[voronoi-basic] Voronoi Diagram for Spatial Partitioning

[MarkusNeusinger]

Voronoi diagram partitioning space into regions based on proximity to data points. Used in spatial analysis, computational geometry, and machine learning.

[claude]

✅ Spec ID: voronoi-basic

Rationale: Voronoi diagram is a fundamental computational geometry visualization for spatial partitioning; 'basic' indicates the standard 2D tessellation variant.

Add label approved to begin generation.

[github-actions]

✅ Specification Ready: voronoi-basic

PR: #3420

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specification.md

voronoi-basic: Voronoi Diagram for Spatial Partitioning

Description

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.

Applications

• Analyzing service area coverage for retail stores or emergency facilities based on customer proximity
• Visualizing territorial boundaries in ecological studies to show animal home ranges or plant distribution zones
• Mapping nearest-neighbor relationships in urban planning for optimizing resource placement like cell towers or hospitals

Data

• x (float) - X-coordinate of seed points
• y (float) - Y-coordinate of seed points
• label (string, optional) - Identifier for each seed point
• Size: 10-50 seed points recommended for clear visualization
• Example: Random or structured point distribution within a bounded region

Notes

• Cells should be clipped to a visible bounding box to prevent infinite regions
• Each Voronoi cell should be visually distinguishable through colors or edge styling
• Seed points should be clearly marked within their respective cells
• Consider using a color palette that allows easy differentiation of adjacent regions

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