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Learning digital geographies through geographical artificial intelligence / Pengyuan Liu (2021)  

Public Titre : Learning digital geographies through geographical artificial intelligence Type de document : Thèse/HDR Auteurs : Pengyuan Liu, Auteur ; Stefano de Sabbata, Directeur de thèse ; Yu-Dong Zhang, Directeur de thèse Editeur : Leicester [Royaume-Uni] : University of Leicester Année de publication : 2021 Importance : 199 p. Format : 21 x 30 cm Note générale : bibliographie A thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy, Geology and Environment Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse socio-économique [Termes IGN] apprentissage profond [Termes IGN] contenu généré par les utilisateurs [Termes IGN] croissance urbaine [Termes IGN] détection de changement [Termes IGN] données issues des réseaux sociaux [Termes IGN] données localisées des bénévoles [Termes IGN] données spatiotemporelles [Termes IGN] géomatique web [Termes IGN] intelligence artificielle [Termes IGN] Londres [Termes IGN] réseau neuronal de graphes [Termes IGN] réseau sémantique [Termes IGN] système d'information urbain [Termes IGN] zone urbaine Index. décimale : THESE Thèses et HDR Résumé : (auteur) As the distinction between online and physical spaces rapidly degrades, digital platforms have become an integral component of how people’s everyday experiences are mediated. User-generated content (UGC) shared on such platforms provides insights into how users want to represent their everyday lives, which augments and reinforces our understanding of local communities through time and layers dynamic information across and over the geographic space. Inspired by the development of the newly arisen scientific disciplines within geography: geographical artificial intelligence (GeoAI), this thesis adopts deep learning approaches on graph representations of human dynamics illustrated through geotagged UGC to explore how place representations are augmented and reinforced through users’ spatial experiences by classifying their multimedia activities and identifying the spatial clusters of UGC at the urban scale. Having the place representations described through UGC, this thesis explores how these representations can be used in conjunction with various official spatial statistics to understand and predict the dynamic changes of the socio-economic characteristics of places. The principal contributions of this thesis are: (1) to provide frameworks with higher classification and prediction accuracy but requiring fewer sample data; thus, contributing to an advanced framework to summarise spatial characteristics of places; (2) to show that multimedia content provides rich information regarding places, the use of space, and people’s experience of the landscape; thus, benefiting a better understanding of place representations; (3) to illustrate that the spatial patterns of UGC can be adopted as a valuable proxy to understand urban development and neighbourhood change; (4) to reinforce the concept that Spatial is Special. Spatial processes are commonly spatially autocorrelated. The mainstream of machine learning methods do not explicitly incorporate the spatial or spatio-temporal component to address such a speciality of spatial data. This thesis highlights the importance of explicitly incorporating spatial or spatio-temporal components in geographical analysis models. Note de contenu : 1- Introduction 2- Towards quantitative digital geographies: Concepts, research and implications 3- Data and methods 4- Classification learning through a graph-based semi-supervised approach 5- Location estimation of social media content through a graph-based linkPrediction 6- Urban change modelling with spatial knowledge graphs 7- Discussion Numéro de notice : 28629 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD Thesis: Geology and Environment: Leicester : 2021 DOI : sans En ligne : https://leicester.figshare.com/articles/thesis/Learning_Digital_Geographies_thro [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99618