Identification and Evaluation of the Polycentric Urban Structure: An Empirical Analysis Based on Multi-Source Big Data Fusion
Identifying and evaluating polycentric urban spatial structure is essential for understanding and optimizing current urban development. In order to accurately identify the urban centers of the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), this study firstly fused nighttime light data, POI data,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/11/2705 |
| _version_ | 1797491833273581568 |
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| author | Yuquan Zhou Xiong He Yiting Zhu |
| author_facet | Yuquan Zhou Xiong He Yiting Zhu |
| author_sort | Yuquan Zhou |
| collection | DOAJ |
| description | Identifying and evaluating polycentric urban spatial structure is essential for understanding and optimizing current urban development. In order to accurately identify the urban centers of the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), this study firstly fused nighttime light data, POI data, and population migration data based on wavelet transform, then identified the polycentric spatial structure of the GBA by carrying out cluster and outlier analysis, and evaluated the level of different urban centers byconducting geographical weighted regression analysis. Using data fusion, we identified 4579.81 km² of the urban poly-center area in the GBA, with an identification accuracy of 93.22%. Although the number and spatial extent of the identified urban poly-centers are consistent with the GBA development plan outline, the poly-center level evaluation results are inconsistent with the development plan, which shows there are great differences in actual development levels among different cities in the GBA. By identifying and grading the polycentric spatial structure of the GBA, this study accurately analyzed the current spatial distribution and could provide policy implications for the GBA’s future development and planning. |
| first_indexed | 2024-03-10T00:53:59Z |
| format | Article |
| id | doaj.art-917fa33d7f784d30b5d556cdce711246 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T00:53:59Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-917fa33d7f784d30b5d556cdce7112462023-11-23T14:46:05ZengMDPI AGRemote Sensing2072-42922022-06-011411270510.3390/rs14112705Identification and Evaluation of the Polycentric Urban Structure: An Empirical Analysis Based on Multi-Source Big Data FusionYuquan Zhou0Xiong He1Yiting Zhu2Department of Urban Planning and Spatial Analysis, Sol Price School of Public Policy, University of Southern California, Los Angeles, CA 90089, USASchool of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, ChinaKey Laboratory of Sustainable Development of Xinjiang’s Historical and Cultural Tourism, College of Tourism, Xinjiang University, Urumqi 830046, ChinaIdentifying and evaluating polycentric urban spatial structure is essential for understanding and optimizing current urban development. In order to accurately identify the urban centers of the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), this study firstly fused nighttime light data, POI data, and population migration data based on wavelet transform, then identified the polycentric spatial structure of the GBA by carrying out cluster and outlier analysis, and evaluated the level of different urban centers byconducting geographical weighted regression analysis. Using data fusion, we identified 4579.81 km² of the urban poly-center area in the GBA, with an identification accuracy of 93.22%. Although the number and spatial extent of the identified urban poly-centers are consistent with the GBA development plan outline, the poly-center level evaluation results are inconsistent with the development plan, which shows there are great differences in actual development levels among different cities in the GBA. By identifying and grading the polycentric spatial structure of the GBA, this study accurately analyzed the current spatial distribution and could provide policy implications for the GBA’s future development and planning.https://www.mdpi.com/2072-4292/14/11/2705urban agglomerationbig datanighttime light datadata fusionspatial planning |
| spellingShingle | Yuquan Zhou Xiong He Yiting Zhu Identification and Evaluation of the Polycentric Urban Structure: An Empirical Analysis Based on Multi-Source Big Data Fusion Remote Sensing urban agglomeration big data nighttime light data data fusion spatial planning |
| title | Identification and Evaluation of the Polycentric Urban Structure: An Empirical Analysis Based on Multi-Source Big Data Fusion |
| title_full | Identification and Evaluation of the Polycentric Urban Structure: An Empirical Analysis Based on Multi-Source Big Data Fusion |
| title_fullStr | Identification and Evaluation of the Polycentric Urban Structure: An Empirical Analysis Based on Multi-Source Big Data Fusion |
| title_full_unstemmed | Identification and Evaluation of the Polycentric Urban Structure: An Empirical Analysis Based on Multi-Source Big Data Fusion |
| title_short | Identification and Evaluation of the Polycentric Urban Structure: An Empirical Analysis Based on Multi-Source Big Data Fusion |
| title_sort | identification and evaluation of the polycentric urban structure an empirical analysis based on multi source big data fusion |
| topic | urban agglomeration big data nighttime light data data fusion spatial planning |
| url | https://www.mdpi.com/2072-4292/14/11/2705 |
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