A spatial join mysteriously matches 80% of features when it should match 100%. The coordinates look fine. The CRS is right. The problem is almost always geometry validity.
Geometry is valid when it follows rules. For polygons: rings must be closed, holes must be inside, rings must not self-intersect, and exterior and interior rings must not overlap. For lines: endpoints matter, self-intersections usually indicate errors. Invalid geometry looks correct on a map but fails in spatial operations.
Here’s what breaks: spatial joins against invalid polygons sometimes skip features. Unions create garbage slivers. Area calculations include phantom space. The database either returns incomplete results or rejects the operation entirely.
The insidious part: desktop tools visualize invalid geometry fine. ArcGIS might show it without complaint. PostGIS or DuckDB will reject it. This gap is why data that “worked fine in QGIS” fails in production pipelines.
Common issues: slivers from imperfect overlays (tiny gaps where polygons should touch), ring orientation flipped (exterior ring wound backwards), and self-intersections from digitization errors or reprojection precision loss.
Validation belongs early in pipelines. Check geometry validity before complex analysis. Most tools have validators: ST_IsValid() in PostGIS, is_valid in GeoPandas, ST_MakeValid() to repair.
Fixing geometry is intentional. ST_MakeValid() removes self-intersections, fills slivers, corrects ring orientation. But it changes topology—you’re altering the data, not correcting it. Use repairs after validation, document changes, and review results.
The rule: Validate geometry immediately after ingestion. If invalid, repair or re-ingest. Never run complex spatial analysis on invalid data. Most spatial join failures are data problems. Check validity before assuming the query is wrong.
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