Voronoi Diagram for Spatial Partitioning — Seaborn

A Voronoi diagram partitions a plane into regions based on the distance to a set of seed points, where each region contains all points closer to its seed than to any other. This visualization is essential for understanding spatial relationships, proximity analysis, and territorial boundaries. It reveals natural clustering patterns and helps identify areas of influence around data points.

Voronoi Diagram for Spatial Partitioning rendered with Seaborn

Python source (Seaborn)

""" anyplot.ai
voronoi-basic: Voronoi Diagram for Spatial Partitioning
Library: seaborn 0.13.2 | Python 3.13.13
Quality: 89/100 | Updated: 2026-05-17
"""

import os
import sys


if sys.path[0] == os.path.dirname(os.path.abspath(__file__)):
    sys.path.pop(0)

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib.patches import Polygon
from scipy.spatial import Voronoi


# 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"
BRAND = "#009E73"

# Okabe-Ito palette (canonical order) — define as seaborn-compatible list
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]

# Data generation with spatial context
np.random.seed(42)
n_points = 20
x = np.random.uniform(10, 90, n_points)
y = np.random.uniform(10, 90, n_points)
points = np.column_stack([x, y])
bbox = [0, 100, 0, 100]

# Mirror points for infinite region handling
margin = 200
mirror_points = []
for px, py in points:
    mirror_points.extend([[px, -margin], [px, 100 + margin], [-margin, py], [100 + margin, py]])
all_points = np.vstack([points, mirror_points])

# Compute Voronoi tessellation
vor = Voronoi(all_points)

# Configure seaborn with sophisticated theming
sns.set_theme(style="ticks")
sns.set_palette(IMPRINT)  # Set global palette to Okabe-Ito
sns.set_context("talk", font_scale=1.1)  # Enhanced font sizing via seaborn context

# Apply theme-adaptive rendering context
plt.rcParams.update(
    {
        "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,
    }
)

# Create figure
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)

# Get seaborn's color palette for dynamic color cycling
pal = sns.color_palette(IMPRINT)

# Draw Voronoi regions with dynamic color cycling from seaborn palette
for i in range(n_points):
    region_idx = vor.point_region[i]
    region = vor.regions[region_idx]
    if not region or -1 in region:
        continue

    polygon_vertices = vor.vertices[region].copy()
    polygon_vertices[:, 0] = np.clip(polygon_vertices[:, 0], bbox[0], bbox[1])
    polygon_vertices[:, 1] = np.clip(polygon_vertices[:, 1], bbox[2], bbox[3])

    # Use seaborn's palette cycling for color selection
    color = pal[i % len(pal)]
    poly = Polygon(polygon_vertices, facecolor=color, edgecolor=INK_SOFT, linewidth=2.5, alpha=0.7)
    ax.add_patch(poly)

# Draw Voronoi edges with seaborn-styled aesthetics
for ridge_idx, (p1, p2) in enumerate(vor.ridge_points):
    if p1 < n_points and p2 < n_points:
        v1, v2 = vor.ridge_vertices[ridge_idx]
        if v1 >= 0 and v2 >= 0:
            x_coords = np.clip([vor.vertices[v1, 0], vor.vertices[v2, 0]], bbox[0], bbox[1])
            y_coords = np.clip([vor.vertices[v1, 1], vor.vertices[v2, 1]], bbox[2], bbox[3])
            ax.plot(x_coords, y_coords, color=INK_SOFT, linewidth=2.5, alpha=0.9)

# Plot seed points with enhanced seaborn styling
df = pd.DataFrame({"x": x, "y": y})
sns.scatterplot(data=df, x="x", y="y", s=350, color=BRAND, edgecolor=INK, linewidth=2.5, ax=ax, zorder=10)

# Labels with seaborn-compatible font sizing
ax.set_xlabel("X Coordinate (km)", fontsize=20, color=INK)
ax.set_ylabel("Y Coordinate (km)", fontsize=20, color=INK)
ax.set_title("voronoi-basic · seaborn · anyplot.ai", fontsize=24, fontweight="medium", color=INK, pad=20)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)
ax.set_xlim(bbox[0], bbox[1])
ax.set_ylim(bbox[2], bbox[3])
ax.set_aspect("equal")

# Spine styling with seaborn theme-adaptive colors
for spine in ax.spines.values():
    spine.set_edgecolor(INK_SOFT)
    spine.set_linewidth(2)

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

Part of Voronoi Diagram for Spatial Partitioning on anyplot.ai.

Other implementations