A simple two-step method for spatio-temporal design-based balanced sampling
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We introduce a two-step method to perform spatio-temporal balanced sampling in a design-based approach. For populations with spatio-temporal trends and with anisotropic effects in the variable of interest, the prediction can be further improved by selecting samples that are well spread over the entire population in space and time. We control the spread of the sample over the population by using the volume of the corresponding three-dimensional Voronoi tessellation. Indeed, spatio-temporal design-based balanced sampling is even more efficient under the presence of a trend and anisotropic effects. We present an intensive simulation study comparing our method to other available methods for spatio-temporal sampling. Finally, we analyze real data by sampling from a population of temperature stations over six European countries.
KeywordsBalanced sampling Design-based sampling Spatio-temporal sampling
The authors are thankful to the referees for their many helpful comments that greatly improved this paper. We also wish to acknowledge for the support from Ordered and Spatial Data Center of Excellence of the Ferdowsi University of Mashhad.
- Aarts EH, Korst J (1989) Simulated annealing and boltzman machines. Wiley, New YorkGoogle Scholar
- Christakos G (2005) Random field models in earth sciences. Dover, New YorkGoogle Scholar
- Cochran WG (1977) Sampling techniques, 3rd edn. Wiley, New YorkGoogle Scholar
- Cressie N, Wikle CK (2011) Statistics for spatio-temporal data. Wiley, HobokenGoogle Scholar
- Du Q, Wang D (2005) The optimal centroidal voronoi tessellations and the Gersho’s conjecture in the three-dimensional space. Comput Ind Eng 49:1355–1373Google Scholar
- Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, OxfordGoogle Scholar
- Mateu J, Müller WG (2013) Spatio-temporal design. Advances in efficient data acquisition. Wiley, ChichesterGoogle Scholar
- Matheron G (1971) The theory of regionalized variables and its application. Ecole Nationale Supérieure des Mines de Paris, FranceGoogle Scholar
- Tillé Y (2006) Sampling algorithms. Spinger, New YorkGoogle Scholar
- Valliant R, Dorfman AH, Royall RM (2000) Finite population sampling and inference: a prediction approach. Wiley, New YorkGoogle Scholar
- Yates F (1949) Sampling methods for censuses and surveys. Griffin, LondonGoogle Scholar