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dc.contributor.authorPérez Foguet, Agustí
dc.contributor.authorGiné Garriga, Ricard
dc.contributor.authorOrtego Martínez, María Isabel
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Química
dc.date.accessioned2017-04-18T15:13:33Z
dc.date.available2019-08-01T00:25:50Z
dc.date.issued2017-07
dc.identifier.citationPérez-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.
dc.identifier.issn0048-9697
dc.identifier.urihttp://hdl.handle.net/2117/103521
dc.description.abstractIntroduction 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%.
dc.format.extent12 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Desenvolupament humà i sostenible::Desenvolupament humà::Aigua i sanejament
dc.subject.lcshDrinking water--International cooperation
dc.subject.lcshSanitation--International cooperation
dc.subject.otherWater
dc.subject.otherSanitation and hygiene
dc.subject.otherService ladder
dc.subject.otherCompositional data
dc.subject.otherLog transformation
dc.subject.otherJoint Monitoring Programme (JMP) of WHO and UNICEF
dc.titleCompositional data for global monitoring: the case of drinking water and sanitation
dc.typeArticle
dc.subject.lemacAigua potable
dc.subject.lemacSanejament -- Cooperació internacional
dc.contributor.groupUniversitat Politècnica de Catalunya. EScGD - Engineering Sciences and Global Development
dc.contributor.groupUniversitat Politècnica de Catalunya. COSDA-UPC - COmpositional and Spatial Data Analysis
dc.identifier.doi10.1016/j.scitotenv.2017.02.220
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0048969717304850
dc.rights.accessOpen Access
local.identifier.drac19857824
dc.description.versionPostprint (author's final draft)
local.citation.authorPérez-Foguet, A.; Gine, R.; Ortego, M.I.
local.citation.publicationNameScience of the total environment
local.citation.volume590-591
local.citation.startingPage554
local.citation.endingPage565


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Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain