The Geometry Data Service (GDS) provides APIs for serving (spatial) geometries as RESTful separable first-class objects to spatial features over the web. The GDS provides access to multiple views of spatial geometries for the relevant reporting geographies included in Loc-I as Linked Data. Geometry data is provided in different forms (e.g. geometry as-is, centroid, metadata-only) and formats/serialisations (e.g. WKT, GeoJSON, SHP, RDF/GeoSPARQL). The GDS achieves this via Content Negotiation on spatial features stored in the Loc-I Geo-database, which hosts the geometries. This allows spatial features in Loc-I to embed URI-references to the geometry instead of embedding a geometry literal, optimising the RDF graph store for semantic queries rather than geospatial.
Background to the GDS can be found at the original geometry data service github issue.
| MIME Type | Description | Returns |
|---|---|---|
| text/html | Web interface | <!DOCTYPE html><html lang="en">... |
| text/plain | Well-Known Text | POLYGON((113.1016 -38.062 ...)) |
| application/json | GeoJSON | {"type":"Polygon","coordinates":...} |
| text/turtle | RDF Turtle representation using GeoSPARQL | _:geom1 a geo:sfPolygon , geo:Geometry ; geo:asWKT "POLYGON((113.1016 -38.062 ...))"^^geo:wktLiteral . |
Future work:
| MIME Type | Description | Returns |
|---|---|---|
| application/geo+json | GeoJSON | {"type":"Polygon","coordinates":...} |
| application/gml+xml | GML | <gml:Polygon><gml:Exterior>... |
| application/octet-stream | Well-Known Binary | 01 06 00 00 20 E6 10 00 00 01... |
Note: The above views extend a specification proposed by Regalia et al. 2017*.
- Regalia et al., Revisiting the Representation of and Need for Raw Geometries on the Linked Data Web, LDOW, 2017, http://ceur-ws.org/Vol-1809/article-04.pdf
$ cd api
$ virtualenv venv
$ source venv/bin/activate
$ pip install -r requirements.txt
$ export FLASK_APP=app.py
$ export FLASK_ENV=development
$ flask run --port=3000 --host=0.0.0.0
Some toy examples are included in the repo. Once flask is running, go to http://localhost:3000 and use links provided
An alternative to the simple flask run is via docker.
To use docker, you'll need to install docker and docker-compose.
You can then deploy via
$ docker-compose up -d
If you would like to deploy to a port other than 3000, you can also set the PORT parameter in a .env file like so:
PORT=3111
Running docker-compose up -d will pick up the .env file settings.
Otherwise, you can use the Docker Hub image at https://hub.docker.com/r/csiroenvinf/geometry-data-service.
docker pull csiroenvinf/geometry-data-service
Run with environment variables
$ docker run -e GSDB_DBNAME=gis -e GSDB_HOSTNAME=localhost -e GSDB_PORT=5432 -e GSDB_USER=user -e GSDB_PASS=pass -p 3000:3000 --network host -it csiroenvinf/geometry-data-service
You can add variables in a .env file to override defaults in the docker-compose.yml file:
GSDB_DBNAME=gis
GSDB_HOSTNAME=db
GSDB_PORT=5432
GSDB_USER=jon
GSDB_PASS=jon
In cases, where there isn't an existing Postgis and you would like to populate a Postgis DB with some geometries, you can use the following to set one up.
The following command will setup a postgis database alongside the geometry API
$ docker-compose -f docker-compose.yml -f docker-compose.postgis.yml up -d
This currently uses the kartoza/postgis docker image (see https://github.com/kartoza/docker-postgis).
You can use psql or other tools to populate the geometries into that DB.
The only requirement from the geometry_data_service of the Postgis DB
is to have a table or view called combined_geoms with the structure:
Column | Type | Collation | Nullable | Default
---------+-------------------+-----------+----------+---------
id | character varying | | |
geom | geometry | | |
dataset | text | | |
Where id is the local identifier for the geometry, dataset is the label for the dataset it comes from (e.g. asgs2016_sa1),
and geom is the actual geometry.
The following command will bring down the containers:
$ docker-compose -f docker-compose.yml -f docker-compose.postgis.yml down