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: letsplot 4.9.0 | Python 3.13.13
Quality: 91/100 | Updated: 2026-05-18
"""
import os
import numpy as np
import pandas as pd
from lets_plot import *
from lets_plot import ggsave
LetsPlot.setup_html()
# 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"
# Okabe-Ito palette - first series always #009E73
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD"]
# Data: Major world cities with population and region
np.random.seed(42)
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",
"Tianjin",
"Kinshasa",
"Guangzhou",
"Los Angeles",
"Moscow",
"Shenzhen",
"Lahore",
"Bangalore",
"Paris",
"Bogota",
"Jakarta",
"Chennai",
"Lima",
"Bangkok",
"Seoul",
"Nagoya",
"Hyderabad",
"London",
"Tehran",
"Chicago",
"Chengdu",
"Nanjing",
"Wuhan",
"Ho Chi Minh City",
"Luanda",
"Ahmedabad",
"Kuala Lumpur",
"Hong Kong",
"Hangzhou",
"Sydney",
],
"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,
39.34,
-4.44,
23.13,
34.05,
55.76,
22.54,
31.56,
12.97,
48.86,
4.71,
-6.21,
13.08,
-12.05,
13.76,
37.57,
35.18,
17.39,
51.51,
35.69,
41.88,
30.57,
32.06,
30.59,
10.82,
-8.84,
23.02,
3.14,
22.32,
30.27,
-33.87,
],
"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,
117.20,
15.27,
113.26,
-118.24,
37.62,
114.06,
74.35,
77.59,
2.35,
-74.07,
106.85,
80.27,
-77.04,
100.50,
127.00,
136.91,
78.49,
-0.13,
51.39,
-87.63,
104.07,
118.80,
114.31,
106.63,
13.23,
72.57,
101.69,
114.17,
120.15,
151.21,
],
"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,
13.6,
17.0,
14.3,
12.5,
12.5,
13.4,
13.5,
13.2,
11.0,
11.3,
11.2,
11.5,
11.0,
10.7,
9.9,
9.5,
10.5,
9.5,
9.4,
8.9,
9.4,
9.0,
8.3,
9.1,
8.9,
8.4,
8.3,
7.5,
8.2,
5.3,
],
"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",
"Asia",
"Africa",
"Asia",
"N. America",
"Europe",
"Asia",
"Asia",
"Asia",
"Europe",
"S. America",
"Asia",
"Asia",
"S. America",
"Asia",
"Asia",
"Asia",
"Asia",
"Europe",
"Asia",
"N. America",
"Asia",
"Asia",
"Asia",
"Asia",
"Africa",
"Asia",
"Asia",
"Asia",
"Asia",
"Oceania",
],
}
df = pd.DataFrame(cities_data)
# Continent basemap with Australia/Oceania
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 = [112, 130, 155, 168, 155, 145, 130, 115, 112]
au_lat = [-10, -10, -5, 0, -20, -35, -40, -30, -10]
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)
# Region color mapping using Okabe-Ito
region_colors = {
"Asia": IMPRINT[0], # #009E73
"Europe": IMPRINT[1], # #C475FD
"N. America": IMPRINT[2], # #4467A3
"S. America": IMPRINT[3], # #BD8233
"Africa": IMPRINT[4], # #AE3030
"Oceania": IMPRINT[5], # #2ABCCD
}
# Basemap color
basemap_fill = "#FFFDF6" if THEME == "light" else "#242420"
basemap_border = "#B0B0B0" if THEME == "light" else "#6B6A63"
# Create the geographic scatter map
plot = (
ggplot()
+ geom_polygon(
aes(x="lon", y="lat", group="continent"),
data=df_continents,
fill=basemap_fill,
color=basemap_border,
size=0.3,
alpha=0.6,
)
+ geom_point(
aes(x="longitude", y="latitude", color="region", size="population"),
data=df,
alpha=0.85,
tooltips=layer_tooltips()
.title("@city")
.line("Population|@population M")
.line("Region|@region"),
)
+ scale_color_manual(values=list(region_colors.values()), name="Region")
+ scale_size(range=[4, 18], name="Population (M)")
+ labs(
title="scatter-map-geographic · python · letsplot · anyplot.ai", x="Longitude", y="Latitude"
)
+ coord_fixed(ratio=1.0, xlim=[-180, 180], ylim=[-60, 80])
+ ggsize(1600, 900)
+ theme_minimal()
+ theme(
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
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),
axis_line=element_line(color=INK_SOFT, size=0.5),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_text=element_text(size=16, color=INK_SOFT),
legend_title=element_text(size=18, color=INK),
legend_position="bottom",
panel_grid_major=element_line(color=INK_SOFT, size=0.2),
panel_grid_minor=element_blank(),
)
)
# Save PNG (scale 3x to get 4800 x 2700 px)
ggsave(plot, filename=f"plot-{THEME}.png", path=".", scale=3)
# Save HTML for interactive version
ggsave(plot, filename=f"plot-{THEME}.html", path=".")
Part of Scatter Map with Geographic Points on anyplot.ai.