What does spatial interpolation allow geospatial analysts to do?

Spatial interpolation is a crucial technique used by geospatial analysts to estimate unknown values at specific locations based on known data points surrounding them. By applying various mathematical methods and models, interpolation allows for the prediction of values in areas where data may not have been collected, effectively filling in gaps in the dataset. This can be especially useful in fields such as meteorology, environmental science, and urban planning, where continuous data across a geographic area is essential for analysis and decision-making.

The correct option emphasizes how spatial interpolation transforms sparse datasets into more comprehensive representations, enhancing the ability to visualize and analyze spatial phenomena across a region. It is a fundamental component of GIS (Geographic Information Systems), enabling users to assess trends, make forecasts, and derive insights that rely on spatial relationships among data points.

In contrast, creating high-resolution images, building three-dimensional models, and performing data mining entail different methodologies and processes that do not directly relate to the specific function of estimating unknown values in spatial datasets. Consequently, they do not capture the essence of what spatial interpolation achieves in the realm of geospatial analysis.