Spatial Interpolation

Spatial interpolation is a method used in data science and artificial intelligence to estimate unknown values of a variable at unsampled locations within a study area. It involves the use of statistical and mathematical techniques to create a continuous surface from a set of sample points. Spatial interpolation is commonly used in environmental modeling, geostatistics, and remote sensing applications to predict values of variables such as temperature, precipitation, and air quality at unsampled locations. The accuracy of spatial interpolation depends on the quality and quantity of the sample data, the spatial distribution of the sample points, and the interpolation method used.
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