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Population-density estimation using regression and area-to-point residual kriging / X. Liu in International journal of geographical information science IJGIS, vol 22 n° 4-5 (april 2008)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 22 n° 4-5 (april 2008) . - pp 431 - 447 Titre : Population-density estimation using regression and area-to-point residual kriging Type de document : Article/Communication Auteurs : X. Liu, Auteur ; P. Kyriakidis, Auteur ; Michael F. Goodchild, Auteur Année de publication : 2008 Article en page(s) : pp 431 - 447 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] densité de population [Termes IGN] figuration de la densité [Termes IGN] interpolation spatiale [Termes IGN] krigeage [Termes IGN] régression Résumé : (Auteur) Census population data are associated with several analytical and cartographic problems. Regression models using remote-sensing covariates have been examined to estimate urban population density, but the performance may not be satisfactory. This paper describes a kriging-based areal interpolation method, namely area-to-point residual kriging, which can be used to disaggregate the residuals remaining from regression. Compared with conventional cokriging, the area-to-point residual kriging is much simpler in that only a semivariogram model for the point residuals is required, as opposed to a set of auto- and cross-semivariogram models involving the dependent variable and all the covariates. In addition, area-to-point residual kriging explicitly accounts for any scale differences between source data and target values. The method is illustrated by disaggregating population from census units to the land-use zones within them. Comparative results for regression with and without area-to-point residual kriging show that area-to-point residual kriging can substantially improve interpolation accuracy. Copyright Taylor & Francis Numéro de notice : A2008-148 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810701492225 En ligne : https://doi.org/10.1080/13658810701492225 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29143 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-08031	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-08032	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Geostatistical solutions for super-resolution land cover mapping / A. Boucher in IEEE Transactions on geoscience and remote sensing, vol 46 n° 1 (January 2008)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 46 n° 1 (January 2008) . - pp 272 - 283 Titre : Geostatistical solutions for super-resolution land cover mapping Type de document : Article/Communication Auteurs : A. Boucher, Auteur ; P. Kyriakidis, Auteur ; C. Cronkite-Ratcliff, Auteur Année de publication : 2008 Article en page(s) : pp 272 - 283 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] carte d'occupation du sol [Termes IGN] géostatistique [Termes IGN] spatiocarte [Termes IGN] variogramme Résumé : (Auteur) Super-resolution land cover mapping aims at producing fine spatial resolution maps of land cover classes from a set of coarse-resolution class fractions derived from satellite information via, for example, spectral unmixing procedures. Based on a prior model of spatial structure or texture that encodes the expected patterns of classes at the fine (target) resolution, this paper presents a sequential simulation framework for generating alternative super-resolution maps of class labels that are consistent with the coarse class fractions. Two modes of encapsulating the prior structural information are investigated-one uses a set of indicator variogram models, and the other uses training images. A case study illustrates that both approaches lead to super-resolution class maps that exhibit a variety of spatial patterns ranging from simple to complex. Using four different examples, it is demonstrated that the structural model controls the patterns seen on the super-resolution maps, even for cases where the coarse fraction data are highly constraining. Copyright IEEE Numéro de notice : A2008-046 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2007.907102 En ligne : https://doi.org/10.1109/TGRS.2007.907102 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29041 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-08011	RAB	Revue	Centre de documentation	En réserve L003	Disponible