Interpolation uses known sample points to predict or estimate values at unknown locations, including two approaches: interpolation and extrapolation. The application provides three interpolation methods: Inverse Distance Weighted (IDW), Kriging, and Radial Basis Function (RBF). This section includes the following:

• About Interpolation: Detailed explanations of basic concepts and principles for various interpolation methods.
• IDW Interpolation: IDW interpolation estimates cell values by calculating the weighted mean of neighboring sample points based on spatial similarity within the interpolation area, thereby generating a surface.
• Spline Interpolation: Spline interpolation employs mathematical expressions of minimum surface curvature to simulate smooth surfaces passing through sample points.
• Ordinary Kriging Interpolation: Ordinary Kriging provides linear estimation of regionalized variables, assuming normally distributed observed data with unknown expectation of the regionalized variable.
• Simple Kriging Interpolation: Simple Kriging offers linear estimation of regionalized variables, assuming normally distributed observed data with fixed constant expectation of the regionalized variable.
• Universal Kriging Interpolation: Universal Kriging is applicable when observable trends exist in data that can be fitted using deterministic functions or polynomials.