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  • However, it's a bit outdated now, with numerous unattended pull requests and uses an unfavourable multiprocessing-based approach.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.

Usage

Install

Distributed as a pip package on PyPi:

pip install pg-nearest-city
# or use your dependency manager of choice

Run The Code

Async

from pg_nearest_city import AsyncNearestCity

# Existing code to get db connection, say from API endpoint
db = await get_db_connection()

async with AsyncNearestCity.connect(db) as geocoder:
    location = await geocoder.query(40.7128, -74.0060)

print(location.city)
# "New York City"
print(location.country)
# "USA"

Sync

from pg_nearest_city import NearestCity

# Existing code to get db connection, say from API endpoint
db = get_db_connection()

with AsyncNearestCity.connect(db) as geocoder:
    location = geocoder.query(40.7128, -74.0060)

print(location.city)
# "New York City"
print(location.country)
# "USA"

Create A New DB Connection

• If your app upstream already has a psycopg connection, this can be passed through.
• If you require a new database connection, the connection parameters can be defined as DbConfig object variables:

from pg_nearest_city import DbConfig, AsyncNearestCity

db_config = DbConfig(
    dbname="db1",
    user="user1",
    password="pass1",
    host="localhost",
    port="5432",
)

async with AsyncNearestCity.connect(db_config) as geocoder:
    location = await geocoder.query(40.7128, -74.0060)

• Or alternatively as variables from your system environment:

PGNEAREST_DB_NAME=cities
PGNEAREST_DB_USER=cities
PGNEAREST_DB_PASSWORD=somepassword
PGNEAREST_DB_HOST=localhost
PGNEAREST_DB_PORT=5432

then

from pg_nearest_city import DbConfig, AsyncNearestCity

async with AsyncNearestCity.connect() as geocoder:
    location = await geocoder.query(40.7128, -74.0060)

Benchmarks

• todo

Testing

Run the tests with:

docker compose run --rm code pytest