A framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data

• Published in: Computers, environment and urban systems Vol. 80; p. 101442
• Main Authors: Hu, Sheng, He, Zhanjun, Wu, Liang, Yin, Li, Xu, Yongyang, Cui, Haifu
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.03.2020; Elsevier Science Ltd
• Subjects: Accessibility; Clustering; Detection; Natural language processing; Points-of-interest; Remote sensing; Spatial clusters; Spatial resolution; Urban areas; Urban functional regions; Word embeddings; Words (language); Spatial clusters; Points-of-interest; Word embeddings; Urban functional regions
• ISSN: 0198-9715; 1873-7587
• DOI: 10.1016/j.compenvurbsys.2019.101442

Many studies are in an effort to explore urban spatial structure, and urban functional regions have become the subject of increasing attention among planners, engineers and public officials. Attempts have been made to identify urban functional regions using high spatial resolution (HSR) remote sensing images and extensive geo-data. However, the research scale and throughput have also been limited by the accessibility of HSR remote sensing data. Recently, big geo-data are becoming increasingly popular for urban studies since research is still accessible and objective with regard to the use of these data. This study aims to build a novel framework to provide an alternative solution for sensing urban spatial structure and discovering urban functional regions based on emerging geo-data – points of interest (POIs) data and an embedding learning method in the natural language processing (NLP) field. We started by constructing the intraurban functional corpus using a center-context pairs-based approach. A word embeddings representation model for training that corpus was used to extract multiprototype vectors in the second step, and the last step aggregated the functional parcels based on an introduced spatial clustering method, hierarchical density-based spatial clustering of applications with noise (HDBSCAN). The clustering results suggested that our proposed framework used in this study is capable of discovering the utilization of urban space with a reasonable level of accuracy. The limitation and potential improvement of the proposed framework are also discussed. •We proposed a framework to infer urban functional regions using POIs data.•We considered ubiquitous homonymy and polysemy of urban regions based on the NLP model.•We introduced an efficient HDBSCAN clustering method to aggregate the parcel functions.•A case study in intraurban area of Wuhan, China is constructed.