d3-voronoi-treemap

This D3 plugin produces a _Voronoï treemap_. Given a convex polygon and nested weighted data, it tesselates/partitions the polygon in several inner cells which represent the hierarchical structure of your data, such that the area of a cell represents the weight of the underlying datum.

Because a picture is worth a thousand words:

!square
!hexagon
!diamond
!circle

Available only for d3 v4, d3 v5 and d3 v6.

If you're interested on one-level map, take a look at the d3-voronoi-map plugin, which may be simpler to use (no need of a d3-hierarchy).

Context

D3 already provides a d3-treemap module which produces a rectangular treemap. Such treemaps could be distorted to fit shapes that are not rectangles (cf. Distorded Treemap - d3-shaped treemap).

This plugin allows to compute a treemap with a unique look-and-feel, where inner areas are not strictly aligned each others, and where the outer shape can be any hole-free convex polygons (squares, rectangles, pentagon, hexagon, ... any regular convex polygon, and also any non regular hole-free convex polygon).

The drawback is that the computation of a Voronoï treemap is based on a iteration/looping process. Hence, it requires _some times_, depending on the number and type of data/weights, the desired representativeness of cell areas.

Examples

- Real life use cases
- [https://unearthed.greenpeace.org/2020/02/20/pesticides-croplife-hazardous-bayer-syngenta-health-bees/] by Nadieh Bremer for Greenpeace (more details at https://www.visualcinnamon.com/portfolio/highly-hazardous-pesticides)
- Brussels. A lovely Melting-Pot. by Karim Douieb, with a really great animation that clearly and easily explains to the reader how to read/understand the Voronoï treemap
- Earth's Land Surface by Luca Urzì in Tableau
- Examples with available code
- The Global Economy by GDP, a remake of HowMuch.net's article

Installing

If you use NPM, npm install d3-voronoi-treemap. Otherwise, load https://rawcdn.githack.com/Kcnarf/d3-voronoi-treemap/v1.1.2/build/d3-voronoi-treemap.js (or its d3-voronoi-treemap.min.js version) to make it available in AMD, CommonJS, or vanilla environments. In vanilla, you must load the d3-weighted-voronoi and d3-voronoi-map plugins prioir to this one, and a d3 global is exported:

``html





`

If you're interested in the latest developments, you can use the master build, available throught:

`html

`

TL;DR;

In your javascript, in order to define the tessellation:

`javascript
function weightAccessor(d) {
return d.weight; // computes the weight of one of your data; depending on your data, it may be 'd.area', or 'd.percentage', ...
}
var rootNode = d3.hierarchy(nestedData); // a d3-hierarchy of your nested data
rootNode.sum(weightAccessor); // assigns the adequate weight to each node of the d3-hierarchy

var voronoiTreemap = d3.voronoiTreemap().clip([
[0, 0],
[0, height],
[width, height],
[width, 0],
]); // sets the clipping polygon
voronoiTreemap(rootNode); // computes the weighted Voronoi tessellation of the d3-hierarchy; assigns a 'polygon' property to each node of the hierarchy
`

Then, later in your javascript, in order to draw cells:

`javascript
var allNodes = rootNode.descendants();
d3.selectAll('path')
.data(allNodes)
.enter()
.append('path')
.attr('d', function (d) {
// d is a node
return d3.line()(d.polygon) + 'z'; // d.polygon is the computed Voronoï cell encoding the relative weight of your underlying original data
})
.style('fill', function (d) {
return fillScale(d.data); // d.data is your original data
});
`

Reference

- based on Computing Voronoï Treemaps - Faster, Simpler, and Resolution-independent
- https://github.com/ArlindNocaj/power-voronoi-diagram for a Java implementation

API

# d3.voronoiTreemap()

Creates a new voronoiTreemap with the default configuration values and functions (_clip_, _extent_, _size_, _convergenceRatio_, _maxIterationCount_, _minWeightRatio_ and _prng_).

# voronoiTreemap(root)

Computes the Voronoï treemap for the specified d3-hierarchy, where _root_ is the root node of the hierarchy, assigning a _polygon_ property on the root and its descendants. A polygon is represented as an array of points \[_x_, _y_\] where _x_ and _y_ are the point coordinates, a _site_ field that refers to its site (ie. with x, y and weight retrieved from the original data), and a _site.originalObject_ field that refers to the corresponding element in _data_. Polygons are open: they do not contain a closing point that duplicates the first point; a triangle, for example, is an array of three points. Polygons are also counterclockwise (assuming the origin ⟨0,0⟩ is in the top-left corner).

As others d3-hierarchy layouts (rectangular treemap, or circle packing), the Voronoï treemap layout considers the weight of a node to be the _value_ propertyof that node. Hence, you must call root.sum before passing the hierarchy to the Voronoï treemap layout, in order to properly set the _value_ property of each node (root, intermediates and leaves). For example, considering that your original nested data have leaves with a _weight_ property, you must use rootNode.sum(function(d){ return d.weight; }).

# voronoiTreemap.clip([clip])

If _clip_ is specified, sets the clipping polygon, , compute the adequate _extent_ and _size_, and returns this layout . _clip_ defines a hole-free convex polygon, and is specified as an array of 2D points \[x, y\], which must be _(i)_ open (no duplication of the first D2 point) and _(ii)_ counterclockwise (assuming the origin ⟨0,0⟩ is in the top-left corner). If _clip_ is not specified, returns the current clipping polygon, which defaults to:

`js
[
[0, 0],
[0, 1],
[1, 1],
[1, 0],
];
`

# voronoiTreemap.extent([extent])

If _extent_ is specified, it is a convenient way to define the clipping polygon as a rectangle. It sets the extent, computes the adequate _clip_ping polygon and _size_, and returns this layout. _extent_ must be a two-element array of 2D points \[x, y\], which defines the clipping polygon as a rectangle with the top-left and bottom-right corners respectively set to the first and second points (assuming the origin ⟨0,0⟩ is in the top-left corner on the screen). If _extent_ is not specified, returns the current extent, which is [[minX, minY], [maxX, maxY]] of current clipping polygon, and which defaults to:

`js
[
[0, 0],
[1, 1],
];
`

# voronoiTreemap.size([size])

If _size_ is specified, it is a convenient way to define the clipping polygon as a rectangle. It sets the size, computes the adequate _clip_ping polygon and _extent_, and returns this layout. _size_ must be a two-element array of numbers [width, height], which defines the clipping polygon as a rectangle with the top-left corner set to [0, 0] and the bottom-right corner set to [width, height] (assuming the origin ⟨0,0⟩ is in the top-left corner on the screen). If _size_ is not specified, returns the current size, which is [maxX-minX, maxY-minY] of current clipping polygon, and which defaults to:

`js
[1, 1];
`

# voronoiTreemap.convergenceRatio([convergenceRatio])

If _convergenceRatio_ is specified, sets the convergence ratio, which stops computation when (cell area errors / (_clip_-ping polygon area) <= _convergenceRatio_. If _convergenceRatio_ is not specified, returns the current _convergenceRatio_ , which defaults to:

`js
var convergenceRatio = 0.01; // stops computation when cell area error <= 1% clipping polygon's area
`

The smaller the _convergenceRatio_, the more representative is the treemap, the longer the computation takes time.

# voronoiTreemap.maxIterationCount([maxIterationCount])

If _maxIterationCount_ is specified, sets the maximum allowed number of iterations, which stops computation when it is reached, even if the _convergenceRatio_ is not reached. If _maxIterationCount_ is not specified, returns the current _maxIterationCount_ , which defaults to:

`js
var maxIterationCount = 50;
`

If you want to wait until computation stops _only_ when the _convergenceRatio_ is reached, just set the _maxIterationCount_ to a large amount. Be warned that computation may take a huge amount of time, due to flickering behaviours in later iterations.

# voronoiTreemap.minWeightRatio([minWeightRatio])

If _minWeightRatio_ is specified, sets the minimum weight ratio, which allows to compute the minimum allowed weight (_= maxWeight \* minWeightRatio_). If _minWeightRatio_ is not specified, returns the current _minWeightRatio_ , which defaults to:

`js
var minWeightRatio = 0.01; // 1% of maxWeight
`

_minWeightRatio_ allows to mitigate flickerring behaviour (caused by too small weights), and enhances user interaction by not computing near-empty cells.

# voronoiTreemap.prng([prng])

If _prng_ is specified, sets the pseudorandom number generator which is used when randomness is required (i.e. when setting intial random position of data/seeds). The given pseudorandom number generator must implement the same interface as Math.random and must only return values in the range 0, 1). If _prng_ is not specified, returns the current _prng_ , which defaults to Math.random.

Considering the same set of data, severall Voronoï treemap computations lead to disctinct final arrangements, due to the non-seedable Math.random number generator. If _prng_ is set to a _seedable_ PRNG which produces repeatable results, then several computations will produce the exact same final arrangement. This is useful if you want the same arrangement for distinct page loads/reloads. For example, using [seedrandom:

`js


``

You can also take a look at d3-random for random number generator from other-than-uniform distributions.

Dependencies

- d3-voronoi-map.voronoiMap

Semantic Versioning

d3-voronoi-treemap attempts to follow semantic versioning and
bump major version only when backwards incompatible changes are released.