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As cities continue to grow, the functions of urban areas change and problems arise from previously constructed urban planning schemes. Hence, the actual distribution of urban functional areas needs to be confirmed. POI data, as a representation of urban facilities, can be used to mine the spatial correlation within the city. Therefore it has been widely used for urban functional area extraction. Previous studies are mostly devoted to mining POI linear location relationships and do not comprehensively mine POI spatial information, such as spatial interaction information. This results in less accurate modeling of the relationship between POI-based and urban function types. In addition, they all use Euclidean distance for proximity assessment, which is not realistic. This paper proposes an urban functional area identification method that considers the nonlinear spatial relationship between POIs. First, POI adjacency is determined according to road network constraints, which forms the basis of a co-occurrence matrix. Then, a Global Vectors (GloVe) model is used to train POI category vectors and the feature vectors for each basic research unit are obtained using weighted averages. This is followed by clustering analysis, which is realized by a K-Means++ algorithm. Lastly, the functional areas are labeled according to the POI category ratio, enrichment factors, and mobile phone signal heat data. The model was tested experimentally, using core areas of Zhengzhou City in China as an example. When the results were compared with a Baidu map, we confirmed that making full use of nonlinear spatial relationships between POIs delivers high levels of identification accuracy for urban functional areas. [ABSTRACT FROM AUTHOR]

As cities continue to grow, the functions of urban areas change and problems arise from previously constructed urban planning schemes. Hence, the actual distribution of urban functional areas needs to be confirmed. POI data, as a representation of urban facilities, can be used to mine the spatial correlation within the city. Therefore it has been widely used for urban functional area extraction. Previous studies are mostly devoted to mining POI linear location relationships and do not comprehensively mine POI spatial information, such as spatial interaction information. This results in less accurate modeling of the relationship between POI-based and urban function types. In addition, they all use Euclidean distance for proximity assessment, which is not realistic. This paper proposes an urban functional area identification method that considers the nonlinear spatial relationship between POIs. First, POI adjacency is determined according to road network constraints, which forms the basis of a co-occurrence matrix. Then, a Global Vectors (GloVe) model is used to train POI category vectors and the feature vectors for each basic research unit are obtained using weighted averages. This is followed by clustering analysis, which is realized by a K-Means++ algorithm. Lastly, the functional areas are labeled according to the POI category ratio, enrichment factors, and mobile phone signal heat data. The model was tested experimentally, using core areas of Zhengzhou City in China as an example. When the results were compared with a Baidu map, we confirmed that making full use of nonlinear spatial relationships between POIs delivers high levels of identification accuracy for urban functional areas.

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.

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. [ABSTRACT FROM AUTHOR]