This helps to reduce bias in the predictions. Clustering of points is also taken into account, so that clusters of points are weighted less heavily (in effect, they contain less information than single points). In a general sense, the kriging weights are calculated such that points nearby to the location of interest are given more weight than those farther away. Kriging also generates estimates of the uncertainty surrounding each interpolated value. It differs from simpler methods, such as Inverse Distance Weighted Interpolation, Linear Regression, or Gaussian decays in that it uses the spatial correlation between sampled points to interpolate the values in the spatial field: the interpolation is based on the spatial arrangement of the empirical observations, rather than on a presumed model of spatial distribution. An example of a value that varies across a random spatial field might be average monthly ozone concentrations over a city, or the availability of healthy foods across neighborhoods. Kriging is one of several methods that use a limited set of sampled data points to estimate the value of a variable over a continuous spatial field. Kriging is a method of spatial interpolation that originated in the field of mining geology as is named after South African mining engineer Danie Krige.
