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Auteur Donato Malerba |
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Leveraging correlation across space and time to interpolate geophysical data via CoKriging / Sonja Pravilovic in International journal of geographical information science IJGIS, vol 32 n° 1-2 (January - February 2018)
[article]
Titre : Leveraging correlation across space and time to interpolate geophysical data via CoKriging Type de document : Article/Communication Auteurs : Sonja Pravilovic, Auteur ; Annalisa Appice, Auteur ; Donato Malerba, Auteur Année de publication : 2018 Article en page(s) : pp 191 - 212 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse multivariée
[Termes IGN] apprentissage automatique
[Termes IGN] corrélation
[Termes IGN] données spatiotemporelles
[Termes IGN] interpolation
[Termes IGN] krigeageRésumé : (Auteur) Managing geophysical data generated by emerging spatiotemporal data sources (e.g. geosensor networks) presents a growing challenge to Geographic Information System science. The presence of correlation poses difficulties with respect to traditional spatial data analysis. This paper describes a novel spatiotemporal analytical scheme that allows us to yield a characterization of correlation in geophysical data along the spatial and temporal dimensions. We resort to a multivariate statistical model, namely CoKriging, in order to derive accurate spatiotemporal interpolation models. These predict unknown data by utilizing not only their own geosensor values at the same time, but also information from near past data. We use a window-based computation methodology that leverages the power of temporal correlation in a spatial modeling phase. This is done by also fitting the computed interpolation model to data which may change over time. In an assessment, using various geophysical data sets, we show that the presented algorithm is often able to deal with both spatial and temporal correlations. This helps to gain accuracy during the interpolation phase, compared to spatial and spatiotemporal competitors. Specifically, we evaluate the efficacy of the interpolation phase by using established machine-learning metrics (i.e. root mean squared error, Akaike information criterion and computation time). Numéro de notice : A2018-024 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1381338 En ligne : https://doi.org/10.1080/13658816.2017.1381338 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89176
in International journal of geographical information science IJGIS > vol 32 n° 1-2 (January - February 2018) . - pp 191 - 212[article]Réservation
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