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Configuration

pygeoapi uses a yaml file as configuration source and the file location is read from the PYGEOAPI_CONFIG env variable

Note

pygeoapi is under high development, and new configuration paramenters are constantely being added. For the lastest parameters please consult the pygeoapi-config.yml file provided on github

Using pygeoapi-config.yml as reference we will have the following sections:

  • server for server related configurations
  • logging for logging configuration
  • metadata server and content metadata (information used to populate multiple content)
  • datasets data content offered by server (collections in WFS3.0)

Structured data

JSON-LD support

pygeoapi supports structured metadata about a deployed instance, and is also capable of presenting feature data as structured data. JSON-LD equivalents are available for each HTML page, and are embedded as data blocks within the corresponding page for search engine optimisation (SEO). Tools such as the Google Structured Data Testing Tool can be used to check the structured representations.

The metadata for an instance is determined by the content of the metadata section of the configuration YAML. This metadata is included automatically, and is sufficient for inclusion in major indices of datasets, including the Google Dataset Search.

For collections, at the level of an item or items, by default the JSON-LD representation adds:

  • The GeoJSON JSON-LD vocabulary and context to the @context.
  • An @id for each feature in a collection, that is the URL for that feature (resolving to its HTML representation in pygeoapi)

Note

While this is enough to provide valid RDF (as GeoJSON-LD), it does not allow the properties of your features to be unambiguously interpretable.

pygeoapi currently allows for the extension of the @context to allow properties to be aliased to terms from vocabularies. This is done by adding a context section to the configuration of a dataset.

The default pygeoapi configuration includes an example for the obs sample dataset:

context:
    - datetime: https://schema.org/DateTime
    - vocab: https://example.com/vocab#
      stn_id: "vocab:stn_id"
      value: "vocab:value"

This is a non-existent vocabulary included only to illustrate the expected data structure within the YAML configuration. In particular, the links for the stn_id and value properties do not resolve. We can extend this example to one with terms defined by schema.org:

context:
    - schema: https://schema.org/
      stn_id: schema:identifer
      datetime:
          "@id": schema:observationDate
          "@type": schema:DateTime
      value:
          "@id": schema:value
          "@type": schema:Number

Now this has been elaborated, the benefit of a structured data representation becomes clearer. What was once an unexplained property called datetime in the source CSV, it can now be expanded to https://schema.org/observationDate, thereby eliminating ambiguity and enhancing interoperability. Its type is also expressed as https://schema.org/DateTime.

This example demonstrates how to use this feature with a CSV data provider, using included sample data. The implementation of JSON-LD structured data is available for any data provider but is currently limited to defining a @context. Relationships between features can be expressed but is dependent on such relationships being expressed by the dataset provider, not pygeoapi.

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