Data-Quality

A Reddit thread in r/gis titled “ZIP codes are a bad spatial abstraction” accumulated 34 upvotes and substantial agreement. The comments covered reasons ranging from USPS policy to census methodology. The frustration is real: analysts keep getting results that don’t survive peer review, and the problem is usually the spatial unit, not the statistics. ZIP codes are the default because they appear in every address dataset and everyone understands them intuitively. Neither of those properties makes them good for spatial analysis. ...

GIS format conversions do not crash loudly when they fail. They produce an output file, report success, and leave you with data that is wrong in ways you may not notice until the analysis is already done. Field names get silently cut at 10 characters. Geometries disappear without warning. Coordinate systems shift without errors. Attribute types coerce to something that looks right but produces wrong numbers. These are not edge cases. They are the default behavior of the tools most GIS teams use daily — including GDAL/OGR, QGIS’s built-in export, and ArcGIS Pro’s geoprocessing toolbox. None warn you by default. ...

A state environmental agency analyst posted on r/gis last week, describing the same problem every public-sector GIS team deals with: contractors, federal agencies, and utility companies all send shapefiles that are technically valid per the format spec, and yet every batch requires hours of cleanup. The analyst was scoping an open-source CLI tool to run preflight checks — field truncation, CRS mismatch, missing indexes, null geometry. The tool they need already exists in pieces inside GDAL. The problem is that nobody publishes the checklist. Here is the full preflight — the specific failure modes, the ogrinfo and ogr2ogr commands that detect them, and the decisions each check forces. ...