Actually I think the issue is that the domain scientists, quite reasonably, are
used to working with some domain-assumptions. For example, when you are an
atmospheric scientist talking to other atmospheric scientists, the subject of your
studies and observations is the atmosphere. Doesn't need to be stated explicitly,
or maybe is just inferred from the fact that elevation component of the location
is >0.
I'm comfortable with this. That's really why the SamplingFeature is useful. It
allows you to work with a feature that is primarily defined by its shape for
everyday purposes, without totally abandoning a rigorous model that recognises
the fact that there is a domain feature underneath, and it is really the domain
feature that has the properties.
To help with this, here's some "convenience" features, that you can use as the "feature of
interest" of an observation, of the "sampled feature" of a sampling-feature ;-)
<https://www.seegrid.csiro.au/twiki/bin/view/CGIModel/CGIFeatureRegister#Register_content>
______
Simon.Cox@xxxxxxxx CSIRO Exploration & Mining
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PO Box 1130, Bentley WA 6102 AUSTRALIA
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-----Original Message-----
From: Gerry Creager [mailto:gerry.creager@xxxxxxxx]
Sent: Friday, 14 March 2008 10:40 AM
To: Cox, Simon (E&M, Kensington)
Cc: galeon@xxxxxxxxxxxxxxxx
Subject: Re: [galeon] Fwd: CDM feature and point types docs
I'll revisit O&M Part 2, and admit now that I gave it, at best, a
cursory review last time. However, I continue to suggest that part of
the problem is that the domain scientists don't all speak "geospatial"
as well as they could, and make inference based less on strict
definitions than preference and bias. The result is that the potential
for interoperability is reduced until we can resolve the semantic
issues, and these will likely not be readily resolved with an RDF
document or two because of mis-communication.
gerry
