Exchanging Uncertainty: Interoperable Geostatistics?

  • Matthew Williams
  • Dan Cornford
  • Lucy Bastin
  • Ben Ingram
Part of the Quantitative Geology and Geostatistics book series (QGAG, volume 16)


This paper discusses a solution to providing interoperable, automatic geostatistical processing through the use of Web services, developed in the INTAMAP project (INTeroperability and Automated MAPping). The project builds upon Open Geospatial Consortium standards for describing observations, typically used within sensor webs, and employs Geography Markup Language (GML) to describe the spatial aspect of the problem domain. Thus, the interpolation service is extremely flexible, being able to support a range of observation types, and can cope with issues such as change of support and differing error characteristics of sensors (by utilising descriptions of the observation process provided by SensorML). XML is accepted as the de facto standard for describing Web services, due to its expressive capabilities which allow automatic discovery and consumption by ‘naïve’ users. Any XML schema employed must, therefore, be capable of describing every aspect of a service and its processes. However, no schema currently exists that can define the complex uncertainties and modelling choices that are often present within geostatistical analysis. We show a solution to this problem, developing a family of XML schemata to enable the description of a full range of uncertainty types. These types will range from simple statistics, such as the kriging mean and variances, through to a range of probability distributions and non-parametric models, such as realisations from a conditional simulation. By employing these schemata within a Web Processing Service (WPS) we show a prototype moving towards a truly interoperable geostatistical software architecture.


Unify Modeling Language Service Orient Architecture Error Characteristic Uniform Resource Identifier Open Geospatial Consortium 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is funded by the European Commission, under the Sixth Framework Programme, by Contract 033811 with DG INFSO, action Line IST-2005-2.5.12 ICT for Environmental Risk Management.


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Matthew Williams
    • 1
  • Dan Cornford
    • 1
  • Lucy Bastin
    • 1
  • Ben Ingram
    • 1
  1. 1.Knowledge Engineering GroupSchool of Engineering and Applied Science, Aston UniversityBirminghamUK

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