IDEAS home Printed from https://ideas.repec.org/a/csb/stintr/v15y2014i1p9-22.html
   My bibliography  Save this article

Triangular Method of Spatial Sampling

Author

Listed:
  • Tomasz Bąk

Abstract

In this paper a new adaptive method of spatial sampling - a triangular method of spatial sampling is presented. The theory of this method is developed. Benefits of decreased size of a sample, when this method is used, are discussed. Initial sampling of the first three elements is described and density of sampling at the initial stage is obtained by Monte Carlo method. The density is defined on the basis of the logarithm of inverse square of the Euclidean distance function. Simulation of the triangular method of spatial sampling is conducted. An example is research on a forest. The aim of this research is to approximate the ability of trees to absorb carbon dioxide. In this example the triangular method of spatial sampling is used at the strata sampling stage. Density of sampling in the simulated forest is obtained using Monte Carlo method.

Suggested Citation

  • Tomasz Bąk, 2014. "Triangular Method of Spatial Sampling," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 15(1), pages 9-22, January.
  • Handle: RePEc:csb:stintr:v:15:y:2014:i:1:p:9-22
    as

    Download full text from publisher

    File URL: http://index.stat.gov.pl/repec/files/csb/stintr/csb_stintr_v15_2014_i1_n2.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lorenzo Fattorini, 2006. "Applying the Horvitz-Thompson criterion in complex designs: A computer-intensive perspective for estimating inclusion probabilities," Biometrika, Biometrika Trust, vol. 93(2), pages 269-278, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tomasz Bąk, 2021. "Spatial sampling methods modified by model use," Statistics in Transition New Series, Polish Statistical Association, vol. 22(2), pages 143-154, June.
    2. Lorenzo Fattorini & Timothy G. Gregoire & Sara Trentini, 2018. "The Use of Calibration Weighting for Variance Estimation Under Systematic Sampling: Applications to Forest Cover Assessment," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(3), pages 358-373, September.
    3. L.-C. Zhang & M. Patone, 2017. "Graph sampling," METRON, Springer;Sapienza Università di Roma, vol. 75(3), pages 277-299, December.
    4. Sara Franceschi & Rosa Maria Di Biase & Agnese Marcelli & Lorenzo Fattorini, 2022. "Some Empirical Results on Nearest-Neighbour Pseudo-populations for Resampling from Spatial Populations," Stats, MDPI, vol. 5(2), pages 1-16, April.
    5. Haoge Chang, 2023. "Design-based Estimation Theory for Complex Experiments," Papers 2311.06891, arXiv.org.
    6. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    7. Grafström Anton & Matei Alina, 2015. "Coordination of Conditional Poisson Samples," Journal of Official Statistics, Sciendo, vol. 31(4), pages 649-672, December.
    8. Lorenzo Fattorini, 2009. "An adaptive algorithm for estimating inclusion probabilities and performing the Horvitz–Thompson criterion in complex designs," Computational Statistics, Springer, vol. 24(4), pages 623-639, December.
    9. Alessandro Chiarucci & Rosa Maria Di Biase & Lorenzo Fattorini & Marzia Marcheselli & Caterina Pisani, 2017. "Joining the Incompatible: Exploiting Floristic Lists for the Sample-based Estimation of Species Richness," Department of Economics University of Siena 753, Department of Economics, University of Siena.
    10. 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.
    11. Lorenzo Fattorini & Alberto Meriggi & Enrico Merli & Paolo Varuzza, 2020. "Sampling Strategies to Estimate Deer Density by Drive Counts," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(2), pages 168-185, June.
    12. Bardia Panahbehagh, 2020. "Estimation in Complex Sampling Designs Based on Resampling Methods," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(2), pages 206-228, June.
    13. Luis Sanguiao Sande & Li-Chun Zhang, 2021. "Design-Unbiased Statistical Learning in Survey Sampling," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 714-744, August.
    14. ak Tomasz B, 2021. "Spatial sampling methods modified by model use," Statistics in Transition New Series, Polish Statistical Association, vol. 22(2), pages 143-154, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:csb:stintr:v:15:y:2014:i:1:p:9-22. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Beata Witek (email available below). General contact details of provider: https://edirc.repec.org/data/gusgvpl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.