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Wstęp do integracji danych przestrzennych metodami kokrigingu
Introduction to integration of spatial data by cokriging method
Wiadomości Statystyczne, 2003, nr 5, s. 7-20, bibliogr. 31 poz.
Słowa kluczowe
Teoria statystyki, Metody statystyczne, Analiza przestrzenna, Geostatystyka
Theory of statistics, Statistical methods, Spatial analysis, Geostatistics
Opisano najważniejsze geostatystyczne techniki estymacji przestrzennej oparte na metodach kokrigingu, wykorzystujące informację dodatkową. Omówiono metody: regresji, krigingu oraz kokrigingu. Przedstawiono również niektóre pakiety geostatystyczne realizujące kokriging.

The goal of geostatistics is the description, analysis and interpretation of uncertainty caused by limited spatial sampling of a property under study. The classical statistical methods are not appropriate for this purpose, because they ignore the spatial autocorrelation in data sets. Geostatistics offers a variety of tools for describing the spatial continuity that is an essential feature of many phenomena. Although, successfully developed during the past few decades geostatistical methods and concepts still remain relatively poor known. The paper presents an introduction to the cokriging methods that are most important geostatistical tools of spatial estimation that take into account secondary information. This type of estimation is often called as data integration. The cokriging methods in which more than one variable is used for prediction are particularly useful when primary - variable is sparse, and at the same time the secondary variables is plentiful or even exhaustive. Cokriging is ideally suited for spatial estimation in many fields, where expensive or difficult measurements are needed, including economy, sociology, geochemistry, remote sensing etc. This technique improves significantly the estimation and reduces the variance of estimation error. The most popular method taking advantage of secondary information is ordinary cokriging. The principles of ordinary cokriging, its assumptions and equation systems are introduced and explained in this paper. The cokriging approach needs an appropriate modeling of the spatial covanances and cross-covariances. Therefore the linear model of coregionalization, which explains this modeling, was also described in detail. For the sake of comparison linear regression model, which ignores the spatial correlation was also presented. The essentials of another important types of cokriging such as simple cokriging, cokriging with trend model and collocated one, were also explained. Finally, the possibilities of practical cokriging estimations by means of two important geostatistical software packages GSLIB and GS+ were presented. (original abstract)
Dostępne w
Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka Szkoły Głównej Handlowej w Warszawie
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach
Biblioteka Główna Uniwersytetu Ekonomicznego w Poznaniu
Biblioteka Główna Uniwersytetu Ekonomicznego we Wrocławiu
  1. Zawadzki, J., Zastosowanie metod geostatystycznych do analizy danych przestrzennych, "Wiadomości Statystyczne", GUS, 12, 2002
  2. Journel, A. G. and Huibregts, C. J., Mining Geostatistics, Academic Press, London 1978
  3. Isaaks, E. H. and Srivastava, R., An Introduction to Applied Geostatistics, Oxford University Press, New York, NY 1989
  4. Rouhani, S., Srivastava, R., Desbarats, A., Cromer, M., Johnson, A., Geostatistics for Environmental and Geotechnical Applications, ASTMP STP 1283, 1996
  5. Goovaerts, P., Geostatistics for Natural Resources Evaluation, Oxford University Press, New York 1997
  6. Deutsch, C. V., Journel, A. G., GSLIB Geostatistical Software Library and User's Guide, Oxford University Press, New York, NY 1992
  7. Pannatier, Y., Variowin, Software for Spatial Data Analysis in 2D, Springer-Verlag, 1996
  8. Gamma Design Software, GS+ Geostatistics for the Environmental Software, 1998
  9. Froideevaux, R., Geostatistical Toolbox Primer, version 1.30. FSS International, Troinex, Switzerland 1990
  10. Englund, E. and Sparks, A., Geo-EAS 1.2.1 User's Guide, EPA Report 60018-91/008.EPA-EMSL, Las Vegas, NV 1998
  11. De Bruin, S., Predicting the Areal Extent of Land-Cover Types Using Classified Imagery and Geostatistics, Remote Sensing of Environment, 74, 2000
  12. Hudak, T. A., Lefsky, M. A., Cohen, W. B., Berterretche, M, Integration of Udar and Landsat ETN - data for estimating and mapping forest canopy height, Remote Sensing of Environment, 5724, 2002
  13. Dungan, J., Spatial prediction of vegetation quantities using ground and image data, International Journal of Remote Sensing, 19, 1998
  14. Krige, D. G., Two-dimensional weighted moving average trend surfaces for ore valuation, in Proceedings of the Symposium on Mathematics, Statistics and Computer Applications in Ore Valuation, Johannesburg, South African Institute of Mining and Metallurgy, Johannesburg 1966
  15. Myers, D., Cokriging - new developments, in G. Verly et al., editors, Geostatistics for Natural Resources Characterization, Reidel, Dordrecht, Holland 1984
  16. Royle, A. G., A practical Introduction to Geostatistics, Mining Sciences Departament, University of Leeds, Leeds 1975
  17. Xu, W., Tran, T., Srivastava, R. and Journel, A., Integrating seismic data in reservoir modeling: the collocated alternative, SPE paper 24742, 1992
  18. Benson, B. E., Using co-kriging to enhance subsurface characterization for prediction of contaminant transport, Geostatistics for Environmental and Geotechnical Applications, ASTM STP 1283, Srivastava, R., Rouhani, S., Cromer, M, V., Johnson, A, I., Eds., American Society for Testing and Materials, Philadelphia 1966
  19. Papritz, A. and Füchler, H., Temporal change of spatially autocorelated soil properties: Optimal estimation by cokriging. Geoderma 25, 1994
  20. Webster, R. and Oliver M., Geostatistics for Environmental Scientists, John Wiley and Sons, Ltd., 2000
  21. Lunetta, R. S., Congalton, R. G., Fenstermaker, L. K., Jensen, J. R., McGwire, K. C. and Tinney, L. R., Remote sensing and geographic information system data integration - error sources and research issues. Photogrametric Engineering and Remote Sensing, 57, 1991
  22. Kacewicz, M., "Fuzzy" geostatistics - An integration of qualitative description into spatial analysis, in Geostatistics for the Next Century, Dimitrakopoulous, R., ed. Kluwer, Dordrecht, Netherlands, 448-463
  23. Journel, A. G., Fundamentals of Geostatistics in Five Lessons, Washington, DC 1989
  24. Cressie, N. A. C., The Origins of Kriging, Mathematical Geology, Vol. 22, No. 3, 1990
  25. David, M., The practice of kriging, in Geostat 75, 1975
  26. Matheron, G., Traite de Geostatistuque Appliquee, Technic, Paris 1962-1963
  27. Goovearts, P., and Journel, A. G., Goovearts, P., Ordinary cokriging revisited, Mathematical Geology, 29, 1997
  28. Wackernagel, H., Cokriging versus kriging in regionalized multivariate data analysis, Geoderma, 62, 1995
  29. Ripley, B. D., Spatial Statistics, New York: Wiley, 1981
  30. Isaaks, E. H., and Srivastava, R. M., Spatial continuity measures for probabilistic and deterministic geostatistics, Mathematical Geology, Vol. 20, No. 4, 1988
  31. Journel, A. G. and Rossi, M. E., When do we need a trend model in kriging? Mathematical Geology, 21, 1989
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