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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)  

Public [article]  inTransactions in GIS > vol 25 n° 6 (December 2021) . - pp 3025 - 3047 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 sanitaire Résumé : (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 [article]

Multiscale geographically and temporally weighted regression with a unilateral temporal weighting scheme and its application in the analysis of spatiotemporal characteristics of house prices in Beijing / Zhi Zhang in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 11 (November 2021) . - pp 2262 - 2286 Titre : Multiscale geographically and temporally weighted regression with a unilateral temporal weighting scheme and its application in the analysis of spatiotemporal characteristics of house prices in Beijing Type de document : Article/Communication Auteurs : Zhi Zhang, Auteur ; Jing Li, Auteur ; Fung, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2262 - 2286 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] coût [Termes IGN] hétérogénéité spatiale [Termes IGN] logement [Termes IGN] marché foncier [Termes IGN] Pékin (Chine) [Termes IGN] régression géographiquement pondérée Résumé : (auteur) Geographically and temporally weighted regression (GTWR) has been demonstrated as an effective tool for exploring spatiotemporal data under spatial and temporal heterogeneity. Exploiting the advantages of the two most popular GTWR methods, we propose an alternative GTWR with a good balance between complexity and interpretability via a unilateral temporal weighting scheme called unilateral GTWR (UGTWR). When compared to the other two popular GTWR methods, the simulation experiment shows that UGTWR has comparable estimation accuracy and model fit, but it is more efficient. Furthermore, we propose its multiscale extension, coined multiscale UGTWR (MUGTWR), to characterize the spatiotemporal dynamic regression relationships at multiple scales. The proposed MUGTWR was applied to the analysis of house prices in the period of 2014–2018 in Beijing as a case study. Our analysis reveals that MUGTWR can effectively capture different levels of spatiotemporal heterogeneity in selected factors affecting house prices at different scales. Therefore, this study is useful for the formulation of housing policy in which the spatiotemporal dynamics of house prices with respect to specific factors can be considered. Numéro de notice : A2021-758 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1912348 Date de publication en ligne : 12/05/2021 En ligne : https://doi.org/10.1080/13658816.2021.1912348 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98773 [article]

Exemplaires(1)

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Predicting user activity intensity using geographic interactions based on social media check-in data / Jing Li in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 8 (August 2021) . - n° 555 Titre : Predicting user activity intensity using geographic interactions based on social media check-in data Type de document : Article/Communication Auteurs : Jing Li, Auteur ; Wenyue Guo, Auteur ; Haiyan Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 555 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage profond [Termes IGN] données issues des réseaux sociaux [Termes IGN] interaction spatiale [Termes IGN] mobilité humaine [Termes IGN] modèle non linéaire [Termes IGN] relation spatiale [Termes IGN] réseau neuronal de graphes [Termes IGN] réseau neuronal récurrent [Termes IGN] utilisateur Résumé : (auteur) Predicting user activity intensity is crucial for various applications. However, existing studies have two main problems. First, as user activity intensity is nonstationary and nonlinear, traditional methods can hardly fit the nonlinear spatio-temporal relationships that characterize user mobility. Second, user movements between different areas are valuable, but have not been utilized for the construction of spatial relationships. Therefore, we propose a deep learning model, the geographical interactions-weighted graph convolutional network-gated recurrent unit (GGCN-GRU), which is good at fitting nonlinear spatio-temporal relationships and incorporates users’ geographic interactions to construct spatial relationships in the form of graphs as the input. The model consists of a graph convolutional network (GCN) and a gated recurrent unit (GRU). The GCN, which is efficient at processing graphs, extracts spatial features. These features are then input into the GRU, which extracts their temporal features. Finally, the GRU output is passed through a fully connected layer to obtain the predictions. We validated this model using a social media check-in dataset and found that the geographical interactions graph construction method performs better than the baselines. This indicates that our model is appropriate for fitting the complex nonlinear spatio-temporal relationships that characterize user mobility and helps improve prediction accuracy when considering geographic flows. Numéro de notice : A2021-588 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10080555 Date de publication en ligne : 17/08/2021 En ligne : https://doi.org/10.3390/ijgi10080555 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98206 [article]

Path length correction for improving leaf area index measurements over sloping terrains: A deep analysis through computer simulation / Gaofei Yin in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 7 (July 2020) . - pp 4573 - 4589 Titre : Path length correction for improving leaf area index measurements over sloping terrains: A deep analysis through computer simulation Type de document : Article/Communication Auteurs : Gaofei Yin, Auteur ; Biao Cao, Auteur ; Jing Li, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 4573 - 4589 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] canopée [Termes IGN] couvert végétal [Termes IGN] densité du feuillage [Termes IGN] incertitude de mesurage [Termes IGN] indice foliaire [Termes IGN] longueur de trajet [Termes IGN] modèle de simulation [Termes IGN] pente [Termes IGN] topographie Résumé : (auteur) The in situ measurement of the leaf area index (LAI) from gap fraction is often affected by terrain slope. Path length correction (PLC) is commonly used to mitigate the topographic effect on the LAI measurements. However, the terrain-induced uncertainty and the accuracy improvement of the PLC for LAI measurements have not been systematically analyzed, hindering the establishment of an appropriate protocol for LAI measurements over mountainous regions. In this article, the above knowledge gap was filled using a computer simulation framework, which enables the estimated LAI before and after PLC to be benchmarked against the known and precise model truth. The simulation was achieved by using CANOPIX software and a dedicatedly designed ray-tracing method for continuous and discrete canopies, respectively. Simulations show that the slope distorts the angular pattern of the gap fraction, i.e., increasing the gap fraction in the down-slope direction and reducing it in the up-slope direction. The horizontally equivalent hemispheric gap fraction from the PLC can reconstruct the azimuthally symmetric angular pattern of the real horizontal surface. The azimuthally averaged gap fraction for sloping terrain can both be underestimated or overestimated depending on the LAI and can be successfully corrected through PLC. The topography-induced uncertainty in LAI measurements is found to be ~14.3% and >20% for continuous and discrete canopies, respectively. This uncertainty can be, respectively, reduced to ~1.8% and Numéro de notice : A2020-379 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2963366 Date de publication en ligne : 30/01/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2963366 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95372 [article]

Automatic cloud resource management for interactive remote geovisualization / Tong Zhang in Transactions in GIS, vol 22 n° 6 (December 2018)  

Public [article]  inTransactions in GIS > vol 22 n° 6 (December 2018) . - pp 1437 - 1461 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] Géovisualisation Résumé : (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 [article]