Descripteur
Documents disponibles dans cette catégorie (103)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Constructing and analyzing spatial-social networks from location-based social media data / Xuebin Wei in Cartography and Geographic Information Science, vol 48 n° 3 (May 2021)
[article]
Titre : Constructing and analyzing spatial-social networks from location-based social media data Type de document : Article/Communication Auteurs : Xuebin Wei, Auteur ; Xiaobai Yao, Auteur Année de publication : 2021 Article en page(s) : pp 258 - 274 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de données
[Termes IGN] collecte de données
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données spatiotemporelles
[Termes IGN] Facebook
[Termes IGN] géolocalisation
[Termes IGN] réseau social géodépendant
[Termes IGN] santé
[Termes IGN] système d'information géographique
[Termes IGN] urbanismeRésumé : (auteur) People interact with each other in space and time. Improved understanding of human interactions in spatial, temporal, and social dimensions are highly beneficial for research and practices in public health, urban planning, and other fields. Traditional methods of collecting social interaction data are time-intensive and resource-consuming, resulting in relatively small sample sizes and limited information. Furthermore, traditional methods often oversimplify the dynamics of human interactions and fail to capture the characteristics of places where the interactions occur. With the popularity of location-based social media (LBSM) platforms, people can publish information about their social events such as time, location, and other participants. This research introduces a framework that formalizes terminologies and concepts related to spatial-social connections for the construction of spatial-social networks from LBSM data in GIS. Supported by the framework, the study presents methods of collecting, analyzing, and visualizing LBSM data in spatial-social dimensions. The methods are implemented and tested in a case study with Facebook data. The case study demonstrates that location-based social media data can be transformed into spatial-social networks and then be analyzed and visualized to answer innovative types of scientific inquiries. Numéro de notice : A2021-612 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2021.1891974 Date de publication en ligne : 09/04/2021 En ligne : https://doi.org/10.1080/15230406.2021.1891974 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97538
in Cartography and Geographic Information Science > vol 48 n° 3 (May 2021) . - pp 258 - 274[article]Réservation
Réserver ce documentExemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 032-2021031 RAB Revue Centre de documentation En réserve L003 Disponible A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media / Yi Bao in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)
[article]
Titre : A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media Type de document : Article/Communication Auteurs : Yi Bao, Auteur ; Zhou Huang, Auteur ; Linna Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 639 - 660 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données spatiotemporelles
[Termes IGN] géopositionnement
[Termes IGN] graphe
[Termes IGN] modèle de simulation
[Termes IGN] point d'intérêt
[Termes IGN] réseau social
[Termes IGN] service fondé sur la position
[Termes IGN] utilisateur
[Termes IGN] Wuhan (Chine)Résumé : (auteur) Location prediction based on spatio-temporal footprints in social media is instrumental to various applications, such as travel behavior studies, crowd detection, traffic control, and location-based service recommendation. In this study, we propose a model that uses geotags of social media to predict the potential area containing users’ next locations. In the model, we utilize HiSpatialCluster algorithm to identify clustering areas (CAs) from check-in points. CA is the basic spatial unit for predicting the potential area containing users’ next locations. Then, we use the LINE (Large-scale Information Network Embedding) to obtain the representation vector of each CA. Finally, we apply BiLSTM-CNN (Bidirectional Long Short-Term Memory-Convolutional Neural Network) for location prediction. The results show that the proposed ensemble model outperforms the single LSTM or CNN model. In the case study that identifies 100 CAs out of Weibo check-ins collected in Wuhan, China, the Top-5 predicted areas containing next locations amount to an 80% accuracy. The high accuracy is of great value for recommendation and prediction on areal unit. Numéro de notice : A2021-268 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1808896 Date de publication en ligne : 26/08/2020 En ligne : https://doi.org/10.1080/13658816.2020.1808896 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97324
in International journal of geographical information science IJGIS > vol 35 n° 4 (April 2021) . - pp 639 - 660[article]A graph-based semi-supervised approach to classification learning in digital geographies / Pengyuan Liu in Computers, Environment and Urban Systems, vol 86 (March 2021)
[article]
Titre : A graph-based semi-supervised approach to classification learning in digital geographies Type de document : Article/Communication Auteurs : Pengyuan Liu, Auteur ; Stefano de Sabbata, Auteur Année de publication : 2021 Article en page(s) : n° 101583 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] apprentissage profond
[Termes IGN] approche participative
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification semi-dirigée
[Termes IGN] données spatiotemporelles
[Termes IGN] partage de données localisées
[Termes IGN] prise en compte du contexte
[Termes IGN] réseau social
[Termes IGN] segmentation sémantique
[Termes IGN] Time-geographyRésumé : (auteur) As the distinction between online and physical spaces rapidly degrades, social media have now become an integral component of how many people's everyday experiences are mediated. As such, increasing interest has emerged in exploring how the content shared through those online platforms comes to contribute to the collaborative creation of places in physical space at the urban scale. Exploring digital geographies of social media data using methods such as qualitative coding (i.e., content labelling) is a flexible but complex task, commonly limited to small samples due to its impracticality over large datasets. In this paper, we propose a new tool for studies in digital geographies, bridging qualitative and quantitative approaches, able to learn a set of arbitrary labels (qualitative codes) on a small, manually-created sample and apply the same labels on a larger set. We introduce a semi-supervised, deep neural network approach to classify geo-located social media posts based on their textual and image content, as well as geographical and temporal aspects. Our innovative approach is rooted in our understanding of social media posts as augmentations of the time-space configurations that places are, and it comprises a stacked multi-modal autoencoder neural network to create joint representations of text and images, and a spatio-temporal graph convolution neural network for semi-supervised classification. The results presented in this paper show that our approach performs the classification of social media content with higher accuracy than traditional machine learning models as well as two state-of-art deep learning frameworks. Numéro de notice : A2021-024 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2020.101583 Date de publication en ligne : 16/12/2020 En ligne : https://doi.org/10.1016/j.compenvurbsys.2020.101583 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96608
in Computers, Environment and Urban Systems > vol 86 (March 2021) . - n° 101583[article]Joint promotion partner recommendation systems using data from location-based social networks / Yi-Chung Chen in ISPRS International journal of geo-information, vol 10 n° 2 (February 2021)
[article]
Titre : Joint promotion partner recommendation systems using data from location-based social networks Type de document : Article/Communication Auteurs : Yi-Chung Chen, Auteur ; Hsi-Ho Huang, Auteur ; Sheng-Min Chiu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 57 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] Facebook
[Termes IGN] Foursquare
[Termes IGN] géomercatique
[Termes IGN] New York (Etats-Unis ; ville)
[Termes IGN] point d'intérêt
[Termes IGN] politique commerciale
[Termes IGN] réseau social géodépendantRésumé : (auteur) Joint promotion is a valuable business strategy that enables companies to attract more customers at lower operational cost. However, finding a suitable partner can be extremely difficult. Conventionally, one of the most common approaches is to conduct survey-based analysis; however, this method can be unreliable as well as time-consuming, considering that there are likely to be thousands of potential partners in a city. This article proposes a framework to recommend Joint Promotion Partners using location-based social networks (LBSN) data. We considered six factors in determining the suitability of a partner (customer base, association, rating and awareness, prices and star ratings, distance, and promotional strategy) and developed efficient algorithms to perform the required calculations. The effectiveness and efficiency of our algorithms were verified using the Foursquare dataset and real-life case studies. Numéro de notice : A2021-152 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10020057 Date de publication en ligne : 30/01/2021 En ligne : https://doi.org/10.3390/ijgi10020057 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97063
in ISPRS International journal of geo-information > vol 10 n° 2 (February 2021) . - n° 57[article]
Titre : COVID-19 pandemic, geospatial information, and community resilience : Global applications and lessons Type de document : Monographie Auteurs : Abbas Rajabifard, Éditeur scientifique ; Daniel Paez, Éditeur scientifique ; Greg Foliente, Éditeur scientifique Editeur : Boca Raton, New York, ... : CRC Press Année de publication : 2021 Importance : 544 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-00-318159-0 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique
[Termes IGN] analyse spatiale
[Termes IGN] données localisées
[Termes IGN] épidémie
[Termes IGN] gestion de crise
[Termes IGN] image satellite
[Termes IGN] maladie virale
[Termes IGN] modélisation spatiale
[Termes IGN] OpenStreetMap
[Termes IGN] planification urbaine
[Termes IGN] réseau socialRésumé : (auteur) Geospatial information plays an important role in managing location dependent pandemic situations across different communities and domains. Geospatial information and technologies are particularly critical to strengthening urban and rural resilience, where economic, agricultural, and various social sectors all intersect. Examining the United Nations' SDGs from a geospatial lens will ensure that the challenges are addressed for all populations in different locations. This book, with worldwide contributions focused on COVID-19 pandemic, provides interdisciplinary analysis and multi-sectoral expertise on the use of geospatial information and location intelligence to support community resilience and authorities to manage pandemics. Note de contenu : 1- Setting the scene
2- Technical and technico-social solutions
3- Regional, country and local applications
4- Stakeholder perspectives
5- The futur directionNuméro de notice : 28628 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.1201/9781003181590 En ligne : https://doi.org/10.1201/9781003181590 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99578 Incorporating memory-based preferences and point-of-interest stickiness into recommendations in location-based social networks / Hang Zhang in ISPRS International journal of geo-information, vol 10 n° 1 (January 2021)PermalinkIntroducing diversion graph for real-time spatial data analysis with location based social networks / Sameera Kannangara (2021)PermalinkRegNet: a neural network model for predicting regional desirability with VGI data / Wenzhong Shi in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)PermalinkSIoT: a new strategy to improve the network lifetime with an efficient search process / Abderrahim Zannou in Future internet, vol 13 n° 1 (January 2021)PermalinkPermalinkExploring the heterogeneity of human urban movements using geo-tagged tweets / Ding Ma in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)PermalinkHow urban places are visited by social groups? Evidence from matrix factorization on mobile phone data / Chaogui Kang in Transactions in GIS, Vol 24 n° 6 (December 2020)PermalinkSocial media as passive geo-participation in transportation planning – how effective are topic modeling & sentiment analysis in comparison with citizen surveys? / Oliver Lock in Geo-spatial Information Science, vol 23 n° 4 (December 2020)PermalinkEvaluating geo-tagged Twitter data to analyze tourist flows in Styria, Austria / Johannes Scholz in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)PermalinkPrivacy-aware visualization of volunteered geographic information (VGI) to analyze spatial activity: A benchmark implementation / Alexander Dunkel in ISPRS International journal of geo-information, vol 9 n° 10 (October 2020)Permalink