Journal of Geographical Sciences >
Spatio-temporal association analysis of county potential in the Pearl River Delta during 1990-2009
*Corresponding author: Xu Songjun, PhD and Professor, specialized in environmental ecology.E-mail:xusj@scnu.edu.cn
Author: Mei Zhixiong, PhD and Associate Professor, specialized in GIS and spatial statistics. E-mail:zhixiongmei76@126.com
Received date: 2014-05-09
Accepted date: 2014-06-05
Online published: 2015-03-15
Supported by
National Natural Science Foundation of China, No.41001078;No.41271060
Copyright
According to the highway data and some socioeconomic data of 1990, 1994, 2000, 2005 and 2009 of county units in the Pearl River Delta, this paper measured urban integrated power of different counties in different years by factor analysis, and estimated each county’s potential in each year by means of expanded potential model. Based on that, the spatio-temporal association patterns and evolution of county potential were analyzed using spatio-temporal autocorrelation methods, and the validity of spatio-temporal association patterns was verified by comparing with spatial association patterns and cross-correlation function. The main results are shown as follows: (1) The global spatio-temporal association of county potential showed a positive effect during the study period. But this positive effect was not strong, and it had been slowly strengthened during 1994-2005 and decayed during 2005-2009. The local spatio-temporal association characteristics of most counties’ potential kept relatively stable and focused on a positive autocorrelation, however, there were obvious transformations in some counties among four types of local spatio-temporal association (i.e., HH, LL, HL and LH). (2) The distribution difference and its change of local spatio-temporal association types of county potential were obvious. Spatio-temporal HH type units were located in the central zone and Shenzhen-Dongguan region of the eastern zone, but the central spatio-temporal HH area shrunk to the Guangzhou-Foshan core metropolitan region only after 2000; the spatio-temporal LL area in the western zone kept relatively stable with a surface-shaped continuous distribution pattern, new LL type units emerged in the south-central zone since 2005, the eastern LL area expanded during 1994-2000, but then gradually shrunk and scattered at the eastern edge in 2009; the spatio-temporal HL and LH areas varied significantly. (3) The local spatio-temporal association patterns of county potential among the three zones presented significant disparity, and obvious difference between the eastern and central zones tended to decrease, whereas that between the western zone and the central and eastern zones further expanded. (4) Spatio-temporal autocorrelation methods can efficiently mine the spatio-temporal association patterns of county potential, and can better reveal the complicated spatio-temporal interaction between counties than ESDA methods.
MEI Zhixiong , XU Songjun , OUYANG Jun . Spatio-temporal association analysis of county potential in the Pearl River Delta during 1990-2009[J]. Journal of Geographical Sciences, 2015 , 25(3) : 319 -336 . DOI: 10.1007/s11442-015-1171-1
Figure 1 The location of the study area and spatial distribution of county units |
Table 1 The evaluation index system of urban integrated power at county level |
First-level indices | Second-level indices |
---|---|
Urban scale level | Urban non-agricultural population (x1) and urban built-up area (x2) |
Urban economic level | GDP (x3), GDP per capita (x4), gross industrial output value (x5), local government financial revenue (x6), local government financial expenditures (x7), savings deposits of urban and rural residents at the year-end (x8), total investment in fixed assets (x9), annual average wage of staff and workers (x10), total retail sales of consumer goods (x11), secondary industry output value (x12), tertiary industry output value (x13), proportion of tertiary industry output value in total output value (x14), foreign capital actually utilized (x15) and total foreign trade export value (x16) |
Social development level | Number of beds occupied per 10,000 persons in health institutions (x17), business volume of postal and telecommunication services (x18), business volume of postal and telecommunication services per capita (x19), number of persons with professional and technical titles (x20) and R&D expenditure (x21) |
Infrastructure level | Urban passenger traffic volume (x22), urban goods traffic volume (x23) and per capita length of highways in operation (x24) |
Table 2 Values of urban potential at county level between 1990 and 2009 |
Study unit | 1990 | 1994 | 2000 | 2005 | 2009 | Study unit | 1990 | 1994 | 2000 | 2005 | 2009 |
---|---|---|---|---|---|---|---|---|---|---|---|
Guangzhou urban district | 0.4138 | 0.6424 | 0.6276 | 0.5735 | 0.5374 | Jiangmen urban district | 0.0569 | 0.0647 | 0.0627 | 0.0578 | 0.0523 |
Huadu | 0.1422 | 0.1143 | 0.1064 | 0.0899 | 0.1152 | Taishan | 0.0557 | 0.0447 | 0.0141 | 0.0268 | 0.0150 |
Conghua | 0.0132 | 0.0161 | 0.0161 | 0.0147 | 0.0201 | Kaiping | 0.0613 | 0.0499 | 0.0207 | 0.0325 | 0.0204 |
Panyu | 0.1605 | 0.1729 | 0.1393 | 0.1734 | 0.1103 | Heshan | 0.0467 | 0.0578 | 0.0514 | 0.0397 | 0.0420 |
Zengcheng | 0.0312 | 0.0397 | 0.0302 | 0.0362 | 0.0475 | Enping | 0.0067 | 0.0084 | 0.0087 | 0.0069 | 0.0069 |
Shenzhen urban district | 0.1098 | 0.1323 | 0.2113 | 0.1667 | 0.1455 | Foshan urban district | 0.1040 | 0.2631 | 0.2521 | 0.1753 | 0.1333 |
Zhuhai urban district | 0.0727 | 0.1317 | 0.0914 | 0.0701 | 0.0665 | Shunde | 0.1863 | 0.1660 | 0.1638 | 0.1491 | 0.1055 |
Doumen | 0.0211 | 0.0251 | 0.0298 | 0.0191 | 0.0096 | Sanshui | 0.0420 | 0.0885 | 0.0707 | 0.0553 | 0.0538 |
Huizhou urban district | 0.0894 | 0.1677 | 0.0672 | 0.1080 | 0.1755 | Gaoming | 0.0161 | 0.0406 | 0.0373 | 0.0349 | 0.0344 |
Huidong | 0.0285 | 0.0255 | 0.0141 | 0.0187 | 0.0184 | Zhaoqing urban district | 0.0102 | 0.0140 | 0.0128 | 0.0146 | 0.0097 |
Huiyang | 0.0239 | 0.0230 | 0.0216 | 0.0212 | 0.0181 | Sihui | 0.0163 | 0.0191 | 0.0166 | 0.0171 | 0.0178 |
Boluo | 0.0393 | 0.1100 | 0.0154 | 0.0447 | 0.1044 | Guangning | 0.0064 | 0.0047 | 0.0032 | 0.0048 | 0.0048 |
Longmen | 0.0043 | 0.0043 | 0.0030 | 0.0042 | 0.0046 | Deqing | 0.0053 | 0.0030 | 0.0039 | 0.0027 | 0.0041 |
Dongguan | 0.0355 | 0.0891 | 0.1076 | 0.0929 | 0.1065 | Fengkai | 0.0044 | 0.0029 | 0.0032 | 0.0021 | 0.0035 |
Zhongshan | 0.0660 | 0.0919 | 0.0785 | 0.0677 | 0.0663 | Huaiji | 0.0036 | 0.0029 | 0.0023 | 0.0031 | 0.0031 |
Table 3 Values of spatio-temporal autocorrelation function |
k=1 | k=2 | k=3 | k=4 | |
---|---|---|---|---|
h=0 | 0.9310 | 0.6492 | 0.3383 | 0.1025 |
h=1 | 0.2639 | 0.1504 | 0.0861 | 0.0204 |
h=2 | 0.0150 | 0.1076 | 0.0626 | 0.0062 |
Table 4 Values of spatio-temporal partial autocorrelation function |
k=1 | k=2 | k=3 | k=4 | |
---|---|---|---|---|
h=0 | -0.6943 | 0.0623 | 0.0426 | 0.0032 |
h=1 | -0.1420 | 0.0364 | 0.0025 | -0.0011 |
h=2 | -0.0717 | 0.0138 | 0.0157 | -0.0019 |
Table 5 STI1,1 and its Z test of county potential at first-order time lag and space lag |
Year | 1994 | 2000 | 2005 | 2009 |
---|---|---|---|---|
Global index STI1,1 | 0.2364 | 0.2388 | 0.2401 | 0.1897 |
Significant level | 0.01 | 0.01 | 0.01 | 0.05 |
Figure 2 Evolvement of local spatio-temporal association patterns of county potential |
Figure 3 Evolvement of local spatial association patterns of county potential |
Figure 4 Comparison between STIi,1,1 index and cross-correlation function (CCF) |
The authors have declared that no competing interests exist.
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