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Geometric quality assessment of trajectory-generated VGI road networks based on the symmetric arc similarity / Yan Lyu in Transactions in GIS, vol 21 n° 5 (October 2017)  

Public [article]  inTransactions in GIS > vol 21 n° 5 (October 2017) . - pp 984 - 1009 Titre : Geometric quality assessment of trajectory-generated VGI road networks based on the symmetric arc similarity Type de document : Article/Communication Auteurs : Yan Lyu, Auteur ; Yehua Sheng, Auteur ; Ningning Guo, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 984 - 1009 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] analyse comparative [Termes IGN] données localisées des bénévoles [Termes IGN] précision des données [Termes IGN] qualité des données [Termes IGN] réalité de terrain [Termes IGN] réseau routier [Termes IGN] similitude Résumé : (Auteur) As large amounts of trajectories from a wide variety of Volunteered Geographic Information (referred to as VGI) contributors pour into the spatial database, the geometric qualities of the VGI road networks generated from these trajectories are different from the ground truth road dataset and so need to be differently assessed. To address this issue, an assessment approach based on symmetric arc similarity is proposed, and the geometric quality of a VGI road network is assessed by its conformity with the corresponding ground truth road network, the results being visualized as hierarchical thematic maps. To compute the conformity, the geometric similarity between the VGI road arc and the corresponding ground truth road arc, which is selected by the adaptive searching distance, is measured based on the symmetric arc similarity method; the geometric quality is assessed based on an assessment matrix. Also, the symmetric arc similarity method is independent of directions and with a feature of shift-independence, which is applicable to assess the geometric qualities of different VGI road networks and makes the assessment result consistent with the actual situation of the real world. The robustness and scalability of the approach are examined using VGI road networks from different sources. Numéro de notice : A2017-633 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12256 En ligne : http://dx.doi.org/10.1111/tgis.12256 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86952 [article]

Exploring cell tower data dumps for supervised learning-based point-of-interest prediction (industrial paper) / Ran Wang in Geoinformatica, vol 20 n° 2 (April - June 2016)  

Public [article]  inGeoinformatica > vol 20 n° 2 (April - June 2016) . - pp 327 - 349 Titre : Exploring cell tower data dumps for supervised learning-based point-of-interest prediction (industrial paper) Type de document : Article/Communication Auteurs : Ran Wang, Auteur ; Chi-Yin Chow, Auteur ; Yan Lyu, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 327 - 349 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] algorithme d'apprentissage [Termes IGN] apprentissage dirigé [Termes IGN] comportement [Termes IGN] données massives [Termes IGN] exploration de données [Termes IGN] histogramme [Termes IGN] point d'intérêt [Termes IGN] positionnement automatique [Termes IGN] téléphonie mobile [Termes IGN] utilisateur Résumé : (auteur) Exploring massive mobile data for location-based services becomes one of the key challenges in mobile data mining. In this paper, we investigate a problem of finding a correlation between the collective behavior of mobile users and the distribution of points of interest (POIs) in a city. Specifically, we use large-scale cell tower data dumps collected from cell towers and POIs extracted from a popular social network service, Weibo. Our objective is to make use of the data from these two different types of sources to build a model for predicting the POI densities of different regions in the covered area. An application domain that may benefit from our research is a business recommendation application, where a prediction result can be used as a recommendation for opening a new store/branch. The crux of our contribution is the method of representing the collective behavior of mobile users as a histogram of connection counts over a period of time in each region. This representation ultimately enables us to apply a supervised learning algorithm to our problem in order to train a POI prediction model using the POI data set as the ground truth. We studied 12 state-of-the-art classification and regression algorithms; experimental results demonstrate the feasibility and effectiveness of the proposed method. Numéro de notice : A2016-375 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Article DOI : 10.1007/s10707-015-0237-7 En ligne : http://dx.doi.org/10.1007/s10707-015-0237-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81140 [article]