The design and analysis of spatial data structures
The design and analysis of spatial data structures
An algorithm for the construction of spatial coverage designs with implementation in SPLUS
Computers & Geosciences
A conditioned Latin hypercube method for sampling in the presence of ancillary information
Computers & Geosciences
Multidimensional binary indexing for neighbourhood calculations in spatial partition trees
Computers & Geosciences
Computers and Electronics in Agriculture
Value of information and mobility constraints for sampling with mobile sensors
Computers & Geosciences
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Spatial sampling schemes are mainly developed to determine sampling locations that can cover the variation of environmental properties in the area of interest. Here we proposed the variance quadtree algorithm for sampling in an area with prior information represented as ancillary or secondary environmental data, and the covariance structure of the ancillary variable is non-stationary. The algorithm is based on the idea of a quadtree decomposition, where an area is successively divided into strata so each stratum has more-or-less equal variation. An observation point is then placed inside each stratum. This scheme samples sparsely in relatively uniform areas and more intensively where variation is large. It samples in the feature space and also takes into consideration the spread in the geographic space. We describe the algorithm, its software implementation, and present some examples of applications.