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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]