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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)
[article]
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]Mask R-CNN-based building extraction from VHR satellite data in operational humanitarian action: An example related to Covid-19 response in Khartoum, Sudan / Dirk Tiede in Transactions in GIS, Vol 25 n° 3 (June 2021)
[article]
Titre : Mask R-CNN-based building extraction from VHR satellite data in operational humanitarian action: An example related to Covid-19 response in Khartoum, Sudan Type de document : Article/Communication Auteurs : Dirk Tiede, Auteur ; Gina Schwendemann, Auteur ; Ahmad Alobaidi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1213-1227 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection du bâti
[Termes IGN] échantillonnage
[Termes IGN] épidémie
[Termes IGN] gestion de crise
[Termes IGN] HRV (capteur)
[Termes IGN] image à très haute résolution
[Termes IGN] image Pléiades-HR
[Termes IGN] itération
[Termes IGN] SoudanRésumé : Auteur) Within the constraints of operational work supporting humanitarian organizations in their response to the Covid-19 pandemic, we conducted building extraction for Khartoum, Sudan. We extracted approximately 1.2 million dwellings and buildings, using a Mask R-CNN deep learning approach from a Pléiades very high-resolution satellite image with 0.5 m pixel resolution. Starting from an untrained network, we digitized a few hundred samples and iteratively increased the number of samples by validating initial classification results and adding them to the sample collection. We were able to strike a balance between the need for timely information and the accuracy of the result by combining the output from three different models, each aiming at distinctive types of buildings, in a post-processing workflow. We obtained a recall of 0.78, precision of 0.77 and F1 score of 0.78, and were able to deliver first results in only 10 days after the initial request. The procedure shows the great potential of convolutional neural network frameworks in combination with GIS routines for dwelling extraction even in an operational setting. Numéro de notice : A2021-464 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12766 Date de publication en ligne : 06/05/2021 En ligne : https://doi.org/10.1111/tgis.12766 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98060
in Transactions in GIS > Vol 25 n° 3 (June 2021) . - pp 1213-1227[article]Emotional cartography as a window into children's well-being: Visualizing the felt geographies of place / Andrew Steger in Emotion, Space and Society, vol 39 (May 2021)
[article]
Titre : Emotional cartography as a window into children's well-being: Visualizing the felt geographies of place Type de document : Article/Communication Auteurs : Andrew Steger, Auteur ; Elly Evans, Auteur ; Bryan Wee, Auteur Année de publication : 2021 Article en page(s) : n° 100772 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] bien-être collectif
[Termes IGN] cartographie sensible
[Termes IGN] émotion
[Termes IGN] enfant
[Termes IGN] épidémie
[Termes IGN] maladie virale
[Termes IGN] sentiment
[Termes IGN] système d'information géographique
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) More often than not, Geographic Information Systems (GIS) excludes emotion and qualitative analysis from studies of people-place relationships in favor of quantitative approaches. We employ emotional cartography as a form of qualitative GIS (qualGIS) to elevate emotions from the periphery to the center of dialogue about children's well-being. We highlight the ontological parallels between qualGIS, emotional cartography and children in society, and advance emotion maps as a way to visualize different spatial and emotional realities. In reflecting upon the felt geography of our own childhood places, we affirm the importance of children's emotional attachments to places as well as the centrality of ‘messy’ human experiences in GIS. To conclude, we discuss the implications of emotional cartography for researchers, planners and GIS, paying special attention to children's well-being amidst the current COVID-19 pandemic. In particular, this includes a call to ‘witness’ and to foster spatial empathy among those advocating for children. Numéro de notice : A2021-949 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.emospa.2021.100772 Date de publication en ligne : 08/02/2021 En ligne : https://doi.org/10.1016/j.emospa.2021.100772 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99769
in Emotion, Space and Society > vol 39 (May 2021) . - n° 100772[article]
Titre : COVID-19 pandemic, geospatial information, and community resilience : Global applications and lessons Type de document : Monographie Auteurs : Abbas Rajabifard, Éditeur scientifique ; Daniel Paez, Éditeur scientifique ; Greg Foliente, Éditeur scientifique Editeur : Boca Raton, New York, ... : CRC Press Année de publication : 2021 Importance : 544 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-00-318159-0 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique
[Termes IGN] analyse spatiale
[Termes IGN] données localisées
[Termes IGN] épidémie
[Termes IGN] gestion de crise
[Termes IGN] image satellite
[Termes IGN] maladie virale
[Termes IGN] modélisation spatiale
[Termes IGN] OpenStreetMap
[Termes IGN] planification urbaine
[Termes IGN] réseau socialRésumé : (auteur) Geospatial information plays an important role in managing location dependent pandemic situations across different communities and domains. Geospatial information and technologies are particularly critical to strengthening urban and rural resilience, where economic, agricultural, and various social sectors all intersect. Examining the United Nations' SDGs from a geospatial lens will ensure that the challenges are addressed for all populations in different locations. This book, with worldwide contributions focused on COVID-19 pandemic, provides interdisciplinary analysis and multi-sectoral expertise on the use of geospatial information and location intelligence to support community resilience and authorities to manage pandemics. Note de contenu : 1- Setting the scene
2- Technical and technico-social solutions
3- Regional, country and local applications
4- Stakeholder perspectives
5- The futur directionNuméro de notice : 28628 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.1201/9781003181590 En ligne : https://doi.org/10.1201/9781003181590 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99578
Titre : Geospatial analysis of the spreading of COVID-19 In the United States Type de document : Mémoire Auteurs : Otto Heimonen, Auteur Editeur : Tampere [Finlande] : Tampere University Année de publication : 2021 Importance : 67 p. Format : 21 x 30 cm Note générale : bibliographie
Master’s Degree Programme in Computational Big Data AnalyticsLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] autocorrélation spatiale
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] épidémie
[Termes IGN] estimation bayesienne
[Termes IGN] Etats-Unis
[Termes IGN] maladie infectieuse
[Termes IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes IGN] méthode du maximum de vraisemblance (estimation)
[Termes IGN] modèle de simulationRésumé : (auteur) The COVID-19 pandemic has been a big threat to public health and there is an increasing need for efficient modelling of pathogens, predicting the daily infection rates to reduce the spread of COVID-19.
The Moran’s and Geary’s statistics showed significant spatial autocorrelation in the infection counts for the
US COVID-19 data. Spatial regression using the simultaneous autoregression (SAR) and conditional autoregression (CAR) models indicate clear association between the confirmed cases and the number of population and the population density in both national county and state specific analyses. The SAR model provided a better model fit with the low AIC value, leaving no significant autocorrelation for the residuals. The approximate Bayesian computation (ABC) methods were used to provide a flexible posterior distribution of the infection rate for COVID-19 based on the first 100 days of the pandemic. Three different simulation methods such as ABC-Rejection, ABC-Markov Chain Monte Carlo (MCMC) and ABC-Sequential Monte Carlo (SMC) were employed and compared. These algorithms seem to give reasonable posterior estimates for the average daily infections when the likelihood calculations for the spread of a harmful pathogen become complex, or intractable entirely. The posterior distributions of ABC-MCMC and ABC-SMC provided plausible estimations covering all of the observed infection rates at different time points.Note de contenu : 1- Introduction
2- Methods
3- Empirical data analysis
4- DiscussionNuméro de notice : 28455 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Mémoire masters divers DOI : sans En ligne : https://trepo.tuni.fi/handle/10024/134567 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99025 NEAT approach for testing and validation of geospatial network agent-based model processes: case study of influenza spread / Taylor Anderson in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)PermalinkIntegration of spatialization and individualization: the future of epidemic modelling for communicable diseases / Meifang Li in Annals of GIS, vol 26 n° 3 (July 2020)PermalinkOnline flu epidemiological deep modeling on disease contact network / Liang Zhao in Geoinformatica, vol 24 n° 2 (April 2020)PermalinkEpidémiologie et géographie / Marc Souris (2019)PermalinkSpatial discontinuities, health and mobility - What do the Google's POIs and tweets tell us about Bangkok's (Thailand) structures and spatial dynamics? / Alexandre Cebeillac in Revue internationale de géomatique, vol 28 n° 4 (octobre - décembre 2018)PermalinkAdapting an existing semi-automatized image processing chain to enable Sentinel-2 data classification. / Hiyam Elbadri (2018)PermalinkMovement analysis of free-grazing domestic ducks in Poyang Lake, China: a disease connection / Dian J. Prosser in International journal of geographical information science IJGIS, vol 30 n° 5-6 (May - June 2016)PermalinkCNES strategy: satellite data and modelling for public health: towards a cooperation with NASA / Murielle Lafaye in Geocarto international, vol 29 n° 5 - 6 (August - October 2014)PermalinkCartes en mains contre Lili la tigresse / Françoise de Blomac in DécryptaGéo le mag, n° 152 (01/12/2013)PermalinkSpatial modeling of the black death in Sweden / Lars Skog in Transactions in GIS, vol 17 n° 4 (August 2013)Permalink