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Geo-spatially modelling dengue epidemics in urban cities: a case study of Lahore, Pakistan / Muhammad Imran in Geocarto international, vol 36 n° 2 ([01/02/2021])
[article]
Titre : Geo-spatially modelling dengue epidemics in urban cities: a case study of Lahore, Pakistan Type de document : Article/Communication Auteurs : Muhammad Imran, Auteur ; Yasra Hamid, Auteur ; Abeer Mazher, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 197 - 211 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] cartographie des risques
[Termes IGN] diptère
[Termes IGN] image Landsat
[Termes IGN] maladie tropicale
[Termes IGN] modélisation spatiale
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Pakistan
[Termes IGN] régression géographiquement pondérée
[Termes IGN] régression logistique
[Termes IGN] risque sanitaire
[Termes IGN] série temporelle
[Termes IGN] zone intertropicale
[Termes IGN] zone urbaineRésumé : (auteur) The study objective is to predict the epidemiological impact of dengue fever arbovirosis in urban tropical areas of Pakistan. To do so, we used the GPS-based data of the Aedes larvae collected during 2014–2015 in Lahore. We developed a Geographically Weighted Logistic Regression (GWLR) model for Geospatially predicting larvae presence or absence in Lahore. Data on rainfall, temperature are included along with time series of the normalized difference vegetation index (NDVI) derived from Landsat imagery. We observed a high spatial variability of the GWLR parameter estimates of these variables in the study area. The GWLR model significantly (R2a = 0.78) explained the presence or absence of Aedes larvae with temperature, rainfall and NDVI variables in South and Southeast of the study area. In the North and North-West, however, GWLR relationships were observed weak in highly populated areas. Interpolating GWLR coefficients generate more accurate maps of Aedes larvae presence or absence. Numéro de notice : A2021-474 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1614100 Date de publication en ligne : 10/06/2020 En ligne : https://doi.org/10.1080/10106049.2019.1614100 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96932
in Geocarto international > vol 36 n° 2 [01/02/2021] . - pp 197 - 211[article]
Titre : COVID-19 geoviz for spatio-temporal structures detection Type de document : Article/Communication Auteurs : Jacques Gautier , Auteur ; María-Jesús Lobo , Auteur ; Benjamin Fau, Auteur ; Armand Drugeon, Auteur ; Sidonie Christophe , Auteur ; Guillaume Touya , Auteur Editeur : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2021 Collection : Proceedings of the ICA num. 4 Projets : 1-Pas de projet / Conférence : ICC 2021, 30th ICA international cartographic conference 14/12/2021 18/12/2021 Florence Italie Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle
[Termes IGN] analyse spatio-temporelle
[Termes IGN] cube espace-temps
[Termes IGN] données spatiotemporelles
[Termes IGN] exploration de données géographiques
[Termes IGN] maladie virale
[Vedettes matières IGN] GéovisualisationMots-clés libres : Grow Ring Map visualization Résumé : (auteur) The spread of COVID-19 has motivated a wide interest in visualization tools to represent the pandemic’s spatio-temporal evolution. This tools usually rely on dashboard environments which depict COVID-19 data as temporal series related to different indicators (number of cases, deaths) calculated for several spatial entities at different scales (countries or regions). In these tools, diagrams (line charts or histograms) display the temporal component of data, and 2D cartographic representations display the spatial distribution of data at one moment in time. In this paper, we aim at proposing novel visualization designs in order to help medical experts to detect spatio-temporal structures such as clusters of cases and spatial axes of propagation of the epidemic, through a visual analysis of detailed COVID-19 event data. In this context, we investigate and revisit two visualizations, one based on the Growth Ring Map technique and the other based on the space-time cube applied on a spatial hexagonal grid. We assess the potential of these visualizations for the visual analysis of COVID-19 event data, through two proofs of concept using synthetic cases data and web-based prototypes. The Grow Ring Map visualization appears to facilitate the identification of clusters and propagation axes in the cases distribution, while the space-time cube appears to be suited for the identification of local temporal trends. Numéro de notice : C2021-046 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/ica-proc-4-37-2021 Date de publication en ligne : 03/12/2021 En ligne : https://doi.org/10.5194/ica-proc-4-37-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99398
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 Is Xylella fastidiosa a serious threat to European forests? / Marie-Laure Desprez-Loustau in Forestry, an international journal of forest research, vol 94 n° 1 (January 2021)
[article]
Titre : Is Xylella fastidiosa a serious threat to European forests? Type de document : Article/Communication Auteurs : Marie-Laure Desprez-Loustau, Auteur ; Yialmaz Balci, Auteur ; Daniele Cornara, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1 - 17 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Acer pseudoplatanus
[Termes IGN] Amérique du nord
[Termes IGN] dépérissement
[Termes IGN] écosystème forestier
[Termes IGN] Europe (géographie politique)
[Termes IGN] Italie
[Termes IGN] maladie bactérienne
[Termes IGN] Olea europaea
[Termes IGN] Quercus (genre)
[Termes IGN] Ulmus (genre)
[Termes IGN] viticulture
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) The recent emergence of Olive Quick Decline Syndrome in Italy, caused by Xylella fastidiosa, has drawn attention to the risks posed by this vector-borne bacterium to important crops in Europe (especially fruit trees and grapevine). Comparatively very little is known on actual and potential impacts of this pathogen in forests, in the native (North American) and introduced (European) regions, respectively. The present review aims to address important questions related to the threat posed by X. fastidiosa to European forests, such as the following: What are the symptoms, hosts and impact of bacterial leaf scorch caused by X. fastidiosa on trees in North America? Which forest tree species have been found infected in the introduction area in Europe? How does X. fastidiosa cause disease in susceptible hosts? Are there any X. fastidiosa genotypes (subspecies and sequence types) specifically associated with forest trees? How is X. fastidiosa transmitted? What are the known and potential vectors for forest trees? How does vector ecology affect disease? Is the distribution of X. fastidiosa, especially the strains associated with trees, restricted by climatic factors? Is disease risk for trees different in forest ecosystems as compared with urban settings? We conclude by pointing to important knowledge gaps related to all these questions and strongly advocate for more research about the Xylella-forest pathosystems, in both North America and Europe. Numéro de notice : A2021-072 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/forestry/cpaa029 Date de publication en ligne : 06/08/2020 En ligne : https://doi.org/10.1093/forestry/cpaa029 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96805
in Forestry, an international journal of forest research > vol 94 n° 1 (January 2021) . - pp 1 - 17[article]Mise en place d’une infrastructure de données spatiales sur le risque de piqures de tiques / Lilian Calas (2021)PermalinkEvaluating the impact of declining tsetse fly (Glossina pallidipes) habitat in the Zambezi valley of Zimbabwe / Farai Matawa in Geocarto international, vol 35 n° 12 ([01/09/2020])PermalinkNEAT 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)PermalinkÉcosystèmes forestiers et maladies infectieuses : des liens complexes / Hélène Soubelet in Revue forestière française, vol 72 n° 3 ([30/06/2020])PermalinkAssessment of malaria hazard, vulnerability, and risks in Dire Dawa City Administration of eastern Ethiopia using GIS and remote sensing / Abdinasir Moha in Applied geomatics, vol 12 n° 1 (April 2020)PermalinkDetection of Xylella fastidiosa infection symptoms with airborne multispectral and thermal imagery: Assessing bandset reduction performance from hyperspectral analysis / T. Poblete in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)PermalinkOnline flu epidemiological deep modeling on disease contact network / Liang Zhao in Geoinformatica, vol 24 n° 2 (April 2020)PermalinkA comprehensive framework for studying diffusion patterns of imported dengue with individual-based movement data / Haiyan Tao in International journal of geographical information science IJGIS, vol 34 n° 3 (March 2020)Permalink10th Colour and Visual Computing Symposium 2020 (CVCS 2020), Gjøvik, Norway, and Virtual, September 16-17, 2020 / Jean-Baptiste Thomas (2020)PermalinkPermalinkOptimizing arbovirus surveillance using risk mapping and coverage modelling / Joni A. Downs in Annals of GIS, Vol 26 n° 1 (January 2020)PermalinkValidating the correct wearing of protection mask by taking a selfie: design of a mobile application "CheckYourMask" to limit the spread of COVID-19 / Karim Hammoudi (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)PermalinkPermalinkAutomatisation de l’acquisition et du traitement des images Sentinel-2 pour le calcul d’indices de végétation aidant à la prévention des pics de paludisme à Madagascar / Charlotte Wolff (2017)PermalinkEvaluating data stability in aggregation structures across spatial scales: revisiting the modifiable areal unit problem / Jonathan K. Nelson in Cartography and Geographic Information Science, Vol 44 n° 1 (January 2017)PermalinkFast and accurate target detection based on multiscale saliency and active contour model for high-resolution SAR images / Song Tu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)Permalink