A spatial join that should match 100% of features matches 87%. The geometries look identical. The problem is math.
Coordinates are stored as floating point numbers—approximations, not exact values. A degree of latitude isn’t stored as a perfect number. It’s approximated. When you reproject, buffer, intersect, or dissolve, floating point operations accumulate rounding error. Coordinates drift by 0.000001 degrees. On screen, invisible. In a spatial join, fatal.
Here’s what happens: two boundaries that should share an edge are offset by 0.0000001 degrees. The spatial join looking for exact matches fails. You get a sliver—a tiny gap between features that should be adjacent. Union a zoning layer with a floodplain, and slivers multiply. Dissolve, and self-intersections appear. All caused by math rounding, not bad data.
Reprojection amplifies this. Converting from geographic (degrees) to projected (meters) involves trigonometric functions that introduce error. Converting back introduces more. Chain multiple projections, and precision compounds.
This is why snapping and tolerances exist. They’re not approximations—they’re protective. A snap tolerance tells the system “if coordinates are within X distance, treat them as equal.” This prevents joins from failing on invisible precision errors.
The rule: Before complex overlay operations (union, intersect, dissolve), snap geometries to a reasonable tolerance. Check for slivers and gaps after union. If joins mysteriously fail, check precision alignment first. Snapping isn’t cheating—it’s accounting for how computers store numbers.
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