What is the purpose of spatial interpolation methods like IDW and Kriging?

Spatial interpolation methods such as Inverse Distance Weighting (IDW) and Kriging are primarily used to estimate values at unmeasured locations based on the values sampled at nearby points. These techniques utilize the spatial correlation that often exists in environmental and geographic data, allowing surveyors and analysts to predict unknown values in a continuous field.

IDW, for instance, operates under the principle that each measured point contributes to the interpolated values, with closer points having more influence than those farther away. This results in a smooth surface that reflects the variation of the data. On the other hand, Kriging offers a more sophisticated statistical approach, incorporating both the distance and the spatial arrangement of the sample points, yielding estimates that include a measure of uncertainty.

These methods are particularly valuable in fields such as environmental science, agriculture, and urban planning, where direct measurements are often limited or impractical. By applying interpolation techniques, decisions can be informed by predicted values in areas that lack direct observation, facilitating better planning and resource management.