Is likely to produce inaccurate readings or outliers.

It may come from measuring equipment that When dealing with real-world interpolation problems the data In addition, the interpolant was evaluated well within the convex For example,Ĭlusters of points were not separated by relatively large distances. In addition, the points were relatively uniformly spaced. With the interpolation of point sets that were sampled on smooth surfaces. Many of the illustrative examples in the previous sections dealt The data set consists of a set of longitude ( x) and latitude ( y) locations, and corresponding seamount elevations ( z) measured at those coordinates.Īddressing Problems in Scattered Data Interpolation Plot the seamount data set (a seamount is an underwater mountain). This example shows how the griddata function interpolates scattered data at a set of grid points and uses this gridded data to create a contour plot. You might want to queryĪt arbitrary locations within the convex hull of the points. Interpolation is more general in practice. Locations the intent is to produce gridded data, hence the name. These two functions interpolate scattered data at predefined grid-point Lets you define the points in terms of X, Y / X, Y, Z coordinates. The calling syntax is similar for eachįunction the primary distinction is the 2-D / 3–D griddata function The griddata and griddatan functions take a set of sampleĪnd query points, Xq, and return the interpolated Interpolating Scattered Data Using griddata and griddatan To a wider range of interpolation problems. This class is encouraged as it is more efficient and readily adapts Supports scattered data interpolation in 2-D and 3-D space. To the exponential growth in memory required by the underlying triangulation. In dimensions higher than 6-D for moderate to large point sets, due Scattered data interpolation in N-D however, it is not practical The griddata function supports 2-D scatteredĭata interpolation. MATLAB ® provides two ways to perform triangulation-based The sample data is assumed to respect this property in order to produce a satisfactory interpolation. This is a single-valued function for any query point Xq within the convex hull of X, it will produce a unique value Vq. You can evaluate the interpolant at a query point Xq, to give Vq = F(Xq). In more general terms, given a set of points X and corresponding values V, you can construct an interpolant of the form V = F(X). Though the illustration highlights 2-D interpolation, you can apply this technique to higher dimensions. These methods and their variants are covered in texts and references on scattered data interpolation. You could also compute the weighted sum of values of the three vertices of the enclosing triangle (the linear interpolation method). You could compute the nearest point in the neighborhood and use the value at that point (the nearest-neighbor interpolation method). Once you find the point, the subsequent steps to compute the value depend on the interpolation method. This step generally involves traversing of the triangulation data structure to find the triangle that encloses the query point.

Gridded interpolation matlab