Efficiency evaluation of MEV spatial sampling strategies: a scenario analysis

The minimum estimation variance (MEV) spatial sampling strategy is compared with some further strategies based on systematic designs and the sample mean. Since in environmental surveys data are usually collected repeatedly in time at sites whose selection is based on practical circumstances only, it seems worth measuring the efficiency of spatial sampling strategies through the expectation of design mean square errors under several superpopulation models assumed about the fixed population generating process. Relating the study to specific superpopulations, MEV efficiencies can be calculated and a quantitative evaluation is made possible by the use of scenario analyses for several sample sizes and different models of spatial drift and correlations. The MEV strategy revealed itself to be the relatively more efficient one under realistic conditions of nonstationary spatial drifts and bounded sample sizes.

[1]  Naser E. Heravi,et al.  Phased sampling for soil remediation , 1994, Environmental and Ecological Statistics.

[2]  Noel A Cressie,et al.  Statistics for Spatial Data. , 1992 .

[3]  Munindar P. Singh,et al.  Unified theory and strategies of survey sampling , 1988 .

[4]  R. J. Martin Comparing and contrasting some environmental and experimental design problems , 2001 .

[5]  B. Matérn Spatial variation : Stochastic models and their application to some problems in forest surveys and other sampling investigations , 1960 .

[6]  K. Juang,et al.  Using sequential indicator simulation to assess the uncertainty of delineating heavy-metal contaminated soils. , 2004, Environmental pollution.

[7]  Giuseppe Arbia,et al.  The use of GIS in spatial surveys , 1993 .

[8]  Pierre Goovaerts,et al.  Evaluating the probability of exceeding a site-specific soil cadmium contamination threshold , 2001 .

[9]  D. Horvitz,et al.  A Generalization of Sampling Without Replacement from a Finite Universe , 1952 .

[10]  Timothy C. Coburn,et al.  Geostatistics for Natural Resources Evaluation , 2000, Technometrics.

[11]  David R. Bellhouse,et al.  Some optimal designs for sampling in two dimensions , 1977 .

[12]  Mike Rees,et al.  5. Statistics for Spatial Data , 1993 .

[13]  Walter W. Piegorsch,et al.  Statistical advances in environmental science , 1998 .

[14]  T. M. Burgess,et al.  Sampling and bulking strategies for estimating soil properties in small regions , 1984 .

[15]  T. Postelnicu,et al.  Foundations of inference in survey sampling , 1977 .

[16]  Giuseppe Arbia,et al.  Evaluating and updating the sample design in repeated environmental surveys: monitoring air quality in Padua , 1997 .

[17]  Giuseppe Arbia,et al.  Anisotropic spatial sampling designs for urban pollution , 2002 .

[18]  M. H. Quenouille Problems in Plane Sampling , 1949 .