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Spatio-temporal building population estimation for highly urbanized areas using GIS: spatio-temporal building population estimation / Konstantin Greger in Transactions in GIS, vol 19 n° 1 (February 2015)  

Public [article]  inTransactions in GIS > vol 19 n° 1 (February 2015) . - pp 129 - 150 Titre : Spatio-temporal building population estimation for highly urbanized areas using GIS: spatio-temporal building population estimation Type de document : Article/Communication Auteurs : Konstantin Greger, Auteur Année de publication : 2015 Article en page(s) : pp 129 - 150 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] bati [Termes IGN] démographie [Termes IGN] données démographiques [Termes IGN] données spatiotemporelles [Termes IGN] estimation statistique [Termes IGN] population urbaine Résumé : (auteur) Detailed population information is crucial for the micro-scale modeling and analysis of human behavior in urban areas. Since it is not available on the basis of individual persons, it has become necessary to derive data from aggregated census data. A variety of approaches have been published in the past, yet they are not entirely suitable for use in the micro-scale context of highly urbanized areas, due mainly to their broad spatial scale and missing temporal scale. Here we introduce an enhanced approach for the spatio-temporal estimation of building populations in highly urbanized areas. It builds upon other estimation methodologies, but extends them by introducing multiple usage categories and the temporal dimension. This allows for a more realistic representation of human activities in highly urbanized areas and the fact that populations change over time as a result of these activities. The model makes use of a variety of micro-scale data sets to operationalize the activities and their spatio-temporal representations. The outcome of the model provides estimated population figures for all buildings at each time step and thereby reveals spatio-temporal behavior patterns. It can be used in a variety of applications concerning the implications of human behavior in urban areas. Numéro de notice : A2015-294 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12086 En ligne : http://dx.doi.org/10.1111/tgis.12086 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76437 [article]

Mining trajectory data and geotagged data in social media for road map inference: Mining social media for road map inference / Jun Li in Transactions in GIS, vol 19 n° 1 (February 2015)  

Public [article]  inTransactions in GIS > vol 19 n° 1 (February 2015) . - pp 1 - 18 Titre : Mining trajectory data and geotagged data in social media for road map inference: Mining social media for road map inference Type de document : Article/Communication Auteurs : Jun Li, Auteur ; Qiming Qin, Auteur ; Jiawei Han, Auteur ; Lu-An Tang, Auteur ; Kin Hou Lei, Auteur Année de publication : 2015 Article en page(s) : pp 1 - 18 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] base de données routières [Termes IGN] contenu généré par les utilisateurs [Termes IGN] données localisées des bénévoles [Termes IGN] exploration de données géographiques [Termes IGN] géobalise [Termes IGN] inférence [Termes IGN] mise à jour de base de données [Termes IGN] traitement du langage naturel Résumé : (auteur) As mapping is costly and labor-intensive work, government mapping agencies are less and less willing to absorb these costs. In order to reduce the updating cycle and cost, researchers have started to use user generated content (UGC) for updating road maps; however, the existing methods either rely heavily on manual labor or cannot extract enough information for road maps. In view of the above problems, this article proposes a UGC-based automatic road map inference method. In this method, data mining techniques and natural language processing tools are applied to trajectory data and geotagged data in social media to extract not only spatial information – the location of the road network – but also attribute information – road class and road name – in an effort to create a complete road map. A case study using floating car data, collected by the National Commercial Vehicle Monitoring Platform of China, and geotagged text data from Flickr and Google Maps/Earth, validates the effectiveness of this method in inferring road maps. Numéro de notice : A2015--118 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12072 Date de publication en ligne : 15/01/2014 En ligne : http://doi.wiley.com/10.1111/tgis.12072 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102453 [article]

Assessing the completeness of bicycle trail and lane features in OpenStreetMap for the United States: Completeness of bicycle features in OpenStreetMap / Hartwig H. Hochmair in Transactions in GIS, vol 19 n° 1 (February 2015)  

Public [article]  inTransactions in GIS > vol 19 n° 1 (February 2015) . - pp 63 - 81 Titre : Assessing the completeness of bicycle trail and lane features in OpenStreetMap for the United States: Completeness of bicycle features in OpenStreetMap Type de document : Article/Communication Auteurs : Hartwig H. Hochmair, Auteur ; Dennis Zielstra, Auteur ; Pascal Neis, Auteur Année de publication : 2015 Article en page(s) : pp 63 - 81 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] bicyclette [Termes IGN] exhaustivité des données [Termes IGN] Miami [Termes IGN] mise à jour de base de données [Termes IGN] OpenStreetMap Résumé : (auteur) This article assesses the completeness of bicycle trail and on-street lane features in OpenStreetMap (OSM). Comparing OSM cycling features with reference data from local planning agencies for selected US Urbanized Areas shows that OSM bicycle trails tend to be more completely mapped than bicycle lanes. Manual evaluation of mapped cycling features in OSM and Google Maps for selected test areas within the Central Business Districts of Portland (OR) and Miami (FL) through comparison with governmental datasets, satellite imagery, and Google Street View, shows that the Bicycle layer in Google Maps can help to identify some missing or erroneously mapped OSM cycling links. However, Google Maps was also found to have some gaps in its data layers, suggesting that consultation of current trail and lane data from local planning authorities, if available, should be considered as an additional data source for bicycle related planning projects. Numéro de notice : A2015--119 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12081 En ligne : http://doi.wiley.com/10.1111/tgis.12081 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102455 [article]