"From my perspective in managing climate data, all of our data is time-series spatial data. It is very important to understand the ‘when’ as well as there ‘where’ with respect to the data (together with the data provenance etc)." Bruce's use case is certainly an important one and we need to make sure we can handle it. But I think it is still possible to separate out time and location concerns in the sense of vocabularies and data representation patterns. For a particular data point (or event or whatever), an approach like RDF Data Cube, or similar n-ary relationship, can link the data point to the appropriate location and time. So (as I see it) the location specification itself does not need to incorporate a concept of time in this case, but rather each data point needs to connect both to a location and a time period. Let me know Bruce if I am missing something important here! (A separate issue is where a location named by an identifier might change it's definition over time - eg 'London' is bigger now than it was a hundred years ago). Cheers Bill
Received on Tuesday, 10 March 2015 11:43:13 UTC