Abstract
The objective of this article is to delve deeper into the understanding and practical implementation of classical areal interpolation methods using R software. Based on a survey paper from Do et al. (Spat Stat 14:412–438, 2015), we focus on four classical methods used in the area-to-area interpolation problem: point-in-polygon, areal weighting interpolation, dasymetric method with auxiliary variable and dasymetric method with control zones. Using the departmental election database for Toulouse in 2015, we find that the point-in-polygon method can be applied if the sources are much smaller than the targets; the areal interpolation method provides good results if the variable of interest is related to the area, but otherwise, a good alternative is to use the dasymetric method with another auxiliary variable; and finally, the dasymetric method with control zones allows us to benefit from both areal interpolation and dasymetric method and, from that perspective, seems to be the best method.
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Acknowledgements
The authors are grateful to Christine Thomas-Agnan who introduced them to this topic of spatial interpolation methods. They also thank two anonymous referees and the editors for their helpful comments. Thibault Laurent acknowledges funding from ANR under grant ANR-17-EURE-0010 (Investissements d’Avenir program).
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Do, V.H., Laurent, T., Vanhems, A. (2021). Guidelines on Areal Interpolation Methods. In: Daouia, A., Ruiz-Gazen, A. (eds) Advances in Contemporary Statistics and Econometrics. Springer, Cham. https://doi.org/10.1007/978-3-030-73249-3_20
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DOI: https://doi.org/10.1007/978-3-030-73249-3_20
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