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Remote Sensing for Natural Resources    2023, Vol. 35 Issue (2) : 132-139     DOI: 10.6046/zrzyyg.2022134
Identification of the polycentric urban structure based on multi-source geographic big data
LYU Yongqiang1(), YU Xinwei1, YANG Shuo1, ZHENG Xinqi2()
1. School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
2. School of Information Engineering, China University of Geosciences(Beijing), Beijing 100083, China
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The emergence of geographic big data provides a new data source for the study of urban spatial structures. Identifying the polycentric urban structure based on geographic big data is currently a hot research topic in academic communities. This study proposed a method for identifying the polycentric urban structure based on multi-source geographic big data. First, the spatial units in the study area were determined using a region segmentation algorithm based on drainage divides. Then, the urban centers and subcenters were identified using the two-stage algorithm for urban center identification. Finally, the identification results were compared and verified. The results of this study are as follows: ① The region segmentation algorithm based on drainage divides can effectively identify the spatial features of nighttime light data, and the basic spatial units acquired using this algorithm can be used to identify urban spatial structures; ② The urban centers identified based on the Weibo (MicroBlog) check-in data, which can effectively reflect urban human activities, and the two-stage algorithm for urban center identification are roughly consistent with those set in the urban planning. Therefore, the method proposed in this study is of great significance for expanding the application scope of geographic big data and enriching the existing research methods for urban spatial structures.

Keywords nighttime lighting      Weibo check-in      watershed algorithm      spatial unit      polycentric structure     
ZTFLH:  TP79  
Issue Date: 07 July 2023
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Yongqiang LYU
Xinwei YU
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Yongqiang LYU,Xinwei YU,Shuo YANG, et al. Identification of the polycentric urban structure based on multi-source geographic big data[J]. Remote Sensing for Natural Resources, 2023, 35(2): 132-139.
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Fig.1  The study area
Fig.2  A diagram of the watershed-based partition approach
Fig.3  Spatial divisions partitioned by the watershed-based partition approach
指标 统计值 北京市 上海市 重庆市
研究区面积/km2 总和 12 186.12 5 448.06 28 657.55
数量/个 总和 1 192 775 1 706
空间单元面积/km2 最大值 98.25 38.00 124.13
平均值 10.22 7.03 16.80
标准差 10.45 4.87 14.67
微博签到点数量/个 总和 20 789 250 16 755 584 3 210 651
平均值 17 440.65 21 620.11 1 881.98
n% 45 595.23 40 933.59 17 918.29
最大值 101 688.70 151 953.60 30 356.67
平均值 2 420.88 3 750.26 1 767.62
Tab.1  Statistic of different urban spatial units
Fig.4  Locations of urban centers identified by a two-stage method in different cities
指标 统计值 北京市 上海市 重庆市
中心数量/个 全市 17 11 12
微博签到点数/个 全市 20 789 250 16 755 584 3 210 651
所有中心 17 418 001 14 452 674 2 931 382
主中心 15 033 714 12 543 156 2 347 317
微博签到点比重/% 所有中心/全市 83.78 86.26 91.30
主中心/全部中心 86.31 86.79 80.08
Tab.2  Statistic of different urban centers in different cities
城市 单元平
参考文献 城市 单元平
北京 38.14 孙铁山等[13] 巴塞罗那 19.51 Garcia-López
上海 26.48 魏旭红等[37] 首尔 12.64 Nam等[42]
广州 44.47 蒋丽等[38] 芝加哥 1.09 McMillen[28]
深圳 44.51 曾海宏等[39] 达拉斯 2.72 McMillen[28]
洛杉矶 4.68 Pereira等[40] 休斯顿 6.73 McMillen[28]
匹兹堡 19.19 Pereira等[40] 洛杉矶 4.51 McMillen[28]
圣保罗 20.45 Pereira等[40] 新奥尔良 14.61 McMillen[28]
巴黎 9.28 Pereira等[40] 旧金山 3.65 McMillen[28]
Tab.3  The average area of spatial units selected in different studies
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