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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
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach
Biblioteka Główna Uniwersytetu Ekonomicznego w Poznaniu
Biblioteka Główna Uniwersytetu Ekonomicznego we Wrocławiu
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