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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]

Building fuzzy areal geographical objects from point sets / Jifa Guo in Transactions in GIS, vol 25 n° 6 (December 2021)  

Public [article]  inTransactions in GIS > vol 25 n° 6 (December 2021) . - pp 3067 - 3087 Titre : Building fuzzy areal geographical objects from point sets Type de document : Article/Communication Auteurs : Jifa Guo, Auteur ; Shihong Du, Auteur Année de publication : 2021 Article en page(s) : pp 3067 - 3087 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse comparative [Termes IGN] habitat animal [Termes IGN] habitat d'espèce [Termes IGN] objet flou [Termes IGN] objet géographique zonal [Termes IGN] réserve naturelle Résumé : (Auteur) Representations of fuzzy geographic objects and analyses of their spatial relationships have received considerable attention in the GIS and spatial database domains over the past 30 years. However, building fuzzy geographical objects from real data is still a challenge. Simple fuzzy areal object models are too restrictive for many applications, and general fuzzy areal models may not be restrictive enough; as a result, the extent of fuzzy regions that satisfy the relevant conditions may be too large to affect the location description and spatial analysis. A condition by which the number of cores is not greater than one is added for the general object model, and an operable method for constructing fuzzy objects from the point set is proposed. Two peak and pass sets are determined for the membership surface by the fuzzy morphometric analysis method. The first set is used to initially divide the footprint of the fuzzy surface into smaller subfootprints, and the second set is used to merge insignificant fuzzy objects with their nearest significant fuzzy objects; thus, unreasonable division is avoided. Cross-validation is adopted to evaluate the generated fuzzy objects. An experiment is provided to verify the effectiveness of the proposed method. Numéro de notice : A2021-933 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12808 Date de publication en ligne : 10/10/2021 En ligne : https://doi.org/10.1111/tgis.12808 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99454 [article]