Inverse distance weighing and Kriging are examples of which of the following?

Inverse distance weighting and Kriging are techniques used for spatial interpolation, which involves estimating values at unmeasured locations based on the values from measured locations. Both methods rely on the spatial correlation of sampled data points to predict unknown values.

Inverse distance weighting operates under the principle that points closer to the location of interest should have more influence on the interpolated value than points further away. It achieves this by assigning weights to data points based on their distance from the target point, typically using an inverse function.

Kriging, on the other hand, is a more sophisticated statistical method that not only considers distance but also the overall spatial arrangement and variability of the sampled data. It uses a mathematical model to describe the spatial correlation among the data points, allowing it to provide not just estimates at unmeasured locations but also a measure of the uncertainty associated with those estimates.

The other options do not encapsulate the purpose and function of these methods. Geographic data models pertain to frameworks for representing spatial data, database linkage methods deal with the integration of different datasets, and processes for visualization focus on displaying data rather than estimating its values. Thus, identifying inverse distance weighing and Kriging as spatial interpolation techniques highlights their role in transforming sparse data into a continuous surface based on