Journal of Geographical Sciences >
Dynamic changes in urban land spatial inequality under the core-periphery structure in urban agglomerations
Fang Xiaoqian, PhD Candidate, specialized in land resources management and landscape ecology. E-mail: fangxiaoqian@zju.edu.cn |
Received date: 2022-03-28
Accepted date: 2022-12-01
Online published: 2023-05-11
Supported by
National Social Science Fund of China(20AGL025)
Relatively coordinated development among cities can typically promote the integration of the whole urban agglomeration, while urbanization of land has been found important to economic development in developing countries. Numerous studies have measured the urban land expansion in urban agglomerations. However, urban land spatial inequality under a specific spatial structure remains poorly understood. Here, we combined the β-convergence model and the core-periphery structure to explore the dynamic changes in urban land spatial inequality in urban agglomerations. The Yangtze River Delta Urban Agglomeration (YRDUA) in China was selected as the study area. Indicators of urban land have been applied in conjunction with a modified conditional β-convergence model, while the existence of the core-periphery structure was tested by analyzing coefficients of the control variable. The results show that although the spatial inequality of urban land area had decreased during 2000-2020, the whole YRDUA had still shown an obvious core-periphery structure. Compared with urban land area, the spatial inequality of urban land economic density and population density had decreased more rapidly, and the core-periphery structure was less obvious. Our findings can help to improve our current understanding of urban agglomeration and serve as a scientific reference for the coordinated development of urban agglomerations.
FANG Xiaoqian , SU Dan , WU Qing , WANG Jiayi , ZHANG Yangjian , LI Guoyu , CAO Yu . Dynamic changes in urban land spatial inequality under the core-periphery structure in urban agglomerations[J]. Journal of Geographical Sciences, 2023 , 33(4) : 760 -778 . DOI: 10.1007/s11442-023-2105-y
Figure 1 Location of the prefecture-level cities (a), and land use types (b) in the Yangtze River Delta Urban Agglomeration in 2020 |
Figure 2 Three types of urban land expansion according to landscape expansion index |
Figure 3 The analytical framework of this paper |
Table 1 Basic characteristics of the borders |
Border | INTRA | INTER | |||||
---|---|---|---|---|---|---|---|
SJ | SZ | SA | JZ | JA | ZA | ||
Count | 92 | 9 | 8 | 8 | 72 | 72 | 64 |
Proportion (%) | 28.31 | 2.77 | 2.46 | 2.46 | 22.15 | 22.15 | 19.69 |
Figure 4 Distribution of urban land in the Yangtze River Delta Urban Agglomeration in 2000, 2010, and 2020 |
Figure 5 The urban area in 2000 and newly increased urban area from 2000 to 2020 for 26 cities in the Yangtze River Delta Urban Agglomeration |
Table 2 The annual growth rate of urban land of 26 cities in the Yangtze River Delta Urban Agglomeration |
City | 00-10 (%) | 10-20 (%) | 00-20 (%) | City | 00-10 (%) | 10-20 (%) | 00-20 (%) |
---|---|---|---|---|---|---|---|
Shanghai | 4.24 | 1.58 | 2.90 | Huzhou | 3.69 | 5.46 | 4.57 |
Nanjing | 2.20 | 6.46 | 4.31 | Shaoxing | 4.19 | 5.84 | 5.01 |
Wuxi | 4.87 | 3.35 | 4.11 | Jinhua | 10.60 | 3.30 | 6.88 |
Changzhou | 5.68 | 4.15 | 4.91 | Zhoushan | 6.32 | 11.63 | 8.94 |
Suzhou | 4.72 | 4.86 | 4.79 | Taizhou2 | 2.43 | 7.24 | 4.81 |
Nantong | 9.15 | 6.97 | 8.05 | Hefei | 1.97 | 1.10 | 1.53 |
Yancheng | 2.99 | 4.13 | 3.56 | Wuhu | 1.17 | 3.90 | 2.52 |
Yangzhou | 3.23 | 7.74 | 5.46 | Maanshan | 1.69 | 1.71 | 1.70 |
Zhenjiang | 2.73 | 7.64 | 5.16 | Tongling | 6.03 | 4.17 | 5.10 |
Taizhou1 | 2.17 | 4.28 | 3.22 | Anqing | 3.50 | 3.32 | 3.41 |
Hangzhou | 4.16 | 4.87 | 4.52 | Chuzhou | 0.77 | 5.18 | 2.95 |
Ningbo | 5.61 | 6.46 | 6.04 | Chizhou | 3.58 | 5.66 | 4.61 |
Jiaxing | 5.74 | 3.76 | 4.75 | Xuancheng | 3.29 | 12.79 | 7.94 |
Figure 6 Spatial patterns of three types of new urban land patches for the Yangtze River Delta Urban Agglomeration from 2000 to 2020 |
Figure 7 The proportion of three types of new urban land patches from the total area |
Table 3 The changes in urban land economic density (ULED) and urban land population density (ULPD) during 2000-2020 |
City | ULED 00-10 | ULED 10-20 | ULPD 00-10 | ULPD 10-20 | City | ULED 00-10 | ULED 10-20 | ULPD 00-10 | ULPD 10-20 |
---|---|---|---|---|---|---|---|---|---|
Shanghai | 3.76 | 5.83 | -546 | -472 | Huzhou | 1.88 | 1.56 | -2928 | -1594 |
Nanjing | 4.6 | 3.37 | 1376 | -3496 | Shaoxing | 2.9 | 1.16 | -4060 | -2549 |
Wuxi | 4.31 | 3.5 | -787 | -1034 | Jinhua | 0.8 | 1.63 | -10227 | 517 |
Changzhou | 3.39 | 3.67 | -2397 | -1727 | Zhoushan | 4.09 | -1.23 | -7749 | -5515 |
Suzhou | 4.9 | 2.42 | 552 | -1438 | Taizhou2 | 2.29 | 0.3 | -2374 | -3782 |
Nantong | 2.3 | 2.21 | -15300 | -4295 | Hefei | 1.67 | 4.6 | -903 | 2107 |
Yancheng | 1.28 | 1.39 | -2926 | -2572 | Wuhu | 2.05 | 3.3 | -428 | 532 |
Yangzhou | 3.25 | 1.42 | -3661 | -5008 | Maanshan | 1.6 | 2.42 | -164 | 1202 |
Zhenjiang | 3.74 | 0.12 | -1088 | -4287 | Tongling | 2.45 | 1.26 | -3739 | 1083 |
Taizhou1 | 2.11 | 1.99 | -2240 | -2390 | Anqing | 1.48 | 2.04 | -8311 | -6062 |
Hangzhou | 4.24 | 4.52 | -2715 | 621 | Chuzhou | 0.5 | 1.59 | -887 | -2167 |
Ningbo | 2.87 | 1.4 | -3340 | -650 | Chizhou | 2.1 | 2.07 | -8295 | -6664 |
Jiaxing | 1.99 | 2.31 | -3799 | 489 | Xuancheng | 1.89 | -0.02 | -8902 | -12557 |
Notes: ULED00-10 represents the ULED value in 2010 minus the ULED value in 2000. The unit of the ULED is million yuan/km2, and the unit of the ULPD is people/km2. |
Figure 8 Urban land economic density (ULED) and urban land population density (ULPD) in 2000, 2010, and 2020 |
Table 4 Estimated models of urban land area (ULA), urban land economic density (ULED) and urban land population density (ULPD) |
Variables | ULA | ULED | ULPD | |||||||
---|---|---|---|---|---|---|---|---|---|---|
00-10 | 10-20 | 00-20 | 00-10 | 10-20 | 00-20 | 00-10 | 10-20 | 00-20 | ||
ln(yt) | -0.126*** | -0.225*** | -0.310*** | -0.312*** | -0.424*** | -0.679*** | -0.352*** | -0.790*** | -0.863*** | |
BSJ | 0.111 | -0.016 | 0.059 | -0.222* | 0.162 | 0.080 | 0.132 | 0.252** | 0.279*** | |
BSZ | 0.053 | -0.003 | 0.023 | -0.002 | 0.312*** | 0.343** | 0.333*** | 0.162 | 0.232** | |
BSA | 0.336*** | 0.247** | 0.488*** | -0.112 | 0.174 | 0.222 | 0.169* | 0.100 | 0.135 | |
BJZ | -0.119*** | 0.080** | -0.012 | 0.111** | -0.097** | -0.053 | 0.203*** | -0.259*** | -0.216*** | |
BJA | 0.164*** | 0.328*** | 0.453*** | -0.001 | -0.236*** | -0.174** | 0.040 | -0.323*** | -0.315*** | |
BZA | 0.222*** | 0.313*** | 0.487*** | -0.221*** | -0.386*** | -0.437*** | -0.160*** | -0.232*** | -0.266*** | |
Constant | 0.061** | -0.065** | -0.023 | 0.109*** | 0.248*** | 0.316*** | -0.002 | 0.169*** | 0.169*** | |
R2 | 0.354 | 0.495 | 0.656 | 0.487 | 0.566 | 0.737 | 0.574 | 0.733 | 0.843 | |
N | 325 | 325 | 325 | 325 | 325 | 325 | 325 | 325 | 325 |
Notes: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, and N is the sample size. |
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