What does 'spatial interpolation' allow you to do?

Spatial interpolation is a vital process in geographic information systems (GIS) that enables you to estimate unknown values at specific locations based on known values from surrounding areas. This technique is particularly useful when data is collected at discrete points, and there is a need to predict values at unmeasured locations within the same area based on the spatial patterns observed in the known data.

For instance, if you have a set of temperature readings collected at various weather stations, spatial interpolation can help estimate the temperature at a location where no measurements were taken by analyzing the values from the nearby stations. Various methods of interpolation exist, such as kriging, inverse distance weighting, and spline techniques, each suited to different types of data and the nature of the spatial relationships.

The other options do not directly relate to the core function of spatial interpolation. Collecting primary data through surveys focuses on data acquisition rather than value estimation. Analyzing relationships using multiple regression pertains more to statistical analysis rather than spatial estimation. Creating digital elevation models involves a specific type of data representation and does not encompass the broader scope of interpolation, which is focused on estimating values across spatial data gaps.