A geographic scatter plot that displays data points on a world or regional map, with each point positioned by its latitude and longitude coordinates. This visualization is ideal for showing spatial distributions of events, locations, or measurements across geographic areas. Points can optionally encode additional variables through size and color, enabling multi-dimensional geographic analysis at a glance.
""" anyplot.ai
scatter-map-geographic: Scatter Map with Geographic Points
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 97/100 | Updated: 2026-05-18
"""
import os
import pandas as pd
from plotnine import (
aes,
coord_fixed,
element_blank,
element_line,
element_rect,
element_text,
geom_point,
geom_polygon,
ggplot,
labs,
scale_color_manual,
scale_size_continuous,
theme,
theme_minimal,
)
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
BASEMAP_COLOR = "#E8E3D8" if THEME == "light" else "#2A2A26"
BASEMAP_EDGE = "#C0B5A8" if THEME == "light" else "#4A4945"
GRID_COLOR = INK if THEME == "light" else INK
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD"]
# Data: Major world cities with population and region
cities_data = {
"city": [
"Tokyo",
"Delhi",
"Shanghai",
"Sao Paulo",
"Mexico City",
"Cairo",
"Mumbai",
"Beijing",
"Dhaka",
"Osaka",
"New York",
"Karachi",
"Buenos Aires",
"Chongqing",
"Istanbul",
"Kolkata",
"Manila",
"Lagos",
"Rio de Janeiro",
"Los Angeles",
"Moscow",
"Paris",
"Bangkok",
"Seoul",
"London",
"Lima",
"Chicago",
"Cape Town",
"Sydney",
"Toronto",
],
"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,
29.43,
41.01,
22.57,
14.60,
6.52,
-22.91,
34.05,
55.76,
48.86,
13.76,
37.57,
51.51,
-12.05,
41.88,
-33.93,
-33.87,
43.65,
],
"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,
106.91,
28.98,
88.36,
120.98,
3.38,
-43.17,
-118.24,
37.62,
2.35,
100.50,
127.00,
-0.13,
-77.04,
-87.63,
18.42,
151.21,
-79.38,
],
"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,
16.9,
15.6,
15.1,
14.4,
15.3,
13.5,
12.5,
12.5,
11.0,
10.7,
9.9,
9.5,
11.0,
8.9,
4.8,
5.3,
6.3,
],
"region": [
"Asia",
"Asia",
"Asia",
"S. America",
"N. America",
"Africa",
"Asia",
"Asia",
"Asia",
"Asia",
"N. America",
"Asia",
"S. America",
"Asia",
"Europe",
"Asia",
"Asia",
"Africa",
"S. America",
"N. America",
"Europe",
"Europe",
"Asia",
"Asia",
"Europe",
"S. America",
"N. America",
"Africa",
"Oceania",
"N. America",
],
}
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, 125, 145, 153, 148, 140, 130, 115, 113]
au_lat = [-20, -14, -16, -28, -40, -38, -32, -34, -20]
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)
# Define region colors using Okabe-Ito palette
region_colors = {
"Africa": IMPRINT[0],
"Asia": IMPRINT[1],
"Europe": IMPRINT[2],
"N. America": IMPRINT[3],
"Oceania": IMPRINT[4],
"S. America": IMPRINT[5],
}
# Create the geographic scatter map
plot = (
ggplot()
+ geom_polygon(
aes(x="lon", y="lat", group="continent"),
data=df_continents,
fill=BASEMAP_COLOR,
color=BASEMAP_EDGE,
size=0.5,
alpha=0.6,
)
+ geom_point(aes(x="longitude", y="latitude", color="region", size="population"), data=df, alpha=0.8, stroke=0.5)
+ scale_color_manual(values=list(region_colors.values()), name="Region")
+ scale_size_continuous(range=(4, 20), name="Population (M)")
+ labs(title="scatter-map-geographic · python · plotnine · anyplot.ai", x="Longitude (°)", y="Latitude (°)")
+ coord_fixed(ratio=1.0, xlim=(-180, 180), ylim=(-60, 80))
+ theme_minimal()
+ theme(
figure_size=(16, 9),
plot_title=element_text(size=24, color=INK),
axis_title=element_text(size=20, color=INK),
axis_text=element_text(size=16, color=INK_SOFT),
legend_title=element_text(size=18, color=INK),
legend_text=element_text(size=14, color=INK_SOFT),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT, size=0.3),
legend_position="right",
panel_grid_major=element_line(color=GRID_COLOR, size=0.3, alpha=0.08),
panel_grid_minor=element_blank(),
panel_background=element_rect(fill=PAGE_BG, color=None),
plot_background=element_rect(fill=PAGE_BG, color=None),
panel_border=element_blank(),
)
)
# Save PNG
plot.save(f"plot-{THEME}.png", dpi=300, verbose=False)
Part of Scatter Map with Geographic Points on anyplot.ai.