Global change research has placed new demands on methods of spatial analysis. In particular, spherical methods for spatial interpolation are required when spatial analyses are performed over large areas of the Earth's surface. In this article, spherical spatial interpolation procedures are reviewed, compared, and evaluated. Three classes of spherical interpolants are evaluated in detail: distance weighting, functional minimization, and tesselation. The strengths and weaknesses of a method from each of these classes—inverse-distance weighting, thin-plate splines, and surfaces fit to triangulated patches—are evaluated using a hypothetical mathematical surface and a global scale representation of topography. For smooth functions, such as the hypothetical mathematical surface, thin-plate splines produce a visually pleasing surface and have low interpolation error. For non-smooth surfaces, such as global topography, inverse-distance weighting, interpolating thin-plate splines, and triangulated C0 patches appear to handle rapid surface changes well. When choosing a spherical interpolant, the properties of the data being analyzed (e.g., smoothness, spatial coherence, etc.) must be taken into account. In addition, multivariate interpolation should be considered when related, ancillary data are available at higher spatial resolution than the original data.
No takes yet. Share an insight, caveat, or question.
Scott M. Robeson (1997) studied this question.