Centre de documentation
scientifique

Human movement patterns of different racial-ethnic and economic groups in U.S. top 50 populated cities: What can social media tell us about isolation? / Meiliu Wu in Annals of GIS, vol 28 n° 2 (April 2022)  

Public [article]  inAnnals of GIS > vol 28 n° 2 (April 2022) . - pp 161 - 183 Titre : Human movement patterns of different racial-ethnic and economic groups in U.S. top 50 populated cities: What can social media tell us about isolation? Type de document : Article/Communication Auteurs : Meiliu Wu, Auteur ; Qunying Huang, Auteur Année de publication : 2022 Article en page(s) : pp 161 - 183 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] données issues des réseaux sociaux [Termes IGN] données socio-économiques [Termes IGN] Etats-Unis [Termes IGN] ethnie [Termes IGN] migration humaine [Termes IGN] mobilité territoriale [Termes IGN] sociologie [Termes IGN] Twitter Résumé : (auteur) Many studies have proven that human movement patterns are strongly impacted by individual socioeconomic and demographic background. While many efforts have been made on exploring the influences of age and gender on movement patterns using social media, this study aims to analyse and compare the movement patterns among different racial-ethnic and economic groups using social media (i.e. geotagged tweets) from the U.S. top 50 populated cities. Results show that there are significant differences in number of activity zones and median travel distance across cities and demographic groups, and that power-laws tend to be captured in both spatial and demographic aspects. Additionally, the analysis of outbound-city travels demonstrates that some cities have slightly stronger interaction with others, and that economically disadvantaged populations and racial-ethnic minorities are more restricted in long distance travels, indicating that their spatial mobility is more limited to the local scale. Lastly, an economically-segregated movement pattern is discovered – upper-class neighbourhoods are mostly visited by the upper-class, while lower-class neighbourhoods are mainly accessed by the lower-class – but some racial-ethnic groups can diversify this segregated pattern in the local scale. Numéro de notice : A2022-501 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475683.2022.2026471 Date de publication en ligne : 22/01/2022 En ligne : https://doi.org/10.1080/19475683.2022.2026471 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100998 [article]

Disaster Image Classification by Fusing Multimodal Social Media Data / Zhiqiang Zou in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 10 (October 2021) . - n° 636 Titre : Disaster Image Classification by Fusing Multimodal Social Media Data Type de document : Article/Communication Auteurs : Zhiqiang Zou, Auteur ; Hongyu Gan, Auteur ; Qunying Huang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 636 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] analyse visuelle [Termes IGN] apprentissage profond [Termes IGN] catastrophe naturelle [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] corpus [Termes IGN] données issues des réseaux sociaux [Termes IGN] données localisées des bénévoles [Termes IGN] données massives [Termes IGN] extraction de traits caractéristiques [Termes IGN] fusion de données multisource [Termes IGN] qualité des données [Termes IGN] traitement de données Numéro de notice : A2021-803 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10100636 Date de publication en ligne : 24/09/2021 En ligne : https://doi.org/10.3390/ijgi10100636 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98856 [article]

Big data computing for geospatial applications / Zhenlong Li (2020)  

Public Titre : Big data computing for geospatial applications Type de document : Monographie Auteurs : Zhenlong Li, Éditeur scientifique ; Wenwu Tang, Éditeur scientifique ; Qunying Huang, Éditeur scientifique ; et al., Auteur Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 222 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-03943-245-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse géovisuelle [Termes IGN] analyse spatio-temporelle [Termes IGN] cyberinfrastructure [Termes IGN] données issues des réseaux sociaux [Termes IGN] données localisées [Termes IGN] données massives [Termes IGN] informatique en nuage [Termes IGN] métadonnées [Termes IGN] représentation géographique [Termes IGN] réseau sémantique Résumé : (éditeur) The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms. Note de contenu : 1- Introduction to Big Data computing for geospatial applications 2- MapReduce-based D-ELT framework to address the challenges of geospatial Big Data 3- High-performance overlay analysis of massive geographic polygons that considers shape complexity in a cloud environment 4- Parallel cellular automata Markov model for land use change prediction over MapReduce framework 5- Terrain analysis in Google Earth Engine: A method adapted for high-gerformance global-scale analysis 6- Integrating geovisual analytics with machine learning for human mobility pattern discovery 7- Social media Big Data mining and spatio-temporal analysis on public emotions for disaster mitigation 8- A novel method of missing road generation in city blocks based on big mobile navigation trajectory data 9- A task-oriented knowledge base for geospatial problem-solving 10- Geographic knowledge graph (GeoKG): A formalized geographic knowledge representation 11- Advanced cyberinfrastructure to enable search of big climate datasets in THREDDS Numéro de notice : 28389 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/SOCIETE NUMERIQUE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03943-245-5 En ligne : https://doi.org/10.3390/books978-3-03943-245-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98688

Exploring 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)  

Public [article]  inInternational journal of geographical information science IJGIS > Vol 33 n° 5-6 (May - June 2019) . - pp 1196 - 1223 Titre : Exploring the uncertainty of activity zone detection using digital footprints with multi-scaled DBSCAN Type de document : Article/Communication Auteurs : Xinyi Liu, Auteur ; Qunying Huang, Auteur ; Song Gao, Auteur Année de publication : 2019 Article en page(s) : pp 1196 - 1223 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] données issues des réseaux sociaux [Termes IGN] mobilité urbaine [Termes IGN] réseau social [Termes IGN] Twitter Mots-clés libres : density-based spatial clustering of applications with noise (DBSCAN) Résumé : (Auteur) The density-based spatial clustering of applications with noise (DBSCAN) method is often used to identify individual activity clusters (i.e., zones) using digital footprints captured from social networks. However, DBSCAN is sensitive to the two parameters, eps and minpts. This paper introduces an improved density-based clustering algorithm, Multi-Scaled DBSCAN (M-DBSCAN), to mitigate the detection uncertainty of clusters produced by DBSCAN at different scales of density and cluster size. M-DBSCAN iteratively calibrates suitable local eps and minpts values instead of using one global parameter setting as DBSCAN for detecting clusters of varying densities, and proves to be effective for detecting potential activity zones. Besides, M-DBSCAN can significantly reduce the noise ratio by identifying all points capturing the activities performed in each zone. Using the historic geo-tagged tweets of users in Washington, D.C. and in Madison, Wisconsin, the results reveal that: 1) M-DBSCAN can capture dispersed clusters with low density of points, and therefore detecting more activity zones for each user; 2) A value of 40 m or higher should be used for eps to reduce the possibility of collapsing distinctive activity zones; and 3) A value between 200 and 300 m is recommended for eps while using DBSCAN for detecting activity zones. Numéro de notice : A2019-445 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1563301 Date de publication en ligne : 09/01/2019 En ligne : https://doi.org/10.1080/13658816.2018.1563301 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92781 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2019051	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2019052	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data / Qunying Huang in Computers, Environment and Urban Systems, vol 66 (November 2017)  

Public [article]  inComputers, Environment and Urban Systems > vol 66 (November 2017) . - pp 23 - 37 Titre : A cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data Type de document : Article/Communication Auteurs : Qunying Huang, Auteur ; Guido Cervone, Auteur ; Guiming Zhang, Auteur Année de publication : 2017 Article en page(s) : pp 23 - 37 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] caractérisation [Termes IGN] catastrophe naturelle [Termes IGN] données issues des réseaux sociaux [Termes IGN] exploration de données géographiques [Termes IGN] exploration de texte [Termes IGN] image numérique [Termes IGN] informatique en nuage [Termes IGN] inondation [Termes IGN] intégration de données [Termes IGN] interface web [Termes IGN] prototype [Termes IGN] tempête [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Social media streams and remote sensing data have emerged as new sources for tracking disaster events, and assessing their damages. Previous studies focus on a case-by-case approach, where a specific event was first chosen and filtering criteria (e.g., keywords, spatiotemporal information) are manually designed and used to retrieve relevant data for disaster analysis. This paper presents a framework that synthesizes multi-sourced data (e.g., social media, remote sensing, Wikipedia, and Web), spatial data mining and text mining technologies to build an architecturally resilient and elastic solution to support disaster analysis of historical and future events. Within the proposed framework, Wikipedia is used as a primary source of different historical disaster events, which are extracted to build an event database. Such a database characterizes the salient spatiotemporal patterns and characteristics of each type of disaster. Additionally, it can provide basic semantics, such as event name (e.g., Hurricane Sandy) and type (e.g., flooding) and spatiotemporal scopes, which are then tuned by the proposed procedures to extract additional information (e.g., hashtags for searching tweets), to query and retrieve relevant social media and remote sensing data for a specific disaster. Besides historical event analysis and pattern mining, the cloud-based framework can also support real-time event tracking and monitoring by providing on-demand and elastic computing power and storage capabilities. A prototype is implemented and tested with data relative to the 2011 Hurricane Sandy and the 2013 Colorado flooding. Numéro de notice : a2017-430 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.compenvurbsys.2017.06.004 En ligne : https://doi.org/10.1016/j.compenvurbsys.2017.06.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86330 [article]

A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data / Guiming Zhang in International journal of geographical information science IJGIS, vol 31 n° 9-10 (September - October 2017)  

Permalink

Enabling point pattern analysis on spatial big data using cloud computing: optimizing and accelerating Ripley’s K function / Guiming Zhang in International journal of geographical information science IJGIS, vol 30 n° 11-12 (November - December 2016)  

Permalink

vol 43 n° 5 - November 2016 - Integrating big social data, computing and modeling for spatial social science (Bulletin de Cartography and Geographic Information Science) / Xinyue Ye  

Permalink

SinoGrids: a practice for open urban data in China / Xinyue Ye in Cartography and Geographic Information Science, vol 43 n° 5 (November 2016)  

Permalink

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)  

Permalink