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Anisotropic spatial sampling designs for urban pollution
Author(s) -
Arbia Giuseppe,
Lafratta Giovanni
Publication year - 2002
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/1467-9876.00265
Subject(s) - sampling (signal processing) , anisotropy , basis (linear algebra) , isotropy , generator (circuit theory) , field (mathematics) , computer science , transformation (genetics) , environmental science , spatial variability , pollution , statistical physics , data mining , statistics , mathematics , physics , ecology , geometry , power (physics) , biochemistry , chemistry , filter (signal processing) , quantum mechanics , pure mathematics , computer vision , gene , biology
Summary. Isotropic processes form an inadequate basis in modelling many spatially distributed data. In particular environmental phenomena often have strong anisotropic spatial variation, especially when the regions monitored are very large. We extend a recently proposed optimal sampling strategy by assuming a spatial anisotropic random field as the basis for the data generator mechanism. The procedure is based on a geographical space transformation indicated by Sampson and Guttorp. We discuss the optimal design and we develop a sequential procedure for selecting a network of monitoring stations in environmental surveys. Some data on sulphur dioxide pollution in Padua (Italy) are analysed to illustrate the method.