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.
""" anyplot.ai
voronoi-basic: Voronoi Diagram for Spatial Partitioning
Library: altair 6.1.0 | Python 3.13.13
Quality: 89/100 | Updated: 2026-05-17
"""
import os
import sys
# Remove current directory from path to avoid shadowing altair library
_current_dir = os.path.dirname(os.path.abspath(__file__))
_sys_path_backup = sys.path.copy()
sys.path = [p for p in sys.path if os.path.abspath(p) != _current_dir]
try:
import altair as alt
finally:
sys.path = _sys_path_backup
import numpy as np
import pandas as pd
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"
# Okabe-Ito palette
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
# Data - Generate seed points for weather stations
np.random.seed(42)
n_points = 15
x_points = np.random.uniform(5, 95, n_points)
y_points = np.random.uniform(5, 95, n_points)
labels = [f"Station {i + 1}" for i in range(n_points)]
points = np.column_stack([x_points, y_points])
# Add boundary points to help with clipping
x_min, x_max = 0, 100
y_min, y_max = 0, 100
margin = 200
boundary_points = []
for px, py in points:
boundary_points.append([2 * x_min - margin - px, py])
boundary_points.append([2 * x_max + margin - px, py])
boundary_points.append([px, 2 * y_min - margin - py])
boundary_points.append([px, 2 * y_max + margin - py])
all_points = np.vstack([points, boundary_points])
# Compute Voronoi diagram
vor = Voronoi(all_points)
# Create filled polygon data for Voronoi cells
polygon_data = []
for point_idx in range(n_points):
region_idx = vor.point_region[point_idx]
region = vor.regions[region_idx]
if not region or -1 in region:
continue
vertices = vor.vertices[region]
clipped_x = np.clip(vertices[:, 0], x_min, x_max)
clipped_y = np.clip(vertices[:, 1], y_min, y_max)
# Sort vertices by angle for proper polygon ordering
center_x = np.mean(clipped_x)
center_y = np.mean(clipped_y)
angles = np.arctan2(clipped_y - center_y, clipped_x - center_x)
sorted_indices = np.argsort(angles)
sorted_x = clipped_x[sorted_indices]
sorted_y = clipped_y[sorted_indices]
color = IMPRINT[point_idx % len(IMPRINT)]
station = labels[point_idx]
# Add vertices for filled polygon
for i in range(len(sorted_x)):
polygon_data.append(
{"x": sorted_x[i], "y": sorted_y[i], "order": i, "cell_id": point_idx, "station": station, "color": color}
)
# Close polygon by repeating first vertex
polygon_data.append(
{
"x": sorted_x[0],
"y": sorted_y[0],
"order": len(sorted_x),
"cell_id": point_idx,
"station": station,
"color": color,
}
)
df_polygons = pd.DataFrame(polygon_data)
# Create DataFrame for seed points
df_points = pd.DataFrame(
{
"x": x_points,
"y": y_points,
"label": labels,
"station": labels,
"color": [IMPRINT[i % len(IMPRINT)] for i in range(n_points)],
}
)
# Create filled Voronoi regions
voronoi_cells = (
alt.Chart(df_polygons)
.mark_line(filled=True, opacity=0.50, strokeWidth=2.5)
.encode(
x=alt.X("x:Q", scale=alt.Scale(domain=[x_min - 2, x_max + 2]), title="X Coordinate"),
y=alt.Y("y:Q", scale=alt.Scale(domain=[y_min - 2, y_max + 2]), title="Y Coordinate"),
color=alt.Color(
"station:N",
scale=alt.Scale(domain=labels, range=IMPRINT[:n_points]),
legend=alt.Legend(
title="Weather Stations",
titleFontSize=16,
labelFontSize=14,
columns=2,
orient="right",
symbolType="square",
symbolSize=150,
),
),
order="order:O",
detail="cell_id:N",
stroke=alt.value(INK_SOFT),
)
)
# Create seed points layer
points_layer = (
alt.Chart(df_points)
.mark_circle(size=300, stroke=PAGE_BG, strokeWidth=3)
.encode(
x="x:Q",
y="y:Q",
color=alt.Color("station:N", scale=alt.Scale(domain=labels, range=IMPRINT[:n_points]), legend=None),
tooltip=[
alt.Tooltip("label:N", title="Station"),
alt.Tooltip("x:Q", format=".1f", title="X"),
alt.Tooltip("y:Q", format=".1f", title="Y"),
],
)
)
# Add station labels with adjusted positioning to reduce overlap
labels_layer = (
alt.Chart(df_points)
.mark_text(dy=-18, fontSize=13, fontWeight="bold")
.encode(x="x:Q", y="y:Q", text="label:N", color=alt.value(INK))
)
# Combine layers
chart = (
(voronoi_cells + points_layer + labels_layer)
.properties(
width=1600,
height=900,
background=PAGE_BG,
title=alt.Title("voronoi-basic · altair · anyplot.ai", fontSize=28, anchor="middle"),
)
.configure_view(fill=PAGE_BG, stroke=INK_SOFT)
.configure_axis(
domainColor=INK_SOFT,
tickColor=INK_SOFT,
gridColor=INK,
gridOpacity=0.10,
labelColor=INK_SOFT,
titleColor=INK,
labelFontSize=18,
titleFontSize=22,
)
.configure_title(color=INK)
.configure_legend(
fillColor=ELEVATED_BG, strokeColor=INK_SOFT, labelColor=INK_SOFT, titleColor=INK, labelFontSize=14
)
)
# Save output
chart.save(f"plot-{THEME}.png", scale_factor=3.0)
chart.save(f"plot-{THEME}.html")
Part of Voronoi Diagram for Spatial Partitioning on anyplot.ai.