A GloVe Model for Urban Functional Area Identification Considering Nonlinear Spatial Relationships between Points of Interest

• Published in: ISPRS international journal of geo-information 2022-10, Vol.11 (10), p.498
• Main Authors: Chen, Yue, Qian, Haizhong, Wang, Xiao, Wang, Di, Han, Lijian
• Format: Article
• Language: English
• Subjects: Algorithms; Cellular telephones; Cities; Cluster analysis; Clustering; Construction planning; Economic activity; Euclidean geometry; GloVe; Identification; Identification methods; Language; Mathematical analysis; Natural language; points-of-interest; Remote sensing; Roads; Roads & highways; Semantics; Social networks; Spatial data; Transportation networks; Urban areas; urban functional regions; Urban planning; Vectors; Zhengzhou
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi11100498

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.