In GIS, what is the practice called that ensures the quality and correctness of spatial data?

In Geographic Information Systems (GIS), the practice that ensures the quality and correctness of spatial data is known as Data Validation. This process involves verifying that the data is accurate, complete, and consistent with defined standards and requirements. During data validation, checks are implemented to catch errors such as incorrect attribute values, missing data, and inconsistencies in spatial relationships.

Data validation serves as a critical quality control measure, enabling users to trust that the spatial data they are working with is reliable for analysis, planning, and decision-making. By confirming that the data adheres to certain rules and standards, GIS professionals can ensure that subsequent analyses yield meaningful and accurate results.

In contrast, while Quality Assurance focuses broadly on maintaining desirable quality levels throughout processes, Data Cleansing specifically deals with the process of correcting or removing inaccuracies or inconsistencies in data. Topology Rule Application is concerned with maintaining spatial relationships in vector data sets but does not encompass the broader validation of data quality.