A new class of $\alpha$-transformations for the spatial analysis of Compositional Data

TL;DR

This paper introduces the Isometric α-transformation, a flexible method for analyzing georeferenced compositional data that includes the ILR and linear transformations as special cases, improving spatial prediction especially when zeros are present.

Contribution

The paper proposes a novel class of transformations, the α-IT, that generalizes existing methods and allows for better modeling of compositional data with zeros, along with maximum likelihood estimation for α.

Findings

α-IT encompasses ILR and linear transformations as special cases.

Optimal transformation depends on the presence of zeros in compositions.

α-IT with estimated α outperforms traditional methods in certain datasets.

Abstract

Georeferenced compositional data are prominent in many scientific fields and in spatial statistics. This work addresses the problem of proposing models and methods to analyze and predict, through kriging, this type of data. To this purpose, a novel class of transformations, named the Isometric $\alpha$-transformation ($\alpha$-IT), is proposed, which encompasses the traditional Isometric Log-Ratio (ILR) transformation. It is shown that the ILR is the limit case of the $\alpha$-IT as $\alpha$ tends to 0 and that $\alpha=1$ corresponds to a linear transformation of the data. Unlike the ILR, the proposed transformation accepts 0s in the compositions when $\alpha>0$. Maximum likelihood estimation of the parameter $\alpha$ is established. Prediction using kriging on $\alpha$-IT transformed data is validated on synthetic spatial compositional data, using prediction scores computed either in the geometry induced by the $\alpha$-IT, or in the simplex. Application to land cover data shows that the relative superiority of the various approaches w.r.t. a prediction objective depends on whether the compositions contained any zero component. When all components are positive, the limit cases (ILR or linear transformations) are optimal for none of the considered metrics. An intermediate geometry, corresponding to the $\alpha$-IT with maximum likelihood estimate, better describes the dataset in a geostatistical setting. When the amount of compositions with 0s is not negligible, some side-effects of the transformation gets amplified as $\alpha$ decreases, entailing poor kriging performances both within the $\alpha$-IT geometry and for metrics in the simplex.