CRS, units, and extent
Confirm coordinate reference systems, unit assumptions, coverage, and spatial extent before analysis or publication.
GIS & data QA
Spatial data needs checks that go beyond looking right on a map.
Review the coordinate, geometry, schema, record, and workflow conditions that determine whether a spatial output is defensible.
Quality-assurance coverage
Inspect the spatial conditions behind the final output.
GIS QA is strongest when it checks both data integrity and the workflow that produces a result.
Geometry and topology
Review invalid geometry, gaps, overlaps, containment, adjacency, and other spatial relationships.
Schema and domain checks
Check field names, types, coded values, required attributes, and constraints against the intended data model.
Duplicates, nulls, and orphans
Identify duplicated records, missing values, and unmatched relationships that can distort an output.
Joins and record counts
Validate join keys, match rates, expected counts, and whether records were added, excluded, or multiplied.
Geocoding review
Review location matches, coordinates, ambiguity, and spatial tolerance instead of accepting a point at face value.
Lineage documentation
Record sources, transformations, assumptions, and limitations so results can be understood and recreated.
Cartographic QA
Check labels, legends, symbology, scale, hierarchy, and whether a map communicates the intended analytical result.
Workflow documentation
Document repeatable steps and Python-assisted checks where they improve consistency and reviewability.
How checks are used
Make review criteria repeatable.
Some checks are manual because professional judgement is essential. Others can be assisted by Python or automated rules when the underlying requirement is objective and repeatable.
The work should make exceptions visible, document their impact, and preserve the instructions and inputs needed to reproduce the review.