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Spatially–encouraged spectral clustering: a technique for blending map typologies and regionalization / Levi John Wolf in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 11 (November 2021) . - pp 2356 - 2373 Titre : Spatially–encouraged spectral clustering: a technique for blending map typologies and regionalization Type de document : Article/Communication Auteurs : Levi John Wolf, Auteur Année de publication : 2021 Article en page(s) : pp 2356 - 2373 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] apprentissage automatique [Termes IGN] exploration de données [Termes IGN] méthode fondée sur le noyau [Termes IGN] optimisation spatiale [Termes IGN] régionalisation (segmentation) Résumé : (auteur) Clustering is a central concern in geographic data science and reflects a large, active domain of research. In spatial clustering, it is often challenging to balance two kinds of ‘goodness of fit:’ clusters should have ‘feature’ homogeneity, in that they aim to represent one ‘type’ of observation, and also ‘geographic’ coherence, in that they aim to represent some detected geographical ‘place’. This divides ‘map typologization’ studies, common in geodemographics, from ‘regionalization’ studies, common in spatial optimization and statistics. Recent attempts to simultaneously typologize and regionalize data into clusters with both feature homogeneity and geographic coherence have faced conceptual and computational challenges. Fortunately, new work on spectral clustering can address both regionalization and typologization tasks within the same framework. This research develops a novel kernel combination method for use within spectral clustering that allows analysts to blend smoothly between feature homogeneity and geographic coherence. I explore the formal properties of two kernel combination methods and recommend multiplicative kernel combination with spectral clustering. Altogether, spatially encouraged spectral clustering is shown as a novel kernel combination clustering method that can address both regionalization and typologization tasks in order to reveal the geographies latent in spatially structured data. Numéro de notice : A2021-762 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1934475 Date de publication en ligne : 05/07/2021 En ligne : https://doi.org/10.1080/13658816.2021.1934475 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98795 [article]

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