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Analysis of the spatial range of service and accessibility of hospitals designated for coronavirus disease 2019 in Yunnan Province, China / Liangting Zheng in Geocarto international, vol 37 n° 22 ([10/10/2022])
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Titre : Analysis of the spatial range of service and accessibility of hospitals designated for coronavirus disease 2019 in Yunnan Province, China Type de document : Article/Communication Auteurs : Liangting Zheng, Auteur ; Jia Li, Auteur ; Wenying Hu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 6519 - 6537 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] accessibilité
[Termes IGN] diagramme de Voronoï
[Termes IGN] données médicales
[Termes IGN] données routières
[Termes IGN] épidémie
[Termes IGN] interpolation inversement proportionnelle à la distance
[Termes IGN] interpolation par pondération de zones
[Termes IGN] maladie virale
[Termes IGN] médecine humaine
[Termes IGN] secours d'urgence
[Termes IGN] Yunnan (Chine)Résumé : (auteur) COVID-19 poses a major threat to global health care systems, and the recent surge in mortality rates confirms the importance of timely access to care. The capacity of medical service providers is reflected both in the spatial accessibility of medical institutions and in the spatial scope of their services. Therefore, this study aims to investigate the spatial scope of services and spatial accessibility of COVID-19-designated hospitals in Yunnan Province, China. Data are collected from multiple sources and included COVID-19 case data, road data, and data from designated hospitals for COVID-19 in Yunnan Province. The optimal spatial service range for designated hospitals is delineated using a weighted Voronoi diagram that takes into account the number of medical staff and the number of beds in the hospital. Traffic accessibility coefficients are introduced to analyze the spatial accessibility of COVID-19-designated hospitals, and the spatial accessibility of each designated hospital is visualized using the inverse distance weighting interpolation algorithm. The results show the following: (1) COVID-19 cases in Yunnan Province are concentrated in the central and northern regions. The largest single cells in the weighted Voronoi diagram are mainly Pu'er (59168 km2), Honghe (35569 km2), and Baoshan (46795 km2), and the time cost of attainting medical treatment is greater for residents in marginal areas. (2) Within the service space of designated hospitals, 90.24% of patients could obtain medical assistance within 2 h. Those in 52 (36.36%) counties within a municipal jurisdiction could obtain medical services within 2 h, and 76.47% of counties have above-average spatial accessibility. (3) Medical resources in Yunnan Province should be shifted toward the high-risk east-central region and the less spatially accessible in southern and western regions. Numéro de notice : A2022-728 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1943008 Date de publication en ligne : 09/07/2021 En ligne : https://doi.org/10.1080/10106049.2021.1943008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101674
in Geocarto international > vol 37 n° 22 [10/10/2022] . - pp 6519 - 6537[article]Investigating the efficiency of deep learning methods in estimating GPS geodetic velocity / Omid Memarian Sorkhabi in Earth and space science, vol 9 n° 10 (October 2022)
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Titre : Investigating the efficiency of deep learning methods in estimating GPS geodetic velocity Type de document : Article/Communication Auteurs : Omid Memarian Sorkhabi, Auteur ; Muhammed Milani, Auteur ; Seyed Mehdi Seyed Alizadeh, Auteur Année de publication : 2022 Article en page(s) : 8 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] apprentissage profond
[Termes IGN] champ de vitesse
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] géodynamique
[Termes IGN] point géodésique
[Termes IGN] positionnement par GPS
[Termes IGN] station GPS
[Termes IGN] tectoniqueRésumé : (auteur) Geodetic velocity (GV) has many applications in tectonic motion determination and geodynamic studies. Due to the high cost of global navigation satellite system stations, deep learning methods have been investigated to estimate GV. In this research, four methods of convolutional neural networks (CNNs), deep Boltzmann machines, deep belief net and recurrent neural networks have been applied. The GV of 42 global positioning system stations is entered the deep learning methods. The outputs of the four methods have successfully passed the normality test. The results show that the CNN method has a lower goodness of fit and root mean square error (RMSE). CNN can learn different dependencies and extract features. Numéro de notice : A2022-757 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1029/2021EA002202 Date de publication en ligne : 22/09/2022 En ligne : https://doi.org/10.1029/2021EA002202 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101763
in Earth and space science > vol 9 n° 10 (October 2022) . - 8 p.[article]Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions / Di Zhu in Geoinformatica, vol 26 n° 4 (October 2022)
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Titre : Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions Type de document : Article/Communication Auteurs : Di Zhu, Auteur ; Yu Liu, Auteur ; Xin Yao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 645 - 676 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse multivariée
[Termes IGN] analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] distribution spatiale
[Termes IGN] échantillonnage
[Termes IGN] intelligence artificielle
[Termes IGN] régression
[Termes IGN] régression géographiquement pondérée
[Termes IGN] réseau neuronal convolutif
[Termes IGN] réseau neuronal de graphesMots-clés libres : Geospatial artificial intelligence (GeoAI) Résumé : (auteur) Geospatial artificial intelligence (GeoAI) has emerged as a subfield of GIScience that uses artificial intelligence approaches and machine learning techniques for geographic knowledge discovery. The non-regularity of data structures has recently led to different variants of graph neural networks in the field of computer science, with graph convolutional neural networks being one of the most prominent that operate on non-euclidean structured data where the numbers of nodes connections vary and the nodes are unordered. These networks use graph convolution – commonly known as filters or kernels – in place of general matrix multiplication in at least one of their layers. This paper suggests spatial regression graph convolutional neural networks (SRGCNNs) as a deep learning paradigm that is capable of handling a wide range of geographical tasks where multivariate spatial data needs modeling and prediction. The feasibility of SRGCNNs lies in the feature propagation mechanisms, the spatial locality nature, and a semi-supervised training strategy. In the experiments, this paper demonstrates the operation of SRGCNNs with social media check-in data in Beijing and house price data in San Diego. The results indicate that a well-trained SRGCNN model is capable of learning from samples and performing reasonable predictions for unobserved locations. The paper also presents the effectiveness of incorporating the idea of geographically weighted regression for handling heterogeneity between locations in the model approach. Compared to conventional spatial regression approaches, SRGCNN-based models tend to generate much more accurate and stable results, especially when the sampling ratio is low. This study offers to bridge the methodological gap between graph deep learning and spatial regression analytics. The proposed idea serves as an example to illustrate how spatial analytics can be combined with state-of-the-art deep learning models, and to enlighten future research at the front of GeoAI. Numéro de notice : A2022-865 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1007/s10707-021-00454-x Date de publication en ligne : 02/11/2021 En ligne : https://doi.org/10.1007/s10707-021-00454-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102158
in Geoinformatica > vol 26 n° 4 (October 2022) . - pp 645 - 676[article]Spherical harmonic synthesis of area-mean potential values on irregular surfaces / Blažej Bucha in Journal of geodesy, vol 96 n° 10 (October 2022)
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Titre : Spherical harmonic synthesis of area-mean potential values on irregular surfaces Type de document : Article/Communication Auteurs : Blažej Bucha, Auteur Année de publication : 2022 Article en page(s) : n° 68 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] champ de gravitation
[Termes IGN] convergence
[Termes IGN] harmonique sphérique
[Termes IGN] surface hétérogène
[Termes IGN] transformation de Legendre
[Termes IGN] transformation rapide de FourierRésumé : (auteur) We present a method to integrate external solid spherical harmonic expansions at geographical grids residing on undulated surfaces. It can be used to evaluate area-mean potential values on planetary surfaces that vary within grid cells. This is in contrast with available methods, which assume cells with a constant spherical radius only. When formulating the technique, we took advantage of 2D spherical Fourier methods to improve the computational speed. The price to be paid are high memory requirements, even with moderate maximum harmonic degrees such as 100 (both of the potential and of the irregular surface). In numerical experiments, we validate the method against independent area-mean potential values to prove its correctness. A study of the series behavior below the sphere of convergence shows that the series may diverge on planetary topographies, similarly as it is with its point-value counterpart. The method can be utilized in numerical studies of the change of boundary method, one of the pivotal concepts of recent high-degree models such as EGM2008. A numerical implementation is made available through CHarm, a C library to work with spherical harmonics up to high degrees. CHarm is accessible via https://github.com/blazej-bucha/charm. Numéro de notice : A2022-736 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-022-01658-1 Date de publication en ligne : 27/09/2022 En ligne : https://doi.org/10.1007/s00190-022-01658-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101708
in Journal of geodesy > vol 96 n° 10 (October 2022) . - n° 68[article]The fractional vegetation cover (FVC) and associated driving factors of modeling in mining areas / Jun Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 10 (October 2022)
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Titre : The fractional vegetation cover (FVC) and associated driving factors of modeling in mining areas Type de document : Article/Communication Auteurs : Jun Li, Auteur ; Tianyu Guo, Auteur ; Chengye Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 665 - 671 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] Chine
[Termes IGN] couvert végétal
[Termes IGN] Google Earth Engine
[Termes IGN] hétérogénéité spatiale
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-OLI
[Termes IGN] industrie minière
[Termes IGN] mine
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] régression géographiquement pondérée
[Termes IGN] régression multiple
[Termes IGN] réseau neuronal artificielRésumé : (auteur) To determine the fractional vegetation cover (FVC) and associated driving factors of modeling in mining areas, six types of data were used as driving factors and three methods —multi-linear regression (MLR), geographically weighted regression (GWR), and geographically weighted artificial neural network (GWANN)— were adopted in the modeling. The experiments, conducted in Shengli mining areas located in Xilinhot city, China, show that the MLR model without consideration of spatial heterogeneity and spatial non-stationarity performs the worst and that the GWR model presents obvious location differences, since it predefines a linear relationship which is unable to describe FVC for some locations. The GWANN model, improving on these defects, is the most suitable model for the FVC driving process in mining areas; it outperforms the other two models, with root-mean-square error (RMSE) and mean absolute percentage error (MAPE) reaching 0.16 and 0.20. It has improvements of approximately 24% in RMSE and 33% in MAPE compared to the MLR model, and those values grow to 59% and 71% when compared with the GWR model. Numéro de notice : A2022-813 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00070R3 Date de publication en ligne : 01/10/2022 En ligne : https://doi.org/10.14358/PERS.21-00070R3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101973
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 10 (October 2022) . - pp 665 - 671[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2022101 SL Revue Centre de documentation Revues en salle Disponible The use of gravity data to determine orthometric heights at the Hong Kong territories / Albertini Nsiah Ababio in Journal of applied geodesy, vol 16 n° 4 (October 2022)
PermalinkPrediction of suspended sediment concentration using hybrid SVM-WOA approaches / Sandeep Samantaray in Geocarto international, vol 37 n° 19 ([15/09/2022])
PermalinkAdaptive block modeling of time dependent variations of datum reference points in a tectonically active area / Chun-Yun Chou in Survey review, vol 54 n° 386 (September 2022)
PermalinkDiscontinuity interpretation and identification of potential rockfalls for high-steep slopes based on UAV nap-of-the-object photogrammetry / Wei Wang in Computers & geosciences, vol 166 (September 2022)
PermalinkA general model for creating robust choropleth maps / Wangshu Mu in Computers, Environment and Urban Systems, vol 96 (September 2022)
PermalinkRapid source models of the 2021 Mw 7.4 Maduo, China, earthquake inferred from high-rate BDS3/2, GPS, Galileo and GLONASS observations / Jianfei Zang in Journal of geodesy, vol 96 n° 9 (September 2022)
PermalinkFull-waveform classification and segmentation-based signal detection of single-wavelength bathymetric LiDAR / Xue Ji in IEEE Transactions on geoscience and remote sensing, vol 60 n° 8 (August 2022)
PermalinkGenerating impact maps from bomb craters automatically detected in aerial wartime images using marked point processes / Christian Kruse in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 5 (August 2022)
PermalinkUse of GIS and dasymetric mapping for estimating tsunami-affected population to facilitate humanitarian relief logistics: a case study from Phuket, Thailand / Kiatkulchai Jitt-Aer in Natural Hazards, vol 113 n° 1 (August 2022)
PermalinkGNSSseg, a statistical method for the segmentation of daily GNSS IWV time series / Annarosa Quarello in Remote sensing, vol 14 n° 14 (July-2 2022)
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