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Geospatial big data handling theory and methods: A review and research challenges / Songnian Li in ISPRS Journal of photogrammetry and remote sensing, vol 115 (May 2016)
[article]
Titre : Geospatial big data handling theory and methods: A review and research challenges Type de document : Article/Communication Auteurs : Songnian Li, Auteur ; Suzana Dragićević, Auteur ; Francesc Antón Castro, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 119 – 133 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] données massives
[Termes IGN] traitement automatique de données
[Termes IGN] traitement de données localiséesRésumé : (auteur) Big data has now become a strong focus of global interest that is increasingly attracting the attention of academia, industry, government and other organizations. Big data can be situated in the disciplinary area of traditional geospatial data handling theory and methods. The increasing volume and varying format of collected geospatial big data presents challenges in storing, managing, processing, analyzing, visualizing and verifying the quality of data. This has implications for the quality of decisions made with big data. Consequently, this position paper of the International Society for Photogrammetry and Remote Sensing (ISPRS) Technical Commission II (TC II) revisits the existing geospatial data handling methods and theories to determine if they are still capable of handling emerging geospatial big data. Further, the paper synthesises problems, major issues and challenges with current developments as well as recommending what needs to be developed further in the near future. Numéro de notice : A2016-547 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.10.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.10.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81699
in ISPRS Journal of photogrammetry and remote sensing > vol 115 (May 2016) . - pp 119 – 133[article]Rethinking big data: A review on the data quality and usage issues / Jianzheng Liu in ISPRS Journal of photogrammetry and remote sensing, vol 115 (May 2016)
[article]
Titre : Rethinking big data: A review on the data quality and usage issues Type de document : Article/Communication Auteurs : Jianzheng Liu, Auteur ; Jie Li, Auteur ; Weifeng Li, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 134 – 142 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] données massives
[Termes IGN] évaluation des données
[Termes IGN] qualité des données
[Termes IGN] traitement de données localiséesRésumé : (auteur) The recent explosive publications of big data studies have well documented the rise of big data and its ongoing prevalence. Different types of “big data” have emerged and have greatly enriched spatial information sciences and related fields in terms of breadth and granularity. Studies that were difficult to conduct in the past time due to data availability can now be carried out. However, big data brings lots of “big errors” in data quality and data usage, which cannot be used as a substitute for sound research design and solid theories. We indicated and summarized the problems faced by current big data studies with regard to data collection, processing and analysis: inauthentic data collection, information incompleteness and noise of big data, unrepresentativeness, consistency and reliability, and ethical issues. Cases of empirical studies are provided as evidences for each problem. We propose that big data research should closely follow good scientific practice to provide reliable and scientific “stories”, as well as explore and develop techniques and methods to mitigate or rectify those ‘big-errors’ brought by big data. Numéro de notice : A2016-548 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.11.006 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.11.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81702
in ISPRS Journal of photogrammetry and remote sensing > vol 115 (May 2016) . - pp 134 – 142[article]Spatiotemporal data model for network time geographic analysis in the era of big data / Bi Yu Chen in International journal of geographical information science IJGIS, vol 30 n° 5-6 (May - June 2016)
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Titre : Spatiotemporal data model for network time geographic analysis in the era of big data Type de document : Article/Communication Auteurs : Bi Yu Chen, Auteur ; H. Yuan, Auteur ; Qingquan Li, Auteur Année de publication : 2016 Article en page(s) : pp 1041 - 1071 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] données massives
[Termes IGN] données spatiotemporelles
[Termes IGN] entité géographique
[Termes IGN] milieu urbain
[Termes IGN] modèle conceptuel de données spatio-temporelles
[Termes IGN] tempsRésumé : (Auteur) There has been a resurgence of interest in time geography studies due to emerging spatiotemporal big data in urban environments. However, the rapid increase in the volume, diversity, and intensity of spatiotemporal data poses a significant challenge with respect to the representation and computation of time geographic entities and relations in road networks. To address this challenge, a spatiotemporal data model is proposed in this article. The proposed spatiotemporal data model is based on a compressed linear reference (CLR) technique to transform network time geographic entities in three-dimensional (3D) (x, y, t) space to two-dimensional (2D) CLR space. Using the proposed spatiotemporal data model, network time geographic entities can be stored and managed in classical spatial databases. Efficient spatial operations and index structures can be directly utilized to implement spatiotemporal operations and queries for network time geographic entities in CLR space. To validate the proposed spatiotemporal data model, a prototype system is developed using existing 2D GIS techniques. A case study is performed using large-scale datasets of space-time paths and prisms. The case study indicates that the proposed spatiotemporal data model is effective and efficient for storing, managing, and querying large-scale datasets of network time geographic entities. Numéro de notice : A2016-294 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1104317 En ligne : https://doi.org/10.1080/13658816.2015.1104317 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80878
in International journal of geographical information science IJGIS > vol 30 n° 5-6 (May - June 2016) . - pp 1041 - 1071[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2016032 RAB Revue Centre de documentation En réserve L003 Disponible 079-2016031 RAB Revue Centre de documentation En réserve L003 Disponible 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)
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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] utilisateurRésumé : (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
in Geoinformatica > vol 20 n° 2 (April - June 2016) . - pp 327 - 349[article]Future trends in geospatial information management : UN expert committee regards connectivity as key to growth / Frédérique Coumans in GIM international [en ligne], vol 30 n° 4 (April 2016)
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Titre : Future trends in geospatial information management : UN expert committee regards connectivity as key to growth Type de document : Article/Communication Auteurs : Frédérique Coumans, Auteur Année de publication : 2016 Article en page(s) : pp 32 - 34 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données
[Termes IGN] base de données localisées
[Termes IGN] données massives
[Termes IGN] droit
[Termes IGN] interopérabilitéRésumé : (éditeur) The most significant changes in the geospatial industry in the next decade will come not through a single technology, but rather from linking multiple technologies together. Especially the development of big data analytics will boost smart use of the location component to integrate data from many sources. The United Nations Committee of Experts on Global Geospatial Information Management (UN-GGIM) sees precise location information forming a core part of tomorrow's all-connecting IT infrastructure. Numéro de notice : A2016-216 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80683
in GIM international [en ligne] > vol 30 n° 4 (April 2016) . - pp 32 - 34[article]Classified and clustered data constellation: An efficient approach of 3D urban data management / Suhaibah Azri in ISPRS Journal of photogrammetry and remote sensing, vol 113 (March 2016)PermalinkA land use/land cover change geospatial cyberinfrastructure to integrate big data and temporal topology / Jin Xing in International journal of geographical information science IJGIS, vol 30 n° 3-4 (March - April 2016)PermalinkBig data, open data et valorisation des données / Jean-Louis Monino (2016)PermalinkPermalinkPermalinkDense image matching / Martin Kodde in GIM international [en ligne], vol 30 n° 1 (January 2016)PermalinkThe iQmulus urban showcase: automatic tree classification and identification in huge mobile mapping point clouds / Jan Böhm (2016)PermalinkTweets analysis for event detection / Soumaya Cherichi in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 21 n° 1 (janvier - février 2016)PermalinkIntergeo 2015 ou la foire du drone / Olivier Reis in XYZ, n° 145 (décembre 2015 - février 2016)Permalink11 Events under One Umbrella / Anonyme in GIM international [en ligne], vol 29 n° 11 (November 2015)Permalink