Geospatial Data Management, Standards, and Quality
Geospatial Data Management, Standards, and Quality
This domain tests metadata, spatial data infrastructure, quality elements, and how accuracy is stated and verified.
Core rules and concepts
- Metadata documents source, lineage, coordinate system, accuracy, currency, and access constraints. ISO 19115 is a widely used geospatial metadata standard; OGC standards support interoperable services.
- Spatial data quality elements include positional accuracy, attribute or thematic accuracy, completeness, logical consistency, and temporal quality.
- RMSE summarises positional error as the square root of the mean squared error. It is not the same as mean error, which can cancel positive and negative errors.
- NSSDA-style reporting expresses horizontal accuracy at a specified confidence level, commonly 95%.
- Versioning, backup, topology validation, and documented workflows support data integrity.
Worked example
Three checkpoints have coordinate errors of +0.2 m, −0.3 m, and +0.1 m. Squared errors are 0.04, 0.09, and 0.01; sum = 0.14; mean = 0.0467; RMSE = √0.0467 ≈ 0.216 m. The mean error is zero, which would falsely suggest no bias. RMSE captures magnitude but still requires separate bias and outlier checks.
Traps
- Reporting mean error as if it were RMSE.
- Omitting the coordinate system or datum from metadata.
- Treating a small RMSE as proof that every point is accurate.
- Mixing accuracy, precision, and resolution in a data quality statement.