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Geo-located community detection in Twitter with enhanced fast-greedy optimization of modularity: the case study of typhoon Haiyan / Mohamed Bakillah in International journal of geographical information science IJGIS, vol 29 n° 2 (February 2015)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 29 n° 2 (February 2015) . - pp 258 - 279 Titre : Geo-located community detection in Twitter with enhanced fast-greedy optimization of modularity: the case study of typhoon Haiyan Type de document : Article/Communication Auteurs : Mohamed Bakillah, Auteur ; Ren-Yu Li, Auteur ; Steve H.L. Liang, Auteur Année de publication : 2015 Article en page(s) : pp 258 - 279 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] communauté virtuelle [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] exploration de données géographiques [Termes descripteurs IGN] géopositionnement [Termes descripteurs IGN] risque naturel [Termes descripteurs IGN] Twitter Résumé : (Auteur) As they increase in popularity, social media are regarded as important sources of information on geographical phenomena. Studies have also shown that people rely on social media to communicate during disasters and emergency situation, and that the exchanged messages can be used to get an insight into the situation. Spatial data mining techniques are one way to extract relevant information from social media. In this article, our aim is to contribute to this field by investigating how graph clustering can be applied to support the detection of geo-located communities in Twitter in disaster situations. For this purpose, we have enhanced the fast-greedy optimization of modularity (FGM) clustering algorithm with semantic similarity so that it can deal with the complex social graphs extracted from Twitter. Then, we have coupled the enhanced FGM with the varied density-based spatial clustering of applications with noise spatial clustering algorithm to obtain spatial clusters at different temporal snapshots. The method was experimented with a case study on typhoon Haiyan in the Philippines, and Twitter’s different interaction modes were compared to create the graph of users and to detect communities. The experiments show that communities that are relevant to identify areas where disaster-related incidents were reported can be extracted, and that the enhanced algorithm outperforms the generic one in this task. Numéro de notice : A2015-579 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2014.964247 En ligne : http://www.tandfonline.com/doi/full/10.1080/13658816.2014.964247 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77841 [article]

Traffic prediction and analysis using a big data and visualisation approach / Declan McHugh (2015) 

Public contenu dans GIS Research UK (GISRUK) 2015 Proceedings / Nick Malleson (2015)   Titre : Traffic prediction and analysis using a big data and visualisation approach Type de document : Article/Communication Auteurs : Declan McHugh, Auteur Editeur : Leeds [Royaume-Uni] : University of Leeds Année de publication : 2015 Importance : pp 408 - 420 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse géovisuelle [Termes descripteurs IGN] Dublin (Irlande ; ville) [Termes descripteurs IGN] exploration de données géographiques [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] prévision [Termes descripteurs IGN] régression multiple [Termes descripteurs IGN] trafic routier [Termes descripteurs IGN] Twitter [Vedettes matières IGN] Géovisualisation Résumé : (auteur) This abstract illustrates an approach of using big data, visualisation and data mining techniques used to predict and analyse traffic. The objective is to understand Traffic patterns in Dublin City. The prediction model was used as an estimator to identify unusual traffic patterns. The generic model was designed using data mining techniques, multivariate regression algorithms, ARIMA and visually correlated with real-time traffic tweets. Using the prediction model and tweet event detection. The result is a high-performance web application containing over 500,000,000,000 traffic observations that produce analytical dashboard providing traffic prediction and analysis. Numéro de notice : C2015-049 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Communication Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83863

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Mapping social activities and concepts with social media (Twitter) and web search engines (Yahoo and Bing): a case study in 2012 US Presidential Election / Ming-Hsiang Tsou in Cartography and Geographic Information Science, vol 40 n° 4 (September 2013)  

Public [article]  inCartography and Geographic Information Science > vol 40 n° 4 (September 2013) . - pp 337 - 348 Titre : Mapping social activities and concepts with social media (Twitter) and web search engines (Yahoo and Bing): a case study in 2012 US Presidential Election Type de document : Article/Communication Auteurs : Ming-Hsiang Tsou, Auteur ; Jiue-An Yang, Auteur ; Brian Spitzberg, Auteur ; Jean Marc Gawron, Auteur ; Dipak Gupta, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 337 - 348 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] comportement [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] élection [Termes descripteurs IGN] estimation par noyau [Termes descripteurs IGN] Etats-Unis [Termes descripteurs IGN] logiciel de navigation [Termes descripteurs IGN] moteur de recherche [Termes descripteurs IGN] ontologie [Termes descripteurs IGN] Twitter Résumé : (Auteur) We introduce a new research framework for analyzing the spatial distribution of web pages and social media (Twitter) messages with related contents, called Visualizing Information Space in Ontological Networks (VISION). This innovative method can facilitate the tracking of ideas and social events disseminated in cyberspace from a spatial-temporal perspective. Thousands of web pages and millions of tweets associated with the same keywords were converted into visualization maps using commercial web search engines (Yahoo application programming interface (API) and Bing API), a social media search engine (Twitter APIs), Internet Protocol (IP) geolocation methods, and Geographic Information Systems (GIS) functions (e.g., kernel density and raster-based map algebra methods). We found that comparing multiple web information landscapes with different keywords or different dates can reveal important spatial patterns and “geospatial fingerprints” for selected keywords. We used the 2012 US Presidential Election candidates as our case study to validate this method. We noticed that the weekly changes of the geographic probability of hosting “Barack Obama” or “Mitt Romney” web pages are highly related to certain major campaign events. Both attention levels and the content of the tweets were deeply impacted by Hurricane Sandy. This new approach may provide a new research direction for studying human thought, human behaviors, and social activities quantitatively. Numéro de notice : A2013-762 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2013.799738 En ligne : https://doi.org/10.1080/15230406.2013.799738 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32898 [article]

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Geo-tagged Twitter collection and visualization system / Hideyuki Fujita in Cartography and Geographic Information Science, vol 40 n° 3 (June 2013)  

Public [article]  inCartography and Geographic Information Science > vol 40 n° 3 (June 2013) . - pp 183 - 191 Titre : Geo-tagged Twitter collection and visualization system Type de document : Article/Communication Auteurs : Hideyuki Fujita, Auteur Année de publication : 2013 Article en page(s) : pp 183 - 191 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] géoétiquetage [Termes descripteurs IGN] partage de données localisées [Termes descripteurs IGN] Twitter [Termes descripteurs IGN] visualisation cartographique Résumé : (Auteur) Mobile social media generate valuable data for analyzing human behavior and events in the real world. In this study, we developed a distributed system for collecting geo-tagged data from Twitter. The proposed system can collect several times as much data as commonly used methods. We also developed a spatio-temporal visualization tool for displaying the collected data. We conducted a data-collection and visualization experiment in central Tokyo and showed that the collected data reflected many events in the real world. Numéro de notice : A2013-751 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2013.800272 En ligne : https://doi.org/10.1080/15230406.2013.800272 Format de la ressource électronique : url Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32887 [article]

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Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr / Linna Li in Cartography and Geographic Information Science, vol 40 n° 2 (March 2013)  

Public [article]  inCartography and Geographic Information Science > vol 40 n° 2 (March 2013) . - pp 61 - 77 Titre : Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr Type de document : Article/Communication Auteurs : Linna Li, Auteur ; Michael F. Goodchild, Auteur ; Bo Xu, Auteur Année de publication : 2013 Article en page(s) : pp 61 - 77 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse de données [Termes descripteurs IGN] analyse socio-économique [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] Californie (Etats-Unis) [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] Etats-Unis [Termes descripteurs IGN] partage de données localisées [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] Twitter Résumé : (Auteur) Online social networking and information sharing services have generated large volumes of spatio-temporal footprints, which are potentially a valuable source of knowledge about the physical environment and social phenomena. However, it is critical to take into consideration the uneven distribution of the data generated in social media in order to understand the nature of such data and to use them appropriately. The distribution of footprints and the characteristics of contributors indicate the quantity, quality, and type of the data. Using georeferenced tweets and photos collected from Twitter and Flickr, this research presents the spatial and temporal patterns of such crowd-sourced geographic data in the contiguous United States and explores the socioeconomic characteristics of geographic data creators by investigating the relationships between tweet and photo densities and the characteristics of local people using California as a case study. Correlations between dependent and independent variables in partial least squares regression suggest that well-educated people in the occupations of management, business, science, and arts are more likely to be involved in the generation of georeferenced tweets and photos. Further research is required to explain why some people tend to produce and spread information over the Internet using social media from the perspectives of psychology and sociology. This study would be informative to sociologists who study the behaviors of social media users, geographers who are interested in the spatial and temporal distribution of social media users, marketing agencies who intend to understand the influence of social media, and other scientists who use social media data in their research. Numéro de notice : A2013-743 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article DOI : 10.1080/15230406.2013.777139 En ligne : https://doi.org/10.1080/15230406.2013.777139 Format de la ressource électronique : url Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32879 [article]

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vol 40 n° 2 - March 2013 - Mapping cyberspace and social media (Bulletin de Cartography and Geographic Information Science) / Ming-Hsiang Tsou  

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#Earthquake: Twitter as a distributed sensor system / Andrew Crooks in Transactions in GIS, vol 17 n° 1 (February 2013)  

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Sites : les outils pour mieux gazouiller sur Twitter / Sophie Paisant in Archimag, n° 232 (mars 2010)

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