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Geographically Weighted Regression Analysis for Two-Factorial Compositional Data

In: Advances in Compositional Data Analysis

Author

Listed:
  • Kamila Fačevicová

    (Palacký University Olomouc, Department of Mathematical Analysis and Applications of Mathematics)

  • Petra Kynčlová

    (United Nation Industrial Development Organization (UNIDO), Statistics Division)

  • Karel Macků

    (Palacký University Olomouc, Department of Geoinformatics)

Abstract

The chapter focuses on the modelling and analysis of spatial dependent two-factorial compositional data. Spatial statistics provides a wide range of methods for the analysis of data with local variations but only a few of them are accommodated for the purposes of modelling relative structures. In this chapter, the geographically weighted regression model is introduced to analyse the relationship between the dependent variable and an explanatory variable reflecting a structure expressed in terms of a compositional table. The methodology is motivated by the problem of modelling local variations of the relationship between at-risk-of-poverty rates and the structure of the highest attained educational level in the German population aged 30–34. The real data study shows how information included in a compositional table and further expressed in real-valued coordinates can be highly valuable in selecting variables and prioritising them with respect to a research interest to facilitate the final interpretation of the model.

Suggested Citation

  • Kamila Fačevicová & Petra Kynčlová & Karel Macků, 2021. "Geographically Weighted Regression Analysis for Two-Factorial Compositional Data," Springer Books, in: Peter Filzmoser & Karel Hron & Josep Antoni Martín-Fernández & Javier Palarea-Albaladejo (ed.), Advances in Compositional Data Analysis, pages 103-124, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-71175-7_6
    DOI: 10.1007/978-3-030-71175-7_6
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