Inverse distance weighting is a deterministic, nonlinear interpolation technique that uses a weighted average of the attribute (i.e., phenomenon) values from nearby sample points to estimate the magnitude of that attribute at non-sampled locations. The weight a particular point is assigned in the averaging calculation depends upon the sampled point's distance to the non-sampled location (see Figure 6.cg.25, below). The method is called inverse distance weighting because according to Tobler's first law of geography, (see Interpolation) the similarity of two locations should decrease with increasing distance.