Inverse distance weighting (IDW)

Inverse Distance Weighting (IDW) is a spatial interpolation technique used to estimate values at unsampled locations based on known values at nearby points. The method assumes that points closer to the location of interest have a greater influence on the predicted value than those farther away. IDW calculates the predicted value by taking a weighted average of the known values, where the weights are inversely proportional to the distances between the known points and the prediction location, raised to a power parameter. This power parameter controls the rate at which the influence of the known points decreases with distance, with higher values giving more weight to closer points. IDW is widely used in fields such as geostatistics, meteorology, and environmental science to interpolate spatial data like rainfall, temperature, and pollution levels.