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Evaluation of Polycentric Spatial Structure in the Urban Agglomeration of the Pearl River Delta (PRD) Based on Multi-Source Big Data Fusion

Remote sensing (Basel, Switzerland), 2021-09, Vol.13 (18), p.3639 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs13183639

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  • Title:
    Evaluation of Polycentric Spatial Structure in the Urban Agglomeration of the Pearl River Delta (PRD) Based on Multi-Source Big Data Fusion
  • Author: He, Xiong ; Cao, Yongwang ; Zhou, Chunshan
  • Subjects: Agglomeration ; anselin local moran’s I (LMI) ; Big Data ; Central business districts ; Cities ; Data integration ; Development policy ; geographically weighted regression (GWR) ; Identification methods ; Methods ; nighttime light (NTL) data ; Optimization ; point of interest (POI) data ; Population ; Regional development ; Remote sensing ; Rivers ; Spatial analysis ; Sustainable development ; tencent migration data (TMG) ; Urban areas ; Urban development ; Urban planning ; Urbanization ; Wavelet transforms
  • Is Part Of: Remote sensing (Basel, Switzerland), 2021-09, Vol.13 (18), p.3639
  • Description: 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.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2072-4292
    EISSN: 2072-4292
    DOI: 10.3390/rs13183639
  • Source: Directory of Open Access Scholarly Resources (ROAD)
    Directory of Open Access Journals
    Coronavirus Research Database
    ProQuest Central
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