pygeoapi is a Python server implementation of the OGC API suite of standards. The project emerged as part of the next generation OGC API efforts in 2018 and provides the capability for organizations to deploy a RESTful OGC API endpoint using OpenAPI, GeoJSON, and HTML. pygeoapi is open source and released under an MIT license.
The setting of the ogc api features track
at inspire may be a bit different then the normal setting of the slide set, we don't need many of the ogc/api intro slides because those are covered by Gobe and Thorsten. On the other hand we may add some slides related to schema-org and json-ld.
We could skip/add the topics in the presentation or create a dedicated copy.
schema-org is relevant because there is a general feel that inspire data is hard to locate, facilitating data search via google dataset search is one of the potential improvements people expect, pygeoapi will facilitate that
json-ld is relevant because inspire has many app-schema xml models which are quite challenging to consume. GeoJson on the other hand is quite limited in hierarchical capabilities.
An option would be the adoption of json-ld as a app-schema alternative. Experiments and findings from the pygeoapi project are interesting
what i like of the approach chosen in pygeoapi (rather then requiring to fit a dataset into a model) publish the data more or less as-is and reference a ld-context file to make the data (inspire) model aware. see also open data principles
rdf vocabularies of some inspire models are published here
The setting of the ogc api features track at inspire may be a bit different then the normal setting of the slide set, we don't need many of the ogc/api intro slides because those are covered by Gobe and Thorsten. On the other hand we may add some slides related to schema-org and json-ld.
We could skip/add the topics in the presentation or create a dedicated copy.
schema-org is relevant because there is a general feel that inspire data is hard to locate, facilitating data search via google dataset search is one of the potential improvements people expect, pygeoapi will facilitate that
json-ld is relevant because inspire has many app-schema xml models which are quite challenging to consume. GeoJson on the other hand is quite limited in hierarchical capabilities. An option would be the adoption of json-ld as a app-schema alternative. Experiments and findings from the pygeoapi project are interesting
what i like of the approach chosen in pygeoapi (rather then requiring to fit a dataset into a model) publish the data more or less as-is and reference a ld-context file to make the data (inspire) model aware. see also open data principles
rdf vocabularies of some inspire models are published here