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Understanding Uncertainty in Geospatial Models: Insights from Aston University's Research

Dan Cornford and his team at Aston University focus on managing uncertainty in geospatial models, particularly in complex environmental systems. Through their work on projects like INTAMAP and UncertWeb, they have developed standards and tools for uncertainty propagation in web service chains. With UncertML, a probabilistic model for representing uncertainties, they aim to enhance interoperability in geospatial data. This initiative addresses the critical need for uncertainty considerations in decision-making processes and aims to improve data quality in spatially distributed systems.

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Understanding Uncertainty in Geospatial Models: Insights from Aston University's Research

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  1. Uncertainty and the OGC:an Aston University take Dan Cornford Computer Science, Aston University, Birmingham, United Kingdom

  2. Some background • I am a bit of everything and nothing: • statistician, mathematician, meteorologist, computer scientist, even geographer • and I even know a bit about GIS • Team includes Lucy Bastin (GIS / ecology), Matt Williams, Richard Jones (computer science) • Our main interest is in geospatial models and associated data, and in particular uncertainty • my main research is in managing uncertainty in complex models, typically of spatially distributed environmental systems

  3. Route into the OGC • We started working on ‘interoperability’ in an EU funded project called INTAMAP • developing an ‘interoperable’ interpolation system • during this we created UncertML to represent uncertainties on inputs and outputs • the service was implemented as a WPS and used GML and SWE standards • This led on to UncertWeb, which I coordinate

  4. The UncertWeb concept • The “model web” ... • When chaining services of limited or unknown quality, uncertainty must be accounted for if rational decisions are to be made “UncertWeb develops mechanisms, standards, tools and test-beds for accountable uncertainty propagation in web service chains.”

  5. UncertWeb and UncertML • We need a model for uncertainty within UncertWeb • UncertML provides a complete probabilistic model for uncertainty • provide an API for using UncertML • UncertML ideally standardised (OGC?, W3C?, IETF?) • UncertML 2.0 is a major revision to the intial proposal: • a very simple conceptual model (distributions, statistics, samples) • controlled vocabulary (still trying to realise how to deploy this) • encoding (XML and JSON, no use of other OGC standards!) • Emphasis on usability and cross domain aspects – keep it simple • e.g. jStat library for visualising UncertML • hard typed to allow easy interoperability

  6. Uncertainty Enabled Services • Current OWS do not explicitly support uncertainty • some recognition in metadata: quality • UncertWeb will develop profiles of WPS, SOS, CSW and WCS and produce implementations of these that can work with uncertain inputs and outputs • will restrict what can be communicated to make it easier to interoperate within uncertainty enabled services • We think uncertainty is an incredibly important issue in terms of true ‘information interoperability’, particularly in workflows

  7. And what about the OGC? • Lots of good things • contacts, open standards • very well considered conceptual models (O&M, GML) • useful service interfaces (WMS, WFS, SOS, WCS) • … but … • too little attention to real deployment (e.g. O&M, GML) – where are the profiles / application schema? • focus on services, not clients, so support often limited • what to do about cross domain concerns? • bit slow with SOAP/WSDL and RESTful web service world, JSON? ...

  8. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n° [248488 and 265178]. The future • Focus remains on uncertainty • further UncertML developments, API, tools, real usage • developing full SOAP/WSDL support for ‘uWPS’ • trying to find the right home for UncertML … • integration of UncertML with GEOSS in GeoViQua, in context of data quality

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