Abstract
The predictive approach and the analysis of survey data are two topics that have only attracted a small amount of attention when compared with the traditional approach of sampling from a finite population.
Furthermore, spatial effects that are very important features in agricultural surveys are often neglected in the predictive approach to sampling, and in the analysis of survey data. The inclusion of spatial information could represent a very important challenge to be addressed by researchers in the near future.
The main aim of this chapter is to properly emphasize these two different and important topics, trying to highlight the basic ideas for developing a unified approach for geographically distributed data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
To avoid confusion, note that in this section the uppercase Y indicates a random vector, while the lowercase y describes the realization of Y.
- 2.
For the sake of simplicity, we have supposed that the function f(.) depends on only one parameter θ. These methods can be straightforwardly extended to the multivariate case.
References
Burrough PA (1986) Principles of geographical information systems for land resources assessment. Oxford University Press, Oxford
Cassel CM, Särndal CE, Wretman JH (1977) Foundations of inference in survey sampling. Wiley, New York
Chambers RL, Skinner CJ (2003) Analysis of survey data. Wiley, Ontario
Chambers RL, Steel DG, Wang S, Welsh A (2012) Maximum likelihood estimation for sample surveys. Chapman & Hall/CRC, Boca Raton, FL
Delmelle EM (2013) Spatial sampling. In: Fischer MM, Nijkamp P (eds) Handbook of regional science. Springer, Berlin, pp 1385–1399
Fischer MM, Nijkamp P (2013) Handbook of regional science. Springer, Berlin
Ford L (1976) Contour reaggregation: another way to integrate data. In: Papers, thirteenth annual URISA conference, 11, pp 528–575
Godambe VP (1955) A unified theory of sampling from finite populations. J R Stat Soc Ser B 17:269–278
Godambe VP (1965) A review of the contributions towards a unified theory of sampling from finite populations. Rev Int Stat Inst 33:242–258
Godambe VP (1975) A reply to my critics. Sankhya C 37:53–76
Goodchild MF, Lam NS (1980) Areal interpolation: a variant of the traditional spatial problem. Geo-Processing 1:297–312
Haining RP (2003) Spatial data analysis: theory and practice. Cambridge University Press, Cambridge
Krieger AM, Pfeffermann D (1992) Maximum likelihood from complex sample surveys. Surv Methodol 18:225–239
Krieger AM, Pfeffermann D (1997) Testing of distribution functions from complex sample surveys. J Off Stat 13:123–142
Lam NS (1983) Spatial interpolation methods: a review. Am Cartogr 10:129–150
Lumley T (2010) Complex surveys. A guide to analysis using R. Wiley, Hoboken, NJ
Palma D, Benedetti R (1998) A transformational view of spatial data analysis. Geogr Syst 5:199–220
Pfeffermann D, Krieger AM, Rinott Y (1998) Parametric distributions of complex survey data under informative probability sampling. Stat Sin 8:1087–1114
Royall RM (1976) The linear least-squares prediction approach to two-stage sampling. J Am Stat Assoc 71:657–664
Särndal CE (1978) Design-based and model-based inference in survey sampling. Scand J Stat 5:27–52
Särndal CE, Swensson B, Wretman J (1992) Model assisted survey sampling. Springer, New York
Schabenberger O, Gotway CA (2005) Statistical methods for spatial data analysis. CRC, Boca Raton, FL
Serfling RJ (1980) Approximation theorems of mathematical statistics. Wiley, New York
Tobler W (1979) Smooth pycnophylactic interpolation for geographical regions. J Am Stat Assoc 74:519–530
Valliant R (2009) Model-based prediction of finite population totals. In: Pfeffermann D, Rao CR (eds) Sample surveys: design, methods and applications, vol 29B. Elsevier, The Netherlands, pp 11–31
Valliant R, Dorfman AH, Royall RM (2000) Finite population sampling and inference: a prediction approach. Wiley, New York
Waters MN (1989) Spatial interpolation I, lecture 40. In: NCGIA Core Curriculum, Technical Issues in GIS. University of California, Santa Barbara, pp 40.3–40.11
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Benedetti, R., Piersimoni, F., Postiglione, P. (2015). Spatial Survey Data Modeling. In: Sampling Spatial Units for Agricultural Surveys. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46008-5_12
Download citation
DOI: https://doi.org/10.1007/978-3-662-46008-5_12
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-46007-8
Online ISBN: 978-3-662-46008-5
eBook Packages: Business and EconomicsEconomics and Finance (R0)