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Evergreen Spatial Data Infrastructure Catalogues: A Novel Approach

Sampo Savolainen, Cameron Wilson, Eric Wright and Simon Riopel

(Submission #148)


An automated method to produce an evergreen collection of spatial data services is being developed for Pan-Arctic and Canadian spatial data infrastructure practitioners in support of policy development.

Spatial Data Infrastructures (SDI) behave as distributed Complex Adaptive Systems providing a rich data sharing environment for data distributors to publish in a standardised manner, thus enabling analysts easy access and methods to combine diverse sources of information. A core component of SDIs is a catalogue of distributed data holdings. Over the years there has been extensive efforts to build catalogues based on known metadata standards that describe data collections. This approach tends to involve metadata editors, manual editing, with ongoing maintenance costs. Service level metadata can be leveraged further to enable automated web harvesting and regular publication of the results for a country or region.

This paper will present a novel approach to an automated method to produce an evergreen catalogue of spatial web services and their respective data holdings. This method crawls and harvests the web for geographic services and their data collections, followed by a filtering process which identifies what is relevant to that particular SDI. The filtering criteria include attributes directly extracted from the service as well as monitored quality information ensuring retired services are promptly removed from the catalogue. Producing a catalogue with these methods allows for rapid development of service and data catalogues. Improving the quality of a catalogue created in this manner is a matter of adjusting selection criteria instead of applying costly manual labour.

This approach has been successfully used for the Canadian Geospatial Data Infrastructure and is being developed for the Arctic SDI to discover the diversity of accessible, interoperable, Arctic data services. This method can discover data services relevant for a particular domain in a very efficient way, even when the service or data was not known. This has been of particular value to the Arctic SDI, which supports varied types of research within their domains. Specifically, this harvesting approach is providing an updated catalogue for Arctic SDI, helping the discovery of data services for a variety of users, across circumpolar jurisdictions, reducing duplicate data, identifying gaps, and also reporting evidence when evaluating on an SDI.


Topic Area:  [4.1]INSPIRE Thinking out of the box – INSPIRE innovation
Abstract Type:  Oral Presentation

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