Spatial interpolation is a mathematical technique used to estimate values between known data points. It involves constructing a function or curve that passes through the given data points and can be used to predict missing values at intermediate locations. Spatial interpolation is influenced by geographic proximity, making it distinct from non-spatial interpolation methods. Two popular spatial interpolation techniques are Inverse Distance Weighting (IDW) and Kriging. IDW calculates interpolated values based on distances between known points, producing smooth surfaces with gradual transitions, but can be oversensitive to outliers. Kriging, a geostatistical technique, takes into account both distance and spatial correlation of data points, providing estimates of uncertainty and optimized estimation, but is computationally demanding and requires careful model selection. The choice between IDW and Kriging depends on the characteristics of the data and desired outcomes, with IDW suitable for simple, fast interpolation and Kriging better suited for datasets with complex patterns and accurate estimation.