Spatial Join Sanity

Spatial Join Sanity

A spatial join is where CRS bugs, geometry bugs, and cardinality bugs all converge. Do it deliberately.

Before the join

1. Confirm both layers share a CRS (see crs-discipline) and have valid geometry (see validate-geometry). A spatial join across mismatched CRSs returns wrong matches with no error.

2. Pick the predicate that matches the question. Don't default to "intersects" for everything:

   • points-in-polygons → within / contains
   • any overlap → intersects
   • shared boundary / adjacency → touches
   • "near, within N meters" → a distance predicate (ST_DWithin, near) — not intersects, and not a post-hoc distance filter.

Handle cardinality on purpose

A spatial join is one-to-many. A point on a shared border matches both polygons; overlapping polygons multiply rows. Decide explicitly:

• keep all matches, or
• keep the largest-overlap / nearest / first match.

Don't let the join silently change your record count.

Sanity-check the result

Compare input vs output row counts and state it. If a 10,000-point layer becomes 14,000 rows after a join to polygons, that's a boundary/overlap multiplication that needs an explanation — not something to hand over unnoticed. A point count that drops means unmatched features fell out of an inner join; decide whether that's intended.

Why this matters

Spatial joins produce a table that looks normal whether or not it's correct. The errors — wrong predicate, CRS mismatch, silent row multiplication — don't surface as crashes; they surface as a deliverable with the wrong counts. A predicate chosen on purpose and a row-count check catch them before they ship.