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Depicting urban boundaries from a mobility network of spatial interactions : a case study of Great Britain with geo-located Twitter data / Junjun Yin in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)
[article]
Titre : Depicting urban boundaries from a mobility network of spatial interactions : a case study of Great Britain with geo-located Twitter data Type de document : Article/Communication Auteurs : Junjun Yin, Auteur ; Aiman Soliman, Auteur ; Dandong Yin, Auteur ; Shaowen Wang, Auteur Année de publication : 2017 Article en page(s) : pp 1293 - 1313 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] comportement
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données socio-économiques
[Termes IGN] géographie humaine
[Termes IGN] Grande-Bretagne
[Termes IGN] interaction humain-espace
[Termes IGN] limite administrative
[Termes IGN] mobilité urbaine
[Termes IGN] réseau social
[Termes IGN] trace GPS
[Termes IGN] urbanisationRésumé : (Auteur) Existing urban boundaries are usually defined by government agencies for administrative, economic, and political purposes. However, it is not clear whether the boundaries truly reflect human interactions with urban space in intra- and interregional activities. Defining urban boundaries that consider socioeconomic relationships and citizen commute patterns is important for many aspects of urban and regional planning. In this paper, we describe a method to delineate urban boundaries based upon human interactions with physical space inferred from social media. Specifically, we depicted the urban boundaries of Great Britain using a mobility network of Twitter user spatial interactions, which was inferred from over 69 million geo-located tweets. We define the non-administrative anthropographic boundaries in a hierarchical fashion based on different physical movement ranges of users derived from the collective mobility patterns of Twitter users in Great Britain. The results of strongly connected urban regions in the form of communities in the network space yield geographically cohesive, nonoverlapping urban areas, which provide a clear delineation of the non-administrative anthropographic urban boundaries of Great Britain. The method was applied to both national (Great Britain) and municipal scales (the London metropolis). While our results corresponded well with the administrative boundaries, many unexpected and interesting boundaries were identified. Importantly, as the depicted urban boundaries exhibited a strong instance of spatial proximity, we employed a gravity model to understand the distance decay effects in shaping the delineated urban boundaries. The model explains how geographical distances found in the mobility patterns affect the interaction intensity among different non-administrative anthropographic urban areas, which provides new insights into human spatial interactions with urban space. Numéro de notice : A2017-303 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1282615 En ligne : http://dx.doi.org/10.1080/13658816.2017.1282615 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85350
in International journal of geographical information science IJGIS > vol 31 n° 7-8 (July - August 2017) . - pp 1293 - 1313[article]Réservation
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Titre : Big data et traçabilité numérique : Les sciences sociales face à la quantification massive des individus Type de document : Monographie Auteurs : Pierre-Michel Menger, Éditeur scientifique ; Simon Paye, Éditeur scientifique Editeur : Paris : Collège de France Année de publication : 2017 Collection : Conférences ISBN/ISSN/EAN : 978-2-7226-0467-4 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Infrastructure de données
[Termes IGN] administration de données
[Termes IGN] comportement
[Termes IGN] données massives
[Termes IGN] données numériques
[Termes IGN] édition en libre accès
[Termes IGN] sciences sociales
[Termes IGN] stockage de données
[Termes IGN] traçabilité
[Termes IGN] web des donnéesRésumé : (éditeur) Les traces numériques de l’activité des individus, des entreprises, des administrations, des réseaux sociaux sont devenues un gisement considérable. Comment ces données sont-elles prélevées, stockées, valorisées, et vendues ? Et que penser des algorithmes qui convertissent en outil de contrôle et de persuasion l’information sur les comportements, les actes de travail et les échanges ? Les big data sont-elles à notre service ou font-elles de nous les rouages consentants du capitalisme informationnel et relationnel ? Les sciences sociales enquêtent sur les enjeux sociaux, éthiques, politiques et économiques de ces transformations. Mais elles sont elles aussi de plus en plus consommatrices de données numériques de masse. Cet ouvrage collectif explore l’expansion de la traçabilité numérique dans ces deux dimensions, marchande et scientifique. L’ouvrage est dirigé par Pierre-Michel Menger, professeur au Collège de France et titulaire de la chaire « Sociologie du travail créateur », et par Simon Paye, maître de conférences à l’université de Lorraine, sociologue du travail et des groupes professionnels. Note de contenu : Introduction
I. Cheminements des big data : technologies, marchés, échanges
II. Big data et configurations sociales en mouvement
III. Données numériques et outils de recherche en sciences sociales
PostfaceNuméro de notice : 25929 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/SOCIETE NUMERIQUE Nature : Monographie DOI : sans En ligne : https://books.openedition.org/cdf/5013 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96193
Titre : Visual analytics of human mobility behavior Type de document : Thèse/HDR Auteurs : Robert Lutz Krüger, Auteur ; Thomas Ertl, Directeur de thèse ; Ross Maciejewski, Directeur de thèse Editeur : Stuttgart : University of Stuttgart Année de publication : 2017 Importance : 212 p. Format : 21 x 30 cm Note générale : Bibliographie
Von der Fakultät Informatik, Elektrotechnik und Informationstechnik der Universität Stuttgart zur Erlangung der Würde eines Doktors der Naturwissenschaften (Dr. rer. nat.), genehmigte AbhandlungLangues : Anglais (eng) Descripteur : [Termes IGN] acquisition de données
[Termes IGN] analyse visuelle
[Termes IGN] base de données localisées
[Termes IGN] comportement
[Termes IGN] données de terrain
[Termes IGN] données socio-économiques
[Termes IGN] enrichissement sémantique
[Termes IGN] exploration de données géographiques
[Termes IGN] mobilité humaine
[Termes IGN] modélisation
[Termes IGN] trajet (mobilité)
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Human mobility plays an important role in many domains of today’s society, such as security, logistics, transportation, urban planning, and geo-marketing. Both, government and industry thus have great interest in understanding mobility patterns and their driving social, economical, and environmental causes and effects. While
stakeholders had to rely on manual traffic surveys for a long time, improvements in tracking technology made analyses based on large digital datasets possible. Recently, the omnipresence of mobile devices significantly increased the amounts of collected movement and context data. People are willing to reveal their position, but also further personal details such as visited places, observations, events, news, and sentiments in exchange for personalized services and social networking. This opens up new possibilities for many domains where a semantic mobility understanding is required but also raises major challenges. To reveal a holistic picture, heterogeneous datasets of different services with different resolution and format have to be fused and analyzed. However, social sensing data is vast, has varying scale, is unevenly distributed, and constantly updated. Especially content from social media services is often inconsistent, unreliable, and incomplete, which requires special treatment. Fully automatic mapping approaches are not trustworthy as they do not take into account these uncertainties. At the same time, manual approaches become insufficient with large amounts of data. Even when data is perfectly aligned, analysts cannot purely rely on existing techniques. Answering questions about reasons for movement requires a broader perspective that takes into account environmental and social context, the driving forces for human mobility behavior. Visual analytics is an emerging research field to tackle such challenges. It creates added value by combining the processing power and accuracy of machines with human capabilities to perceive information visually. Automatic means are used to fuse and aggregate data and to detect hidden patterns therein. Interactive visualizations allow to explore and query the data and to steer the automatic processes with domain knowledge. This increases trust in data, models, and results, which is especially important when critical decisions need to be made. The strengths of visual analytics have been shown to be particularly advantageous when problems and goals are underspecified and exploratory means are needed to discover yet unknown patterns.
This thesis presents novel visual analytics approaches to derive meaning and reasons behind movement, by taking into account the aforementioned characteristics. The approaches are aligned in a holistic process model covering all steps from data retrieval, enrichment, exploration, and verification to externalization of gained knowledge for various fields of application such as electric mobility, event management, and law enforcement. It is shown how data from social media can not only be used to retrieve up-to-date movement information, but also to enrich movement trajectories from other sources with structured and unstructured information about places, events, transactions, and other observations. Through highly interactive visual interfaces analysts can bring in domain knowledge to deal with uncertainties during data fusion and to steer the subsequent semantic analysis. Exploratory and confirmatory analysis techniques are presented to create hypotheses, refine them, and find support in the data. Analysts can discover routines and abnormal behavior with assistance of automatic pattern detection methods to cope with the vast amounts of data. Spatial drill-down is supported by a set-based focus+context technique, while a more abstract visual query language allows to explicitly formulate, extract, and query for movement patterns. The approaches are applied in different scenarios and are integrated in a visual analytics system. Evaluation with experts and novice users, case studies, and comparisons to ground truth data reveal the need and effectiveness of the contributions. Overall, the thesis contributes a visual analytics process for human mobility behavior with novel semantic analysis approaches, ranging from global movements of many to local activities of a few people, for a wide range of application domains.Note de contenu : Introduction
1 - From Foundations to Applications
2 - Movement Data Retrieval and Visual Representation
3 - Semantic Enrichment with Context Data
4 - Interactive Filtering
5 - Pattern Detection and Verification
6 - MOBY - The Mobility Analysis System
Conclusion and OutlookNuméro de notice : 21573 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Thèse étrangère Note de thèse : PhD Dissertation : Informatik, Elektrotechnik und Informationstechnik : Universität Stuttgart : 2017 DOI : sans En ligne : http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-97337 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90574 A probabilistic approach to detect mixed periodic patterns from moving object data / Jun Li in Geoinformatica, vol 20 n° 4 (October - December 2016)
[article]
Titre : A probabilistic approach to detect mixed periodic patterns from moving object data Type de document : Article/Communication Auteurs : Jun Li, Auteur ; Jingjing Wang, Auteur ; Junfei Zhang, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 715 - 739 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] comportement
[Termes IGN] détection automatique
[Termes IGN] détection d'objet
[Termes IGN] estimation par noyau
[Termes IGN] objet mobile
[Termes IGN] séquence d'images
[Termes IGN] variable aléatoireRésumé : (Auteur) The prevalence of moving object data (MOD) brings new opportunities for behavior related research. Periodic behavior is one of the most important behaviors of moving objects. However, the existing methods of detecting periodicities assume a moving object either does not have any periodic behavior at all or just has a single periodic behavior in one place. Thus they are incapable of dealing with many real world situations whereby a moving object may have multiple periodic behaviors mixed together. Aiming at addressing this problem, this paper proposes a probabilistic periodicity detection method called MPDA. MPDA first identifies high dense regions by the kernel density method, then generates revisit time sequences based on the dense regions, and at last adopts a filter-refine paradigm to detect mixed periodicities. At the filter stage, candidate periods are identified by comparing the observed and reference distribution of revisit time intervals using the chi-square test, and at the refine stage, a periodic degree measure is defined to examine the significance of candidate periods to identify accurate periods existing in MOD. Synthetic datasets with various characteristics and two real world tracking datasets validate the effectiveness of MPDA under various scenarios. MPDA has the potential to play an important role in analyzing complicated behaviors of moving objects. Numéro de notice : A2016-814 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s10707-016-0261-2 En ligne : http://dx.doi.org/10.1007/s10707-016-0261-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82615
in Geoinformatica > vol 20 n° 4 (October - December 2016) . - pp 715 - 739[article]Integrating social network data into GISystems / Clio Andris in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)
[article]
Titre : Integrating social network data into GISystems Type de document : Article/Communication Auteurs : Clio Andris, Auteur Année de publication : 2016 Article en page(s) : pp 2009 - 2031 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse spatio-temporelle
[Termes IGN] classe d'objets
[Termes IGN] comportement
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] service fondé sur la positionRésumé : (Auteur) Today, online social media outlets provide new and plentiful sources of data on social networks (SNs) and location-based social networks (LBSNs), i.e., geolocated evidence of connections between individuals. While SNs have been used to show how the magnitude of social connectivity decreases with distance, there are few examples of how to include SNs as layers in a GISystem. If SNs, and thus, interpersonal relationships, could be analyzed in a geographic information system (GIS) setting, we could better model how humans socialize, share information, and form social groups within the complex geographic landscape.
Our goal is to facilitate a guide for analyzing SNs (as derived from online social media, telecommunications, surveys, etc.) within geographic space by combining the mature fields of social network analysis (SNA) and GISystems. First, we describe why modeling socialization in geographic space is essential for understanding human behavior. We then outline best practices and techniques for embedding SN nodes and edges in GISystems by introducing terms like ‘social flow’ and ‘anthrospace’, and categorizations for data and spatial aggregation types. Finally, we explore case study vignettes of SNA within GISystems from diverse regions located in Bolivia, China, Côte d’Ivoire, Singapore, the United Kingdom, and the United States, using concepts such as geolocated dyads, ego–alter relationships, node feature roles, modularity, and network transitivity.Numéro de notice : A2016-574 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1153103 En ligne : http://dx.doi.org/10.1080/13658816.2016.1153103 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81730
in International journal of geographical information science IJGIS > vol 30 n° 9-10 (September - October 2016) . - pp 2009 - 2031[article]Réservation
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