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How We Build Geostatistical Models and Deal with Their Output

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Interfacing Geostatistics and GIS

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References

  1. Armstrong, M. (1984) Improving the estimation and modelling of the variogram. In: G. Verly et al, (Eds.), Geostatistics for Natural Resource Characterization, pp 1–20, Reidel, Dordrecht, Holland.

    Google Scholar 

  2. Abrahamsen, P. and F.E. Benth (2001) Kriging with inequality constraints. Mathematical Geology 33 (6), 719–744.

    Article  Google Scholar 

  3. Bivand, R.S. (2003) Approaches to classes for spatial data in R. In: K. Hornik and F. Leisch (Eds.), Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003) March 20–22, Vienna, Austria. http://www.ci.tuwien.ac.at/Conferences/DSC-2003/

  4. Burrough, P.A. and R.A. McDonnell (1998) Principles of geographical information systems. Oxford University Press, New York. 431 pp.

    Google Scholar 

  5. Chilès, J-P. and P. Delfiner (1999) Geostatistics, modeling spatial uncertainty. Wiley, New York.

    Google Scholar 

  6. Cleveland, W.S. (1993) Visualizing data. Hobart Press, Summit, New Jersey.

    Google Scholar 

  7. Cressie, N.A.C. (1993) Statistics for Spatial Data, revised edition. Wiley, New York.

    Google Scholar 

  8. Deutsch, C.V. and A.G. Journel (1990) GSLIB geostatistical software library and user’s guide, second edition. Oxford University Press, New York.

    Google Scholar 

  9. Draper, N.R. and H. Smith (1981) Applied regression analysis, second edition. Wiley, New York.

    Google Scholar 

  10. Goovaerts, P. (1997) Geostatistics for natural resources evaluation. Oxford University Press, New York.

    Google Scholar 

  11. Goulard, M. and M. Voltz (1992) Linear coregionalization model: tools for estimation and choice of cross-variogram matrix. Mathematical Geology 24 (3), 269–286.

    Article  Google Scholar 

  12. Ihaka, R. and R. Gentleman (1996) R: a language for data analysis and graphics. Journal of Computational and Graphical Statistics 5(3) 299–314.

    Article  Google Scholar 

  13. Journel, A.G. and Ch.J. Huijbregts (1978) Mining geostatistics. Academic Press, London, 600 pp.

    Google Scholar 

  14. Journel, A.G. and M.E. Rossi (1989) When do we need a trend model in kriging? Mathematical Geology 21 (7), 715–739.

    Article  Google Scholar 

  15. Kitanidis, P.K. (1993) Generalized covariance functions in estimation. Mathematical Geology 25 (5), 525–540.

    Article  Google Scholar 

  16. Krivoruchko, K. (2007)...., this volume.

    Google Scholar 

  17. Laane, R.W.P.M., H.L.A. Sonneveldt, A.J. Van der Weyden, J.P.G. Loch and G. Groeneveld (1999) Trends in the spatial and temporal distribution of metals (Cd, Cu, Zn and Pb) and organic compounds (PCBs and PAHs) in Dutch coastal zone sediments from 1981 to 1996: a model case study for Cd and PCBs. Journal of Sea Research 41, 1–17.

    Article  Google Scholar 

  18. Pebesma, E.J. and C.G. Wesseling (1998) Gstat, a program for geostatistical modelling, prediction and simulation. Computers & Geosciences, 24 (1), 17–31.

    Article  Google Scholar 

  19. Pebesma, E.J. and G.B.M. Heuvelink (1999) Latin hypercube sampling of Gaussian random fields. Technometrics 41 (4), 303–312.

    Article  Google Scholar 

  20. Pebesma, E.J. (2004) Multivariable geostatistics in S: the gstat package. Computers & Geosciences 30, 683–691.

    Article  Google Scholar 

  21. Remy, N. (2001) GsTL: the geostatistical template library in C++. MSc thesis, Dept. of Petroleum Engineering, Stanford University.

    Google Scholar 

  22. Stein, M.L. (1999) Interpolation of spatial data: some theory for kriging. Springer, New York, 247 pp.

    Google Scholar 

  23. Ver Hoef, J.M. and N.A.C. Cressie (1993) Multivariable spatial prediction. Mathematical Geology, 25 (2), pp. 219–240.

    Article  Google Scholar 

  24. Yao, T. and A.G. Journel (1997) Automatic modeling of (cross) covariance tables using fast fourier transform. Mathematical Geology 30, 589–616.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute for Geoinformatics (ifgi), University of Münster, Münster, Germany

    Edzer J. Pebesma

Authors
  1. Edzer J. Pebesma
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Editor information

Editors and Affiliations

  1. Universität Klagenfurt Institut für Statistik, Universitätsstr. 65-67, 9020 Klagenfurt, Austria

    Jürgen Pilz

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Pebesma, E.J. (2009). How We Build Geostatistical Models and Deal with Their Output. In: Pilz, J. (eds) Interfacing Geostatistics and GIS. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-33236-7_1

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