Exercise 2 - Vector data via OGC API - Features

OGC API - Features provides a Web API to access vector data (geometries and their attributes). While the core specification covers basic data access and query, additional related standards and extensions are in development for the following capabilities:

OGC API - Features - Part 1: Core provides basic access and query capabilities
OGC API - Features - Part 2: Coordinate Reference Systems by Reference enables the import and export of any data according to dedicated projections
OGC API - Features - Part 3: Filtering (draft) adds the ability for complex queries using Common Query Language (CQL)
OGC API - Features - Part 4: Create, Replace, Update and Delete (draft) adds transactional capabilities

pygeoapi support

pygeoapi supports all of the above OGC API - Features specification parts (Part 3 is dependent on backend support i.e. Elasticsearch).

Note

See the official documentation for more information on supported vector backends

Note

See the official documentation for more information on CQL support

Next, we are going to explore how-to publish vector data using a GeoPackage and a Elasticsearch backend.

Publish a GeoPackage

In the previous section we demonstrated the steps involved to add a dataset to pygeoapi and update the configuration. In this exercise we are going to publish another vector file, this time from a GeoPackage (SQLite3) data source.

Tip

It may be helpful to open the dataset in QGIS while adding and updating your pygeoapi server to easily evaluate table attributes, names, spatial properties and CRS.

Let's add the file workshop/exercises/data/cp-tartu2.gpkg.zip:

Update the pygeoapi configuration

First, let's unzip the file cptartu2.gpkg.zip to cptartu2.gpkg.

Then, open the pygeoapi configuration file in a text editor. Find the line # START - EXERCISE 2 - cadastral parcels Tartu.

Add a new dataset section by uncommenting the lines up to # END - EXERCISE 2 - cadastral parcels Tartu:

cp-tartu:
    type: collection
    title: Tartu Cadastral Parcels
    description: Cadasral parcels in downtown Tartu
    keywords:
        - Cadastral parcels
        - Tartu
    links:
        -   type: OGC/WFS
            rel: canonical
            title: data
            href: https://metadata.geoportaal.ee/geonetwork/srv/metadata/9949ec81-f758-42e6-9c1c-6fb604a3f053
            hreflang: EE
    extents:
        spatial:
            bbox: [26.6, 58.3, 26.8, 58.5]
            crs: http://www.opengis.net/def/crs/OGC/1.3/CRS84
    providers:
        - type: feature
          name: SQLiteGPKG
          data: /data/cptartu2.gpkg # place correct path here
          id_field: nationalca
          title_field: label
          table: cptartu2 # table name within gpkg

Save the file and restart Docker Compose. Navigate to http://localhost:5000/collections to evaluate whether the new dataset with title "Tartu Cadastral Parcels" has been published.

Note

The SQLite driver incidentally has challenges to open the GeoPackage extension on MacOS. Consult the official documentation or try with an alternative data format.

Publish a GeoJSON using Elasticsearch

If you want to explore publishing vector tiles using Elasticsearch clone this fork of pygeoapi:

git checkout -b ogcapi-ws https://github.com/doublebyte1/pygeoapi.git

In alternative, you can download a zip file from this link.

Then change into the docker/examples/elastic folder, and run the docker-compose file:

cd docker/examples/elastic

docker-compose up

This configuration snippet, enables publishing the file greater_hyderabad_municipal_corporation_ward_boundaries.geojson as OGC API - Features:

    greater_hyderabad_municipal_corporation_ward_boundaries:
        type: collection
        title: Greater Hyderabad Municipal Corporation ward boundaries
        description: The city ward boundaries represent the administrative and electoral boundary areas of the city. It plays a great role in planning of the city, for each council of the municipal corporation.
        keywords:
           - Boundaries
           - Administrative
           - Ward
        links:
            - type: text/html
              rel: canonical
              title: information
              href: https://livingatlas-dcdev.opendata.arcgis.com/datasets/a090c89d52f1498f96a82e97b8bfb83e_0/about
              hreflang: en-US
        extents:
            spatial:
                bbox: [78.2379194985166180,17.2908061510471995,78.6217049083810764,17.5618443356918768]
                crs: http://www.opengis.net/def/crs/OGC/1.3/CRS84
            temporal:
                begin: 2011-11-11
                end: null  # or empty
        providers:
            - type: feature
              name: Elasticsearch
              # note: elastic_search is the Docker container name as defined in docker-compose.ym
              data: http://elastic_search:9200/greater_hyderabad_municipal_corporation_ward_boundaries
              id_field: objectid

Wait until the data was ingested into an elastic index, and pygeoapi starts. You can check the logs using:

docker-compose logs --follow

After the server has started you can access the collection page here:

http://localhost:5000/collections/greater_hyderabad_municipal_corporation_ward_boundaries

And the feature items here:

http://localhost:5000/collections/greater_hyderabad_municipal_corporation_ward_boundaries/items

pygeoapi as a WFS proxy

You can check the "pygeoapi as a Bridge to Other Services" section to learn how to publish WFS as OGC API - Features.

Client access

QGIS

QGIS is one of the first GIS Desktop clients which added support for OGC API - Features. Support has been integrated into the existing WFS provider.

Open an OGC API - Features collection in QGIS

Follow the steps to add some collections from an OGC API - Features enpoint:

Open QGIS (if you don't have QGIS, you can use OSGeoLive)
From the Layer menu, select Add Layer > Add WFS layer
From the Data source manager panel, choose 'New connection'

New connection

Add the URL https://demo.pygeoapi.io/master (or the address of a local server)
You can now click the detect button and QGIS will notice you are configuring an OGC API - Features endpoint
QGIS facilitates to set page size (request is split in multiple requests)
- for points you can easily set it to 2500
- for some polygons with high density, 100 can already be slow
Press OK to save the connection and return to the previous screen
Now click the Connect button to retireve the collections of the service

Collection list

You can now add collections to your QGIS project
You can also build a query to add a subset of the collection
Close the Data source manager. Notice that QGIS applied a default styling just like it would if you add a file based layer. You can work with the collection in a similar way; identify, apply styling, filter, export, etc.

Tip

Activate the Debugging/Development Tools Panel (right click anywhere on the toolbar icons and select). It will display HTTP traffic within QGIS and is a valuable tool in debugging failing connections.

Note

An increasing number of GIS Desktop clients add support for OGC API's in subsequent releases. For example ArcGIS Pro supports OGC API - Features since release 2.8.

GDAL/OGR - Advanced

GDAL/OGR provides support for OGC API - Features. This means you can use ogrinfo, ogr2ogr to query and convert data from OGC API - Features endpoints just like any other vector data source. This also means you can make connections to OGC API - Features endpoints from any software which has an interface to GDAL, such as MapServer, GeoServer, Manifold, FME, ArcGIS, etc.

Use OGR to interact with OGC API - Features

Verify you have a recent GDAL installed, else use GDAL from OSGeoLive
Run ogrinfo on the command line to verify a connection to OGC API - Features

ogrinfo OAPIF:https://demo.pygeoapi.io/master/collections/obs

Now, let's convert the observations into a shapefile

ogr2ogr -f "ESRI Shapefile" obs.shp OAPIF:https://demo.pygeoapi.io/master/collections/obs

Note

You can even use OGR to append new features to an OGC API - Features collection which supports transactions (pygeoapi transaction support is planned for future implementation)

OWSLib - Advanced

OWSLib is a Python library to interact with OGC Web Services and supports a number of OGC APIs including OGC API - Features.

Interact with OGC API - Features via OWSLib

If you do not have Python installed, consider running this exercise in a Docker container. See the Setup Chapter.

pip3 install owslib

Then start a Python console session with: python3 (stop the session by typing exit()).

>>> from owslib.ogcapi.features import Features
>>> w = Features('https://demo.pygeoapi.io/master')
>>> w.url
'https://demo.pygeoapi.io/master'
>>> conformance = w.conformance()
{u'conformsTo': [u'http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/core', u'http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/oas30', u'http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/html', u'http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/geojson']}
>>> api = w.api()  # OpenAPI document
>>> collections = w.collections()
>>> len(collections['collections'])
13
>>> feature_collections = w.feature_collections()
>>> len(feature_collections)
13
>>> lakes = w.collection('lakes')
>>> lakes['id']
'lakes'
>>> lakes['title']
'Large Lakes'
>>> lakes['description']
'lakes of the world, public domain'
>>> lakes_queryables = w.collection_queryables('lakes')
>>> len(lakes_queryables['properties'])
6
>>> lakes_query = w.collection_items('lakes')
>>> lakes_query['features'][0]['properties']
{u'scalerank': 0, u'name_alt': None, u'admin': None, u'featureclass': u'Lake', u'id': 0, u'name': u'Lake Baikal'}

Note

See the official OWSLib documentation for more examples.

Summary

Congratulations! You are now able to publish vector data to pygeoapi.