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Human movement patterns of different racial-ethnic and economic groups in U.S. top 50 populated cities: What can social media tell us about isolation? / Meiliu Wu in Annals of GIS, vol 28 n° 2 (April 2022)  

Public [article]  inAnnals of GIS > vol 28 n° 2 (April 2022) . - pp 161 - 183 Titre : Human movement patterns of different racial-ethnic and economic groups in U.S. top 50 populated cities: What can social media tell us about isolation? Type de document : Article/Communication Auteurs : Meiliu Wu, Auteur ; Qunying Huang, Auteur Année de publication : 2022 Article en page(s) : pp 161 - 183 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] données issues des réseaux sociaux [Termes IGN] données socio-économiques [Termes IGN] Etats-Unis [Termes IGN] ethnie [Termes IGN] migration humaine [Termes IGN] mobilité territoriale [Termes IGN] sociologie [Termes IGN] Twitter Résumé : (auteur) Many studies have proven that human movement patterns are strongly impacted by individual socioeconomic and demographic background. While many efforts have been made on exploring the influences of age and gender on movement patterns using social media, this study aims to analyse and compare the movement patterns among different racial-ethnic and economic groups using social media (i.e. geotagged tweets) from the U.S. top 50 populated cities. Results show that there are significant differences in number of activity zones and median travel distance across cities and demographic groups, and that power-laws tend to be captured in both spatial and demographic aspects. Additionally, the analysis of outbound-city travels demonstrates that some cities have slightly stronger interaction with others, and that economically disadvantaged populations and racial-ethnic minorities are more restricted in long distance travels, indicating that their spatial mobility is more limited to the local scale. Lastly, an economically-segregated movement pattern is discovered – upper-class neighbourhoods are mostly visited by the upper-class, while lower-class neighbourhoods are mainly accessed by the lower-class – but some racial-ethnic groups can diversify this segregated pattern in the local scale. Numéro de notice : A2022-501 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475683.2022.2026471 Date de publication en ligne : 22/01/2022 En ligne : https://doi.org/10.1080/19475683.2022.2026471 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100998 [article]

Assessing COVID-induced changes in spatiotemporal structure of mobility in the United States in 2020: a multi-source analytical framework / Evgeny Noi in International journal of geographical information science IJGIS, vol 36 n° 3 (March 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 3 (March 2022) . - pp 585 - 616 Titre : Assessing COVID-induced changes in spatiotemporal structure of mobility in the United States in 2020: a multi-source analytical framework Type de document : Article/Communication Auteurs : Evgeny Noi, Auteur ; Alexander Rudolph, Auteur ; Somayeh Dodge, Auteur Année de publication : 2022 Article en page(s) : pp 585 - 616 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] autocorrélation spatiale [Termes IGN] comportement [Termes IGN] données multisources [Termes IGN] données spatiotemporelles [Termes IGN] épidémie [Termes IGN] Etats-Unis [Termes IGN] hétérogénéité spatiale [Termes IGN] maladie virale [Termes IGN] mobilité [Termes IGN] mobilité territoriale Résumé : (auteur) The COVID-19 pandemic resulted in profound changes in mobility patterns and altered travel behaviors locally and globally. As a result, movement metrics have widely been used by researchers and policy makers as indicators to study, model, and mitigate the impacts of the COVID-19 pandemic. However, the veracity and variability of these mobility metrics have not been studied. This paper provides a systematic review of mobility and social distancing metrics available to researchers during the pandemic in 2020 in the United States. Twenty-six indices across nine different sources are analyzed and assessed with respect to their spatial and temporal coverage as well as sample representativeness at the county-level. Finally global and local indicators of spatial association are computed to explore spatial and temporal heterogeneity in mobility patterns. The structure of underlying changes in mobility and social distancing is examined in different US counties and across different data sets. We argue that a single measure might not describe all aspects of mobility perfectly. Numéro de notice : A2022-207 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.2005796 Date de publication en ligne : 21/12/2021 En ligne : https://doi.org/10.1080/13658816.2021.2005796 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100023 [article]

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)  

Public [article]  inJournal of location-based services > vol 16 n° 1 (March 2022) . - pp 1 - 24 Titre : Changing mobility patterns in the Netherlands during COVID-19 outbreak Type de document : Article/Communication Auteurs : Sander Van Der Drift, Auteur ; Luc Wismans, Auteur ; Marie-José Olde-Kalter, Auteur Année de publication : 2022 Article en page(s) : pp 1 - 24 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] bicyclette [Termes IGN] comportement [Termes IGN] épidémie [Termes IGN] estimation bayesienne [Termes IGN] mobilité territoriale [Termes IGN] Pays-Bas [Termes IGN] téléphone intelligent [Termes IGN] transport [Termes IGN] transport public [Termes IGN] travail à domicile [Termes IGN] véhicule automobile Résumé : (auteur) The COVID-19 outbreak and associated measures taken had an enormous impact on society as well as a disruptive, but not necessarily negative, impact on mobility. The Ministry of Infrastructure and Water Management received the most recent insights from the Dutch Mobility Panel (DMP) on a weekly basis. These insights were used to monitor the travel behaviour and to analyse changes in the behaviour of different groups and usage of modes of transport during COVID-19. The analysis shows an enormous decrease in travel at the beginning of the implementation of the so-called ‘intelligent’ lockdown and gradual increase again towards comparable levels as before this ‘intelligent lockdown, although the distribution over time, motives and used modes has changed. It becomes clear that not everyone needs to travel during peak hours and commuter travel is also not the main reason for the increase in car usage. Furthermore, cycling has shown to be an alternative option for travellers and public transport is hardly used anymore. If it is possible to sustain the lower level of car usage and integrate public transport as an important alternative for travel again, the COVID-19 impact on mobility could have a substantial remaining positive impact on mobility. Numéro de notice : A2022-391 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/17489725.2021.1876259 Date de publication en ligne : 11/03/2021 En ligne : https://doi.org/10.1080/17489725.2021.1876259 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100682 [article]

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]

Contextual location recommendation for location-based social networks by learning user intentions and contextual triggers / Seyyed Mohammadreza Rahimi in Geoinformatica [en ligne], vol 26 n° 1 (January 2022)  

Public [article]  inGeoinformatica [en ligne] > vol 26 n° 1 (January 2022) . - pp 1 - 28 Titre : Contextual location recommendation for location-based social networks by learning user intentions and contextual triggers Type de document : Article/Communication Auteurs : Seyyed Mohammadreza Rahimi, Auteur ; Behrouz Far, Auteur ; Xin Wang, Auteur Année de publication : 2022 Article en page(s) : pp 1 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] analyse spatiale [Termes IGN] comportement [Termes IGN] contenu généré par les utilisateurs [Termes IGN] covariance [Termes IGN] données spatiotemporelles [Termes IGN] historique des données [Termes IGN] interface web [Termes IGN] mobilité territoriale [Termes IGN] prise en compte du contexte [Termes IGN] réseau social géodépendant [Termes IGN] service fondé sur la position [Termes IGN] système de recommandation Résumé : (auteur) Location recommendation methods suggest unvisited locations to their users. Many existing location recommendation methods focus on the spatial, social and temporal aspects of human movements. However, contextual information is also invaluable to location recommendation methods and has the great potential for explaining what triggers users to show different behaviors. CLR learns the response of the users to contextual variables based on their own history and the history of similar behaving users. In this paper, we propose a contextual location recommendation method named Contextual Location Recommendation (CLR) that learns the intention and spatial responses of users to various contextual triggers using the historical check-in and contextual information. CLR starts with a co-variance analysis to reduce dimensionality of the check-in data and then uses an optimized version of the random walk with restart to extract hidden user responses to contextual triggers. A tensor factorization is used to build a latent-factor model to predict the user’s intention response with the given set of contextual triggers. Based on the intention response of the user, a contextual spatial component identifies a set of matching locations accessible to the user by estimating the probability distribution of the location of the user and the popularity probability of locations under the contextual settings. Experimental results on three real-world datasets show that CLR improves the recommendation precision by 35% compared to the best-performing baseline recommendation method. Numéro de notice : A2022-203 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-021-00437-y Date de publication en ligne : 02/06/2021 En ligne : https://doi.org/10.1007/s10707-021-00437-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100008 [article]

Understanding and predicting the spatio-temporal spread of COVID-19 via integrating diffusive graph embedding and compartmental models / Tong Zhang in Transactions in GIS, vol 25 n° 6 (December 2021)  

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Understanding collective human movement dynamics during large-scale events using big geosocial data analytics / Junchuan Fan in Computers, Environment and Urban Systems, vol 87 (May 2021)  

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A 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)  

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L’accessibilité ferroviaire à Paris des grandes aires urbaines françaises : approche par la time geography / Laurent Chapelon in Mappemonde, n° 127 (juillet 2019)  

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Human mobility, spatiotemporal context, and environmental health / Mei-Po Kwan (2019)  

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Création d’un réseau routier Sapeurs-Pompiers / Maud Desjardins (2018) 

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Distributed processing of big mobility data as spatio-temporal data streams / Zdravko Galić in Geoinformatica [en ligne], vol 21 n° 2 (April - June 2017)  

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Potentiel des données géolocalisées issues de la foule pour les questions de mobilité et tourisme : quelques exemples issus de la littérature / Laurence Jolivet (2017) 

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Développement d’un outil de webmapping pour l’optimisation de l’offre de soins en dialyse / Clémentine Chasles (2016) 

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Le plan de mobilité rurale / Cécile Clément-Werny (2016)

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