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Advanced Methods to Design Samples for Land Use/Land Cover Surveys

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Topics on Methodological and Applied Statistical Inference

Abstract

The particular characteristics of geographically distributed data should be taken into account in designing land use/land cover survey. The traditional sampling designs might not address the specificity of this survey. In fact, in the presence of spatial homogeneity of the phenomenon to be sampled, it is desirable to make use of this information in the sampling design. This paper discusses several methods for sampling spatial units that have been recently introduced in literature. The main assumption is to consider the geographical space as a finite population. The methodological framework is of design-based typology. The techniques outlined are: the GRTS, the cube, the SPCS, the LPMs, and the PPDs. These methods will be verified on data deriving from LUCAS 2012.

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References

  1. Arbia, G.: The use of GIS in spatial statistical surveys. Int. Stat. Rev. 61, 339–359 (1993)

    Article  Google Scholar 

  2. Benedetti, R., Piersimoni, F.: A spatially balanced design with probabilities proportional to the within sample distance. Submitted (2014)

    Google Scholar 

  3. Benedetti, R., Piersimoni, F., Postiglione, P.: Sampling Spatial Units for Agricultural Surveys, Advances in Spatial Science Series. Springer, Heidelberg (2015)

    Google Scholar 

  4. Benedetti, R., Palma, D.: Optimal sampling designs for dependent spatial units. Environmetrics 6, 101–114 (1995)

    Article  Google Scholar 

  5. Bondesson, L., Thorburn, D.: A list sequential sampling method suitable for real-time sampling. Scand. J. Stat. 35, 466–483 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  6. Breidt, F.J., Chauvet, G.: Penalized balanced sampling. Biometrika 99, 945–958 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  7. Delmelle, E.M.: Spatial sampling. In: Fischer, M.M., Nijkamp, P. (eds.) Handbook of Regional Science, pp. 1385–1399. Springer, Berlin (2013)

    Google Scholar 

  8. Deville, J.C., Tillé, Y.: Unequal probability sampling without replacement through a splitting method. Biometrika 85, 89–101 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  9. Deville, J.C., Tillé, Y.: Efficient balanced sampling: the cube method. Biometrika 91, 893–912 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  10. Dickson, M.M., Benedetti, R., Giuliani, D., Espa, G.: The use of spatial sampling designs in business surveys. Open J. Stat. 4, 345–354 (2014)

    Article  Google Scholar 

  11. Gallego, J., Delincè, J.: The European land use and cover area-frame statistical survey. In: Benedetti, R., Bee, M., Espa, G., Piersimoni, F. (eds.) Agricultural Survey Methods, pp. 151–168. John Wiley & Sons Ltd, Chichester (2010)

    Google Scholar 

  12. Grafström, A.: Spatially correlated Poisson sampling. J. Stat. Plan. Inference 142, 139–147 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  13. Grafström, A., Schelin, L.: How to select representative samples. Scand. J. Stat. 41, 277–290 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  14. Grafström, A., Lundström, N.L.P., Schelin, L.: Spatially balanced sampling through the pivotal method. Biometrics 68, 514–520 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  15. Rogerson, P., Delmelle, E.: Optimal sampling design for variables with varying spatial importance. Geograph. Anal. 36, 177–194 (2004)

    Article  Google Scholar 

  16. Stevens Jr., D.L., Olsen, A.R.: Spatially balanced sampling of natural resources. J. Am. Stat. Assoc. 99, 262–278 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  17. Traat, I., Bondesson, L., Meister, K.: Sampling design and sample selection through distribution theory. J. Stat. Plan. Inference 123, 395–413 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  18. Wang, J.F., Stein, A., Gao, B.B., Ge, Y.: A review of spatial sampling. Spat. Stat. 2, 1–14 (2012)

    Article  Google Scholar 

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Correspondence to Roberto Benedetti .

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Benedetti, R., Piersimoni, F., Postiglione, P. (2016). Advanced Methods to Design Samples for Land Use/Land Cover Surveys. In: Di Battista, T., Moreno, E., Racugno, W. (eds) Topics on Methodological and Applied Statistical Inference. Studies in Theoretical and Applied Statistics(). Springer, Cham. https://doi.org/10.1007/978-3-319-44093-4_4

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