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: matplotlib 3.10.9 | Python 3.13.13
Quality: 87/100 | Updated: 2026-05-18
"""
import os
import matplotlib.pyplot as plt
import numpy as np
# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
OCEAN = "#C8DDF0" if THEME == "light" else "#1E3050"
LAND = "#E5E5E0" if THEME == "light" else "#3A3A34"
LAND_EDGE = "#888888" if THEME == "light" else "#555550"
GRATICULE = "#AAAAAA" if THEME == "light" else "#555550"
BUBBLE_COLOR = "#009E73" # Okabe-Ito position 1
# Data - Major world cities with GDP (in billion USD) as primary value for bubble size
np.random.seed(42)
cities = {
"Tokyo": (35.6762, 139.6503, 1920),
"New York": (40.7128, -74.0060, 1775),
"Los Angeles": (34.0522, -118.2437, 1050),
"London": (51.5074, -0.1278, 980),
"Paris": (48.8566, 2.3522, 815),
"Shanghai": (31.2304, 121.4737, 810),
"Beijing": (39.9042, 116.4074, 750),
"Seoul": (37.5665, 126.9780, 710),
"Chicago": (41.8781, -87.6298, 680),
"Osaka": (34.6937, 135.5023, 650),
"Mumbai": (19.0760, 72.8777, 400),
"São Paulo": (-23.5505, -46.6333, 430),
"Moscow": (55.7558, 37.6173, 520),
"Sydney": (-33.8688, 151.2093, 390),
"Hong Kong": (22.3193, 114.1694, 380),
"Singapore": (1.3521, 103.8198, 360),
"Toronto": (43.6532, -79.3832, 340),
"Mexico City": (19.4326, -99.1332, 310),
"Bangkok": (13.7563, 100.5018, 270),
"Jakarta": (-6.2088, 106.8456, 260),
"Istanbul": (41.0082, 28.9784, 250),
"Buenos Aires": (-34.6037, -58.3816, 240),
"Dubai": (25.2048, 55.2708, 230),
"Delhi": (28.6139, 77.2090, 220),
"Frankfurt": (50.1109, 8.6821, 210),
}
# Extract data
names = list(cities.keys())
lats = np.array([cities[c][0] for c in names])
lons = np.array([cities[c][1] for c in names])
gdp = np.array([cities[c][2] for c in names])
# Scale bubble area proportionally to GDP
min_size = 80
max_size = 900
sizes = min_size + (gdp - gdp.min()) / (gdp.max() - gdp.min()) * (max_size - min_size)
# Simplified world map coastlines
continents = [
# Alaska
[(-168, 66), (-165, 60), (-141, 60), (-141, 70), (-156, 71), (-168, 66)],
# North America main
[
(-168, 52),
(-162, 55),
(-152, 60),
(-141, 60),
(-130, 56),
(-125, 50),
(-124, 42),
(-117, 33),
(-110, 32),
(-105, 29),
(-97, 26),
(-97, 28),
(-95, 30),
(-90, 30),
(-85, 30),
(-82, 25),
(-81, 25),
(-80, 32),
(-75, 35),
(-70, 41),
(-67, 45),
(-65, 45),
(-64, 47),
(-67, 48),
(-70, 47),
(-75, 45),
(-80, 45),
(-84, 46),
(-88, 48),
(-95, 49),
(-102, 49),
(-120, 49),
(-123, 49),
(-130, 55),
(-140, 60),
(-148, 60),
(-153, 58),
(-162, 55),
(-168, 52),
],
# Mexico + Central America
[
(-117, 33),
(-115, 30),
(-112, 29),
(-110, 25),
(-105, 22),
(-100, 20),
(-97, 20),
(-95, 18),
(-92, 16),
(-88, 18),
(-87, 16),
(-84, 10),
(-82, 9),
(-78, 9),
(-77, 8),
(-80, 8),
(-80, 15),
(-88, 21),
(-90, 22),
(-97, 26),
(-105, 29),
(-110, 32),
(-117, 33),
],
# South America
[
(-78, 10),
(-71, 12),
(-67, 11),
(-63, 10),
(-60, 8),
(-55, 5),
(-50, 0),
(-45, -2),
(-40, -3),
(-35, -6),
(-35, -10),
(-37, -15),
(-40, -20),
(-42, -23),
(-47, -25),
(-50, -28),
(-53, -33),
(-58, -38),
(-66, -55),
(-74, -52),
(-76, -48),
(-75, -42),
(-72, -37),
(-72, -30),
(-71, -20),
(-70, -18),
(-78, -6),
(-81, -3),
(-80, 0),
(-78, 3),
(-77, 7),
(-78, 10),
],
# Iberian Peninsula
[(-10, 36), (-6, 37), (-2, 36), (3, 43), (0, 44), (-2, 43), (-8, 44), (-9, 42), (-10, 36)],
# UK
[(-6, 50), (-5, 54), (-4, 58), (-8, 58), (-6, 55), (-6, 50)],
# European mainland
[
(-5, 43),
(0, 43),
(3, 43),
(6, 44),
(8, 44),
(12, 46),
(14, 45),
(14, 41),
(16, 40),
(20, 40),
(24, 37),
(26, 38),
(28, 41),
(30, 42),
(32, 42),
(34, 42),
(37, 45),
(40, 46),
(44, 42),
(50, 37),
(52, 30),
(56, 27),
(60, 25),
(70, 25),
(75, 25),
(78, 22),
(78, 8),
(76, 8),
(72, 18),
(70, 22),
(66, 25),
(60, 25),
(56, 27),
(50, 30),
(42, 31),
(37, 32),
(33, 30),
(30, 31),
(25, 35),
(22, 36),
(18, 40),
(14, 41),
(12, 44),
(10, 47),
(8, 48),
(5, 49),
(4, 51),
(3, 51),
(5, 54),
(10, 54),
(10, 56),
(12, 56),
(14, 54),
(19, 55),
(22, 56),
(24, 55),
(28, 56),
(30, 60),
(32, 65),
(28, 70),
(20, 70),
(12, 65),
(10, 62),
(5, 58),
(3, 54),
(-2, 50),
(-5, 48),
(-5, 43),
],
# Africa
[
(-17, 14),
(-17, 21),
(-13, 28),
(-10, 32),
(-6, 35),
(0, 36),
(10, 37),
(11, 34),
(15, 32),
(20, 32),
(25, 32),
(30, 31),
(33, 30),
(35, 28),
(37, 22),
(42, 14),
(44, 11),
(51, 11),
(51, 3),
(42, 0),
(42, -4),
(40, -10),
(38, -18),
(35, -22),
(32, -28),
(28, -33),
(20, -35),
(17, -30),
(15, -25),
(12, -17),
(12, -6),
(9, 4),
(5, 5),
(0, 6),
(-5, 5),
(-10, 7),
(-15, 11),
(-17, 14),
],
# Asia main
[
(28, 70),
(40, 70),
(50, 68),
(60, 70),
(80, 72),
(100, 77),
(120, 75),
(140, 72),
(160, 65),
(170, 60),
(165, 55),
(160, 52),
(150, 46),
(140, 44),
(135, 35),
(129, 33),
(125, 35),
(120, 32),
(122, 25),
(118, 23),
(110, 18),
(105, 16),
(100, 14),
(100, 20),
(105, 22),
(108, 22),
(100, 10),
(104, 2),
(98, 0),
(96, 6),
(92, 22),
(88, 22),
(92, 22),
(88, 26),
(82, 28),
(80, 28),
(78, 33),
(74, 35),
(72, 25),
(66, 25),
(60, 25),
(56, 27),
(52, 30),
(50, 37),
(44, 42),
(40, 46),
(37, 45),
(34, 42),
(32, 42),
(30, 42),
(28, 56),
(28, 70),
],
# Japan
[
(130, 32),
(132, 34),
(136, 35),
(140, 36),
(141, 40),
(141, 45),
(145, 44),
(145, 42),
(144, 38),
(140, 36),
(136, 35),
(132, 32),
(130, 32),
],
# Australia
[
(113, -22),
(115, -21),
(117, -20),
(122, -18),
(130, -12),
(135, -12),
(137, -16),
(139, -17),
(141, -13),
(145, -15),
(150, -23),
(153, -28),
(152, -33),
(150, -37),
(145, -38),
(140, -38),
(136, -35),
(130, -32),
(125, -32),
(117, -35),
(115, -34),
(115, -30),
(113, -25),
(113, -22),
],
]
# Plot
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
ax.set_facecolor(OCEAN)
# Draw continents
for continent in continents:
poly = plt.Polygon(continent, facecolor=LAND, edgecolor=LAND_EDGE, linewidth=0.6, zorder=1)
ax.add_patch(poly)
# Add graticule
for lat in range(-60, 90, 30):
ax.axhline(y=lat, color=GRATICULE, linewidth=0.4, linestyle=":", alpha=0.6, zorder=0)
for lon in range(-150, 181, 30):
ax.axvline(x=lon, color=GRATICULE, linewidth=0.4, linestyle=":", alpha=0.6, zorder=0)
# Plot bubbles — Okabe-Ito position 1 with edge matching page background
ax.scatter(lons, lats, s=sizes, c=BUBBLE_COLOR, alpha=0.6, edgecolors=PAGE_BG, linewidths=2, zorder=5)
ax.set_xlim(-180, 180)
ax.set_ylim(-60, 80)
# Style
ax.set_xlabel("Longitude (°)", fontsize=20, color=INK)
ax.set_ylabel("Latitude (°)", fontsize=20, color=INK)
ax.set_title(
"City GDP by Location · bubble-map-geographic · python · matplotlib · anyplot.ai",
fontsize=24,
fontweight="medium",
color=INK,
pad=15,
)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT, labelcolor=INK_SOFT)
for spine in ax.spines.values():
spine.set_color(INK_SOFT)
# Size legend
gdp_values = [250, 750, 1500]
legend_sizes = [min_size + (v - gdp.min()) / (gdp.max() - gdp.min()) * (max_size - min_size) for v in gdp_values]
size_labels = ["$250B", "$750B", "$1.5T"]
size_handles = []
for sz, label in zip(legend_sizes, size_labels, strict=True):
handle = ax.scatter([], [], s=sz, c=BUBBLE_COLOR, alpha=0.6, edgecolors=PAGE_BG, linewidths=1.5, label=label)
size_handles.append(handle)
leg = ax.legend(
handles=size_handles,
title="Metro GDP",
loc="lower left",
fontsize=14,
title_fontsize=16,
fancybox=True,
labelspacing=0.8,
borderpad=1.2,
)
leg.get_frame().set_facecolor(ELEVATED_BG)
leg.get_frame().set_edgecolor(INK_SOFT)
leg.get_frame().set_alpha(0.95)
plt.setp(leg.get_texts(), color=INK_SOFT)
leg.get_title().set_color(INK)
# Save
plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
Part of Bubble Map with Sized Geographic Markers on anyplot.ai.