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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