Geographic Heatmap for Spatial Density — Seaborn

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.

Geographic Heatmap for Spatial Density rendered with Seaborn

Python source (Seaborn)

""" anyplot.ai
heatmap-geographic: Geographic Heatmap for Spatial Density
Library: seaborn 0.13.2 | Python 3.13.13
Quality: 83/100 | Updated: 2026-05-19
"""

import os

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns


# 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"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"

sns.set_theme(
    style="ticks",
    rc={
        "figure.facecolor": PAGE_BG,
        "axes.facecolor": PAGE_BG,
        "axes.edgecolor": INK_SOFT,
        "axes.labelcolor": INK,
        "text.color": INK,
        "xtick.color": INK_SOFT,
        "ytick.color": INK_SOFT,
        "grid.color": INK,
        "grid.alpha": 0.10,
        "legend.facecolor": ELEVATED_BG,
        "legend.edgecolor": INK_SOFT,
    },
)

# Data: Mobile app check-in density across central Tokyo districts
np.random.seed(42)

# Shinjuku — major rail hub and entertainment district
shinjuku_lat = np.random.normal(35.690, 0.012, 280)
shinjuku_lon = np.random.normal(139.700, 0.012, 280)

# Shibuya — commercial and youth culture centre
shibuya_lat = np.random.normal(35.660, 0.010, 220)
shibuya_lon = np.random.normal(139.699, 0.010, 220)

# Ginza — luxury retail and business district
ginza_lat = np.random.normal(35.672, 0.008, 180)
ginza_lon = np.random.normal(139.763, 0.008, 180)

# Akihabara — electronics and pop-culture district
akiba_lat = np.random.normal(35.700, 0.007, 140)
akiba_lon = np.random.normal(139.773, 0.007, 140)

# Scattered activity across central Tokyo
scattered_lat = np.random.uniform(35.63, 35.73, 180)
scattered_lon = np.random.uniform(139.67, 139.80, 180)

latitude = np.concatenate([shinjuku_lat, shibuya_lat, ginza_lat, akiba_lat, scattered_lat])
longitude = np.concatenate([shinjuku_lon, shibuya_lon, ginza_lon, akiba_lon, scattered_lon])

# Plot
fig, ax = plt.subplots(figsize=(16, 9))

# KDE geographic heatmap
sns.kdeplot(
    x=longitude,
    y=latitude,
    ax=ax,
    fill=True,
    cmap="YlOrRd",
    levels=30,
    thresh=0.02,
    alpha=0.85,
    cbar=True,
    cbar_kws={"label": "Check-in Density", "shrink": 0.8},
)

# Scatter overlay: individual check-in locations
ax.scatter(longitude, latitude, s=12, alpha=0.25, color=INK_MUTED, edgecolors="none", zorder=5)

# District labels provide geographic context
districts = [
    ("Shinjuku", 139.700, 35.701),
    ("Shibuya", 139.699, 35.649),
    ("Ginza", 139.763, 35.681),
    ("Akihabara", 139.773, 35.709),
]
for name, lon, lat in districts:
    ax.text(lon, lat, name, fontsize=13, color=INK, ha="center", va="center", fontweight="semibold", alpha=0.85)

# Axis style
ax.set_xlabel("Longitude (°E)", fontsize=20)
ax.set_ylabel("Latitude (°N)", fontsize=20)
ax.set_title(
    "Tokyo Check-ins · heatmap-geographic · python · seaborn · anyplot.ai",
    fontsize=22,
    fontweight="medium",
    color=INK,
    pad=15,
)
ax.tick_params(axis="both", labelsize=16)

ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f"{x:.2f}°"))
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda y, _: f"{y:.2f}°"))

ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
for s in ("left", "bottom"):
    ax.spines[s].set_color(INK_SOFT)

ax.grid(True, alpha=0.10, linewidth=0.8)
ax.set_aspect("equal", adjustable="box")

# Style colorbar axes (theme-adaptive tick labels and label color)
for cbar_ax in fig.axes:
    if cbar_ax is not ax:
        cbar_ax.tick_params(labelsize=14, colors=INK_SOFT)
        cbar_ax.yaxis.label.set_color(INK)
        cbar_ax.yaxis.label.set_fontsize(18)
        break

plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)

Part of Geographic Heatmap for Spatial Density on anyplot.ai.

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