Play with gdal.vectorTranslate in Node.js

Gist 4ab7840c00aa352f278636ea3f3f0d3e

This example demonstrates streaming merge of two French cadastral parcel GeoJSON files into a single unified feature collection, output as a continuous JSON stream to standard output. It shows how to combine multiple geospatial datasets without loading them entirely into memory. The code uses the `geojson-stream` library for parsing and stringifying GeoJSON features, Node.js `fs.createReadStream` for file input, and the `zlib` module to decompress gzipped source files. Data originates from the Etalab cadastre dataset, with separate scripts handling zipped and unzipped input files.

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Execute Spatialite within Node.js

This example demonstrates how to execute SpatiaLite spatial operations within a Node.js application using the `sqlite3` database driver in memory. The code initializes a spatial metadata table, creates a point geometry column with EPSG 4326 projection, and adds a spatial index to a `places_spatialite` table. It then inserts a sample point using `GeomFromText` and queries the table with `db.each`, logging the resulting rows. The workflow relies on `db.loadExtension` to load the native `mod_spatialite` library, with the extension path hard-coded for Linux systems.

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GeoJSON Transforms

This example demonstrates how GeoJSON geometry types can be transformed into simplified or filtered representations by applying custom point, line, and polygon handlers. It builds on the D3 geo module’s type dispatch system, explicitly defining default handlers for Feature, FeatureCollection, and GeometryCollection objects alongside primitive geometries. The code uses d3_geo_type and d3_noop to create a reusable translator, showing how to override or extend processing for each GeoJSON type. The rendering approach is not shown, focusing solely on the data-transformation pipeline.

Mmbostock

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mapshaper-info-recreated-via-gdal

This example recreates the command-line tool mapshaper-info using the gdal-async library to inspect a GeoJSON TopoJSON file containing German administrative regions. It downloads the dataset from GitHub, then prints layer metadata including geometry type, record count, bounding box, and coordinate reference system, followed by a formatted table of attribute names and their first values. The script uses the got library for streaming downloads and the pipeline function from stream/promises to handle the file writing asynchronously.

Load GTFS as GPKG with GDAL 3.7

This example demonstrates converting an IDFM GTFS transit feed into a GeoPackage database using GDAL’s new GTFS driver. The Bash script first checks that GDAL 3.7 is installed, then uses `ogrinfo` with `-json` to list all available layers. For each layer, it runs `ogr2ogr` with the GeoPackage format, appending layers incrementally with the `-update` flag. The source data is loaded directly from a URL via `/vsicurl/`, and the script allows customizing input and output names through environment variables.

Clean Up for Natural Earth GeoJSON

This example presents a command-line Node.js script for cleaning Natural Earth GeoJSON data. It reads a GeoJSON collection from standard input and outputs a simplified version, retaining only the `name` property and an `id` derived from either the `ISO_A3` or `FIPS` code. The script uses `JSON.parse` and `JSON.stringify` for data processing, and it rounds all coordinate values to six decimal places via a custom `round` function applied to `Polygon`, `MultiPolygon`, and `LineString` geometries. The process logs warnings for invalid country codes.

Mmbostock

Simplify GeoJSON using d3-plugins/simplify (thanks @jasondavies for preserving topology!)

This example demonstrates how to simplify complex GeoJSON geometry while preserving topological relationships, using the `d3.simplify` API from the d3-plugins/simplify package. It shows a command-line conversion workflow that reads a GeoJSON input file, applies a simplification algorithm with zero area tolerance and topology-aware processing, and writes the simplified output to a new file. The implementation uses Node.js file I/O with `fs`, JSON parsing, and a custom projection function that passes coordinates through unchanged, making it a practical tool for reducing map data size without breaking shared boundaries.

RRandomEtc

shape rendering in nodejs with LearnBoost's node-canvas

This example demonstrates dynamic, tile-based map rendering in Node.js using LearnBoost's node-canvas library. It projects GeoJSON feature coordinates onto a spherical Mercator grid and serves individual 256x256 pixel tiles over HTTP. The code loads multiple geographic layers, such as park boundaries, water bodies, and street centerlines from naturalearthdata.com or datasf.org, applies fill and stroke styles, and caches rendered canvases for reuse. It uses the canvas context's `moveTo`, `lineTo`, `scale`, and `translate` methods to draw the parsed geometries, then streams PNG output to the client.

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