A geographic heatmap visualizes spatial density or intensity values across a map using continuous color gradients. Unlike choropleth maps that color discrete regions, this plot shows smooth density variations computed from point data or gridded values. The color intensity at each location represents the concentration or magnitude of the underlying data, making it ideal for identifying hotspots, clusters, and spatial patterns in geographic data.
""" anyplot.ai
heatmap-geographic: Geographic Heatmap for Spatial Density
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 87/100 | Updated: 2026-05-19
"""
import os
import numpy as np
import pandas as pd
from plotnine import (
aes,
coord_fixed,
element_blank,
element_line,
element_rect,
element_text,
geom_polygon,
geom_tile,
ggplot,
labs,
scale_fill_gradient,
theme,
theme_minimal,
)
from scipy.ndimage import gaussian_filter
from scipy.stats import gaussian_kde
# 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_BG = "#D4E8F7" if THEME == "light" else "#1A2A3A"
CONTINENT_COLOR = "#505050" if THEME == "light" else "#A0A09A"
# Data
np.random.seed(42)
# Generate synthetic earthquake event data (Pacific Ring of Fire and major seismic zones)
japan_lon = np.random.normal(140, 3, 400)
japan_lat = np.random.normal(36, 4, 400)
indo_lon = np.random.normal(115, 8, 350)
indo_lat = np.random.normal(-5, 4, 350)
chile_lon = np.random.normal(-72, 2, 300)
chile_lat = np.random.normal(-30, 10, 300)
calif_lon = np.random.normal(-120, 3, 250)
calif_lat = np.random.normal(37, 5, 250)
phil_lon = np.random.normal(122, 3, 200)
phil_lat = np.random.normal(12, 4, 200)
nz_lon = np.random.normal(175, 3, 150)
nz_lat = np.random.normal(-40, 3, 150)
med_lon = np.random.normal(25, 8, 200)
med_lat = np.random.normal(38, 3, 200)
scatter_lon = np.random.uniform(-170, 170, 150)
scatter_lat = np.random.uniform(-50, 60, 150)
all_lon = np.concatenate([japan_lon, indo_lon, chile_lon, calif_lon, phil_lon, nz_lon, med_lon, scatter_lon])
all_lat = np.concatenate([japan_lat, indo_lat, chile_lat, calif_lat, phil_lat, nz_lat, med_lat, scatter_lat])
# Kernel density estimation on a geographic grid
lon_grid = np.linspace(-180, 180, 120)
lat_grid = np.linspace(-60, 80, 70)
lon_mesh, lat_mesh = np.meshgrid(lon_grid, lat_grid)
points = np.vstack([all_lon, all_lat])
kde = gaussian_kde(points, bw_method=0.08)
density = kde(np.vstack([lon_mesh.ravel(), lat_mesh.ravel()]))
density = density.reshape(lon_mesh.shape)
density = gaussian_filter(density, sigma=1.5)
heatmap_rows = []
for i in range(len(lat_grid)):
for j in range(len(lon_grid)):
heatmap_rows.append({"longitude": lon_grid[j], "latitude": lat_grid[i], "density": density[i, j]})
df_heatmap = pd.DataFrame(heatmap_rows)
# Filter low-density ocean tiles to show clear hotspots only
threshold = df_heatmap["density"].quantile(0.45)
df_heatmap = df_heatmap[df_heatmap["density"] > threshold].copy()
# Simplified continent outlines for geographic context
continents = []
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]})
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]})
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]})
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]})
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]})
au_lon = [113, 125, 135, 145, 152, 150, 140, 130, 115, 113]
au_lat = [-22, -15, -12, -15, -25, -38, -38, -33, -35, -22]
for i in range(len(au_lon)):
continents.append({"continent": "Australia", "order": i, "lon": au_lon[i], "lat": au_lat[i]})
df_continents = pd.DataFrame(continents)
# Plot
plot = (
ggplot()
+ geom_tile(aes(x="longitude", y="latitude", fill="density"), data=df_heatmap, width=3.1, height=2.1, alpha=0.90)
+ scale_fill_gradient(low="#FFFFCC", high="#BD0026", name="Density")
+ geom_polygon(
aes(x="lon", y="lat", group="continent"), data=df_continents, fill="none", color=CONTINENT_COLOR, size=0.8
)
+ coord_fixed(ratio=1.0, xlim=(-180, 180), ylim=(-60, 80))
+ labs(
title="Seismic Activity Density · heatmap-geographic · python · plotnine · anyplot.ai",
x="Longitude (°)",
y="Latitude (°)",
)
+ theme_minimal()
+ theme(
figure_size=(16, 9),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=OCEAN_BG),
panel_grid_major=element_blank(),
panel_grid_minor=element_blank(),
axis_line=element_line(color=INK_SOFT, size=0.5),
plot_title=element_text(size=24, weight="bold", 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),
legend_position="right",
)
)
# Save
plot.save(f"plot-{THEME}.png", dpi=300, verbose=False)
Part of Geographic Heatmap for Spatial Density on anyplot.ai.