Name two common methods used to estimate values at unsampled locations in spatial interpolation.

Name two common methods used to estimate values at unsampled locations in spatial interpolation.

• A. Nearest-neighbor and linear regression
• B. Inverse distance weighting and kriging
• C. Interpolation and extrapolation
• D. Rasterization and vectorization

Answer: (B)

Estimating values at unsampled locations relies on using nearby observations to infer what lies between them. Two widely used methods are inverse distance weighting and kriging. Inverse distance weighting fills in a value by taking a weighted average of nearby measurements, with closer points having more influence than farther ones. The weights decline with distance, often controlled by a power parameter. This method is simple, fast, and makes no strong assumptions about the exact spatial pattern beyond the idea that nearby observations are more relevant. Kriging treats the data as a realization of a spatial random field and uses a variogram to model how similarity between observations changes with distance. It then computes the estimator that minimizes prediction error (a best linear unbiased predictor) and also provides an uncertainty estimate for each prediction. Because it explicitly accounts for spatial autocorrelation, kriging can yield more accurate estimates when a spatial structure exists. Other options either resemble general data processing rather than interpolation between points, or combine concepts in ways that aren’t standard interpolation methods (for example, simple nearest-neighbor is often too blocky, while linear regression isn’t an interpolation technique by itself in geographic space).

Estimating values at unsampled locations relies on using nearby observations to infer what lies between them. Two widely used methods are inverse distance weighting and kriging.

Inverse distance weighting fills in a value by taking a weighted average of nearby measurements, with closer points having more influence than farther ones. The weights decline with distance, often controlled by a power parameter. This method is simple, fast, and makes no strong assumptions about the exact spatial pattern beyond the idea that nearby observations are more relevant.

Kriging treats the data as a realization of a spatial random field and uses a variogram to model how similarity between observations changes with distance. It then computes the estimator that minimizes prediction error (a best linear unbiased predictor) and also provides an uncertainty estimate for each prediction. Because it explicitly accounts for spatial autocorrelation, kriging can yield more accurate estimates when a spatial structure exists.

Other options either resemble general data processing rather than interpolation between points, or combine concepts in ways that aren’t standard interpolation methods (for example, simple nearest-neighbor is often too blocky, while linear regression isn’t an interpolation technique by itself in geographic space).