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Measuring spatial nonstationary effects of POI-based mixed use on urban vibrancy using Bayesian spatially varying coefficients model / Zensheng Wang in International journal of geographical information science IJGIS, vol 37 n° 2 (February 2023)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 37 n° 2 (February 2023) . - pp 339 - 359 Titre : Measuring spatial nonstationary effects of POI-based mixed use on urban vibrancy using Bayesian spatially varying coefficients model Type de document : Article/Communication Auteurs : Zensheng Wang, Auteur ; Feidong Lu, Auteur ; Zhaohui Liu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 339 - 359 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] approche hiérarchique [Termes IGN] classification bayesienne [Termes IGN] dynamique spatiale [Termes IGN] estimation bayesienne [Termes IGN] hétérogénéité spatiale [Termes IGN] modèle de simulation [Termes IGN] planification urbaine [Termes IGN] point d'intérêt [Termes IGN] Shenzhen [Termes IGN] téléphonie mobile [Termes IGN] urbanisation [Termes IGN] utilisation du sol Résumé : (auteur) Understanding the relationship between mixed land use and urban vibrancy is vital in advanced urban planning applications. This study presents a Bayesian spatially varying coefficient (SVC) model to explore the spatially nonstationary relationship between mixed land use and urban vibrancy after controlling for other factors. We first use the convolutional conditional autoregressive prior to accommodate the ecological bias resulting from unobserved confounders. Then we develop our approach in the case of a single predictor to allow the spatially varying coefficient process. We further introduce a type of the Bayesian SVC model that considers the stratified heterogeneity of the outcome, allowing the coefficients to simultaneously vary at the local and subregion level. We illustrate the proposed model by conducting a case study in Shenzhen using mobile phone data, an officially registered point-of-interest (POI) dataset, and several supplementary datasets. The model evaluation results show that including spatially unstructured and structured component combinations can improve the model's fitness and predictive ability; additionally, considering spatial stratified heterogeneity can further enhance the model's performance. Our findings provide an alternative for measuring the variable local-scale association between mixed-use and urban vibrancy and offer new insights that broaden the fields of environmental science and spatial statistics. Numéro de notice : A2023-057 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2117363 En ligne : https://doi.org/10.1080/13658816.2022.2117363 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102393 [article]

Bayesian hyperspectral image super-resolution in the presence of spectral variability / Fei Ye in IEEE Transactions on geoscience and remote sensing, vol 60 n° 12 (December 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 12 (December 2022) . - n° 5545613 Titre : Bayesian hyperspectral image super-resolution in the presence of spectral variability Type de document : Article/Communication Auteurs : Fei Ye, Auteur ; Zebin Wu, Auteur ; Yang Xu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5545613 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] fusion d'images [Termes IGN] image hyperspectrale [Termes IGN] image multibande [Termes IGN] processus gaussien [Termes IGN] réflectance [Termes IGN] signature spectrale [Termes IGN] théorème de Bayes Résumé : (auteur) Synthesizing a high-resolution (HR) hyperspectral image (HSI) by merging a low-resolution (LR) HSI with a corresponding HR multispectral image (MSI) has become a promising HSI super-resolution scheme. Most existing HSI-MSI fusion methods are effective to some extent, while several challenges remain. First, the spectral response of a given material exhibits considerable variability due to different acquisition times and conditions, however, variations in spectral signatures are often neglected. Second, a majority of off-the-shelf methods require predefined degradation operators, which can be unavailable in practice. To tackle the above issues, we introduce a novel fusion approach with a Bayesian framework. Specifically, we regard the up-sampled LR-HSI as the low-frequency component of the underlying HR-HSI. We characterize the texture features of high- and low-frequency components, respectively, which can enlarge modeling capacity and bypass the absence of degradation operators. Furthermore, we depict the relative smoothness of reflectance spectra with the Gaussian process. Extensive experiments on synthesized and real datasets illustrate the superiority of the proposed strategy in terms of fusion performance and robustness to spectral variability. Numéro de notice : A2022-908 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3228313 Date de publication en ligne : 12/12/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3228313 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102339 [article]

A data-driven framework to manage uncertainty due to limited transferability in urban growth models / Jingyan Yu in Computers, Environment and Urban Systems, vol 98 (December 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 98 (December 2022) . - n° 101892 Titre : A data-driven framework to manage uncertainty due to limited transferability in urban growth models Type de document : Article/Communication Auteurs : Jingyan Yu, Auteur ; Alex Hagen-Zanker, Auteur ; Naratip Santitissadeekorn, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101892 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] automate cellulaire [Termes IGN] changement d'utilisation du sol [Termes IGN] croissance urbaine [Termes IGN] estimation bayesienne [Termes IGN] étalement urbain [Termes IGN] Europe (géographie politique) [Termes IGN] méthode de Monte-Carlo par chaînes de Markov [Termes IGN] modèle stochastique [Termes IGN] simulation dynamique Résumé : (auteur) The processes of urban growth vary in space and time. There is a lack of model transferability, which means that models estimated for a particular study area and period are not necessarily applicable for other periods and areas. This problem is often addressed through scenario analysis, where scenarios reflect different plausible model realisations based typically on expert consultation. This study proposes a novel framework for data-driven scenario development which, consists of three components - (i) multi-area, multi-period calibration, (ii) growth mode clustering, and (iii) cross-application. The framework finds clusters of parameters, referred to as growth modes: within the clusters, parameters represent similar spatial development trajectories; between the clusters, parameters represent substantially different spatial development trajectories. The framework is tested with a stochastic dynamic urban growth model across European functional urban areas over multiple time periods, estimated using a Bayesian method on an open global urban settlement dataset covering the period 1975–2014. The results confirm a lack of transferability, with reduced confidence in the model over the validation period, compared to the calibration period. Over the calibration period the probability that parameters estimated specifically for an area outperforms those for other areas is 96%. However, over an independent validation period, this probability drops to 72%. Four growth modes are identified along a gradient from compact to dispersed spatial developments. For most training areas, spatial development in the later period is better characterized by one of the four modes than their own historical parameters. The results provide strong support for using identified parameter clusters as a tool for data-driven and quantitative scenario development, to reflect part of the uncertainty of future spatial development trajectories. Numéro de notice : A2022-799 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101892 Date de publication en ligne : 08/10/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101892 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101966 [article]

Hybrid XGboost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling / Saeid Janizadeh in Geocarto international, vol 37 n° 25 ([01/12/2022])  

Public [article]  inGeocarto international > vol 37 n° 25 [01/12/2022] . - pp 8273 - 8292 Titre : Hybrid XGboost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling Type de document : Article/Communication Auteurs : Saeid Janizadeh, Auteur Année de publication : 2022 Article en page(s) : pp 8273 - 8292 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] ArcGIS [Termes IGN] bassin hydrographique [Termes IGN] cartographie des risques [Termes IGN] classification par arbre de décision [Termes IGN] colinéarité [Termes IGN] estimation bayesienne [Termes IGN] Extreme Gradient Machine [Termes IGN] inondation [Termes IGN] modèle numérique de surface [Termes IGN] modélisation spatiale [Termes IGN] optimisation (mathématiques) [Termes IGN] Téhéran Résumé : (auteur) The purpose of this investigation is to develop an optimal model to flood susceptibility mapping in the Kan watershed, Tehran, Iran. Therefore, in this study, three Bayesian optimization hyper-parameter algorithms including Upper confidence bound (UCB), Probability of improvement (PI) and Expected improvement (EI) in order to Extreme Gradient Boosting (XGB) machine learning model optimization and Extreme randomize tree (ERT) model for modeling flood hazard were used. In order to perform flood susceptibility mapping, 118 historic flood locations were identified and analyzed using 17 geo-environmental explanatory variables to predict flooding susceptibility. Flood locations data were divided into 70% for training and 30% for testing of models developed. The receiver operating characteristic (ROC) curve parameters were used to evaluate the performance of the models. The evaluation results based on the criterion area under curve (AUC) in the testing stage showed that the ERT and XGB models have efficiencies of 91.37% and 91.95%, respectively. The evaluation of the efficiency of Bayesian hyperparameters optimization methods on the XGB model also showed that these methods increase the efficiency of the XGB model, so that the model efficiency using these methods EI-XGB, POI-XGB and UCB-XGB based on the AUC in the testing stage were 95.89%, 96.87% and 96.38%, respectively. The results of the relative importance of the five models shows that the variables of elevation and distance from the river are the significant compared to other variables in predicting flood hazard in the Kan watershed. Numéro de notice : A2022-931 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2021.1996641 Date de publication en ligne : 29/10/2021 En ligne : https://doi.org/10.1080/10106049.2021.1996641 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102666 [article]

Exploring the spatial disparity of home-dwelling time patterns in the USA during the COVID-19 pandemic via Bayesian inference / Xiao Huang in Transactions in GIS, vol 26 n° 4 (June 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 4 (June 2022) . - pp 1939 - 1961 Titre : Exploring the spatial disparity of home-dwelling time patterns in the USA during the COVID-19 pandemic via Bayesian inference Type de document : Article/Communication Auteurs : Xiao Huang, Auteur ; Yang Xu, Auteur ; Rui Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1939 - 1961 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse multiéchelle [Termes IGN] disparité [Termes IGN] distribution spatiale [Termes IGN] données socio-économiques [Termes IGN] épidémie [Termes IGN] estimation bayesienne [Termes IGN] hétérogénéité spatiale [Termes IGN] inférence statistique [Termes IGN] logement [Termes IGN] maladie virale [Termes IGN] méthode de Monte-Carlo par chaînes de Markov [Termes IGN] méthode robuste Résumé : (auteur) In this study, we aim to reveal hidden patterns and confounders associated with policy implementation and adherence by investigating the home-dwelling stages from a data-driven perspective via Bayesian inference with weakly informative priors and by examining how home-dwelling stages in the USA varied geographically, using fine-grained, spatial-explicit home-dwelling time records from a multi-scale perspective. At the U.S. national level, two changepoints are identified, with the former corresponding to March 22, 2020 (9 days after the White House declared the National Emergency on March 13) and the latter corresponding to May 17, 2020. Inspections at U.S. state and county level reveal notable spatial disparity in home-dwelling stage-related variables. A pilot study in the Atlanta Metropolitan area at the Census Tract level reveals that the self-quarantine duration and increase in home-dwelling time are strongly correlated with the median household income, echoing existing efforts that document the economic inequity exposed by the U.S. stay-at-home orders. To our best knowledge, our work marks a pioneering effort to explore multi-scale home-dwelling patterns in the USA from a purely data-driven perspective and in a statistically robust manner. Numéro de notice : A2022-533 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Article DOI : 10.1111/tgis.12918 Date de publication en ligne : 17/03/2022 En ligne : https://doi.org/10.1111/tgis.12918 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101081 [article]

GIS-based assessment of long-term traffic accidents using spatiotemporal and empirical Bayes analysis in Turkey / Saffet Erdoğan in Applied geomatics, vol 14 n° 2 (June 2022)  

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Mapping and prediction of soil organic carbon by an advanced geostatistical technique using remote sensing and terrain data / Santanu Malik in Geocarto international, vol 37 n° 8 ([01/05/2022])  

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Potential of Bayesian formalism for the fusion and assimilation of sequential forestry data in time and space / Cheikh Mohamedou in Canadian Journal of Forest Research, Vol 52 n° 4 (April 2022)  

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Changing mobility patterns in the Netherlands during COVID-19 outbreak / Sander Van Der Drift in Journal of location-based services, vol 16 n° 1 (March 2022)  

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Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles / Nico Lang in Remote sensing of environment, vol 268 (January 2022)  

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A method for precisely predicting satellite clock bias based on robust fitting of ARMA models / Guochao Zhang in GPS solutions, vol 26 n° 1 (January 2022)  

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Estimating timber volume loss due to storm damage in Carinthia, Austria, using ALS/TLS and spatial regression models / Arne Nothdurft in Forest ecology and management, vol 502 (December-15 2021)  

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Radiative transfer modeling in structurally complex stands: towards a better understanding of parametrization / Frédéric André in Annals of Forest Science, vol 78 n° 4 (December 2021)  

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Calibration of cellular automata urban growth models from urban genesis onwards - a novel application of Markov chain Monte Carlo approximate Bayesian computation / Jingyan Yu in Computers, Environment and Urban Systems, vol 90 (November 2021)  

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Variational bayesian compressive multipolarization indoor radar imaging / Van Ha Tang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)  

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