What term describes the way to organize spatial data effectively instead of creating multiple feature classes?

Using attribute domains is the correct approach to organizing spatial data effectively without the need to create multiple feature classes. Attribute domains allow you to define the set of valid values for an attribute of a feature class, ensuring consistency and integrity in the data. This means that you can maintain control over what data can be entered for specific fields, which reduces redundancy and enhances data quality.

For example, if you have a feature class representing different types of land use, you can define an attribute domain for the land use type, allowing only predefined categories (such as residential, commercial, industrial, etc.). This organization method streamlines the management of spatial data as it keeps related attributes in a single feature class while still enforcing data standards.

In contrast, subtypes allow for categorization within a feature class but do not inherently restrict the values that can be assigned to those categories. Merging datasets may create a single dataset but does not address the nuances of data validation and attribute integrity. Applying spatial joins performs a spatial relationship operation between two datasets but does not focus on the organization of data within a single feature class. Therefore, attribute domains prove to be the most effective method for maintaining organized, reliable spatial data.