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Auteur Meng Chen |
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Estimating urban functional distributions with semantics preserved POI embedding / Weiming Huang in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)
[article]
Titre : Estimating urban functional distributions with semantics preserved POI embedding Type de document : Article/Communication Auteurs : Weiming Huang, Auteur ; Lizhen Cui, Auteur ; Meng Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1905 - 1930 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] Chine
[Termes IGN] classe sémantique
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] distribution spatiale
[Termes IGN] échantillonnage
[Termes IGN] lissage de données
[Termes IGN] matrice de co-occurrence
[Termes IGN] Perceptron multicouche
[Termes IGN] point d'intérêt
[Termes IGN] triangulation de Delaunay
[Termes IGN] zone urbaineRésumé : (auteur) We present a novel approach for estimating the proportional distributions of function types (i.e. functional distributions) in an urban area through learning semantics preserved embeddings of points-of-interest (POIs). Specifically, we represent POIs as low-dimensional vectors to capture (1) the spatial co-occurrence patterns of POIs and (2) the semantics conveyed by the POI hierarchical categories (i.e. categorical semantics). The proposed approach utilizes spatially explicit random walks in a POI network to learn spatial co-occurrence patterns, and a manifold learning algorithm to capture categorical semantics. The learned POI vector embeddings are then aggregated to generate regional embeddings with long short-term memory (LSTM) and attention mechanisms, to take account of the different levels of importance among the POIs in a region. Finally, a multilayer perceptron (MLP) maps regional embeddings to functional distributions. A case study in Xiamen Island, China implements and evaluates the proposed approach. The results indicate that our approach outperforms several competitive baseline models in all evaluation measures, and yields a relatively high consistency between the estimation and ground truth. In addition, a comprehensive error analysis unveils several intrinsic limitations of POI data for this task, e.g. ambiguous linkage between POIs and functions. Numéro de notice : A2022-738 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658816.2022.2040510 Date de publication en ligne : 08/03/2022 En ligne : https://doi.org/10.1080/13658816.2022.2040510 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101714
in International journal of geographical information science IJGIS > vol 36 n° 10 (October 2022) . - pp 1905 - 1930[article]