Interpolation is a process of predicting or estimating values at unmeasured points using sample points with known values. An interpolation is either performed within a region or across regions. The application program provides three interpolation methods: Inverse Distance Weighted (IDW), Kriging, and Radial Basis Function (RBF). Below are the links to their specific descriptions:
Introduction to Interpolation
The basic concepts and principles of the supported methods are described in details here.
Inverse Distance Weighted Interpolation
The Inverse Distance Weighted (IDW) interpolation method estimates the value of a cell using the weighted average of sample points around that cell based on the similarity between the points within the region. A surface is then generated.
Spline Interpolation
The spline interpolation method uses a mathematical expression with the minimum surface curvature to simulate a smooth curved surface that passes through a series of sample points.
Ordinary Kriging Interpolation
The ordinary Kriging method performs a linear estimation on a regional variable. It assumes that the observation data are normally distributed and the expected value of the regional variable is unknown.
Simple Kriging Interpolation
The simple Kriging method performs a linear estimation on a regional variable. It assumes that the observation data are normally distributed and the expected value of the regional variable is a fixed constant.
Universal Kriging Interpolation
The universal Kriging method is applied when a certain trend exists in the observation data and this trend can be simulated using a determinate function or polynomial.