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A geovisual analytics exploration of the OpenStreetMap crowd / Sterling Quinn in Cartography and Geographic Information Science, Vol 45 n° 2 (March 2018)  

Public [article]  inCartography and Geographic Information Science > Vol 45 n° 2 (March 2018) . - pp 140 - 155 Titre : A geovisual analytics exploration of the OpenStreetMap crowd Type de document : Article/Communication Auteurs : Sterling Quinn, Auteur ; Alan M. MacEachren, Auteur Année de publication : 2018 Article en page(s) : pp 140 - 155 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle [Termes IGN] conception orientée utilisateur [Termes IGN] données localisées des bénévoles [Termes IGN] OpenStreetMap [Termes IGN] outil d'aide à la comparaison [Termes IGN] production participative [Termes IGN] utilisateur [Vedettes matières IGN] Géovisualisation Résumé : (Auteur) It is sometimes easy to forget that massive crowdsourced data products such as Wikipedia and OpenStreetMap (OSM) are the sum of individual human efforts stemming from a variety of personal and institutional interests. We present a geovisual analytics tool called Crowd Lens for OpenStreetMap designed to help professional users of OSM make sense of the characteristics of the “crowd” that constructed OSM in specific places. The tool uses small multiple maps to visualize each contributor’s piece of the crowdsourced whole, and links OSM features with the free-form commit messages supplied by their contributors. Crowd Lens allows sorting and filtering contributors by characteristics such as number of contributions, most common language used, and OSM attribute tags applied. We describe the development and evaluation of Crowd Lens, showing how a multiple-stage user-centered design process (including testing by geospatial technology professionals) helped shape the tool’s interface and capabilities. We also present a case study using Crowd Lens to examine cities in six continents. Our findings should assist institutions deliberating OSM’s fitness for use for different applications. Crowd Lens is also potentially informative for researchers studying Internet participation divides and ways that crowdsourced products can be better comprehended with visual analytics methods. Numéro de notice : A2018-007 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2016.1276479 En ligne : https://doi.org/10.1080/15230406.2016.1276479 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88976 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2018021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

The effect of acquisition error and level of detail on the accuracy of spatial analyses / Filip Biljecki in Cartography and Geographic Information Science, Vol 45 n° 2 (March 2018)  

Public [article]  inCartography and Geographic Information Science > Vol 45 n° 2 (March 2018) . - pp 156 - 176 Titre : The effect of acquisition error and level of detail on the accuracy of spatial analyses Type de document : Article/Communication Auteurs : Filip Biljecki, Auteur ; Gerard B.M. Heuvelink, Auteur ; Hugo Ledoux, Auteur ; Jantien E. Stoter, Auteur Année de publication : 2018 Article en page(s) : pp 156 - 176 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] CityGML [Termes IGN] erreur de positionnement [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] niveau de détail [Termes IGN] précision des données [Termes IGN] propagation d'erreur Résumé : (Auteur) There has been a great deal of research about errors in geographic information and how they affect spatial analyses. A typical GIS process introduces various types of errors at different stages, and such errors usually propagate into errors in the result of a spatial analysis. However, most studies consider only a single error type thus preventing the understanding of the interaction and relative contributions of different types of errors. We focus on the level of detail (LOD) and positional error, and perform a multiple error propagation analysis combining both types of error. We experiment with three spatial analyses (computing gross volume, envelope area, and solar irradiation of buildings) performed with procedurally generated 3D city models to decouple and demonstrate the magnitude of the two types of error, and to show how they individually and jointly propagate to the output of the employed spatial analysis. The most notable result is that in the considered spatial analyses the positional error has a much higher impact than the LOD. As a consequence, we suggest that it is pointless to acquire geoinformation of a fine LOD if the acquisition method is not accurate, and instead we advise focusing on the accuracy of the data. Numéro de notice : A2018-008 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2017.1279986 En ligne : https://doi.org/10.1080/15230406.2017.1279986 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88977 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2018021	RAB	Revue	Centre de documentation	En réserve L003	Disponible