Voronoi diagrams, or Dirichlet tessellation, are everywhere in nature. It’s a simple pattern that has incredible properties with applications in many fields, including computer science.
Written by Francesco Bellelli Published on Feb. 22, 2023
Voronoi diagrams, also known as Dirichlet tessellation or Thiessen polygons, are everywhere in nature. You’ve likely encountered them thousands of times, but perhaps didn’t know what they were called. Voronoi diagrams are simple, yet they have incredible properties that have applications in fields ranging from cartography, biology, computer science, statistics, archaeology, all the way to architecture and arts.
A Voronoi diagram is a type of tessellation pattern in which a number of points scattered on a plane subdivides in exactly n cells enclosing a portion of the plane that is closest to each point. The pattern can be found in nature, such as in cells and a giraffe’s coat, architecture, art and computer science.
Suppose you have n points scattered on a plane, the Voronoi diagram of those points subdivides the plane in exactly n cells enclosing the portion of the plane that is the closest to each point. This produces a tessellation that completely covers the plane. In the illustration below, I plotted 100 random points and their corresponding Voronoi diagram. As you can see, every point is enclosed in a cell, whose boundaries are equidistant between two or more points. In other words, the area enclosed in the cell is closer to the point in the cell than to any other point.
Voronoi patterns can be found in a number of places, including nature, architecture and the arts. Let’s look at some examples.
\Voronoi diagram patterns are common in nature. From microscopic cells in onion skins, to the shell of jackfruits and the coat of giraffes, these patterns are everywhere.
A reason for their omnipresence is that they form efficient shapes. As we mentioned earlier, a Voronoi diagram completely tessellates the plane. All space is used. This is very convenient if you are trying to squeeze as much as possible in a limited space — such as in muscle fibers or bee hives. Voronoi diagrams are also a spontaneous pattern whenever something is growing at a uniform growth rate from separate points as in the illustration below. For instance, this explains why giraffes exhibit such a pattern. Giraffe embryos have a scattered distribution of melanin-secreting cells, which is responsible for the dark pigmentation of the giraffe’s spots. Over the course of the gestation these cells release melanin — hence spots radiate outward. A study from researchers Marcelo Walter, Alan Fournier and Menevaux also explores this concept of using Voronoi diagrams to model computer rendering of spots on animal coats.
Perhaps because of their spontaneous, natural look, or simply because of their mesmerizing randomness, Voronoi patterns have intentionally been implemented in human-made structures. An architectural example is the “Water cube,” which was built to house water sports during the 2008 Beijing Olympics. It features Voronoi diagrams on its ceiling and façades. The Voronoi diagrams were chosen because they recall bubbles . This analogy is clear at night, when the entire façade is illuminated in blue and comes alive.
But appreciation for the Voronoi pattern is surely older than this building in China. Guan and Ge ware from the Song dynasty have a distinctive crackled glaze. Ceramics can easily crack during the cooling process, however the crackles from the Guan and Ge ware are different because they are intentional. They were sought after because of their aesthetic qualities. Thanks to the Voronoi-like patterns on their surface, each piece is unique. To date, they are one of the most imitated styles of porcelain.
Voronoi diagrams are also common in graphic arts for creating “abstract” patterns. I think they make excellent background images. For example, I created the thumbnail of this post by generating random points and constructing a Voronoi diagram. Then, I coloured each cell based on the distance of its point from a randomly selected spot in the box. Endless abstract backgrounds images could be generated this way.
So far, we have presented a simple two-dimensional Voronoi diagram. However, the same type of structure can be generalized to an n-dimensional space. Suppose P= is a set of m points in our n -dimensional space. Then the space can be partitioned in m Voronoi cells, Vi , containing all points in Rn that are closer to pi than to any other point.
Where the function d(x,y) gives the distance ( a ) between its two arguments. Typically, the Euclidean distance is used (l2 distance):
However, Voronoi diagrams could be designed using other distance functions. For instance, the equation below shows a Voronoi diagram obtained with the Manhattan or cityblock distance ( l1 distance). The Manhattan distance is the distance between two points if you had to follow a regular grid — such as the city blocks of Manhattan. The result is a more “boxy” Voronoi diagram.
Euclidean distance is the most common distance measure in scientific applications of the Voronoi diagram. It also has the advantage of generating Voronoi cells that are convex. That is to say, if you take any two points within a cell, the line that connects the two points will lie entirely within the cell.
Finally, it should also be noted that Voronoi diagrams are tightly linked with the k-nearest neighbors algorithm (k-NN) — a very popular algorithm in classification , regression and clustering problems. The algorithm uses the k closest examples in the training data set to make value predictions. Since the Voronoi diagrams partition the space in polygons containing the closest points to each seed, the edges of Voronoi cells correspond exactly to the decision boundaries of a simple 1-nearest neighbor problem.
If you take each of the points from a Voronoi diagram and link it with the points in its neighboring cells, you will obtain a graph called the Delaunay triangulation. In mathematical terms, the Delaunay triangulation is the dual graph of the Voronoi diagram. In the Figure below, a Voronoi diagram (black) and Delaunay triangulation (gray) is plotted from a set of points.
Delaunay triangulation is just as amazing as Voronoi diagrams. As the name suggests, it produces a set of triangles linking our points. These triangles are such that if one were to draw a circle across the vertices of these triangles, there would be no other point inside the circle. Moreover, Delaunay triangulation also has the property of maximizing the smallest angle in the triangles of the triangulation. Hence, Delaunay triangulation tends to avoid triangles with acute angles.
These properties make it very useful in modeling surfaces and objects from a set of points. For instance, the Delaunay triangulation is used to generate meshes for the finite element method , construct 3D models for computer animations and model terrain in geographic information system (GIS) analysis.
Llyod’s algorithm is a useful algorithm related to Voronoi diagrams. The algorithm consists of repeatedly alternating between constructing Voronoi diagrams and finding the centroids (i.e. center of mass) of each cell. At each iteration, the algorithm spaces the points apart and produces more homogeneous Voronoi cells.
After a few iterations, the cells will have a “rounder” aspect, and points will be more evenly distributed. This is illustrated in the figure below, in which I have plotted the first 30 iterations of the Lloyd’s algorithm for a random set of points. For each point, I also record their starting position with a gray hollow circle to better trace the movement of each cell. For a high number of iterations, the diagram tends to converge towards a stable Voronoi diagram in which every seed is also the centroid of the cell — also known as the centroidal Voronoi diagram. Interestingly, in 2D, Voronoi cells will tend to turn into hexagons because they provide the most efficient way of packing shapes in a plane. As any bee building their hive can certify, hexagonal cells have two big advantages:
In data science, Lloyd’s algorithm is the basis of k-means clustering — one of the most popular clustering algorithms. K-means clustering is typically initiated by taking k random “centroids” in space. Then, data points are grouped in k clusters by alternating between assigning data points to the closest “centroid” (this is equivalent to building a Voronoi diagram for the centroid and checking which point are inside the cell) and updating the centroid by calculating the mean of the points inside each cell.
Lloyd’s algorithm is used in a variety of applications. For instance, it is very common in quantization and lossy data compression algorithms, for example the Lloyd-Max algorithm . It is also useful whenever one wants random points that are nicely spaced. For instance, it could be used to generate smooth meshes from the Delaunay triangulation, for dithering images, or as a basis for procedural maps generation in video games.
A tutorial on how to construct a Voronoi diagram. | Video: Blue Shirt Khaki PantsOne could construct Voronoi diagrams by building each cell one by one. If one extends the bisector of the segments linking every combination of points, it’s possible to obtain the outline of Voronoi cells. However, this technique is quite inefficient. Considering there are 0.5*(n-1)n combinations of points, the complexity of such an algorithm would increase quadratically with the number of points.
More efficient alternatives have been proposed. For example, the Sweep line algorithm builds Voronoi cells progressively by sequentially using binary search tree and priority queue operations. Another way to construct Voronoi diagrams is to first build Delaunay triangulations. Once the triangulation is obtained, extending the bisectors of the triangle edges leads to the Voronoi diagram. Delaunay triangulation can be obtained without considering every pair of points. For instance, an efficient technique consists of projecting the points on a paraboloid in a higher dimension. Re-projecting the convex hull onto the original space gives the Delaunay triangulation.
There are also different algorithms for computing Voronoi diagrams. New algorithms are continuously being proposed to improve computation efficiency in different circumstances. There are also techniques requiring constant time that generate approximate Voronoi diagrams, such as the jump flooding algorithm.
