The quality of environmental data analysis and propagation of errors
are heavily affected by the representativity of the initial sampling
design [CRE 93, DEU 97, KAN 04a, LEN 06, MUL07]. Geostatistical methods
such as kriging are related to field samples, whose spatial distribution
is crucial for the correct detection of the phenomena. Literature
about the design of environmental monitoring networks (MN) is widespread
and several interesting books have recently been published [GRU 06,
LEN 06, MUL 07] in order to clarify the basic principles of spatial
sampling design (monitoring networks optimization) based on Support
Vector Machines was proposed.
Nonetheless, modelers often receive real data coming from environmental
monitoring networks that suffer from problems of non-homogenity (clustering).
Clustering can be related to the preferential sampling or to the
impossibility of reaching certain regions.