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Auteur Tong Zhang |
Documents disponibles écrits par cet auteur (4)
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Interactive visual analytics of moving passenger flocks using massive smart card data / Tong Zhang in Cartography and Geographic Information Science, Vol 49 n° 4 (July 2022)
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Titre : Interactive visual analytics of moving passenger flocks using massive smart card data Type de document : Article/Communication Auteurs : Tong Zhang, Auteur ; Wei He, Auteur ; Jing Huang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 354 - 369 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse spatiale
[Termes IGN] analyse visuelle
[Termes IGN] carte à puce
[Termes IGN] données massives
[Termes IGN] mobilité urbaine
[Termes IGN] objet mobile
[Termes IGN] Shenzhen
[Termes IGN] trajet (mobilité)
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Understanding urban mobility patterns is constrained by our limited capabilities to extract and visualize spatio-temporal regularities from large amounts of mobility data. Moving flocks, defined as groups of people traveling along over a pre-defined time duration, can reveal collective moving patterns at aggregated spatio-temporal scales, thereby facilitating the discovery of urban mobility structure and travel demand patterns. In this study, we extend classical trajectory-oriented flock mining algorithms to discover moving flocks of transit passengers, accounting for the constraints of multi-modal transit networks. We develop a map-centered visual analytics approach by integrating the flock mining algorithm with interactive visualization designs of discovered flocks. Novel interactive visualizations are designed and implemented to support the exploration and analyses of discovered moving flocks at different spatial and temporal scales. The visual analytics approach is evaluated using a real-world smart card dataset collected in Shenzhen City, China, validating its applicability in capturing and mapping dynamic mobility patterns over a large metropolitan area. Numéro de notice : A2022-480 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2022.2039775 Date de publication en ligne : 09/03/2022 En ligne : https://doi.org/10.1080/15230406.2022.2039775 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100886
in Cartography and Geographic Information Science > Vol 49 n° 4 (July 2022) . - pp 354 - 369[article]Understanding and predicting the spatio-temporal spread of COVID-19 via integrating diffusive graph embedding and compartmental models / Tong Zhang in Transactions in GIS, vol 25 n° 6 (December 2021)
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Titre : Understanding and predicting the spatio-temporal spread of COVID-19 via integrating diffusive graph embedding and compartmental models Type de document : Article/Communication Auteurs : Tong Zhang, Auteur ; Jing Li, Auteur Année de publication : 2021 Article en page(s) : pp 3025 - 3047 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] Colorado (Etats-Unis)
[Termes IGN] données spatiotemporelles
[Termes IGN] épidémie
[Termes IGN] maladie virale
[Termes IGN] mobilité territoriale
[Termes IGN] modèle de simulation
[Termes IGN] modélisation spatio-temporelle
[Termes IGN] outil d'aide à la décision
[Termes IGN] quartier
[Termes IGN] réseau de transport
[Termes IGN] risque sanitaire
[Termes IGN] surveillance sanitaireRésumé : (Auteur) In order to find useful intervention strategies for the novel coronavirus (COVID-19), it is vital to understand how the disease spreads. In this study, we address the modeling of COVID-19 spread across space and time, which facilitates understanding of the pandemic. We propose a hybrid data-driven learning approach to capture the mobility-related spreading mechanism of infectious diseases, utilizing multi-sourced mobility and attributed data. This study develops a visual analytic approach that identifies and depicts the strength of the transmission pathways of COVID-19 between areal units by integrating data-driven deep learning and compartmental epidemic models, thereby engaging stakeholders (e.g., public health officials, managers from transportation agencies) to make informed intervention decisions and enable public messaging. A case study in the state of Colorado, USA was performed to demonstrate the applicability of the proposed transmission modeling approach in understanding the spatio-temporal spread of COVID-19 at the neighborhood level. Transmission path maps are presented and analyzed, demonstrating their utility in evaluating the effects of mitigation measures. In addition, integrated embeddings also support daily prediction of infected cases and role analysis of each area unit during the transmission of the virus. Numéro de notice : A2021-932 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12803 Date de publication en ligne : 16/07/2021 En ligne : https://doi.org/10.1111/tgis.12803 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99447
in Transactions in GIS > vol 25 n° 6 (December 2021) . - pp 3025 - 3047[article]Automatic cloud resource management for interactive remote geovisualization / Tong Zhang in Transactions in GIS, vol 22 n° 6 (December 2018)
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Titre : Automatic cloud resource management for interactive remote geovisualization Type de document : Article/Communication Auteurs : Tong Zhang, Auteur ; Jing Li, Auteur Année de publication : 2018 Article en page(s) : pp 1437 - 1461 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] automatisation
[Termes IGN] informatique en nuage
[Termes IGN] modèle dynamique
[Vedettes matières IGN] GéovisualisationRésumé : (Auteur) Remote geovisualization has gained momentum to support large‐scale geospatial data analysis and complex decision‐making over the last few years. Cloud computing, due to its capabilities to deliver on‐demand computing resources, has been embraced to develop and deploy interactive and scalable remote geovisualization applications. However, current cloud computing frameworks do not offer a versatile resource management scheme that is readily applicable for online remote visualization services, which usually require maintaining a satisfactory service level over time under dynamic workloads. To address this gap, we propose an automatic cloud resource management approach based on a bi‐level scheduling and horizontal scaling scheme to exploit cloud resources efficiently. At the lower level, a dynamic task‐scheduling scheme using collaborative filtering techniques is proposed to allocate virtual cloud resources to execute sub‐tasks. The scheduling scheme considers spatio‐temporal patterns presented in visualization views. At the upper level, reinforcement learning is adopted to perform resource auto‐scaling based on a reward function that integrates three different facets, namely: time cost, resource cost, and service stability. The original reinforcement learning algorithm is improved in two main aspects: (1) considering the delay of resource provisioning that is common in cloud environments; and (2) using online Gaussian estimation to estimate Q values. Task scheduling and auto‐scaling interact with each other and are integrated to deliver a comprehensive and responsive resource management solution. Experimental results demonstrate that our approach outperforms several existing cloud resource management methods. The proposed approach is also applicable for other interactive visualization applications, which have similar workload characteristics and performance requirements as interactive remote geovisualization. Numéro de notice : A2018-567 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12479 Date de publication en ligne : 10/12/2018 En ligne : https://doi.org/10.1111/tgis.12479 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92286
in Transactions in GIS > vol 22 n° 6 (December 2018) . - pp 1437 - 1461[article]A catalogue service for internet GIServices supporting active service evaluation and real-time quality monitoring / Shengyu Shen in Transactions in GIS, vol 16 n° 6 (December 2012)
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Titre : A catalogue service for internet GIServices supporting active service evaluation and real-time quality monitoring Type de document : Article/Communication Auteurs : Shengyu Shen, Auteur ; Tong Zhang, Auteur ; Huayi Wu, Auteur ; Zhijia Liu, Auteur Année de publication : 2012 Article en page(s) : pp 745 - 761 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] architecture orientée services
[Termes IGN] Catalog Service Web
[Termes IGN] catalogue de données localisées
[Termes IGN] contrôle qualité
[Termes IGN] données localisées
[Termes IGN] prototype
[Termes IGN] qualité des données
[Termes IGN] ressources web
[Termes IGN] service web géographique
[Termes IGN] temps réel
[Termes IGN] Web Map ServiceRésumé : (Auteur) Internet-based Geographical Information Services (GIServices) have exploded in depth and variety over the past few years. More and more academic institutions, private companies, and even individuals publish their own geographical information resources in the form of GIServices on the Internet. In today's resource-rich environment, fast and accurate service discovery is a significant problem for industry and academia, as well as regular users. To solve this problem, researchers introduced Service-Oriented Architecture (SOA) to the domain of GIServices. Being the core of SOA, a registry catalogue implements message exchange services. But current catalog service mechanisms lack the capability to search services, to monitor and to provide accurate descriptions for quality of service in an active manner. Therefore, current catalogue services are not able to overcome problems such as limited registered resources, inaccurate registration information and the uncertainty of service status in various geospatial applications. A catalogue framework for GIServices is proposed that effectively integrates the Quality of GIServices (QoGIService) theory, as well as service crawling/search and dynamic monitoring techniques. A prototype application focused on Web Map Services (WMS) was established that mitigated the abovementioned problems through active service evaluation and real-time quality monitoring mechanisms. Numéro de notice : A2012-615 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/j.1467-9671.2012.01363.x En ligne : https://doi.org/10.1111/j.1467-9671.2012.01363.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32061
in Transactions in GIS > vol 16 n° 6 (December 2012) . - pp 745 - 761[article]