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Ajouter le résultat dans votre panierThe spatial structure of socioeconomic disadvantage: a Bayesian multivariate spatial factor analysis / Matthew Quick in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)
[article]
Titre : The spatial structure of socioeconomic disadvantage: a Bayesian multivariate spatial factor analysis Type de document : Article/Communication Auteurs : Matthew Quick, Auteur ; Hui Luan, Auteur Année de publication : 2021 Article en page(s) : pp 63 - 83 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse multivariée
[Termes IGN] analyse socio-économique
[Termes IGN] classification bayesienne
[Termes IGN] pauvreté
[Termes IGN] quartier
[Termes IGN] revenu
[Termes IGN] structure spatiale
[Termes IGN] TorontoRésumé : (auteur) Neighborhood socioeconomic disadvantage is a measure of socio-spatial inequality that has been shown to be associated with a variety of social, economic, and health outcomes. Existing studies that explore the local patterning of disadvantage often construct composite indices that summarize the interactions between multiple dimensions of social status, but do not consider if, and how, disadvantage exhibits spatial structure. This study applies a Bayesian multivariate factor analytic modeling approach to examine the spatial structure of socioeconomic disadvantage in Toronto, Canada. Socioeconomic disadvantage is modeled as an area-based composite index associated with three variables measuring low income, low-educational attainment, and low occupational status, and a series of models with different assumptions regarding the spatial structure of disadvantage are compared. The best-fitting model shows that the prevalence of low-income households has the strongest positive association with disadvantage and that spatial clustering is three times more important than spatial heterogeneity for explaining the spatial structure of disadvantage. The implications of this study for analyzing multivariate spatial data and for understanding the interactions amongst multiple dimensions of disadvantage are discussed. Numéro de notice : A2021-020 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1759807 Date de publication en ligne : 07/05/2020 En ligne : https://doi.org/10.1080/13658816.2020.1759807 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96519
in International journal of geographical information science IJGIS > vol 35 n° 1 (January 2021) . - pp 63 - 83[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2021011 SL Revue Centre de documentation Revues en salle Disponible Sherloc: a knowledge-driven algorithm for geolocating microblog messages at sub-city level / Laura Di Rocco in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)
[article]
Titre : Sherloc: a knowledge-driven algorithm for geolocating microblog messages at sub-city level Type de document : Article/Communication Auteurs : Laura Di Rocco, Auteur ; Michela Bertolotto, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 84 - 115 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] données localisées
[Termes IGN] géolocalisation
[Termes IGN] inférence
[Termes IGN] microblogue
[Termes IGN] répertoire toponymique
[Termes IGN] segmentation sémantique
[Termes IGN] système à base de connaissances
[Termes IGN] toponyme
[Termes IGN] zone urbaineRésumé : (auteur) Many solutions for coarse geolocating of users at the time they post a message exist. However, for many important applications, like traffic monitoring and event detection, finer geolocation at the level of city neighborhoods, i.e., at a sub-city level, is needed. Data-driven approaches often do not guarantee good accuracy and efficiency due to the higher number of sub-city level positions to be estimated and the low availability of balanced and large training sets. We claim that external information sources overcome limitations of data-driven approaches in achieving good accuracy for sub-city level geolocation and we present a knowledge-driven approach achieving good results once the reference area of a message is known. Our algorithm, called Sherloc, exploits toponyms in the message, extracts their semantic from a geographic gazetteer, and embeds them into a metric space that captures the semantic distance among them. We identify the semantically closest toponyms to a message and then cluster them with respect to their spatial locations. Sherloc requires no prior training, it can infer the location at sub-city level with high accuracy, and it is not limited to geolocating on a fixed spatial grid. Numéro de notice : A2021-021 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1764003 Date de publication en ligne : 16/06/2020 En ligne : https://doi.org/10.1080/13658816.2020.1764003 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96521
in International journal of geographical information science IJGIS > vol 35 n° 1 (January 2021) . - pp 84 - 115[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2021011 SL Revue Centre de documentation Revues en salle Disponible Local fuzzy geographically weighted clustering: a new method for geodemographic segmentation / George Grekousis in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)
[article]
Titre : Local fuzzy geographically weighted clustering: a new method for geodemographic segmentation Type de document : Article/Communication Auteurs : George Grekousis, Auteur Année de publication : 2021 Article en page(s) : pp 152 - 174 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] classification floue
[Termes IGN] données démographiques
[Termes IGN] New York (Etats-Unis ; ville)
[Termes IGN] optimisation par essaim de particules
[Termes IGN] pondération
[Termes IGN] régression géographiquement pondérée
[Termes IGN] santé
[Termes IGN] segmentation
[Termes IGN] voisinage (relation topologique)Résumé : (auteur) Fuzzy geographically weighted clustering has been proposed as an approach for improving fuzzy c-means algorithm when applied to geodemographic analysis. This clustering method allows a spatial entity to belong to more than one cluster with varying degrees, namely, membership values. Although fuzzy geographically weighted clustering attempts to create geographically aware clusters, it partially fails to trace spatial dependence and heterogeneity because, as a global metric, the membership values are calculated across the entire set of spatial entities. Here we introduce the first local version of fuzzy geographically weighted clustering, ‘local fuzzy geographically weighted clustering.’ In local fuzzy geographically weighted clustering, the membership values of a spatial entity are updated only according to the membership values of the spatial entities within its neighborhood and not across the entire set of entities, as originally proposed by the global metric. Additionally, we apply particle swarm optimization meta-heuristic to overcome the random initialization problem regarding the fuzzy c-means algorithm. To evaluate our method we compare local fuzzy geographically weighted clustering to global fuzzy geographically weighted clustering using a cancer incident benchmark dataset for Manhattan, New York. The results show that local fuzzy geographically weighted clustering outperforms the global version in all experimental settings. Numéro de notice : A2021-022 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1808221 Date de publication en ligne : 21/08/2020 En ligne : https://doi.org/10.1080/13658816.2020.1808221 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96525
in International journal of geographical information science IJGIS > vol 35 n° 1 (January 2021) . - pp 152 - 174[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2021011 SL Revue Centre de documentation Revues en salle Disponible RegNet: a neural network model for predicting regional desirability with VGI data / Wenzhong Shi in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)
[article]
Titre : RegNet: a neural network model for predicting regional desirability with VGI data Type de document : Article/Communication Auteurs : Wenzhong Shi, Auteur ; Zhewei Liu, Auteur ; Zhenlin An, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 175 - 192 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] classification par réseau neuronal
[Termes IGN] données localisées des bénévoles
[Termes IGN] Hong-Kong
[Termes IGN] modèle de simulation
[Termes IGN] niveau local
[Termes IGN] participation du public
[Termes IGN] régression
[Termes IGN] réseau social géodépendantRésumé : (auteur) Volunteered geographic information can be used to predict regional desirability. A common challenge regarding previous works is that intuitive empirical models, which are inaccurate and bring in perceptual bias, are traditionally used to predict regional desirability. This results from the fact that the hidden interactions between user online check-ins and regional desirability have not been revealed and clearly modelled yet. To solve the problem, a novel neural network model ‘RegNet’ is proposed. The user check-in history is input into a neural network encoder structure firstly for redundancy reduction and feature learning. The encoded representation is then fed into a hidden-layer structure and the regional desirability is predicted. The proposed RegNet is data-driven and can adaptively model the unknown mappings from input to output, without presumed bias and prior knowledge. We conduct experiments with real-world datasets and demonstrate RegNet outperforms state-of-the-art methods in terms of ranking quality and prediction accuracy of rating. Additionally, we also examine how the structure of encoder affects RegNet performance and suggest on choosing proper sizes of encoded representation. This work demonstrates the effectiveness of data-driven methods in modelling the hidden unknown relationships and achieving a better performance over traditional empirical methods. Numéro de notice : A2021-023 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1768261 Date de publication en ligne : 18/05/2020 En ligne : https://doi.org/10.1080/13658816.2020.1768261 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96526
in International journal of geographical information science IJGIS > vol 35 n° 1 (January 2021) . - pp 175 - 192[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2021011 SL Revue Centre de documentation Revues en salle Disponible