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Exploring 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)
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[article]
Titre : Exploring the heterogeneity of human urban movements using geo-tagged tweets Type de document : Article/Communication Auteurs : Ding Ma, Auteur ; Toshihiro Osaragi, Auteur ; Takuya Oki, Auteur ; Bin Jiang, Auteur Année de publication : 2020 Article en page(s) : pp 2475 -2 496 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes descripteurs IGN] analyse spatio-temporelle
[Termes descripteurs IGN] données issues des réseaux sociaux
[Termes descripteurs IGN] données localisées des bénévoles
[Termes descripteurs IGN] espace urbain
[Termes descripteurs IGN] flux de données
[Termes descripteurs IGN] géoétiquetage
[Termes descripteurs IGN] géolocalisation
[Termes descripteurs IGN] hétérogénéité
[Termes descripteurs IGN] Londres
[Termes descripteurs IGN] migration humaine
[Termes descripteurs IGN] modèle de simulation
[Termes descripteurs IGN] modèle orienté agent
[Termes descripteurs IGN] population urbaine
[Termes descripteurs IGN] Tokyo (Japon)
[Termes descripteurs IGN] TwitterRésumé : (auteur) The availability of vast amounts of location-based data from social media platforms such as Twitter has enabled us to look deeply into the dynamics of human movement. The aim of this paper is to leverage a large collection of geo-tagged tweets and the street networks of two major metropolitan areas—London and Tokyo—to explore the underlying mechanism that determines the heterogeneity of human mobility patterns. For the two target cities, hundreds of thousands of tweet locations and road segments were processed to generate city hotspots and natural streets. User movement trajectories and city hotspots were then used to build a hotspot network capable of quantitatively characterizing the heterogeneous movement patterns of people within the cities. To emulate observed movement patterns, the study conducts a two-level agent-based simulation that includes random walks through the hotspot networks and movements in the street networks using each of three distance types—metric, angular and combined. Comparisons of the simulated and observed movement flows at the segment and street levels show that the heterogeneity of human urban movements at the collective level is mainly shaped by the scaling structure of the urban space. Numéro de notice : A2020-692 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1718153 date de publication en ligne : 24/01/2020 En ligne : https://doi.org/10.1080/13658816.2020.1718153 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96233
in International journal of geographical information science IJGIS > vol 34 n° 12 (December 2020) . - pp 2475 -2 496[article]Evaluating 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)
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Titre : Evaluating geo-tagged Twitter data to analyze tourist flows in Styria, Austria Type de document : Article/Communication Auteurs : Johannes Scholz, Auteur ; Janja Jeznik, Auteur Année de publication : 2020 Article en page(s) : n° 681 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes descripteurs IGN] analyse sémantique
[Termes descripteurs IGN] Autriche
[Termes descripteurs IGN] coefficient de corrélation
[Termes descripteurs IGN] contenu généré par les utilisateurs
[Termes descripteurs IGN] distribution spatiale
[Termes descripteurs IGN] méthode fondée sur le noyau
[Termes descripteurs IGN] tourisme
[Termes descripteurs IGN] TwitterRésumé : (auteur) The research focuses on detecting tourist flows in the Province of Styria in Austria based on crowdsourced data. Twitter data were collected in the time range from 2008 until August 2018. Extracted tweets were submitted to an extensive filtering process within non-relational database MongoDB. Hotspot Analysis and Kernel Density Estimation methods were applied, to investigate spatial distribution of tourism relevant tweets under temporal variations. Furthermore, employing the VADER method an integrated semantic analysis provides sentiments of extracted tweets. Spatial analyses showed that detected Hotspots correspond to typical Styrian touristic areas. Apart from mainly successful sentiment analysis, it pointed out also a problematic aspect of working with multilingual data. For evaluation purposes, the official tourism data from the Province of Styria and federal Statistical Office of Austria played a role of ground truth data. An evaluation with Pearson’s correlation coefficient was employed, which proves a statistically significant correlation between Twitter data and reference data. In particular, the paper shows that crowdsourced data on a regional level can serve as accurate indicator for the behaviour and movement of users. Numéro de notice : A2020-731 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9110681 date de publication en ligne : 15/11/2020 En ligne : https://doi.org/10.3390/ijgi9110681 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96344
in ISPRS International journal of geo-information > vol 9 n° 11 (November 2020) . - n° 681[article]Volunteered geographic information research in the first decade: a narrative review of selected journal articles in GIScience / Yingwei Yan in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)
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Titre : Volunteered geographic information research in the first decade: a narrative review of selected journal articles in GIScience Type de document : Article/Communication Auteurs : Yingwei Yan, Auteur ; Chen-Chieh Feng, Auteur ; Wei Huang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1765 - 1791 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes descripteurs IGN] données localisées des bénévoles
[Termes descripteurs IGN] GeoWeb
[Termes descripteurs IGN] littérature
[Termes descripteurs IGN] occupation du sol
[Termes descripteurs IGN] OpenStreetMap
[Termes descripteurs IGN] presse (media)
[Termes descripteurs IGN] problème de Dirichlet
[Termes descripteurs IGN] qualité des données
[Termes descripteurs IGN] recherche
[Termes descripteurs IGN] réseau social
[Termes descripteurs IGN] SIG participatif
[Termes descripteurs IGN] source de données
[Termes descripteurs IGN] système d'information géographique
[Termes descripteurs IGN] Twitter
[Termes descripteurs IGN] utilisation du sol
[Termes descripteurs IGN] WikimapiaRésumé : (auteur) More than 10 years have passed since the coining of the term volunteered geographic information (VGI) in 2007. This article presents the results of a review of the literature concerning VGI. A total of 346 articles published in 24 international refereed journals in GIScience between 2007 and 2017 have been reviewed. The review has uncovered varying levels of popularity of VGI research over space and time, and varying interests in various sources of VGI (e.g. OpenStreetMap) and VGI-related terms (e.g. user-generated content) that point to the multi-perspective nature of VGI. Content-wise, using latent Dirichlet allocation (LDA), this study has extracted 50 specific research topics pertinent to VGI. The 50 topics have been subsequently clustered into 13 intermediate topics and three overarching themes to allow a hierarchical topic review. The overarching VGI research themes include (1) VGI contributions and contributors, (2) main fields applying VGI, and (3) conceptions and envisions. The review of the articles under the three themes has revealed the progress and the points that demand attention regarding the individual topics. This article also discusses the areas that the existing research has not yet adequately explored and proposes an agenda for potential future research endeavors. Numéro de notice : A2020-476 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1730848 date de publication en ligne : 26/02/2020 En ligne : https://doi.org/10.1080/13658816.2020.1730848 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95623
in International journal of geographical information science IJGIS > vol 34 n° 9 (September 2020) . - pp 1765 - 1791[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2020091 SL Revue Centre de documentation Revues en salle Disponible Exploration of OpenStreetMap missing built-up areas using twitter hierarchical clustering and deep learning in Mozambique / Hao Li in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)
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Titre : Exploration of OpenStreetMap missing built-up areas using twitter hierarchical clustering and deep learning in Mozambique Type de document : Article/Communication Auteurs : Hao Li, Auteur ; Benjamin Herfort, Auteur ; Wei Huang, Auteur Année de publication : 2020 Article en page(s) : pp 41-51 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes descripteurs IGN] analyse de groupement
[Termes descripteurs IGN] analyse spatiale
[Termes descripteurs IGN] apprentissage profond
[Termes descripteurs IGN] carte sanitaire
[Termes descripteurs IGN] cartographie collaborative
[Termes descripteurs IGN] données localisées des bénévoles
[Termes descripteurs IGN] géographie sociale
[Termes descripteurs IGN] inventaire du bâti
[Termes descripteurs IGN] Mozambique
[Termes descripteurs IGN] OpenStreetMap
[Termes descripteurs IGN] qualité des données
[Termes descripteurs IGN] TwitterRésumé : (auteur) Accurate and detailed geographical information digitizing human activity patterns plays an essential role in response to natural disasters. Volunteered geographical information, in particular OpenStreetMap (OSM), shows great potential in providing the knowledge of human settlements to support humanitarian aid, while the availability and quality of OSM remains a major concern. The majority of existing works in assessing OSM data quality focus on either extrinsic or intrinsic analysis, which is insufficient to fulfill the humanitarian mapping scenario to a certain degree. This paper aims to explore OSM missing built-up areas from an integrative perspective of social sensing and remote sensing. First, applying hierarchical DBSCAN clustering algorithm, the clusters of geo-tagged tweets are generated as proxies of human active regions. Then a deep learning based model fine-tuned on existing OSM data is proposed to further map the missing built-up areas. Hit by Cyclone Idai and Kenneth in 2019, the Republic of Mozambique is selected as the study area to evaluate the proposed method at a national scale. As a result, 13 OSM missing built-up areas are identified and mapped with an over 90% overall accuracy, being competitive compared to state-of-the-art products, which confirms the effectiveness of the proposed method. Numéro de notice : A2020-350 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.05.007 date de publication en ligne : 07/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.05.007 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95233
in ISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 41-51[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020081 SL Revue Centre de documentation Revues en salle Disponible 081-2020083 DEP-RECP Revue MATIS Dépôt en unité Exclu du prêt 081-2020082 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Los Angeles as a digital place: The geographies of user‐generated content / Andrea Ballatore in Transactions in GIS, Vol 24 n° 4 (August 2020)
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Titre : Los Angeles as a digital place: The geographies of user‐generated content Type de document : Article/Communication Auteurs : Andrea Ballatore, Auteur ; Stefano de Sabbata, Auteur Année de publication : 2020 Article en page(s) : 23 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes descripteurs IGN] analyse spatiale
[Termes descripteurs IGN] centre urbain
[Termes descripteurs IGN] contenu généré par les utilisateurs
[Termes descripteurs IGN] distribution spatiale
[Termes descripteurs IGN] données multisources
[Termes descripteurs IGN] données socio-économiques
[Termes descripteurs IGN] exploration de données géographiques
[Termes descripteurs IGN] Foursquare
[Termes descripteurs IGN] Los Angeles
[Termes descripteurs IGN] modèle de régression
[Termes descripteurs IGN] OpenStreetMap
[Termes descripteurs IGN] participation du public
[Termes descripteurs IGN] représentation géographique
[Termes descripteurs IGN] réseau social
[Termes descripteurs IGN] réseau social géodépendant
[Termes descripteurs IGN] TwitterRésumé : (auteur) Online representations of places are becoming pivotal in informing our understanding of urban life. Content production on online platforms is grounded in the geography of their users and their digital infrastructure. These constraints shape place representation, that is, the amount, quality, and type of digital information available in a geographic area. In this article we study the place representation of user‐generated content (UGC) in Los Angeles County, relating the spatial distribution of the data to its geo‐demographic context. Adopting a comparative and multi‐platform approach, this quantitative analysis investigates the spatial relationship between four diverse UGC datasets and their context at the census tract level (about 685,000 geo‐located tweets, 9,700 Wikipedia pages, 4 million OpenStreetMap objects, and 180,000 Foursquare venues). The context includes the ethnicity, age, income, education, and deprivation of residents, as well as public infrastructure. An exploratory spatial analysis and regression‐based models indicate that the four UGC platforms possess distinct geographies of place representation. To a moderate extent, the presence of Twitter, OpenStreetMap, and Foursquare data is influenced by population density, ethnicity, education, and income. However, each platform responds to different socio‐economic factors and clusters emerge in disparate hotspots. Unexpectedly, Twitter data tend to be located in denser, more deprived areas, and the geography of Wikipedia appears peculiar and harder to explain. These trends are compared with previous findings for the area of Greater London. Numéro de notice : A2020-671 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12600 date de publication en ligne : 02/01/2020 En ligne : https://doi.org/10.1111/tgis.12600 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96156
in Transactions in GIS > Vol 24 n° 4 (August 2020) . - 23 p.[article]A name‐led approach to profile urban places based on geotagged Twitter data / Juntao Lai in Transactions in GIS, Vol 24 n° 4 (August 2020)
PermalinkDelineating and modeling activity space using geotagged social media data / Lingqian Hu in Cartography and Geographic Information Science, vol 47 n° 3 (May 2020)
PermalinkSpatio-temporal mobility and Twitter: 3D visualisation of mobility flows / Joaquín Osorio Arjona in Journal of maps, vol 16 n° 1 ([02/01/2020])
PermalinkExploring the uncertainty of activity zone detection using digital footprints with multi-scaled DBSCAN / Xinyi Liu in International journal of geographical information science IJGIS, Vol 33 n° 5-6 (May - June 2019)
PermalinkUnderstanding demographic and socioeconomic biases of geotagged Twitter users at the county level / Jiang Juqin in Cartography and Geographic Information Science, vol 46 n° 3 (May 2019)
PermalinkModeling and visualizing semantic and spatio-temporal evolution of topics in interpersonal communication on Twitter / Caglar Koylu in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)
PermalinkCarSenToGram: geovisual text analytics for exploring spatiotemporal variation in public discourse on Twitter / Caglar Koylu in Cartography and Geographic Information Science, Vol 46 n° 1 (January 2019)
PermalinkSpatialities, social Media and sentiment analysis: Exploring the potential of the detection tool SentiStrength / Christina Reithmeier in GI Forum, vol 2018 n° 2 ([01/09/2018])
PermalinkSensePlace3: a geovisual framework to analyze place–time–attribute information in social media / Scott Pezanowski in Cartography and Geographic Information Science, Vol 45 n° 5 (August 2018)
PermalinkA spatial analysis of non‐English Twitter activity in Houston, TX / Matthew Haffner in Transactions in GIS, vol 22 n° 4 (August 2018)
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