In GIS, interpolation is made possible by a principle called:

• A. Spatial Homogeneity
• B. Spatial Randomness
• C. Spatial Autocorrelation
• D. Spatial Uniformity

Answer: (C)

Interpolation in GIS relies on spatial autocorrelation—the idea that observations near each other tend to be more similar than those farther apart. This tendency means that known values from sampled locations carry information about values in nearby, unsampled locations, so we can estimate a continuous surface. The strength and range of this similarity are captured by the spatial structure of the data (for example, a variogram used in kriging). Techniques like inverse distance weighting or kriging exploit this relationship to produce plausible estimates across space.

Other terms describe uniformity or randomness and do not explain why nearby values inform predictions; they would imply little to no usable pattern for interpolation, which is why they are not the basis for interpolation.