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The socio-environmental data explorer (SEDE) : a social media–enhanced decision support system to explore risk perception to hazard events / Eric Shook in Cartography and Geographic Information Science, vol 43 n° 5 (November 2016)
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Titre : The socio-environmental data explorer (SEDE) : a social media–enhanced decision support system to explore risk perception to hazard events Type de document : Article/Communication Auteurs : Eric Shook, Auteur ; Victoria K. Turner, Auteur Année de publication : 2016 Article en page(s) : pp 427 - 441 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] analyse de données
[Termes IGN] données environnementales
[Termes IGN] Etats-Unis
[Termes IGN] exploration de données
[Termes IGN] neige
[Termes IGN] outil d'aide à la décision
[Termes IGN] perception
[Termes IGN] réseau social
[Termes IGN] risque environnemental
[Termes IGN] risque technologique
[Termes IGN] tempête
[Termes IGN] temps réelRésumé : (Auteur) Social media are increasingly recognized as a useful data source for understanding social response to hazard events in real time and in post-event analysis. This article establishes social media–enhanced decision support systems (SME-DSS) as a synergistic integration of social media and decision support systems (DSSs) to provide structured access to native, near real-time data from a large and diverse population to assess social response to social, environmental, and technological risk and hazard events. We introduce a prototype SME-DSS entitled socio-environmental data explorer (SEDE) to explore the opportunities and challenges of leveraging social media for decision support. We use a winter storm during 25–28 January 2015 that accumulated record amounts of snow along the East Coast of the United States as a case study to evaluate SEDE in helping assess social response to environmental risk and hazard events as well as evaluate social media as a theoretical component within the social amplification of risk framework (SARF) that serves as a theoretical foundation for SME-DSS. Numéro de notice : A2016-693 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2015.1131627 En ligne : https://doi.org/10.1080/15230406.2015.1131627 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82030
in Cartography and Geographic Information Science > vol 43 n° 5 (November 2016) . - pp 427 - 441[article]Réservation
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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)
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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)
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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]Réservation
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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)
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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]Réservation
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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)
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Titre : Finding spatial outliers in collective mobility patterns coupled with social ties Type de document : Article/Communication Auteurs : Monica Wachowicz, Auteur ; Tianyu Liu, Auteur Année de publication : 2016 Article en page(s) : pp 1806 - 1831 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] centroïde
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données massives
[Termes IGN] interface en langage naturel
[Termes IGN] mobilité humaine
[Termes IGN] statistiques d'appels détaillés
[Termes IGN] traitement de données localisées
[Termes IGN] traitement du langage naturel
[Termes IGN] Twitter
[Termes IGN] valeur aberranteRésumé : (Auteur) Currently the increase in the variety and volume of data sources is demanding new data analytical workflows for exploring them concurrently, especially if the goal is to detect spatial outliers. In this paper, we propose a data analytical workflow for exploring Call Detail Records in conjunction with geotagged tweets. The aim was to investigate how massive data point observations can be analyzed to detect spatial outliers in collective mobility patterns that are coupled with social ties. This workflow consists of analytical tasks that are developed based on the a-priori assumption of two isometric spaces where Natural Language Processing techniques are used to find spatial clusters from geotagged tweets in a Social Space which are later used to aggregate the Call Detail Records generated by antennas located in the Mobility Space. The dynamic weighted centroids that are given by the mean location of the number of calls per hour of all antennas that belong to a particular cluster are used to compute Standard Deviation Ellipses. The longer the period of time a weighted centroid stays outside of the 99.7% probability region of an ellipse, the highest the likelihood that they are spatial outliers. The workflow was implemented for the city of Dakar in Senegal. The results indicate that the further the hourly weighted centroids are skewed from the normal mean of an ellipse, the stronger the influence of a cluster is in finding spatial outliers. Furthermore, the longer the period of time the outliers stays outside of the 99.7% probability region of an ellipse, the highest the likelihood that the outliers are genuine and can be associated to extraordinary events such as natural disasters and national holidays. Numéro de notice : A2016-569 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=81713
in International journal of geographical information science IJGIS > vol 30 n° 9-10 (September - October 2016) . - pp 1806 - 1831[article]Réservation
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