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Crowdsourcing functions of the living city from Twitter and Foursquare data / Xiaolu Zhou in Cartography and Geographic Information Science, vol 43 n° 5 (November 2016)
[article]
Titre : Crowdsourcing functions of the living city from Twitter and Foursquare data Type de document : Article/Communication Auteurs : Xiaolu Zhou, Auteur ; Liang Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 393 - 404 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse spatio-temporelle
[Termes IGN] Boston (Massachusetts)
[Termes IGN] Chicago (Illinois)
[Termes IGN] dimension temporelle
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données localisées des bénévoles
[Termes IGN] géobalise
[Termes IGN] planification urbaine
[Termes IGN] réseau social
[Termes IGN] système d'information géographique
[Termes IGN] villeRésumé : (Auteur) Urban functions are closely related to people’s spatiotemporal activity patterns, transportation needs, and a city’s business distribution and development trends. Studies investigating urban functions have used different data sources, such as remotely sensed imageries, observation, photography, and cognitive maps. However, these data sources usually suffer from low spatial, temporal, and thematic resolution. This article attempts to investigate human activities to understand urban functions through crowdsourcing social media data. In this study, we mined Twitter and Foursquare data to extract and analyze six types of human activities. The spatiotemporal analysis revealed hotspots for different activity intensities at different temporal resolution. We also applied the classified model in a real-time system to extract information of various urban functions. This study demonstrates the significance and usefulness of social sensing in analyzing urban functions. By combining different platforms of social media data and analyzing people’s geo-tagged city experience, this article contributes to leverage voluntary local knowledge to better depict human dynamics, discover spatiotemporal city characteristics, and convey information about cities. Numéro de notice : A2016-690 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1080/15230406.2015.1128852 En ligne : https://doi.org/10.1080/15230406.2015.1128852 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82018
in Cartography and Geographic Information Science > vol 43 n° 5 (November 2016) . - pp 393 - 404[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 032-2016051 RAB Revue Centre de documentation En réserve L003 Disponible Automatic targeted-domain spatiotemporal event detection in twitter / Ting Hua in Geoinformatica, vol 20 n° 4 (October - December 2016)
[article]
Titre : Automatic targeted-domain spatiotemporal event detection in twitter Type de document : Article/Communication Auteurs : Ting Hua, Auteur ; Feng Chen, Auteur ; Liang Zhao, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 765 - 795 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse spatio-temporelle
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] positionnement automatique
[Termes IGN] TwitterRésumé : (Auteur) Twitter has become an important data source for detecting events, especially tracking detailed information for events of a specific domain. Previous studies on targeted-domain Twitter information extraction have used supervised learning techniques to identify domain-related tweets, however, the need for extensive manual labeling makes these supervised systems extremely expensive to build and maintain. What’s more, most of these existing work fail to consider spatiotemporal factors, which are essential attributes of target-domain events. In this paper, we propose a semi-supervised method for Automatical Targeted-domain Spatiotemporal Event Detection (ATSED) in Twitter. Given a targeted domain, ATSED first learns tweet labels from historical data, and then detects on-going events from real-time Twitter data streams. Specifically, an efficient label generation algorithm is proposed to automatically recognize tweet labels from domain-related news articles, a customized classifier is created for Twitter data analysis by utilizing tweets’ distinguishing features, and a novel multinomial spatial-scan model is provided to identify geographical locations for detected events. Experiments on 305 million tweets demonstrated the effectiveness of this new approach. Numéro de notice : A2016-815 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article DOI : 10.1007/s10707-016-0263-0 En ligne : http://dx.doi.org/10.1007/s10707-016-0263-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82616
in Geoinformatica > vol 20 n° 4 (October - December 2016) . - pp 765 - 795[article]Activity patterns, socioeconomic status and urban spatial structure: what can social media data tell us? / Qunying Huang in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)
[article]
Titre : Activity patterns, socioeconomic status and urban spatial structure: what can social media data tell us? Type de document : Article/Communication Auteurs : Qunying Huang, Auteur ; David W. S. Wong, Auteur Année de publication : 2016 Article en page(s) : pp 1871 - 1898 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse socio-économique
[Termes IGN] base de données spatiotemporelles
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données massives
[Termes IGN] données socio-économiques
[Termes IGN] morphologie urbaine
[Termes IGN] surveillance
[Termes IGN] TwitterRésumé : (Auteur) Individual activity patterns are influenced by a wide variety of factors. The more important ones include socioeconomic status (SES) and urban spatial structure. While most previous studies relied heavily on the expensive travel-diary type data, the feasibility of using social media data to support activity pattern analysis has not been evaluated. Despite the various appealing aspects of social media data, including low acquisition cost and relatively wide geographical and international coverage, these data also have many limitations, including the lack of background information of users, such as home locations and SES. A major objective of this study is to explore the extent that Twitter data can be used to support activity pattern analysis. We introduce an approach to determine users’ home and work locations in order to examine the activity patterns of individuals. To infer the SES of individuals, we incorporate the American Community Survey (ACS) data. Using Twitter data for Washington, DC, we analyzed the activity patterns of Twitter users with different SESs. The study clearly demonstrates that while SES is highly important, the urban spatial structure, particularly where jobs are mainly found and the geographical layout of the region, plays a critical role in affecting the variation in activity patterns between users from different communities. Numéro de notice : A2016-570 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1145225 En ligne : http://dx.doi.org/10.1080/13658816.2016.1145225 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81714
in International journal of geographical information science IJGIS > vol 30 n° 9-10 (September - October 2016) . - pp 1871 - 1898[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2016051 RAB Revue Centre de documentation En réserve L003 Disponible Discovery of local topics by using latent spatio-temporal relationships in geo-social media / Kyoung-Sook Kim in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)
[article]
Titre : Discovery of local topics by using latent spatio-temporal relationships in geo-social media Type de document : Article/Communication Auteurs : Kyoung-Sook Kim, Auteur ; Isao Kojima, Auteur ; Hirotaka Ogawa, Auteur Année de publication : 2016 Article en page(s) : pp 1899 - 1922 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données massives
[Termes IGN] données spatiotemporelles
[Termes IGN] géovisualisation
[Termes IGN] positionnement automatique
[Termes IGN] temps réel
[Termes IGN] traitement de donnéesRésumé : (Auteur) Social networks have played a crucial role as information channels for people to understanding their daily lives beyond merely being communication tools. In particular, coupling social networks with geographic location has boosted the worth of social media to not only enable comprehension of the effects of natural phenomena such as global warming and disasters, but also the social patterns of human societies. However, the high rate of social data generation and the large amounts of noisy data makes it difficult to directly apply social media to decision-making processes. This article proposes a new system of analyzing the spatio-temporal patterns of social phenomena in real time and the discovery of local topics based on their latent spatio-temporal relationships. We will first describe a model that represents the local patterns of populations of geo-tagged social media. We will then define a local topic whose keywords share a region in space and time and present a system implementation based on existing open source technologies. We evaluated the model of local topics with several ways of visualization in experiments and demonstrated a certain social pattern from a dataset of daily Twitter streams. The results obtained from experiments revealed certain keywords had a strong spatio-temporal proximity even though they did not occur in the same message. Numéro de notice : A2016-571 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1146956 En ligne : http://dx.doi.org/10.1080/13658816.2016.1146956 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81715
in International journal of geographical information science IJGIS > vol 30 n° 9-10 (September - October 2016) . - pp 1899 - 1922[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2016051 RAB Revue Centre de documentation En réserve L003 Disponible Exploration of spatiotemporal and semantic clusters of Twitter data using unsupervised neural networks / Enrico Steiger in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)
[article]
Titre : Exploration of spatiotemporal and semantic clusters of Twitter data using unsupervised neural networks Type de document : Article/Communication Auteurs : Enrico Steiger, Auteur ; Bernd Resch, Auteur ; Alexander Zipf, Auteur Année de publication : 2016 Article en page(s) : pp 1694 - 1716 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] carte de Kohonen
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] données hétérogènes
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données massives
[Termes IGN] traitement de données localisées
[Termes IGN] TwitterRésumé : (Auteur) The investigation of human activity patterns from location-based social networks like Twitter is an established approach of how to infer relationships and latent information that characterize urban structures. Researchers from various disciplines have performed geospatial analysis on social media data despite the data’s high dimensionality, complexity and heterogeneity. However, user-generated datasets are of multi-scale nature, which results in limited applicability of commonly known geospatial analysis methods. Therefore in this paper, we propose a geographic, hierarchical self-organizing map (Geo-H-SOM) to analyze geospatial, temporal and semantic characteristics of georeferenced tweets. The results of our method, which we validate in a case study, demonstrate the ability to explore, abstract and cluster high-dimensional geospatial and semantic information from crowdsourced data. Numéro de notice : A2016-566 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1144887 En ligne : http://dx.doi.org/10.1080/13658816.2016.1144887 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81707
in International journal of geographical information science IJGIS > vol 30 n° 9-10 (September - October 2016) . - pp 1694 - 1716[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2016051 RAB Revue Centre de documentation En réserve L003 Disponible Finding spatial outliers in collective mobility patterns coupled with social ties / Monica Wachowicz in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)PermalinkIntegrating social network data into GISystems / Clio Andris in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)PermalinkGeo-temporal Twitter demographics / Paul A. Longley in International journal of geographical information science IJGIS, vol 30 n° 1-2 (January - February 2016)PermalinkJoining spatial distribution visualisation tools with social media data using free and open source software : extended abstract / Mayra Zurbaran (2016)PermalinkAn advanced systematic literature review on spatiotemporal analyses of twitter-data / Enrico Steiger in Transactions in GIS, vol 19 n° 6 (December 2015)PermalinkTriangulating social multimedia content for event localization using Flickr and Twitter / George Panteras in Transactions in GIS, vol 19 n° 5 (October 2015)PermalinkAnalyse du comportement d’annotation du réseau social d’un utilisateur pour la détection des intérêts. Application sur Delicious / Manel Mezghani in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 20 n° 4 (juillet - août 2015)PermalinkPermalinkA geographic approach for combining social media and authoritative data towards identifying useful information for disaster management / João Porto de Albuquerque in International journal of geographical information science IJGIS, vol 29 n° 4 (April 2015)PermalinkObject selection in map generalization using geosocial network data: A case study in Wuhan, China / Hao Luo in Geomatica, vol 69 n° 1 (March 2015)Permalink