GeoConvert — GIS Data Conversion

The r/gis thread from August 2026 asked a question that comes up every few months: which Python web mapping library handles large datasets? The common answer — “use PyDeck” — is partly right but misses the key distinction that determines whether your map loads in two seconds or crashes the browser. The real question is not “which library is faster” but “where does the data go and in what format?” Each library makes a different choice. That choice determines the practical upper limit of what you can render. ...

The EU Deforestation Regulation applies from 30 December 2026. For operators placing timber, cocoa, coffee, palm oil, soy, cattle, and rubber on the EU market, compliance requires a Due Diligence Statement submitted through the TRACES NT system. That statement must include plot-level geolocation data — and the format specification is not flexible. TRACES accepts GeoJSON in WGS84 (EPSG:4326) with coordinates expressed as decimal degrees to at least six decimal places. Plots over 4 hectares require a complete boundary polygon. Plots under 4 hectares may be represented by a single point. ...

If you have found a tutorial that includes a line like ogr2ogr -f GFT ... or links to http://www.gdal.org/ogr/drv_gft.html, the tutorial is dead. The GFT driver connected to Google Fusion Tables, which Google shut down on December 3, 2019. The gdal.org/ogr/drv_gft.html URL itself no longer resolves — the entire GDAL documentation was restructured and the GFT driver pages were not preserved. Links to it from GIS community sites still circulate and lead nowhere. ...

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. ...

Safe Software’s partial acquisition by a private equity firm in early 2024 triggered price increases that blindsided teams that had built their GIS infrastructure around FME. Users in the community forums reported annual maintenance increases ranging from substantial to a 400% jump for some license tiers. The reaction was predictable: teams that could absorb the cost renewed; teams that couldn’t started auditing what FME was actually doing for them. What most found: the majority of their FME usage was format translation and straightforward transformation chains — work that GDAL, Python, and QGIS can handle without a license fee. The minority was complex branching pipelines and proprietary format readers that have no drop-in replacement. ...

Most municipalities and utility contractors have their underground infrastructure documented somewhere — in CAD as-built drawings, in PDFs, in someone’s head. Getting it into a proper GIS layer is the difference between calling 811 and hoping versus knowing where a 6-inch water main runs before you start excavating. QGIS handles this well. It’s free, it imports CAD files, and it exports GeoPDF files that field crews can use offline on a tablet. The setup isn’t complicated, but there are decisions you need to make before you start drawing — particularly around file format, CRS, and attribute schema — that determine whether the data is useful for the next 20 years or becomes another file nobody trusts. ...

Data scientists encounter GIS when spatial data shows up in a project: a dataset with latitude/longitude columns, a shapefile from a government portal, a GeoJSON file someone dropped in the repo. The first few operations usually work. Then something breaks silently — a spatial join returns an empty DataFrame, a buffer produces geometry the size of a continent, or exported shapefiles arrive at the lab with every field name truncated. ...

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. ...

If you’re moving from ArcGIS Pro to QGIS, Attribute Rules are one of the first things you’ll miss. ArcGIS Pro’s Attribute Rules let you attach Arcade scripts to a geodatabase layer that fire automatically on edit — calculating fields, enforcing constraints, validating records. QGIS has equivalents for all three categories, but they work differently, and none of them are geodatabase-level rules that execute independently of the client application. This matters: QGIS’s field rules live in the project file, not the data source. If someone opens your GeoPackage or Shapefile in a different QGIS project — or in a different GIS tool — those rules don’t travel with the data. That’s a significant architectural difference from ArcGIS Pro, where rules are stored in the geodatabase and enforced by the database engine. ...

If you’re still reaching for Shapefile or GeoJSON as your default format for large static datasets, FlatGeobuf is worth understanding. It’s been in GDAL since version 3.1 (2020) and QGIS since 3.16 — but most teams still haven’t adopted it where it provides the clearest advantage: large datasets served from cloud storage. Here’s what it does, how its internals work, and where it outperforms the alternatives. What FlatGeobuf Is FlatGeobuf is a binary encoding for vector geographic data built on FlatBuffers, a cross-platform serialization library developed at Google. A FlatGeobuf file has four sections in fixed order: ...