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Alternative and complementary approaches to spatially balanced samples

Author

Listed:
  • R. Benedetti

    (“G. d’Annunzio” University)

  • F. Piersimoni

    (Istat, Directorate for Methodology and Statistical Process Design)

  • P. Postiglione

    (“G. d’Annunzio” University)

Abstract

The spatial distribution of a population represents an important tool in sampling designs that use the geographical coordinates of the units in the frame as auxiliary information. These data may represent a source of auxiliaries that can be helpful to design effective sampling strategies, which, assuming that the observed phenomenon is related with the spatial features of the population, could gather a considerable gain in their efficiency by a proper use of this particular information. We present and compare various methods to select spatially balanced samples. These selection algorithms are compared with the intuitive principle of partitioning the space into n strata and selecting only one unit per stratum. The fundamental interest is not only to evaluate the effectiveness of such different approaches, but also to understand if it is possible to combine them to obtain more efficient sampling designs. The performances of the spatially balanced designs are compared in terms of their root mean squared error using the simple random sampling without replacement as benchmark. An important result is that these complex designs provide better results than the simple principle of stratifying the study area. It also does not help so much to improve efficiencies even if it is combined with balancing on known totals of some auxiliary variables, such as the geographic coordinates.

Suggested Citation

  • R. Benedetti & F. Piersimoni & P. Postiglione, 2017. "Alternative and complementary approaches to spatially balanced samples," METRON, Springer;Sapienza Università di Roma, vol. 75(3), pages 249-264, December.
    • Handle: RePEc:spr:metron:v:75:y:2017:i:3:d:10.1007_s40300-017-0123-1
    DOI: 10.1007/s40300-017-0123-1
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    References listed on IDEAS

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    1. Maria Michela Dickson & Yves Tillé, 2016. "Ordered spatial sampling by means of the traveling salesman problem," Computational Statistics, Springer, vol. 31(4), pages 1359-1372, December.
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    5. Roberto Benedetti & Federica Piersimoni & Paolo Postiglione, 2015. "Sampling Spatial Units for Agricultural Surveys," Advances in Spatial Science, Springer, edition 127, number 978-3-662-46008-5, February.
    6. Jean-Claude Deville & Yves Tille, 2004. "Efficient balanced sampling: The cube method," Biometrika, Biometrika Trust, vol. 91(4), pages 893-912, December.
    7. Bivand, Roger & Piras, Gianfranco, 2015. "Comparing Implementations of Estimation Methods for Spatial Econometrics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i18).
    8. Roberto Benedetti & Federica Piersimoni & Paolo Postiglione, 2017. "Spatially Balanced Sampling: A Review and A Reappraisal," International Statistical Review, International Statistical Institute, vol. 85(3), pages 439-454, December.
    9. Anton Grafström & Niklas L. P. Lundström & Lina Schelin, 2012. "Spatially Balanced Sampling through the Pivotal Method," Biometrics, The International Biometric Society, vol. 68(2), pages 514-520, June.
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    Cited by:

    1. Jean D. Opsomer & M. Giovanna Ranalli & Maria Michela Dickson, 2017. "Foreword to the special issue on “Advances in Survey Statistics”," METRON, Springer;Sapienza Università di Roma, vol. 75(3), pages 245-247, December.
    2. Huan Xie & Fang Wang & Yali Gong & Xiaohua Tong & Yanmin Jin & Ang Zhao & Chao Wei & Xinyi Zhang & Shicheng Liao, 2022. "Spatially Balanced Sampling for Validation of GlobeLand30 Using Landscape Pattern-Based Inclusion Probability," Sustainability, MDPI, vol. 14(5), pages 1-19, February.
    3. Robertson, Blair & Price, Chris, 2024. "One point per cluster spatially balanced sampling," Computational Statistics & Data Analysis, Elsevier, vol. 191(C).

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