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.
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