Assessing environmental quality usually requires the observation of two or more correlated variables, which are measured in several points of the study area. Sometimes, the characteristic of interest is sparsely sampled over the area, then it is convenient to incorporate some auxiliary variables, correlated with the variable of interest, into the estimation procedure. Indeed they carry relevant information for the variable being estimated, especially if they are more densely available over the domain. In this paper three different spatial interpolation approaches have been used in order to obtain spatial predictions of the variable of interest characterized by a severe lack of data.
Modeling environmental quality: a case study
DE IACO, Sandra;MAGGIO, Sabrina;PALMA, Monica;POSA, Donato
2014-01-01
Abstract
Assessing environmental quality usually requires the observation of two or more correlated variables, which are measured in several points of the study area. Sometimes, the characteristic of interest is sparsely sampled over the area, then it is convenient to incorporate some auxiliary variables, correlated with the variable of interest, into the estimation procedure. Indeed they carry relevant information for the variable being estimated, especially if they are more densely available over the domain. In this paper three different spatial interpolation approaches have been used in order to obtain spatial predictions of the variable of interest characterized by a severe lack of data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
