Descripteur
Termes IGN > mathématiques > statistique mathématique > régression > régression logistique
régression logistique |
Documents disponibles dans cette catégorie (49)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Support vector machines for urban growth modeling / B. Huang in Geoinformatica, vol 14 n° 1 (January 2010)
[article]
Titre : Support vector machines for urban growth modeling Type de document : Article/Communication Auteurs : B. Huang, Auteur ; C. Xie, Auteur ; R. Tay, Auteur Année de publication : 2010 Article en page(s) : pp 83 - 99 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] croissance urbaine
[Termes IGN] Delaware (Etats-Unis)
[Termes IGN] modèle de simulation
[Termes IGN] régression logistique
[Termes IGN] utilisation du solRésumé : (Auteur) This paper presents a novel method to model urban land use conversion using support vector machines (SVMs), a new generation of machine learning algorithms used in the classification and regression domains. This method derives the relationship between rural-urban land use change and various factors, such as population, distance to road and facilities, and surrounding land use. Our study showed that SVMs are an effective approach to estimating the land use conversion model, owing to their ability to model non-linear relationships, good generalization performance, and achievement of a global and unique optimum. The rural-urban land use conversions of New Castle County, Delaware between 1984-1992, 1992-1997, and 1997-2002 were used as a case study to demonstrate the applicability of SVMs to urban expansion modeling. The performance of SVMs was also compared with a commonly used binomial logistic regression (BLR) model, and the results, in terms of the overall modeling accuracy and McNamara's test, consistently corroborated the better performance of SVMs. Copyright Springer Numéro de notice : A2010-012 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-009-0077-4 Date de publication en ligne : 25/02/2009 En ligne : https://doi.org/10.1007/s10707-009-0077-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30208
in Geoinformatica > vol 14 n° 1 (January 2010) . - pp 83 - 99[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 057-2010011 RAB Revue Centre de documentation En réserve L003 Disponible Spatiotemporal analysis of rural-urban land conversion / B. Huang in International journal of geographical information science IJGIS, vol 23 n°3-4 (march - april 2009)
[article]
Titre : Spatiotemporal analysis of rural-urban land conversion Type de document : Article/Communication Auteurs : B. Huang, Auteur ; L. Zhang, Auteur ; B. Wu, Auteur Année de publication : 2009 Article en page(s) : pp 379 - 398 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse spatio-temporelle
[Termes IGN] conversion
[Termes IGN] géostatistique
[Termes IGN] milieu rural
[Termes IGN] milieu urbain
[Termes IGN] régression logistique
[Termes IGN] utilisation du solRésumé : (Auteur) Understanding the complexity of urban expansion requires an analysis of the factors influencing the spatial and temporal processes of rural-urban land conversion. This study aims at building a statistical land conversion model to assist in understanding land use change patterns. Specifically, GIS coupled with a logistic regression model and exponential smoothing techniques is used for exploring the effects of various factors on land use change. These factors include population density, slope, proximity to roads, and surrounding land use, and their influence on land use change is studied for generating a predictive model. Methods to reduce spatial autocorrelation in a logistic regression framework are also discussed. Primarily, an optimal sampling scheme that can eliminate spatial autocorrelation while maintaining adequate samples to allow the model to achieve the comparable accuracy as the spatial autoregressive model is developed. Since many of the previous studies on modeling the spatial complexity of urban growth ignored temporal complexity, a modified exponential smoothing technique is employed to produce a smoothed model from a series of bi-temporal models obtained from different time periods. The proposed model is validated using the multi-temporal land use data in New Castle County, DE, USA. It is demonstrated that our approach provides an effective option for multi-temporal land use change modeling and the modeling results help interpret the land use change patterns. Numéro de notice : A2009-155 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810802119685 En ligne : https://doi.org/10.1080/13658810802119685 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78209
in International journal of geographical information science IJGIS > vol 23 n°3-4 (march - april 2009) . - pp 379 - 398[article]Réservation
Réserver ce documentExemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 079-09021 RAB Revue Centre de documentation En réserve L003 Disponible 079-09022 RAB Revue Centre de documentation En réserve L003 Disponible Modelling and mapping potential hooded warbler (Wilsonia citrina) habitat using remotely sensed imagery / J. Pasher in Remote sensing of environment, vol 107 n° 3 (12 April 2007)
[article]
Titre : Modelling and mapping potential hooded warbler (Wilsonia citrina) habitat using remotely sensed imagery Type de document : Article/Communication Auteurs : J. Pasher, Auteur ; Dominique King, Auteur ; K. Lindsay, Auteur Année de publication : 2007 Article en page(s) : pp 471 - 483 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Aves
[Termes IGN] carte thématique
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] habitat animal
[Termes IGN] image Ikonos
[Termes IGN] image Landsat
[Termes IGN] luminance lumineuse
[Termes IGN] Ontario (Canada)
[Termes IGN] photo-interprétation
[Termes IGN] précision de la classification
[Termes IGN] régression logistiqueRésumé : (Auteur) Modelling and mapping of hooded warbler (Wilsonia citrina) nesting habitat in forests of southern Ontario were conducted using Ikonos and Landsat data. The study began with an analysis of skyward hemispherical photography to determine canopy characteristics associated with nest sites. It showed that nest sites had significantly less overhead canopy cover and larger maximum gap size than in non-nest areas. These findings led to the hypothesis that brightness variability in high to moderate resolution remotely sensed imagery may be greater at nest sites than in non-nest areas due to larger shadows and greater shadow variability related to these gap characteristics. This was confirmed when, in addition to some spectral band brightness variables, several image texture and spectrally unmixed fraction (shadow, bare soil) variables were found to be significantly different for nest and non-nest sites in Ikonos and Landsat imagery. These significantly different variables were used in maximum likelihood classification (MLC) and logistic regression (LR) to produce maps of potential nesting habitat. Mapping was conducted with Ikonos and Landsat in a local area where most known nest sites occur, and regionally using Landsat data for almost all of the hooded warbler range in southern Ontario. For the local area mapping using Ikonos data, a posteriori probabilities for both the MLC and LR methods showed that about 62% of the nest sites set aside for validation had been classified with high probability (p > 0.70) in the nest class. MLC mapping accuracy was 70% for the validation nest sites and 87% of validation nest sites were within 10 m of classified nesting habitat, a distance approximately equivalent to expected positional error in the data. LR accuracy was slightly lower. Nest site MLC mapping accuracy in the local area using Landsat data was 87% but the map was much coarser due to the larger pixel size. Regional mapping with Landsat imagery produced lower classification accuracy due to high errors of commission for the habitat class. This resulted from a poor spatial distribution and low number of observations of nest sites throughout the region compared to the local area, while the non-nest site data distribution was too narrow, having been defined and assessed (using standard accepted methods) as areas with no ground shrubs. If either of these problems can be ameliorated, regional mapping accuracy may improve. Copyright Elsevier Numéro de notice : A2007-139 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.09.022 En ligne : https://doi.org/10.1016/j.rse.2006.09.022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28502
in Remote sensing of environment > vol 107 n° 3 (12 April 2007) . - pp 471 - 483[article]