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scientifique

Novel model for predicting individuals’ movements in dynamic regions of interest / Xiaoqi Shen in GIScience and remote sensing, vol 59 n° 1 (2022)  

Public [article]  inGIScience and remote sensing > vol 59 n° 1 (2022) . - pp 250 - 271 Titre : Novel model for predicting individuals’ movements in dynamic regions of interest Type de document : Article/Communication Auteurs : Xiaoqi Shen, Auteur ; Wenzhong Shi, Auteur ; Pengfei Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 250 - 271 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] chaîne de Markov [Termes IGN] Chine [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] données issues des réseaux sociaux [Termes IGN] données spatiotemporelles [Termes IGN] épidémie [Termes IGN] extraction de données [Termes IGN] migration humaine [Termes IGN] mobilité territoriale [Termes IGN] modèle de simulation [Termes IGN] réseau social [Termes IGN] zone d'activité économique [Termes IGN] zone d'intérêt Résumé : (auteur) The increasing amount of geotagged social media data provides a possible resource for location prediction. However, existing location prediction methods rarely incorporate temporal changes in mobility patterns, which could lead to unreliable predictions. In particular, human mobility patterns have changed greatly in the COVID-19 era. We propose a novel model to predict individuals’ movements in dynamic regions of interest (ROIs), taking into account changes in activity areas and movement regularity. To address changes in the activity areas, we design a new updating strategy that can ensure the realistic extraction of an individual’s ROIs. Then, we develop an integration model for changes in the movement regularity based on two newly proposed prediction methods that consider both rapid and slow changes. The proposed integration model is evaluated based on five real-world social media datasets; three Weibo datasets related to COVID-19 collected in three Chinese cities, one Twitter dataset collected in New York and one dense GPS dataset. The results demonstrate that the proposed model can achieve better performances than state-of-the-art models, especially when mobility patterns change greatly. Combined with related pandemic data, this study will benefit pandemic prevention and control. Numéro de notice : A2022-131 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15481603.2022.2026637 Date de publication en ligne : 13/01/2022 En ligne : https://doi.org/10.1080/15481603.2022.2026637 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99719 [article]

RegNet: a neural network model for predicting regional desirability with VGI data / Wenzhong Shi in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 1 (January 2021) . - pp 175 - 192 Titre : RegNet: a neural network model for predicting regional desirability with VGI data Type de document : Article/Communication Auteurs : Wenzhong Shi, Auteur ; Zhewei Liu, Auteur ; Zhenlin An, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 175 - 192 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] classification par réseau neuronal [Termes IGN] données localisées des bénévoles [Termes IGN] Hong-Kong [Termes IGN] modèle de simulation [Termes IGN] niveau local [Termes IGN] participation du public [Termes IGN] régression [Termes IGN] réseau social géodépendant Résumé : (auteur) Volunteered geographic information can be used to predict regional desirability. A common challenge regarding previous works is that intuitive empirical models, which are inaccurate and bring in perceptual bias, are traditionally used to predict regional desirability. This results from the fact that the hidden interactions between user online check-ins and regional desirability have not been revealed and clearly modelled yet. To solve the problem, a novel neural network model ‘RegNet’ is proposed. The user check-in history is input into a neural network encoder structure firstly for redundancy reduction and feature learning. The encoded representation is then fed into a hidden-layer structure and the regional desirability is predicted. The proposed RegNet is data-driven and can adaptively model the unknown mappings from input to output, without presumed bias and prior knowledge. We conduct experiments with real-world datasets and demonstrate RegNet outperforms state-of-the-art methods in terms of ranking quality and prediction accuracy of rating. Additionally, we also examine how the structure of encoder affects RegNet performance and suggest on choosing proper sizes of encoded representation. This work demonstrates the effectiveness of data-driven methods in modelling the hidden unknown relationships and achieving a better performance over traditional empirical methods. Numéro de notice : A2021-023 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1768261 Date de publication en ligne : 18/05/2020 En ligne : https://doi.org/10.1080/13658816.2020.1768261 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96526 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2021011	SL	Revue	Centre de documentation	Revues en salle	Disponible

Urban Informatics / Wenzhong Shi (2021)  

Public Titre : Urban Informatics Type de document : Monographie Auteurs : Wenzhong Shi, Éditeur scientifique ; Michael F. Goodchild, Éditeur scientifique ; Michael Batty, Éditeur scientifique ; et al., Auteur Editeur : Springer Nature Année de publication : 2021 Collection : The Urban Book Series Importance : 941 p. ISBN/ISSN/EAN : 978-981-1589836-- Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Urbanisme [Termes IGN] données massives [Termes IGN] infrastructure urbaine de données localisées [Termes IGN] mobilité urbaine [Termes IGN] planification urbaine [Termes IGN] pollution [Termes IGN] protection civile [Termes IGN] système d'information géographique [Termes IGN] ville durable [Termes IGN] ville intelligente Résumé : (éditeur) This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity. Note de contenu : 1- Introduction 2- Dimensions of Urban Science 3- Urban Systems and Applications Numéro de notice : 28559 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/URBANISME Nature : Monographie DOI : 10.1007/978-981-15-8983-6 En ligne : https://link.springer.com/book/10.1007/978-981-15-8983-6 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97592

Building change detection using a shape context similarity model for LiDAR data / Xuzhe Lyu in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 11 (November 2020) . - n° 678 Titre : Building change detection using a shape context similarity model for LiDAR data Type de document : Article/Communication Auteurs : Xuzhe Lyu, Auteur ; Ming Hao, Auteur ; Wenzhong Shi, Auteur Année de publication : 2020 Article en page(s) : n° 678 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse d'image orientée objet [Termes IGN] détection de changement [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] fusion d'images [Termes IGN] modèle numérique de surface [Termes IGN] reconnaissance de formes [Termes IGN] segmentation d'image [Termes IGN] semis de points Résumé : (auteur) In this paper, a novel building change detection approach is proposed using statistical region merging (SRM) and a shape context similarity model for Light Detection and Ranging (LiDAR) data. First, digital surface models (DSMs) are generated from LiDAR acquired at two different epochs, and the difference data D-DSM is created by difference processing. Second, to reduce the noise and registration error of the pixel-based method, the SRM algorithm is applied to segment the D-DSM, and multi-scale segmentation results are obtained under different scale values. Then, the shape context similarity model is used to calculate the shape similarity between the segmented objects and the buildings. Finally, the refined building change map is produced by the k-means clustering method based on shape context similarity and area-to-length ratio. The experimental results indicated that the proposed method could effectively improve the accuracy of building change detection compared with some popular change detection methods. Numéro de notice : A2020-732 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9110678 Date de publication en ligne : 15/11/2020 En ligne : https://doi.org/10.3390/ijgi9110678 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96345 [article]

An empirical study on the intra-urban goods movement patterns using logistics big data / Pengxiang Zhao in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 6 (June 2020) . - pp 1089 - 1116 Titre : An empirical study on the intra-urban goods movement patterns using logistics big data Type de document : Article/Communication Auteurs : Pengxiang Zhao, Auteur ; Wenzhong Shi, Auteur ; Tao Jia, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1089 - 1116 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] analyse systémique [Termes IGN] fret [Termes IGN] gestion urbaine [Termes IGN] Hong-Kong [Termes IGN] interaction spatiale [Termes IGN] logistique [Termes IGN] objet mobile [Termes IGN] origine - destination [Termes IGN] plan de déplacement urbain [Termes IGN] réseau de transport [Termes IGN] série temporelle [Termes IGN] trafic urbain Résumé : (auteur) Movement patterns of intra-urban goods/things and the ways they differ from human mobility and traffic flow patterns have seldom been explored due to data access and methodological limitations, especially from systemic and long timescale perspectives. However, urban logistics big data are increasingly available, enabling unprecedented spatial and temporal resolutions to this issue. This research proposes an analytical framework for exploring intra-urban goods movement patterns by integrating spatial analysis, network analysis and spatial interaction analysis. Using daily urban logistics big data (over 10 million orders) provided by the largest online logistics company in Hong Kong (GoGoVan) from 2014 to 2016, we analyzed two spatial characteristics (displacement and direction) of urban goods movement. Results showed that the distribution of goods displaceFower law or exponential distribution of human mobility trends. The origin–destination flows of goods were used to build a spatially embedded network, revealing that Hong Kong became increasingly connected through intra-urban freight movement. Finally, spatial interaction characteristics were revealed using a fitting gravity model. Distance lacked substantial influence on the spatial interaction of goods movement. These findings have policy implications to intra-urban logistics and urban transport planning. Numéro de notice : A2020-268 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1520236 Date de publication en ligne : 20/09/2018 En ligne : https://doi.org/10.1080/13658816.2018.1520236 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95039 [article]

Robust M–M unscented Kalman filtering for GPS/IMU navigation / Cheng Yang in Journal of geodesy, vol 93 n° 8 (August 2019)  

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Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change / Hao Wu in International journal of geographical information science IJGIS, Vol 33 n° 5-6 (May - June 2019)  

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Uncertainty modeling and analysis of surface area calculation based on a regular grid digital elevation model (DEM) / Chang Li in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)  

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A measure of average error variance of line features / Eryong Liu in Cartography and Geographic Information Science, Vol 43 n° 4 (September 2016)  

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Spatiotemporal subpixel mapping of time-series images / Qunming Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)  

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Uncertainty modelling and quality control for spatial data / Wenzhong Shi (2016)

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Fast subpixel mapping algorithms for subpixel resolution change detection / Qunming Wang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)  

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Advances in spatial data handling and GIS / Wenzhong Shi (2012)  

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Proceedings of the 2nd international symposium on spatial data quality '03, SDQ 2003, 19 - 20 March 2003, The Hong Kong Polytechnic University, Hong Kong / Wenzhong Shi (2003)

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Development of Voronoi-based cellular automata: an integrated dynamic model for Geographical Information Systems / Wenzhong Shi in International journal of geographical information science IJGIS, vol 14 n° 5 (july 2000)  

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