Which method validates the features and attributes of overlapping spatial data?

The method that validates the features and attributes of overlapping spatial data is the union process. When performing a union operation, two or more spatial datasets are combined (overlaid) to create a new dataset that captures all attributes and features from the input datasets. This is particularly useful for ensuring that any overlapping areas are accounted for, and the resulting data reflects the characteristics of all layers involved.

In the context of geospatial analysis, the union method allows for a comprehensive view of the spatial relationships between different datasets, preserving both the geometry and the attributes of the original layers. This enhances the ability to analyze overlaps, interactions, and integrations of features across different classifications, which can provide valuable insights in various applications such as land use planning, environmental analyses, and resource management.

While other methods like join, merge, and clip are important in geospatial data manipulation, they do not specifically address the validation of features and attributes in the context of overlapping spatial data to the same extent as the union operation does.