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Bumps and bruises in the digital skins of cities: unevenly distributed user-generated content across US urban areas / Colin Robertson in Cartography and Geographic Information Science, Vol 43 n° 4 (September 2016)  

Public [article]  inCartography and Geographic Information Science > Vol 43 n° 4 (September 2016) . - pp 283 - 300 Titre : Bumps and bruises in the digital skins of cities: unevenly distributed user-generated content across US urban areas Type de document : Article/Communication Auteurs : Colin Robertson, Auteur ; Robert Feick, Auteur Année de publication : 2016 Article en page(s) : pp 283 - 300 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] analyse de données [Termes IGN] Dallas (Texas) [Termes IGN] données descriptives [Termes IGN] données localisées des bénévoles [Termes IGN] données socio-économiques [Termes IGN] géobalise [Termes IGN] image 2D [Termes IGN] Nouvelle-Orléans (Louisiane) [Termes IGN] qualité des données [Termes IGN] représentation des données [Termes IGN] representativité [Termes IGN] Seattle (Washington) Résumé : (Auteur) As momentum and interest build to leverage new forms of user-generated content that contains geographical information, classical issues of data quality remain significant research challenges. In this article, we explore issues of representativeness for one form of user-generated content, geotagged photographs in US urban centers. Generalized linear models were developed to associate photograph distribution with underlying socioeconomic descriptors at the city-scale, and examine intra-city variation in relation to income inequality. We conclude our analyses with a detailed examination of Dallas, Seattle, and New Orleans. Our findings add to the growing volume of evidence outlining uneven representativeness in user-generated data, and our approach contributes to the stock of methods available to investigate geographic variations in representativeness. In addition to city-scale variables relating to distribution of user-generated content, variability remains at localized scales that demand an individual and contextual understanding of their form and nature. The findings demonstrate that careful analysis of representativeness at both macro and micro scales can simultaneously provide important insights into the processes giving rise to user-generated data sets and potentially shed light on their embedded biases and suitability as inputs to analysis. Numéro de notice : A2016-415 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2015.1088801 En ligne : https://doi.org/10.1080/15230406.2015.1088801 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81309 [article]

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