Postgis

The problem with most “free GIS tools” roundup lists is that they treat GDAL, QGIS, GRASS, and PostGIS as competing products when they’re actually layers of the same stack. GDAL underpins QGIS. QGIS calls GRASS as a processing backend. PostGIS sits behind both for production data. Choosing between them isn’t “which is best” — it’s “which layer do I need to interact with directly.” This is a functional comparison: what each tool does well, where it breaks down, and which one to reach for when you receive a shapefile, a GeoJSON, or a KML and need it in something your software actually reads. ...

GeoJSON keeps showing up in places it shouldn’t. Someone drops a 2 GB .geojson into a processing pipeline, the script that used to run in 20 seconds runs for eight minutes, and the team spends a Thursday wondering whether the problem is their query planner, their RAM, or their patience. The problem is the format. GeoJSON is a perfectly good interchange format — human-readable, one-spec wonder, works everywhere a text editor opens. It’s a terrible working format as soon as your dataset has more than a few hundred thousand features. This post is about when to stop loading GeoJSON directly and what to convert to instead, based on what teams handling real-scale data actually reach for. ...