Several Spatial Interpolation Methods Implemented in MATLAB

This document provides detailed explanations of several spatial interpolation programs implemented in MATLAB, designed to assist beginners in understanding and applying these techniques. Spatial interpolation methods are computational approaches that transform limited data points into continuous surfaces, with common implementations including Kriging, spherical harmonic function interpolation, radial basis function (RBF) interpolation, and others. When programming these methods, developers must consider selecting the most appropriate algorithm for their specific dataset characteristics while implementing robust handling for potential noise and missing values in the data. Key programming considerations include parameter optimization through techniques like cross-validation to enhance interpolation accuracy. The implementation typically involves MATLAB's spatial statistics toolbox functions such as kriging for variogram modeling, or custom RBF functions utilizing Euclidean distance calculations with basis functions like Gaussian or multiquadric. Understanding the mathematical principles behind these interpolation methods and their corresponding MATLAB implementations is crucial for effective spatial analysis. We hope these explanations prove helpful, and please feel free to contact us with any questions or inquiries regarding the implementation details.