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Visualizing demographic trajectories with self-organizing maps / A. Skupin in Geoinformatica, vol 9 n° 2 (June - August 2005)  

Public [article]  inGeoinformatica > vol 9 n° 2 (June - August 2005) . - pp 159 - 179 Titre : Visualizing demographic trajectories with self-organizing maps Type de document : Article/Communication Auteurs : A. Skupin, Auteur ; R. Hagelman, Auteur Année de publication : 2005 Article en page(s) : pp 159 - 179 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse diachronique [Termes IGN] analyse spatiale [Termes IGN] carte de Kohonen [Termes IGN] données démographiques [Termes IGN] données multidimensionnelles [Termes IGN] données socio-économiques [Termes IGN] graphe [Termes IGN] noeud [Termes IGN] représentation spatiale [Termes IGN] réseau neuronal artificiel [Termes IGN] système d'information géographique [Termes IGN] trajectoire (véhicule non spatial) [Termes IGN] visualisation de données [Vedettes matières IGN] Géovisualisation Résumé : (Auteur) In recent years, the proliferation of multi-temporal census data products and the increased capabilities of geospatial analysis and visualization techniques have encouraged longitudinal analyses of socioeconomic census data. Traditional cartographic methods for illustrating socioeconomic change tend to rely either on comparison of multiple temporal snapshots or on explicit representation of the magnitude of change occurring between different time periods. This paper proposes to add another perspective to the visualization of temporal change, by linking multi-temporal observations to a geometric configuration that is not based on geographic space, but on a spatialized representation of n-dimensional attribute space. The presented methodology aims at providing a cognitively plausible representation of changes occurring inside census areas by representing their attribute space trajectories as line features traversing a two-dimensional display space. First, the self-organizing map (SOM) method is used to transform n-dimensional data such that the resulting two-dimensional configuration can be represented with standard GIS data structures. Then, individual census observations are mapped onto the neural network and linked as temporal vertices to represent attribute space trajectories as directed graphs. This method is demonstrated for a data set containing 254 counties and 32 demographic variables. Various transformations and visual results are presented and discussed in the paper, from the visualization of individual component planes and trajectory clusters to the mapping of different attributes onto temporal trajectories. Numéro de notice : A2005-225 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1007/s10707-005-6670-2 En ligne : https://doi.org/10.1007/s10707-005-6670-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27362 [article]

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