Compositional data for global monitoring: the case of drinking water and sanitation
10.1016/j.scitotenv.2017.02.220
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/103521
Tipus de documentArticle
Data publicació2017-07
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
Introduction
At a global level, access to safe drinking water and sanitation has been monitored by the Joint Monitoring Programme (JMP) of WHO and UNICEF. The methods employed are based on analysis of data from household surveys and linear regression modelling of these results over time. However, there is evidence of non-linearity in the JMP data. In addition, the compositional nature of these data is not taken into consideration. This article seeks to address these two previous shortcomings in order to produce more accurate estimates.
Methods
We employed an isometric log-ratio transformation designed for compositional data. We applied linear and non-linear time regressions to both the original and the transformed data. Specifically, different modelling alternatives for non-linear trajectories were analysed, all of which are based on a generalized additive model (GAM).
Results and discussion
Non-linear methods, such as GAM, may be used for modelling non-linear trajectories in the JMP data. This projection method is particularly suited for data-rich countries. Moreover, the ilr transformation of compositional data is conceptually sound and fairly simple to implement. It helps improve the performance of both linear and non-linear regression models, specifically in the occurrence of extreme data points, i.e. when coverage rates are near either 0% or 100%.
CitacióPérez-Foguet, A., Gine, R., Ortego, M.I. Compositional data for global monitoring: the case of drinking water and sanitation. "Science of the total environment", Juliol 2017, vol. 590-591, p. 554-565.
ISSN0048-9697
Versió de l'editorhttp://www.sciencedirect.com/science/article/pii/S0048969717304850
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Perez et al (20 ... oring_STOTEN_pre-print.pdf | 1,232Mb | Visualitza/Obre |