Error-Aware Spatio-Temporal Aggregation in the Model Web

  • Christoph StaschEmail author
  • Edzer Pebesma
  • Benedikt Graeler
  • Lydia Gerharz
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Spatio-temporal aggregation of observed or predicted values for environmental phenomena is needed for fusing sensor data or coupling sensors and environmental models. However, estimates from sensors or environmental models can never represent our world precisely and are subject to errors. Hence, there is uncertainty in the estimates that needs to be considered in environmental model workflows. This chapter presents an approach for an error-aware spatio-temporal aggregation in the Web, where probabilistic uncertainties are used within a Monte Carlo simulation. The approach is applied in a Web-based model chain that provides uncertain crop yield predictions on field parcel level that are aggregated to larger regions.


Aggregation Process Aggregation Function Probability Distribution Function Monte Carlo Simulation Approach Aggregation Service 
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.



The research leading to these results has received funding from the European Union Seventh Framework Programme [FP7/2007-2013] under grant agreement no 248488. We are thankful to Jill Johnson and Sarah Knight from the Food and Environment Research Agency and Richard Jones from Aston University for the support during the integration of our approach in the yield prediction workflow.


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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Christoph Stasch
    • 1
    Email author
  • Edzer Pebesma
    • 1
  • Benedikt Graeler
    • 1
  • Lydia Gerharz
    • 1
  1. 1.Institute for GeoinformaticsUniversity of MünsterMünsterGermany

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