Voronoi Diagram for Spatial Partitioning — Matplotlib

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 Matplotlib

Python source (Matplotlib)

""" anyplot.ai
voronoi-basic: Voronoi Diagram for Spatial Partitioning
Library: matplotlib 3.10.9 | Python 3.13.13
Quality: 89/100 | Updated: 2026-05-17
"""

import os

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.collections import PolyCollection
from scipy.spatial import Voronoi


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"

# Okabe-Ito palette for Voronoi regions
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]

# Data - Generate seed points for Voronoi diagram
np.random.seed(42)
n_points = 15
x = np.random.uniform(1, 9, n_points)
y = np.random.uniform(1, 9, n_points)
points = np.column_stack([x, y])

# Create Voronoi tessellation with mirror points for bounded regions
# Add mirror points outside the boundary to ensure all regions are finite
x_min, x_max, y_min, y_max = 0, 10, 0, 10
mirror_points = np.vstack(
    [
        points,
        np.column_stack([x, 2 * y_min - y]),
        np.column_stack([x, 2 * y_max - y]),
        np.column_stack([2 * x_min - x, y]),
        np.column_stack([2 * x_max - x, y]),
    ]
)
vor = Voronoi(mirror_points)

# Create figure with theme-aware background
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)

# Collect polygons for original points only (first n_points)
polygons = []
poly_colors = []

for idx in range(n_points):
    region_idx = vor.point_region[idx]
    region = vor.regions[region_idx]

    if not region or -1 in region:
        continue

    # Get polygon vertices
    polygon = np.array([vor.vertices[v] for v in region])

    # Clip polygon to bounding box
    polygon[:, 0] = np.clip(polygon[:, 0], x_min, x_max)
    polygon[:, 1] = np.clip(polygon[:, 1], y_min, y_max)

    polygons.append(polygon)
    poly_colors.append(IMPRINT[idx % len(IMPRINT)])

# Draw all Voronoi regions at once
collection = PolyCollection(polygons, facecolors=poly_colors, edgecolors=INK_SOFT, linewidths=2.5, alpha=0.6)
ax.add_collection(collection)

# Plot seed points prominently with brand green
ax.scatter(x, y, s=350, c="#009E73", edgecolors="white", linewidths=3, zorder=5)

# Set bounds and styling
ax.set_xlim(-0.2, 10.2)
ax.set_ylim(-0.2, 10.2)
ax.set_xlabel("X Coordinate", fontsize=20, color=INK)
ax.set_ylabel("Y Coordinate", fontsize=20, color=INK)
ax.set_title("voronoi-basic · matplotlib · anyplot.ai", fontsize=24, color=INK)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)
for s in ("left", "bottom"):
    ax.spines[s].set_color(INK_SOFT)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.set_aspect("equal")
ax.yaxis.grid(True, alpha=0.12, linewidth=0.8, color=INK)

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.

Voronoi Diagram for Spatial Partitioning — Matplotlib

Python source (Matplotlib)

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