Geospatial Data Quality Audits & Metadata Documentation
Audit geospatial data quality and auto-generate comprehensive metadata
What You Can Do
Systematically evaluate geospatial datasets for quality issues, completeness, and compliance with spatial standards. You'll receive detailed audit reports identifying data gaps, CRS inconsistencies, geometric errors, and attribute validation failures. The skill also auto-generates structured metadata documentation following ISO 19115 and OGC standards, complete with data lineage, processing history, and recommended corrections.
Features
Detect invalid geometries, self-intersections, topology errors, and dimensional mismatches. Validates against OGC Simple Features spec.
Verify coordinate reference system consistency, identify misaligned projections, and detect datum shifts. Flag coordinate range anomalies.
Analyze missing values, null geometries, and attribute sparsity. Quantify data coverage by spatial extent and attribute fields.
Auto-generate ISO 19115 and Dublin Core compliant metadata records with lineage, keywords, and access constraints.
Verify attribute data types, field cardinality, domain constraints, and uniqueness rules against defined schemas.
Quantify positional accuracy, attribute accuracy, and logical consistency using statistical methods and standard metrics.
Generate standardized quality reports with numeric scorecards, pass/fail criteria, and prioritized remediation steps.
Process multiple datasets or layers simultaneously and compare quality metrics across the portfolio.
Example Output
Audit Report: Road Network Dataset
- Geometric Validity: 94.2% (2,847 of 3,021 linestrings valid)
- CRS Check: EPSG:4326 ✓ Consistent across all features
- Missing Attributes: 156 NULL values in "surface_type" (5.2% of dataset)
- Topology: 23 self-intersecting ways detected at intersections
- Completeness Score: 87/100
- Recommendation: Repair 23 topology errors, backfill 156 missing attributes
Generated Metadata Snippet
<MD_Metadata>
<MD_SpatialRepresentationInfo>
<MD_VectorSpatialRepresentation>
<geometricObjects>
<MD_GeometricObjects>
<geometricObjectType>line</geometricObjectType>
<geometricObjectCount>3021</geometricObjectCount>
</MD_GeometricObjects>
</geometricObjects>
</MD_VectorSpatialRepresentation>
</MD_SpatialRepresentationInfo>
<contact>roads_admin@gis.example.org</contact>
<dateStamp>2026-08-10</dateStamp>
</MD_Metadata>
What's Included
- Quality Audit Workflow: Step-by-step validation process covering geometry, attributes, CRS, and completeness with detailed error reporting.
- Metadata Templates: ISO 19115, OGC WCS/WFS compliance templates, and Dublin Core schemas pre-configured for spatial data.
- Scoring Framework: Quantitative quality metrics (0-100 scale) aligned with USGS, NRCAN, and EU INSPIRE data quality standards.
- Remediation Checklist: Prioritized action items with SQL/GDAL recipes and sample code for fixing common geometry and attribute errors.
- Report Templates: Markdown and JSON formatted audit reports suitable for stakeholder reviews and compliance documentation.
Who It's For
- GIS Data Managers
- Spatial Data Engineers
- Cartographers & Map Custodians
- Environmental Data Auditors
- Urban Planners & City Data Teams
Best For
- Pre-publication data quality certification
- Compliance audits for open data portals
- Data lineage and provenance documentation
- Multi-dataset quality benchmarking
- Automated metadata generation for data catalogs
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