pg-nearest-city 0.0.0

Given a geopoint, find the nearest city using PostGIS (reverse geocode).

Simple PostGIS Reverse Geocoder

Given a geopoint, find the nearest city using PostGIS (reverse geocode).

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📖 Documentation: https://hotosm.github.io/pg-nearest-city/

🖥️ Source Code: https://github.com/hotosm/pg-nearest-city

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Why do we need this?

This package was developed primarily as a basic reverse geocoder for use within web frameworks (APIs) that have an existing PostGIS connection to utilise.

• The reverse geocoding package in Python here is probably the original and canonincal implementation using K-D tree.
  • However, it's a bit outdated now, with numerous unattended pull requests and uses an unfavourable multiprocessing-based approach.
• The package here is an excellent revamp of the package above, an likely the best choice in many scenarios.

The K-D tree implementation in Python is performant (see benchmarks) and an excellent choice for scripts.

However, it does leave a large memory footprint of approximately 160Mb to load the K-D tree in memory (see benchmarks).

Once computed, the K-D tree remains in memory! This is an unacceptable compromise for a web server, for such a small amount of functionality, particularly if the web server is run via a container orchestrator as replicas with minimal memory.

As we already have a Postgres database running alongside our webserver, an approach to simply query via pre-loaded data via PostGIS is much more memory efficient (~2Mb) and has an acceptable performance penalty (see benchmarks).

[!NOTE] We don't discuss web based geocoding services here, such as Nominatim, as simple offline reverse-geocoding has two purposes:

• Reduced latency, when very precise locations are not required.
• Reduced load on free services such as Nominatim (particularly when running in automated tests frequently).

Priorities

• Lightweight package size.
• Minimal memory footprint.
• Reasonably good performance.

How This Package Works

• geonames.org data.
• Voronoi polygons based on geopoints.
• Gzipped data bundled with package.
• Query the Voronois.

Benchmarks

• todo

Testing

Run the tests with:

docker compose run --rm code pytest