, 70, 70, 70, 65, 60, 50, 40, 35]
for i in range(len(eu_lon)):
continents.append({"continent": "Europe", "order": i, "lon": eu_lon[i], "lat": eu_lat[i]})
# Africa
af_lon = [-17, -5, 10, 35, 50, 52, 43, 35, 30, 15, 0, -17, -17]
af_lat = [15, 37, 37, 32, 12, 0, -25, -35, -35, -25, 5, 20, 15]
for i in range(len(af_lon)):
continents.append({"continent": "Africa", "order": i, "lon": af_lon[i], "lat": af_lat[i]})
# Asia
as_lon = [60, 80, 100, 120, 140, 145, 140, 130, 105, 100, 80, 60, 45, 30, 25, 30, 35, 50, 60]
as_lat = [55, 70, 75, 70, 55, 45, 35, 30, 0, 5, 10, 25, 30, 35, 42, 55, 70, 70, 55]
for i in range(len(as_lon)):
continents.append({"continent": "Asia", "order": i, "lon": as_lon[i], "lat": as_lat[i]})
# Australia/Oceania
au_lon = [113, 130, 150, 155, 153, 145, 130, 115, 113]
au_lat = [-25, -12, -15, -25, -35, -40, -35, -35, -25]
for i in range(len(au_lon)):
continents.append({"continent": "Oceania", "order": i, "lon": au_lon[i], "lat": au_lat[i]})
df_continents = pd.DataFrame(continents)
# Create the geographic bubble map
# Size is the PRIMARY visual encoding - use scale_size to map area proportionally
plot = (
ggplot()
+ geom_polygon(
aes(x="lon", y="lat", group="continent"),
data=df_continents,
fill="#E5E5E5",
color="#A0A0A0",
size=0.4,
alpha=0.8,
)
+ geom_point(
aes(x="longitude", y="latitude", size="population", color="region"),
data=df,
alpha=0.65,
stroke=0.8,
tooltips=layer_tooltips()
.title("@city")
.line("Population|@population million")
.line("Region|@region"),
)
# Scale bubble area proportionally - larger range for bubble emphasis
+ scale_size(range=[4, 28], name="Population (M)", breaks=[5, 10, 20, 30])
+ scale_color_manual(
values=["#306998", "#DC2626", "#2CA02C", "#9467BD", "#FFD43B", "#17BECF"], name="Region"
)
+ labs(
title="World City Populations · bubble-map-geographic · letsplot · pyplots.ai", x="Longitude", y="Latitude"
)
+ coord_fixed(ratio=1.0, xlim=[-180, 180], ylim=[-60, 80])
+ ggsize(1600, 900)
+ theme_minimal()
+ theme(
plot_title=element_text(size=26, face="bold"),
axis_title=element_text(size=20),
axis_text=element_text(size=16),
legend_title=element_text(size=18),
legend_text=element_text(size=14),
legend_position="right",
panel_grid_major=element_line(color="#D0D0D0", size=0.3),
panel_grid_minor=element_blank(),
plot_background=element_rect(fill="#F8F8F8"),
)
)
# Save PNG (scale 3x to get 4800 x 2700 px)
export_ggsave(plot, filename="plot.png", path=".", scale=3)
# Save HTML for interactive version
export_ggsave(plot, filename="plot.html", path=".")
Part of Bubble Map with Sized Geographic Markers on anyplot.ai.