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Children’s walking to urban services: an analysis of pedestrian access to social infrastructures and its relationship with land use / Wonjun No in International journal of geographical information science IJGIS, vol 37 n° 1 (January 2023)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 37 n° 1 (January 2023) . - pp 189 - 214 Titre : Children’s walking to urban services: an analysis of pedestrian access to social infrastructures and its relationship with land use Type de document : Article/Communication Auteurs : Wonjun No, Auteur ; Junyong Choi, Auteur ; Youngchul Kim, Auteur Année de publication : 2023 Article en page(s) : pp 189 - 214 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse spatiale [Termes IGN] enfant [Termes IGN] matrice [Termes IGN] milieu urbain [Termes IGN] navigation pédestre [Termes IGN] origine - destination [Termes IGN] Séoul [Termes IGN] service public [Termes IGN] système d'information géographique [Termes IGN] utilisation du sol Résumé : (auteur) The conceptual framework of child-friendly cities guarantees children’s equal access to public urban services. Despite the widespread application of geographical information systems (GISs) and pedestrian network analysis, studies have yet to analyze children’s comprehensive pedestrian access to urban services in a large-scale city. This study demonstrates GIS-based approaches to measuring children’s pedestrian access to urban services using a pedestrian path layer and the spatial layers of social infrastructure locations in Seoul, South Korea. We show the spatial inequities in children’s access to urban services, which depend on the locational characteristics of social infrastructures and the urban development patterns around children. We analyze how children’s access to social infrastructures is differentiated by land use composition. Our statistical analysis finds that low-rise residential areas, consisting of impermeable street patterns, increase children’s walking distance and restrict children from accessing urban services within their walkable area. In addition, there is potential for key infrastructures such as schools and local community centers to promote pedestrian access to urban services for children. Considering pedestrian access at the street level will help pinpoint vulnerable areas with children who have less access overall and maximize the users served within the service areas of infrastructures. Numéro de notice : A2023-039 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658816.2022.2104455 Date de publication en ligne : 27/07/2022 En ligne : https://doi.org/10.1080/13658816.2022.2104455 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102312 [article]

Correlation of road network structure and urban mobility intensity: An exploratory study using geo-tagged tweets / Li Geng in ISPRS International journal of geo-information, vol 12 n° 1 (January 2023)  

Public [article]  inISPRS International journal of geo-information > vol 12 n° 1 (January 2023) . - n° 7 Titre : Correlation of road network structure and urban mobility intensity: An exploratory study using geo-tagged tweets Type de document : Article/Communication Auteurs : Li Geng, Auteur ; Ke Zhang, Auteur Année de publication : 2023 Article en page(s) : n° 7 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] corrélation automatique de points homologues [Termes IGN] données issues des réseaux sociaux [Termes IGN] Etats-Unis [Termes IGN] géobalise [Termes IGN] mobilité urbaine [Termes IGN] OpenStreetMap [Termes IGN] réseau routier [Termes IGN] Twitter Résumé : (auteur) Urban planners have been long interested in understanding how urban structure and activities are mutually influenced. Human mobility and economic activities naturally drive the formation of road network structure and the accessibility of the latter shapes the patterns of movement flow across urban space. In this paper, we perform an exploratory study on the relationship between the street network structure and the intensity of human movement in urban areas. We focus on two cities and we utilize a dataset of geo-tagged tweets that can form a proxy to urban mobility and the corresponding street networks as obtained from OpenStreetMap. We apply three network centrality measures, including closeness, betweenness and straightness centrality, calculated at a global or local scale, as well as under mixed or individual transportation mode (e.g., driving, biking and walking) with its directional accessibility, to uncover the structural properties of urban street networks. We further design an urban area transition network and apply PageRank to capture the intensity of human mobility. Our correlation analysis indicates different centrality metrics have different levels of correlation with the intensity of human movement. The closeness centrality consistently shows the highest correlation (with a coefficient around 0.6) with human movement intensity when calculated at a global scale, while straightness centrality often shows no correlation at the global scale or weaker correlation ρ≈0.4 at the local scale. The correlation levels further depend on the type of directional accessibility and of various types of transportation modes. Hence, the directionality and transportation mode, largely ignored in the analysis of road networks, are crucial. Furthermore, the strength of the correlation varies in the two cities examined, indicating potential differences in urban spatial structure and human mobility patterns. Numéro de notice : A2023-105 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.3390/ijgi12010007 Date de publication en ligne : 28/12/2022 En ligne : https://doi.org/10.3390/ijgi12010007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102433 [article]

MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction / Du Yin in Geoinformatica, vol 27 n° 1 (January 2023)  

Public [article]  inGeoinformatica > vol 27 n° 1 (January 2023) . - pp 77 - 105 Titre : MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction Type de document : Article/Communication Auteurs : Du Yin, Auteur ; Renhe Jiang, Auteur ; Jiewen Deng, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 77 - 105 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] déformation temporelle dynamique (algorithme) [Termes IGN] données multitemporelles [Termes IGN] données spatiotemporelles [Termes IGN] flux [Termes IGN] gestion de trafic [Termes IGN] origine - destination [Termes IGN] réseau neuronal de graphes [Termes IGN] système de transport intelligent [Termes IGN] trafic urbain [Termes IGN] transport public [Termes IGN] utilisateur Résumé : (auteur) The passenger flow prediction of the public metro system is a core and critical part of the intelligent transportation system, and is essential for traffic management, metro planning, and emergency safety measures. Most methods chose the recent segment from historical data as input to predict the future traffic flow; however, this would lead to the loss of the inherent characteristic information of the metro passenger flow’s daily morning and evening peak. Therefore, this study aggregates the recent-term and long-term information and use a long-term Gated Convolutional Neural Network (Gated CNN) to extract the temporal feature from the complex historical data. On the other hand, typical models did not consider the different spatial dependencies between different metro stations; this work proposes various adjacent relationships to characterize the degree of association between nodes. In order to extract spatial and temporal features at the same time, the historical data of recent-term and long-term is merged together to extract spatial features through a multi-graph neural network module. By combining Gated CNN and multi-graph module, we propose a multi-time multi-graph neural network named MTMGNN for metro passenger flow prediction. The result of our experiment on real-world datasets shows that our model MTMGNN is better than all state-of-art methods. Numéro de notice : A2023-113 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-022-00466-1 Date de publication en ligne : 25/04/2022 En ligne : https://doi.org/10.1007/s10707-022-00466-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102478 [article]

Human mobility and COVID-19 transmission: a systematic review and future directions / Mengxi Zhang in Annals of GIS, vol 28 n° 4 (November 2022)  

Public [article]  inAnnals of GIS > vol 28 n° 4 (November 2022) . - pp 501 - 514 Titre : Human mobility and COVID-19 transmission: a systematic review and future directions Type de document : Article/Communication Auteurs : Mengxi Zhang, Auteur ; Siqin Wang, Auteur ; Tao Hu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 501 - 514 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] corrélation automatique de points homologues [Termes IGN] données spatiotemporelles [Termes IGN] épidémie [Termes IGN] hétérogénéité spatiale [Termes IGN] littérature [Termes IGN] maladie virale [Termes IGN] mobilité humaine [Termes IGN] mobilité territoriale [Termes IGN] modèle dynamique [Termes IGN] modélisation spatio-temporelle [Termes IGN] régression linéaire Résumé : (auteur) Without a widely distributed vaccine, controlling human mobility has been identified and promoted as the primary strategy to mitigate the transmission of COVID-19. Many studies have reported the relationship between human mobility and COVID-19 transmission by utilizing the spatial-temporal information of mobility data from various sources. To better understand the role of human mobility in the pandemic, we conducted a systematic review of articles that measure the relationship between human mobility and COVID-19 in terms of their data sources, mathematical models, and key findings. Following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we selected 47 articles from the Web of Science Core Collection up to September 2020. Restricting human mobility reduced the transmission of COVID-19, although the effectiveness and stringency of policy implementation vary temporally and spatially across different stages of the pandemic. We call for prompt and sustainable measures to control the pandemic. We also recommend researchers 1) to enhance multi-disciplinary collaboration; 2) to adjust the implementation and stringency of mobility-control policies in corresponding to the rapid change of the pandemic; 3) to improve mathematical models used in analysing, simulating, and predicting the transmission of the disease; and 4) to enrich the source of mobility data to ensure data accuracy and suability. Numéro de notice : A2022-863 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475683.2022.2041725 Date de publication en ligne : 03/03/2022 En ligne : https://doi.org/10.1080/19475683.2022.2041725 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102153 [article]

An analysis of twitter as a relevant human mobility proxy / Fernando Terroso-Saenz in Geoinformatica, vol 26 n° 4 (October 2022)  

Public [article]  inGeoinformatica > vol 26 n° 4 (October 2022) . - pp 677 - 706 Titre : An analysis of twitter as a relevant human mobility proxy Type de document : Article/Communication Auteurs : Fernando Terroso-Saenz, Auteur ; Andres Muñoz, Auteur ; Francisco Arcas, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 677 - 706 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] données issues des réseaux sociaux [Termes IGN] données spatiotemporelles [Termes IGN] épidémie [Termes IGN] Espagne [Termes IGN] géobalise [Termes IGN] maladie virale [Termes IGN] mobilité territoriale [Termes IGN] Twitter Résumé : (auteur) During the last years, the analysis of spatio-temporal data extracted from Online Social Networks (OSNs) has become a prominent course of action within the human-mobility mining discipline. Due to the noisy and sparse nature of these data, an important effort has been done on validating these platforms as suitable mobility proxies. However, such a validation has been usually based on the computation of certain features from the raw spatio-temporal trajectories extracted from OSN documents. Hence, there is a scarcity of validation studies that evaluate whether geo-tagged OSN data are able to measure the evolution of the mobility in a region at multiple spatial scales. For that reason, this work proposes a comprehensive comparison of a nation-scale Twitter (TWT) dataset and an official mobility survey from the Spanish National Institute of Statistics. The target time period covers a three-month interval during which Spain was heavily affected by the COVID-19 pandemic. Both feeds have been compared in this context by considering different mobility-related features and spatial scales. The results show that TWT could capture only a limited number features of the latent mobility behaviour of Spain during the study period. Numéro de notice : A2022-866 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-021-00460-z Date de publication en ligne : 15/02/2022 En ligne : https://doi.org/10.1007/s10707-021-00460-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102159 [article]

Predicting the variability in pedestrian travel rates and times using crowdsourced GPS data / Michael J. Campbell in Computers, Environment and Urban Systems, vol 97 (October 2022)  

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Simulating multiple urban land use changes by integrating transportation accessibility and a vector-based cellular automata: a case study on city of Toronto / Xiaocong Xu in Geo-spatial Information Science, vol 25 n° 3 (October 2022)  

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Design and construction of a colourblind-friendly Surabaya city angkot route map prototype / Arzakhy Indhira Pramesti in Cartographica, vol 57 n° 3 (September 2022)  

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Interactive visual analytics of moving passenger flocks using massive smart card data / Tong Zhang in Cartography and Geographic Information Science, Vol 49 n° 4 (July 2022)  

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Modeling human–human interaction with attention-based high-order GCN for trajectory prediction / Yanyan Fang in The Visual Computer, vol 38 n° 7 (July 2022)  

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Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction / Tianhong Zhao in Computers, Environment and Urban Systems, vol 94 (June 2022)  

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A geospatial workflow for the assessment of public transit system performance using near real-time data / Anastassios Dardas in Transactions in GIS, vol 26 n° 4 (June 2022)  

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The effect of intra-urban mobility flows on the spatial heterogeneity of social media activity: investigating the response to rainfall events / Sidgley Camargo de Andrade in International journal of geographical information science IJGIS, vol 36 n° 6 (June 2022)  

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HiPerMovelets: high-performance movelet extraction for trajectory classification / Tarlis Tortelli Portela in International journal of geographical information science IJGIS, vol 36 n° 5 (May 2022)  

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How do voice-assisted digital maps influence human wayfinding in pedestrian navigation? / Yawei Xu in Cartography and Geographic Information Science, vol 49 n° 3 (May 2022)  

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