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MATLAB^{®} provides the necessary functions for performing a spatial search using either
a Delaunay triangulation or a general triangulation. The search queries that MATLAB supports are:

Nearest-neighbor search (sometimes called closest-point search or proximity search).

Point-location search (sometimes called point-in-triangle search or point-in-simplex search, where a simplex is a triangle, tetrahedron or higher dimensional equivalent).

Given a set of points `X`

and a query point `q`

in
Euclidean space, the nearest-neighbor search locates a point `p`

in
`X`

that is closer to `q`

than to any other point in
`X`

. Given a triangulation of `X`

, the point-location
search locates the triangle or tetrahedron that contains the query point
`q`

. Since these methods work for both Delaunay as well as general
triangulations, you can use them even if a modification of the points violates the Delaunay
criterion. You also can search a general triangulation represented in matrix format.

While MATLAB supports these search schemes in `N`

dimensions, exact
spatial searches usually become prohibitive as the number of dimensions extends beyond 3-D.
You should consider approximate alternatives for large problems in up to 10
dimensions.

There are a few ways to compute nearest-neighbors in MATLAB, depending on the dimensionality of the problem:

For 2-D and 3-D searches, use the

`nearestNeighbor`

method provided by the`triangulation`

class and inherited by the`delaunayTriangulation`

class.For 4-D and higher, use the

`delaunayn`

function to construct the triangulation and the complementary`dsearchn`

function to perform the search. While these N-D functions support 2-D and 3-D, they are not as general and efficient as the triangulation search methods.

This example shows how to perform a nearest-neighbor search in 2-D with `delaunayTriangulation`

.

Begin by creating a random set of 15 points.

X = [3.5 8.2; 6.8 8.3; 1.3 6.5; 3.5 6.3; 5.8 6.2; 8.3 6.5;... 1 4; 2.7 4.3; 5 4.5; 7 3.5; 8.7 4.2; 1.5 2.1; 4.1 1.1; ... 7 1.5; 8.5 2.75];

Plot the points and add annotations to show the ID labels.

plot(X(:,1),X(:,2),'ob') hold on vxlabels = arrayfun(@(n) {sprintf('X%d', n)}, (1:15)'); Hpl = text(X(:,1)+0.2, X(:,2)+0.2, vxlabels, 'FontWeight', ... 'bold', 'HorizontalAlignment','center', 'BackgroundColor', ... 'none'); hold off

Create a Delaunay triangulation from the points.

dt = delaunayTriangulation(X);

Create some query points and for each query point find the index of its corresponding nearest neighbor in `X`

using the `nearestNeighbor`

method.

```
numq = 10;
rng(0,'twister');
q = 2+rand(numq,2)*6;
xi = nearestNeighbor(dt, q);
```

Add the query points to the plot and add line segments joining the query points to their nearest neighbors.

xnn = X(xi,:); hold on plot(q(:,1),q(:,2),'or'); plot([xnn(:,1) q(:,1)]',[xnn(:,2) q(:,2)]','-r'); vxlabels = arrayfun(@(n) {sprintf('q%d', n)}, (1:numq)'); Hpl = text(q(:,1)+0.2, q(:,2)+0.2, vxlabels, 'FontWeight', ... 'bold', 'HorizontalAlignment','center', ... 'BackgroundColor','none'); hold off

Performing a nearest-neighbor search in 3-D is a direct extension of the 2-D example based on `delaunayTriangulation`

.

For 4-D and higher, use the `delaunayn`

and `dsearchn`

functions as illustrated in the following example:

Create a random sample of points in 4-D and triangulate the points using
`delaunayn`

:

X = 20*rand(50,4) -10; tri = delaunayn(X);

`X`

using the `dsearchn`

function:q = rand(5,4); xi = dsearchn(X,tri, q)

`nearestNeighbor`

method and the `dsearchn`

function allow the Euclidean distance between the query point and its
nearest-neighbor to be returned as an optional argument. In the 4-D example, you can compute
the distances, `dnn`

, as
follows:[xi,dnn] = dsearchn(X,tri, q)

A point-location search is a triangulation search algorithm that locates the simplex (triangle, tetrahedron, and so on) enclosing a query point. As in the case of the nearest-neighbor search, there are a few approaches to performing a point-location search in MATLAB, depending on the dimensionality of the problem:

For 2-D and 3-D, use the class-based approach with the

`pointLocation`

method provided by the`triangulation`

class and inherited by the`delaunayTriangulation`

class.For 4-D and higher, use the

`delaunayn`

function to construct the triangulation and the complementary`tsearchn`

function to perform the point-location search. Although supporting 2-D and 3-D, these N-D functions are not as general and efficient as the triangulation search methods.

This example shows how to use the `delaunayTriangulation`

class to perform a point location search in 2-D.

Begin with a set of 2-D points.

X = [3.5 8.2; 6.8 8.3; 1.3 6.5; 3.5 6.3; 5.8 6.2; ... 8.3 6.5; 1 4; 2.7 4.3; 5 4.5; 7 3.5; 8.7 4.2; ... 1.5 2.1; 4.1 1.1; 7 1.5; 8.5 2.75];

Create the triangulation and plot it showing the triangle ID labels at the incenters of the triangles.

dt = delaunayTriangulation(X); triplot(dt); hold on ic = incenter(dt); numtri = size(dt,1); trilabels = arrayfun(@(x) {sprintf('T%d', x)}, (1:numtri)'); Htl = text(ic(:,1), ic(:,2), trilabels, 'FontWeight', ... 'bold', 'HorizontalAlignment', 'center', 'Color', ... 'blue'); hold off

Now create some query points and add them to the plot. Then find the index of the corresponding enclosing triangles using the `pointLocation`

method.

q = [5.9344 6.2363; 2.2143 2.1910; 7.0948 3.6615; 7.6040 2.2770; 6.0724 2.5828; 6.5464 6.9407; 6.4588 6.1690; 4.3534 3.9026; 5.9329 7.7013; 3.0271 2.2067]; hold on; plot(q(:,1),q(:,2),'*r'); vxlabels = arrayfun(@(n) {sprintf('q%d', n)}, (1:10)'); Hpl = text(q(:,1)+0.2, q(:,2)+0.2, vxlabels, 'FontWeight', ... 'bold', 'HorizontalAlignment','center', ... 'BackgroundColor', 'none'); hold off

ti = pointLocation(dt,q);

Performing a point-location search in 3-D is a direct extension of performing a point-location search in 2-D with `delaunayTriangulation`

.

For 4-D and higher, use the `delaunayn`

and
`tsearchn`

functions as illustrated in the following example:

Create a random sample of points in 4-D and triangulate them using
`delaunayn`

:

X = 20*rand(50,4) -10; tri = delaunayn(X);

`tsearchn`

function:q = rand(5,4); ti = tsearchn(X,tri,q)

`pointLocation`

method and the `tsearchn`

function allow the
corresponding barycentric coordinates to be returned as an optional argument. In the 4-D
example, you can compute the barycentric coordinates as
follows:[ti,bc] = tsearchn(X,tri,q)

`nearestNeighbor`

| `delaunayTriangulation`

| `triangulation`

| `delaunayn`

| `dsearchn`

| `pointLocation`

| `triangulation`

| `tsearchn`

| `delaunay`