Error-Aware Spatio-Temporal Aggregation in the Model Web

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

Abstract

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.

References

  1. Balazinska M, Deshpande A, Franklin M, Gibbons P, Gray J, Nath S, Hansen M, Liebhold M, Szalay A, Tao V (2007) Data management in the worldwide sensor web. Pervasive Comput IEEE 6(2):30–40 (April–June 2007)Google Scholar
  2. Bastin L, Cornford D, Jones R, Heuvelink GBM, Stasch C, Pebesma E, Nativi S, Mazzetti P, Williams M (2012) Managing uncertainty in integrated environmental modelling frameworks: the uncertweb framework. Environ Model Softw 39:116–134Google Scholar
  3. Benjelloun O, Sarma AD, Halevy A, Widom J (2006) ULDBs: databases with uncertainty and lineage. In: Proceedings of the 32nd international conference on very large data bases. VLDB ’06, VLDB Endowment, pp 953–964Google Scholar
  4. Beven K, Buytaert W, Smith LA (2012) On virtual observatories and modelled realities (or why discharge must be treated as a virtual variable). Hydrol Process 26(12):1905–1908CrossRefGoogle Scholar
  5. Bierkens M, Finke P, De Willingen P (2000) Upscaling and downscaling methods for environmental research. Kluwer Academic PublishersGoogle Scholar
  6. Bigagli L, Nativi S, (eds) (2011) NetCDF uncertainty conventions (NetCDF-U). OGC 11–163. Open geospatial consortium, Inc, pp 17 (accessed 24 July 2012)Google Scholar
  7. Bröring A, Echterhoff J, Jirka S, Simonis I, Everding T, Stasch C, Liang S, Lemmens R (2011) New generation sensor web enablement. Sensors 11(3):2652–2699CrossRefGoogle Scholar
  8. Domenico B (2011) OGC network common data form (NetCDF) core encoding standard version 1.0. OGC 10–090r3. Open geospatial consortium, Inc, pp 21 (Accessed on 01 Nov 2012)Google Scholar
  9. Geller G, Turner, W.: The model web: a concept for ecological forecasting. In: Geoscience and Remote Sensing Symposium, (2007) IGARSS 2007. IEEE International. 2007:2469–2472Google Scholar
  10. Gerharz L, Autermann C, Hopmann H, Stasch C, Pebesma E (2012) Uncertainty visualisation in the model web. European Geosciences Union (EGU) General AssemblyGoogle Scholar
  11. Gerharz L, Pebesma E (2012) Using geostatistical simulation to disaggregate air quality model results for individual exposure estimation on GPS tracks. Stoch Env Res Risk Assess 27:223–234Google Scholar
  12. Graeler B, Stasch C (2012) Flexible representation of spatio-temporal random fields in the model web. European Geosciences Union (EGU) General AssemblyGoogle Scholar
  13. Heuvelink G (1998) Error propagation in environmental modelling with GIS. Taylor & FrancisGoogle Scholar
  14. Heuvelink G, Pebesma E (1999) Spatial aggregation and soil process modelling. Geoderma 89:47–65CrossRefGoogle Scholar
  15. ISO/TC211: ISO/FDIS 19156:2010: geographic information—observations and measurements. ISO/TC 211 (2010)Google Scholar
  16. Jampani R, Xu F, Wu M, Perez LL, Jermaine C, Haas PJ (2008) MCDB: a Monte Carlo approach to managing uncertain data. In: Proceedings of the (2008) ACM SIGMOD international conference on Management of data. SIGMOD ’08. New York, NY, USA, ACM, pp 687–700Google Scholar
  17. Jeong SH, Fernandes AAA, Paton NW, Griffiths T (2004) A generic algorithmic framework for aggregation of spatio-temporal data. In: SSDBM ’04: proceedings of the 16th international conference on scientific and statistical database management, Washington, DC, USA, IEEE Computer Society, p 245Google Scholar
  18. Jirka S, Bröring A, Stasch C (2009) Discovery mechanisms for the sensor web. Sensors 9(4):2661–2681CrossRefGoogle Scholar
  19. Jones R, Cornford D, Bastin L (2012) UncertWeb processing service: making models easier to access on the web. Trans GIS 14(6):921–939Google Scholar
  20. Maue P, Stasch C, Athanasopoulos G, Gerharz L (2011) Geospatial standards for web-enabled environmental models. Int J Spatial Data Infrastruct Res 6:145–167Google Scholar
  21. Nativi S, Bigagli L (2009) Discovery, mediation, and access services for earth observation data. IEEE J Sel Top Appl Earth Observ Rem Sens 2(4):233–240CrossRefGoogle Scholar
  22. Nativi S, Mazzetti P, Geller GN (2012) Environmental model access and interoperability: the GEO model web initiative. Environ Model Softw 39:214–228. doi:10.1016/j.envsoft.2012.03.007 CrossRefGoogle Scholar
  23. Pebesma E (2012) Spacetime: spatio-temporal data in R. J Stat Softw 51(7):1–30Google Scholar
  24. Pross B, Gerharz L, Stasch C, Pebesma E (2012) Tools for uncertainty propagation in the model web using Monte Carlo simulation. In: Seppelt R, Voinov A, Lange S, Bankamp D (eds) Proceedings of the iEMSs sixth Biennial meeting: Managing resources of a limited planet. International congress on environmental modelling and software (iEMS 2012), international environmental modelling and software society (iEMSs)Google Scholar
  25. R Development Core Team: R (2011) A Language and environment for statistical computing. R Foundation for statistical computing, Vienna, Austria. ISBN 3-900051-07-0Google Scholar
  26. Schut P (2007) OpenGIS web processing service. OGC 05–007r7. Open Geospatial Consortium, Inc., 87pp. (Accessed on 24 July 2012)Google Scholar
  27. Sheth A, Henson C, Sahoo S (2008) Semantic sensor web. IEEE Int Comput, pp 78–83Google Scholar
  28. Stasch C, Foerster T, Autermann C, Pebesma E (2012) Spatio-temporal aggregation of European air quality observations in the sensor web. Comput Geosci 47:111–118CrossRefGoogle Scholar
  29. Stasch C, Autermann C, Foerster T, Pebesma E (2011) Towards a spatiotemporal aggregation service in the sensor web. Poster presentation. In: The 14th AGILE international conference on geographic information, scienceGoogle Scholar
  30. Stasch C, Jones R, Cornford D, Kiesow M, Williams M, Pebesma E (2012) Representing Uncertainties in the Sensor Web. In: Proceedings of Workshop Sensing A Changing WorldGoogle Scholar
  31. Taylor JR (1997) An introduction to error analysis: the study of uncertainties in physical measurements. University Science BooksGoogle Scholar
  32. Vega Lopez IF, Snodgrass RT, Moon B (2005) Spatiotemporal aggregate computation: a survey. IEEE Trans Knowl Data. Engineering 17(2):271–286Google Scholar
  33. Williams M, Conford D, Bastin L, Pebesma E (2009) Uncertainty markup language (UncertML) (OGC 08–122r2)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

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

Personalised recommendations