Geoconvert

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. ...

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. ...

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. ...

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: ...

Drop a DXF file onto your GIS and the geometry lands in the middle of the ocean, rotated 90 degrees, scaled by 25.4. Or it lands in the right country but offset by a few hundred metres. Or it imports cleanly into QGIS but every feature ends up on a single layer called “0” with no attributes beyond the original AutoCAD entity type. This is not a bug you can fix with a setting. The CAD-GIS format gap is structural. DWG and DXF were designed for engineering drawings, not for placing things on Earth. GIS formats were designed for the opposite. The two worlds have different answers to three fundamental questions: where are these objects, what do we know about them, and how do they relate to each other? ...

A shapefile arrives in your inbox, or you need to verify what’s in a .shp file before importing it into your pipeline. The two questions that come up immediately: what software do you need, and do you have to install anything? The short answer: no install required for a quick look. For anything involving CRS inspection, attribute filtering, or geometry validation, a desktop tool is worth the effort. What You’re Actually Inspecting A shapefile isn’t a single file — it’s a bundle that requires at least four components to be useful: ...

You have a CSV of postal addresses — a membership list, a customer database, a survey response set. Someone wants to know where these people are concentrated. A heatmap is the right output: it shows density without exposing individual addresses, it scales from 100 to 100,000 records without changing the workflow, and it overlays cleanly on basemaps, zoning layers, or any other spatial data you have. The full pipeline in QGIS: geocode addresses to coordinates using MMQGIS and Nominatim (OpenStreetMap’s free geocoding service), reproject the resulting point layer to a metric CRS, and run kernel density estimation in the QGIS Processing Toolbox. Three steps. The traps are in the rate limits, the CRS handling, and the distinction between two different “heatmap” tools inside QGIS that look alike and behave differently. ...

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. ...

The thread A post in r/gis hit 101 upvotes this spring after a demo showed Claude performing CAD-adjacent spatial tasks. The 96 comments divided into three camps: alarm (entry-level GIS is gone), pragmatism (analysts who use AI will outcompete those who don’t), and dismissal (spatial analytics requires domain knowledge that LLMs don’t have). All three positions contain something true. The useful question is not “will AI replace GIS analysts?” — that framing produces either denial or panic, neither of which helps. The useful question is: which specific tasks are genuinely automatable, and what does that mean for how you spend your time? ...