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: plotly 6.7.0 | Python 3.13.13
Quality: 91/100 | Updated: 2026-05-18
"""
import os
import numpy as np
import plotly.graph_objects as go
# Theme tokens
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"
LAND_COLOR = "#E5E5E5" if THEME == "light" else "#3A3A36"
OCEAN_COLOR = "#D4E8F2" if THEME == "light" else "#2A3F4D"
COAST_COLOR = "#999999" if THEME == "light" else "#666666"
COUNTRY_COLOR = "#CCCCCC" if THEME == "light" else "#555555"
# Data: Global environmental sensor network monitoring air quality
np.random.seed(42)
# Sensor locations distributed across continents
# Urban and industrial regions with higher sensor density
n_points = 85
# North America - Eastern US industrial corridor
n_na = 25
na_lat = np.concatenate(
[
np.random.uniform(40, 45, 15), # Northeast corridor
np.random.uniform(32, 38, 10), # Southeast
]
)
na_lon = np.concatenate(
[
np.random.uniform(-82, -70, 15), # Northeast
np.random.uniform(-85, -75, 10), # Southeast
]
)
# Europe - Industrial centers
n_eu = 20
eu_lat = np.concatenate(
[
np.random.uniform(50, 55, 10), # Central Europe
np.random.uniform(45, 50, 10), # Mediterranean
]
)
eu_lon = np.concatenate(
[
np.random.uniform(5, 15, 10), # Central Europe
np.random.uniform(10, 25, 10), # Mediterranean
]
)
# Asia - Rapid development zones
n_asia = 25
asia_lat = np.concatenate(
[
np.random.uniform(30, 40, 12), # China, India
np.random.uniform(10, 20, 8), # Southeast Asia
np.random.uniform(-10, 10, 5), # Indonesia
]
)
asia_lon = np.concatenate(
[
np.random.uniform(100, 120, 12), # China, India
np.random.uniform(95, 110, 8), # Southeast Asia
np.random.uniform(110, 140, 5), # Indonesia
]
)
# Africa - Growing urban centers
n_africa = 10
africa_lat = np.random.uniform(-35, 20, n_africa)
africa_lon = np.random.uniform(-20, 55, n_africa)
# Australia-Pacific
n_pac = 5
pac_lat = np.random.uniform(-40, -15, n_pac)
pac_lon = np.random.uniform(110, 180, n_pac)
# Combine all data
latitudes = np.concatenate([na_lat, eu_lat, asia_lat, africa_lat, pac_lat])
longitudes = np.concatenate([na_lon, eu_lon, asia_lon, africa_lon, pac_lon])
# Air quality index (AQI) values scaled 0-500 (higher = worse air)
aqi_values = np.concatenate(
[
np.random.uniform(20, 120, n_na), # North America - moderate
np.random.uniform(25, 140, n_eu), # Europe - moderate to high
np.random.uniform(30, 250, n_asia), # Asia - wide range
np.random.uniform(15, 180, n_africa), # Africa - growing industrial
np.random.uniform(20, 80, n_pac), # Pacific - cleaner
]
)
# Measurement counts (number of readings per sensor in the last month)
measurement_counts = np.concatenate(
[
np.random.uniform(15, 30, n_na),
np.random.uniform(20, 30, n_eu),
np.random.uniform(10, 30, n_asia),
np.random.uniform(8, 25, n_africa),
np.random.uniform(12, 28, n_pac),
]
)
# Scale point sizes based on measurement counts
sizes = (measurement_counts - measurement_counts.min()) / (
measurement_counts.max() - measurement_counts.min()
)
sizes = sizes * 28 + 6 # Scale to 6-34 range for visibility
# Create hover text
hover_texts = [
f"AQI: {aqi:.0f}<br>Readings: {count:.0f}"
for aqi, count in zip(aqi_values, measurement_counts, strict=True)
]
# Create figure with geographic scatter
fig = go.Figure()
fig.add_trace(
go.Scattergeo(
lat=latitudes,
lon=longitudes,
mode="markers",
marker={
"size": sizes,
"color": aqi_values,
"colorscale": "Viridis",
"colorbar": {
"title": {"text": "AQI", "font": {"size": 20}},
"tickfont": {"size": 16},
"len": 0.6,
"thickness": 25,
"x": 1.02,
},
"line": {"width": 1, "color": INK},
"opacity": 0.85,
},
text=hover_texts,
hovertemplate="<b>Lat:</b> %{lat:.2f}°<br><b>Lon:</b> %{lon:.2f}°<br>%{text}<extra></extra>",
)
)
# Layout with geographic projection
fig.update_layout(
title={
"text": "scatter-map-geographic · python · plotly · anyplot.ai",
"font": {"size": 28, "color": INK},
"x": 0.5,
"xanchor": "center",
},
geo={
"projection_type": "natural earth",
"showland": True,
"landcolor": LAND_COLOR,
"showocean": True,
"oceancolor": OCEAN_COLOR,
"showcoastlines": True,
"coastlinecolor": COAST_COLOR,
"coastlinewidth": 1,
"showcountries": True,
"countrycolor": COUNTRY_COLOR,
"countrywidth": 0.5,
"showlakes": True,
"lakecolor": OCEAN_COLOR,
"bgcolor": PAGE_BG,
},
paper_bgcolor=PAGE_BG,
plot_bgcolor=PAGE_BG,
margin={"l": 20, "r": 100, "t": 80, "b": 20},
font={"color": INK},
)
# Add size legend annotation
fig.add_annotation(
x=1.02,
y=0.15,
xref="paper",
yref="paper",
text="<b>Point Size</b><br>= # Readings",
showarrow=False,
font={"size": 16, "color": INK},
align="left",
)
# Save as PNG and HTML
script_dir = os.path.dirname(os.path.abspath(__file__))
fig.write_image(
os.path.join(script_dir, f"plot-{THEME}.png"), width=1600, height=900, scale=3
)
fig.write_html(os.path.join(script_dir, f"plot-{THEME}.html"), include_plotlyjs="cdn")
Part of Scatter Map with Geographic Points on anyplot.ai.