Abstract
The rapid development of the urban city has led to great changes in the urban spatial structure. Thus, analyses of polycentric urban spatial structures are important for understanding these kinds of structures. In order to accurately evaluate the polycentric spatial structure of urban agglomerations and judge the differences between the actual development situation and overall planning of urban agglomerations, this study proposes a new method to identify the polycentric spatial structure of urban agglomerations in the Pearl River Delta based on the fusion of nighttime light (NTL) data, point of interest (POI) data, and Tencent migration data (TMG). In the first step, the NTL, POI, and TMG data are fused via wavelet transform; in the second step, Anselin local Moran’s I (LMI) and geographically weighted regression (GWR) were used to identify the main centers and subcenters, respectively. In the third step, the accuracy of the results of this study was further verified and discussed in the context of overall planning. The results show that the accuracy of urban polycenter identification via LMI and GWR after data fusion was 92.84%, and the Kappa value was 0.8971, which was higher than the results of polycenter identification via the traditional relative threshold. After comparing the identification results with the overall planning, firstly, we see that the fusion of multi-source big data can help to accurately evaluate the polycentric spatial structure within the urban agglomeration. Secondly, the fusion of dynamic data and static data can help identify the polycentric spatial structure of urban space more accurately. Therefore, this study can provide a new design for urban polycentric spatial structures, and further provide a reliable reference for the spatial optimization of urban agglomeration and the formulation of regional spatial development policies.
Highlights
China has made rapid progress in its urbanization construction in more than 40 years since 1978 [1], which is embodied in the following aspects: first of all, the urbanization rate increased by 42.44% from 17.90% in 1978 to 60.34% in 2020 [2], and the urbanization rate of first-tier cities, including Beijing, Shanghai, and Guangzhou exceeded 70% [3], especially Shenzhen, whose urbanization rate reached 88% in 2020 [4]
Polycentric Spatial Structure of Urban Agglomerations Identified by nighttime light (NTL) Data
In order to evaluate the effectiveness of the implementation of urban spatial overall planning, this study evaluates the accuracy of the polycentric spatial structure identification results for the PRD urban agglomeration and compares them with the overall planning results of the PRD
Summary
China has made rapid progress in its urbanization construction in more than 40 years since 1978 [1], which is embodied in the following aspects: first of all, the urbanization rate increased by 42.44% from 17.90% in 1978 to 60.34% in 2020 [2], and the urbanization rate of first-tier cities, including Beijing, Shanghai, and Guangzhou exceeded 70% [3], especially Shenzhen, whose urbanization rate reached 88% in 2020 [4]. The rapid growth of population has led to great changes in the spatial structure, function, and nature of the urban interior, and many polycentric cities have gradually emerged [7,8]. The reason for such change is that previously relatively closed. A large number of rural industrial workers living in cities are regarded as urban populations, they have not settled in cities. This phenomenon is obvious in developing countries. The polycentric urban spatial structure is a rapid and sustainable urban development model, which can alleviate these urban problems [18,19]
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