Mathematics    

Triangulation and Interpolation of Scattered Data

MATLAB provides routines that aid in the analysis of closest-point problems and geometric analysis.

Functions for Analysis of Closest-Point Problems and Geometric Analysis  
Function
Description
convhull
Convex hull.
delaunay
Delaunay triangulation.
delaunay3
3-D Delaunay tessellation.
dsearch
Nearest point search of Delaunay triangulation.
inpolygon
True for points inside polygonal region.
polyarea
Area of polygon.
rectint
Area of intersection for two or more rectangles.
tsearch
Closest triangle search.
voronoi
Voronoi diagram.

This section applies the following techniques to the seamount data set supplied with MATLAB:

See also Tessellation and Interpolation of Scattered Data in Higher Dimensions.

Convex Hulls

The convhull function returns the indices of the points in a data set that comprise the convex hull for the set. For example, to view the convex hull for the seamount data.

Delaunay Triangulation

The delaunay function returns a set of triangles such that no data points are contained in any triangle's circumcircle. To try delaunay, load the seamount data set and view the data as a simple scatter plot.

Note For information on seamount, see Parker [2], pp 17-40.

Apply Delaunay triangulation and overplot the resulting triangles on the scatter plot.

Here's a contour plot.

The arguments for meshgrid encompass the largest and smallest x and y values in the original seamount data. To obtain these values, use

and

Closest-Point Searches.   You can search through the Delaunay triangulation data with two functions:

Voronoi Diagrams

Voronoi diagrams are a closest-point plotting technique related to Delaunay triangulation. Use the voronoi function to produce the Voronoi diagram for the seamount data.


 Interpolation and Multidimensional Arrays Tessellation and Interpolation of Scattered Data in Higher Dimensions