Abstract
Points-of-interest (POIs) are an important carriers of location text information in smart cities and have been widely used to extract and identify urban functional regions. However, it is difficult to model the relationship between POIs and urban functional types using existing methods due to insufficient POIs information mining. In this study, we propose a Global Vectors (GloVe)-based, POI type embedding model (GPTEM) to extract and identify urban functional regions at the scale of traffic analysis zones (TAZs) by integrating the co-occurrence information and spatial context of POIs. This method has three main steps. First, we utilize buffer zones centered on each POI to construct the urban functional corpus. Second, we use the constructed corpus and GPTEM to train POI type vectors. Third, we cluster the TAZs and annotate the urban functional types in clustered regions by calculating enrichment factors. The results are evaluated by comparing them against manual annotations and food takeout delivery data, showing that the overall identification accuracy of the proposed method (78.44%) is significantly higher than that of a baseline method based on word2vec. Our work can assist urban planners to efficiently evaluate the development of and changes in the functions of various urban regions.
Highlights
Remote sensing images with high spatial resolution have become a widely used data source for extracting urban functional regions because they accurately reflect the physical features of the land surface, such as its shape, texture, and spectral characteristics [4,5,6]
With the objective of extracting and identifying urban functional regions, we propose a POI information mining method that takes into account both the global co-occurrence frequency of POIs and their geographical relevance in a specific geographic window, integrating POI statistics with spatial semantic features to construct a Global Vectors (GloVe)-based POI type embedding model (GPTEM), in which POI type vectors can more accurately express the role of POIs in urban spaces
We evaluate the urban functions of the clustering regions by calculating the enrichment factors (EF) of the POI types in each clustering region, as follows: j j
Summary
Remote sensing images with high spatial resolution have become a widely used data source for extracting urban functional regions because they accurately reflect the physical features of the land surface, such as its shape, texture, and spectral characteristics [4,5,6]. In addition to these natural attributes, the function of an urban region is strongly influenced by human social and economic activities, the characteristics of which cannot be adequately extracted from remote sensing images
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
