Natural Neighbors

A rich duke with 100 children decides to divide his property in an unusual way. First, he tells each child to stand somewhere (anywhere they like) on the property. When each child has found a spot, a team of surveyors comes and marks every square of ground according to the child closest to it. For instance, each piece of ground that is closer to Child 43 than to any other child is marked with a 43; each piece that is closer to Child 57 than to anyone else is marked with a 57, and so on.

When the process is finished, each child stands inside a boundary, drawn with chalk, that marks the land that is closer to him or her than to anyone else. This is the property he or she inherits.

If you think of these children as a set of points, the lines drawn around their properties would be Voronoi polygons. Voronoi polygons are important in Natural Neighbors interpolation.

After a long afternoon’s work, the entire property has been divided. Suddenly, the duke’s young wife shows up and announces that she is pregnant with his 101st child. She demands that this child, too, receive a share of the inheritance.

The duke, with a sigh, tells her to pick a spot of ground and stand on it. The surveyors redraw the property lines. They don’t have to start from the beginning—they just have to reshape the boundaries in the area around the duke’s wife. Now she, too, stands inside her own Voronoi polygon. Its area is made up of (or “stolen” according to the disgruntled children) parts of the half dozen or so polygons around her.

Think of the pregnant duchess as an estimation location. The six children—think of them as points—who had some of their property taken away are the "natural neighbors" of the estimation location. (There can be more or less than six, but six turns out to be a common number.)

Now forget about the duke and his children so we can talk about z-values without overloading the metaphor. In Natural Neighbors interpolation, the value of an estimated location is a weighted average of the values of the natural neighbors. The weighting is proportional to the area in the estimation location’s Voronoi polygon that was contributed by each natural neighbor’s polygon.

Since the output is a raster, the estimation locations are a regularly-spaced array equal to the number of raster cells.

Top: Voronoi polygons are constructed from the set of input points. Bottom: For each cell in the output raster, a weighted average of the cell center's natural neighbors is calculated.

 The Natural Neighbors Nitty Gritty

You may recall that a TIN is made by applying Delaunay triangulation to the input points. A location’s elevation value is then interpolated from the node values of the triangle in which it lies. In Natural Neighbors, a set of Voronoi polygons, rather than triangles, is constructed from the input points.

Left: Voronoi polygons for a set of input points. The boundary around each point encloses the area that is closer to that point than to any other point. Right: Voronoi polygons are closely related to Delaunay triangles. Polygon sides bisect triangle edges.

To estimate an unknown value at any location, such as the pink dot in the following graphic, a new Voronoi polygon is created for the location. The new polygon encroaches on the surrounding polygons. The input points that belong to these surrounding polygons are the natural neighbors of the point being estimated.

Left: The location to be estimated is marked by the pink dot. Right: the red points are the natural neighbors of the estimation location.

The value of the estimation location is a weighted average of the natural neighbor values.

Top left: The new Voronoi polygon is analyzed according to what percentage of its area came from each of the original polygons. The percentages are weights that are multiplied by the values of the Natural Neighbors. Bottom right: the value for the estimation location is 133. This is equal to (159*.3) + (115*.02) + (121*.35) + (113*.25) + (156*.03) + (146*.05.