What process is used to correct random geometric errors in spatial data?

The process used to correct random geometric errors in spatial data is rubber sheeting. This technique involves adjusting the spatial features of a dataset to better fit a real-world coordinate system or other reference. When spatial datasets are collected, they may suffer from geometric distortions due to various factors such as sensor inaccuracies, projection problems, or variations in data collection methods.

Rubber sheeting works by stretching and deforming the spatial dataset so that specific control points align with their correct positions in a known coordinate system or geographical context. This is particularly useful for correcting irregularities in data that may have been collected over large areas or from different sources, ensuring that the spatial relationships within the data remain accurately represented.

In contrast, georeferencing involves establishing a known coordinate system to map the spatial data but does not specifically focus on correcting geometric errors. Data interpolation refers to estimating unknown values within the data based on known values, which does not address geometrical inaccuracies directly. Cartographic generalization simplifies the representation of geographical features for map creation but does not serve to correct geometric errors. Therefore, rubber sheeting is the most appropriate method for addressing and correcting random geometric errors in spatial data.