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A framework for extracting urban functional regions based on multi prototype word embeddings using points-of-interest data
چارچوبی برای استخراج مناطق عملکردی شهری بر اساس تعبیه چند کلمه نمونه اولیه با استفاده از داده های مورد علاقه-2020 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 geodata.
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 centercontext
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. Keywords: Urban functional regions | Word embeddings | Points-of-interest | Spatial clusters |
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