Spatial Combination of Finance and Center Level Identify Based on K-means Clustering: A Case Study of the Changjiang River Delta
Received date: 2014-01-12
Request revised date: 2014-05-06
Online published: 2015-02-15
Copyright
The article constructs the financial spatial combination model and analyzes quantitatively the spatial differentiation characteristics of spatial combination by applying the number of financial institutions RMB deposit and loan in 2001, 2006 and 2011, taking cities in the Changjiang River Delta as examples. Based on the analysis, the financial center level identifying model with K-means is built to identify the financial center level of the cities in the Changjiang River Delta. The conclusions can be drawn as follows: 1) The spatial trend of the cities’ finance “quality” in the Changjiang River Delta is relatively stable, showing overall that the finance “quality” of the cities in the east is bigger than that of the cities in the west, and the finance “quality” of the cities in the center is bigger than those of the cities in the south and north, presenting the down “U” shaped distribution in the past ten years. 2) On the whole, the largest attracting linkages pattern of finance spatial combination is relatively stable. The largest attracting linkages pattern of finance spatial combination of Shanghai changes significantly, decreasing mainly the connection with the Zhejiang Province. The largest attracting linkages pattern of finance spatial combination of Jiangsu Province is relatively stable, meanwhile, that of Zhejiang Province has been strengthened. 3) The network structure of finance spatial combination has changed significantly. It was mainly a simple “polyline-based” spatial network structure with integrated financial cities among “Shanghai-Suzhou-Wuxi” in 2001. Then it was mainly developed into a simple “network-based” spatial network structure with networked finance cities, covering the partial cities around the core city Shanghai in 2006. In 2011, it has been developed into a complex “network-based” spatial network structure with regionalized financial cities, covering most of the cities in Changjiang River Delta. 4) The spatial distribution pattern of the financial center level is stable, Shanghai is the most prominent financial center, and Suzhou, Wuxi and Hangzhou were followed.
Key words: finance; spatial combination; center level identify; gravity model; K-means
YANG Zhi-min , HUA Xiang-yu , YE Ya-fen , SHAO Yuan-hai . Spatial Combination of Finance and Center Level Identify Based on K-means Clustering: A Case Study of the Changjiang River Delta[J]. SCIENTIA GEOGRAPHICA SINICA, 2015 , 35(2) : 144 -150 . DOI: 10.13249/j.cnki.sgs.2015.01.144
Fig.1 Finance “quality”图1 金融“质量” |
Fig.2 Spatial trends of finance quality图2 金融“质量”空间趋势 |
Fig.3 The largest attracting linkages of finance spatial combination图3 金融空间联系最大引力线 |
Table 1 The number of the potential value of finance表1 金融潜能值 |
2001年 | 2006年 | 2011年 | 2001年 | 2006年 | 2011年 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
潜能 | 比重(%) | 潜能 | 比重(%) | 潜能 | 比重(%) | 潜能 | 比重(%) | 潜能 | 比重(%) | 潜能 | 比重(%) | ||
舟山市 | 122 | 0.50 | 1018 | 0.53 | 7304 | 0.60 | 无锡市 | 2543 | 10.48 | 23728 | 12.48 | 154167 | 12.85 |
温州市 | 246 | 1.01 | 1900 | 0.99 | 12754 | 1.06 | 常州市 | 978 | 4.03 | 7810 | 4.10 | 49445 | 4.12 |
台州市 | 212 | 0.87 | 1721 | 0.90 | 10806 | 0.90 | 南京市 | 1211 | 4.99 | 7948 | 4.18 | 46801 | 3.90 |
丽水市 | 71 | 0.29 | 533 | 0.28 | 3649 | 0.30 | 镇江市 | 634 | 2.61 | 3718 | 1.95 | 24810 | 2.06 |
衢州市 | 58 | 0.23 | 400 | 0.21 | 2664 | 0.22 | 泰州市 | 364 | 1.50 | 2014 | 1.05 | 14854 | 1.23 |
金华市 | 263 | 1.08 | 2037 | 1.07 | 13593 | 1.13 | 南通市 | 983 | 4.05 | 6795 | 3.57 | 44272 | 3.69 |
绍兴市 | 1064 | 4.38 | 9384 | 4.93 | 56741 | 4.73 | 盐城市 | 155 | 0.63 | 756 | 0.39 | 5112 | 0.42 |
宁波市 | 997 | 4.11 | 8771 | 4.61 | 53824 | 4.48 | 淮安市 | 62 | 0.25 | 358 | 0.18 | 2535 | 0.21 |
杭州市 | 2546 | 10.49 | 20516 | 10.79 | 123755 | 10.31 | 宿迁市 | 25 | 0.10 | 151 | 0.07 | 1436 | 0.11 |
湖州市 | 491 | 2.02 | 3506 | 1.84 | 26090 | 2.17 | 徐州市 | 58 | 0.23 | 277 | 0.14 | 1967 | 0.16 |
嘉兴市 | 1216 | 5.01 | 8582 | 4.51 | 55004 | 4.58 | 连云港 | 35 | 0.14 | 190 | 0.09 | 1410 | 0.11 |
上海市 | 5715 | 23.56 | 39602 | 20.83 | 234927 | 19.58 | 扬州市 | 625 | 2.57 | 3304 | 1.73 | 22748 | 1.89 |
苏州市 | 3575 | 14.74 | 35097 | 18.46 | 228560 | 19.05 |
注:金融潜能值没有量纲,大小仅反映联系强度;比重值为各市潜能值占全部潜能值总和的百分比。 |
Fig.4 The network structure of finance spatial combination图4 金融空间联系网络结构 |
Table 2 The clusters in different K of K-means表2 K-means不同K值下聚类结果 |
K | 年 | 样本编号 | K | 年 | 样本编号 |
---|---|---|---|---|---|
3 | 2001 | 12 | 4 | 2001 | 12 |
9、13、14 | 9、13、14 | ||||
其他 | 7、8、11、15、16、17、19、25 | ||||
其他 | |||||
2006 | 9、12、13、14 | 2006 | 12 | ||
7、8、11、15、16、19 | 9、13、14 | ||||
其他 | 7、8、11、15、16、19 | ||||
其他 | |||||
2011 | 9、12、13、14 | 2011 | 12 | ||
7、8、11、15、16、19 | 9、13、14 | ||||
其他 | 7、8、11、15、16、19 | ||||
其他 | |||||
K | 年 | 样本编号 | K | 年 | 样本编号 |
5 | 2001 | 12 | 6 | 2001 | 12 |
9、13、14 | 9、13、14 | ||||
16、25 | 7、8、11、15、16、19 | ||||
7、8、11、15、17、19 | 2、6、25 | ||||
其他 | 10、17、18 | ||||
其他 | |||||
2006 | 12 | 2006 | 12 | ||
9、13、14 | 9、13、14 | ||||
16 | 16 | ||||
7、8、11、15、19 | 7、8、11、15、19 | ||||
其他 | 2、6、17 | ||||
其他 | |||||
2011 | 12 | 2011 | 12 | ||
9、13、14 | 13 | ||||
7、8、11、15、16、19 | 9、14 | ||||
2、6、10、17、25 | 7、8、11、15、16、19 | ||||
其他 | 10、17、18、25 | ||||
其他 |
注:样本编号1-25,依次代表舟山、温州、台州、丽水、衢州、金华、绍兴、宁波、杭州、湖州、嘉兴、上海、苏州、无锡、常州、南京、镇江、泰州、南通、盐城、淮安、宿迁、徐州、连云港、扬州。 |
Table 3 The classification of finance center表3 金融中心等级 |
等级 | 年 | 市 | |||
---|---|---|---|---|---|
一级 | 2001 | 上海市 | |||
2006 | 上海市 | ||||
2011 | 上海市 | ||||
二级 | 2001 | 苏州市 | 无锡市 | 杭州市 | |
2006 | 苏州市 | 无锡市 | 杭州市 | ||
2011 | 苏州市 | 无锡市 | 杭州市 | ||
三级 | 2001 | 南京市 | 宁波市 | 嘉兴市 | 绍兴市 |
常州市 | 南通市 | 镇江市 | 扬州市 | ||
2006 | 南京市 | 宁波市 | 嘉兴市 | ||
绍兴市 | 常州市 | 南通市 | |||
2011 | 南京市 | 宁波市 | 嘉兴市 | ||
绍兴市 | 常州市 | 南通市 | |||
四级 | 2001 | 其余市,共13个,名单略 | |||
2006 | 其余市,共15个,名单略 | ||||
2011 | 其余市,共15个,名单略 |
The authors have declared that no competing interests exist.
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