[OSGeo-Discuss] Should OSGeo get involved in the Information Architecture realm and nurture the development of definitive spatial ontologies?

[Bruce Bannerman]

Hi Jo,

Thank you for your considered reply (...and no, I don't consider it
trollish   ;-)   )


We need robust debate on these types of issues if we are to progress
them.


OK, I'll try and put some more context on the original query.


I see that there are two main ways of utilising spatial information:

- producing a pretty picture that helps people understand an issue. We
have a number of types of products that fall in this realm, including
Google Maps, Google Earth, Virtual Earth, Slippy Maps etc.

- as an input into structured analysis that is used as an aid to
answering a particular question and also as an aid to exploring
inter-relationships between spatial, business, scientific data etc. The
output from this analysis could be a 'map', but of equal relevance it
could be in tabular, graphical or textual form. This is the realm of
traditional spatial analysis, image analysis or a range of spatial
products that I like to term 'Spatial Intelligence Frameworks' e.g.
Cohga's Weave, NGIS' GeoSamba, ESRI Australia's Eview. 


I fall into the second camp and try to implement systems that help end
users to explore and better utilise their data.


For effective analysis to be undertaken, you need to understand your
data and ensure that there are appropriate aspatial attributes to query
and analyse to find an answer to your problem.

While this is relatively straight forward for project work where you
control the data capture and QA processes, it starts becoming very messy
as soon as you start to try and take advantage of data captured by other
people and organisations.

Typically we find that another organisation has captured data describing
the same geographic phenomena for a different purpose, modelled the data
differently, with different fields and data types. This requires lost
time and effort in trying to massage the data into a format that we can
use and requires compromises in what can be considered an acceptable
outcome.


Throw into this the big picture issues that we are facing, e.g. Climate
Change, Water Shortage (in Australia) etc that require analysis at a
continental or global scale and we have a big problem.

How can we as an industry help this work to progress quickly with
minimal impact on the analysis, minimal double handling of data and in
many cases the use of dynamic data from multiple sources?


This is the context in which I made my original post.



As I discussed, I think that the geoscience community is showing us a
potential way forward with their community work developing the GeoSciML
profile. Anyone who has worked with geological data will appreciate the
magnitude of their accomplishments to date. This includes a way of
describing one of the most abstract types of spatial data an a
consistent way that can be understood by people of different cultures
and different languages.


This effort has taken a community four to five years to develop to its
current state with considerable effort.

How do we get consistent schema / ontologies / profiles for other
spatial phenomena? 


You are right in that it could be a GSDI responsibility. It could also
be an Enterprise Architecture responsibility (e.g. FEA Data Reference
Model).

In the end, I suspect that we will need community driven involvement to
get it right. Communities of practice (like the geoscience community)
will need to work together to develop *their* profiles describing
*their* data. 

Is it an OSGeo responsibility? Probably not. I take the point of your
earlier email that OSGeo is predominantly about OS software.


Is this an issue that OSGeo can help with?  Possibly.



When you consider the analysis requirement for spatial data, I suspect
that we as an industry may be heading in the wrong direction. 

Some of the issues that are are attracting a lot of effort are about
simplifying spatial data (GeoRSS, GeoJSON, BXFS etc). These appear to be
about catering to the 'pretty picture' use of spatial information.


I'm regularly seeing serious efforts to address the analysis use of
spatial data (e.g. GML 3 and complex features) ridiculed.


I'm not saying that there is no use for the pretty pictures. There
certainly is and Google in particular is catering to this very well and
increasing the awareness of spatial information amongst decision makers
and the public alike.

Meanwhile 2050 is fast approaching, if we are to believe the climate
change predictions.



Bruce Bannerman











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