Delineating urban functional use from points of interest data with neural network embedding: a case study in Greater London
Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. A neural network embedding model is employed in delineating urban functional use from POI (Points of Interest) data. Doc2Vec model directly trains vector representations for spatial areas while considering the spatial distribution of POIs. This paper explores the functional similarity among 574 POI classes and 4836 LSOAs (Lower Layer Super Output Areas) in Greater London. Doc2Vec model outperforms other semantic models (Word2Vec, LDA and TF-IDF) in urban functional areas identification. Similarity of POI classes trained by Doc2Vec model Create your slides in Markdown - click the Slides button to check out the example. Add the publication’s full text or supplementary notes here. You can use rich formatting such as including code, math, and images.
May 10, 2021
