Arid Land Geography ›› 2023, Vol. 46 ›› Issue (9): 1524-1535.doi: 10.12118/j.issn.1000-6060.2022.581
• Regional Development • Previous Articles Next Articles
YANG Yu1(),SONG Futie1(),ZHANG Jie2
Received:
2022-11-08
Revised:
2022-12-13
Online:
2023-09-25
Published:
2023-09-28
YANG Yu, SONG Futie, ZHANG Jie. Spatial structure characteristics and influencing factors of financial network of China based on geodetectors[J].Arid Land Geography, 2023, 46(9): 1524-1535.
Tab. 2
Geographical concentration of headquarters in 2010 and 2020"
2010年 | 2020年 | 2010年 | 2020年 | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
地址 | 总部/个 | 占比/% | 地址 | 总部/个 | 占比/% | 地址 | 出度 | 占比/% | 地址 | 出度 | 占比/% | |||
上海市 | 141 | 22.24 | 上海市 | 199 | 23.86 | 北京市 | 93509 | 68.55 | 北京市 | 119875 | 56.41 | |||
北京市 | 118 | 18.61 | 北京市 | 156 | 18.71 | 上海市 | 16953 | 12.43 | 上海市 | 26605 | 12.52 | |||
深圳市 | 63 | 9.94 | 深圳市 | 100 | 11.99 | 深圳市 | 9283 | 6.81 | 深圳市 | 19395 | 9.13 | |||
杭州市 | 18 | 2.84 | 广州市 | 23 | 2.76 | 广州市 | 1081 | 0.79 | 福州市 | 3069 | 1.01 | |||
广州市 | 16 | 2.52 | 杭州市 | 21 | 2.52 | 西安市 | 989 | 0.73 | 天津市 | 2741 | 1.06 | |||
天津市 | 15 | 2.37 | 南京市 | 16 | 1.92 | 天津市 | 972 | 0.71 | 南京市 | 2474 | 1.29 | |||
南京市 | 13 | 2.05 | 天津市 | 16 | 1.92 | 南京市 | 961 | 0.70 | 杭州市 | 2254 | 1.16 | |||
成都市 | 11 | 1.74 | 成都市 | 10 | 1.20 | 武汉市 | 873 | 0.64 | 武汉市 | 2162 | 0.92 | |||
重庆市 | 10 | 1.58 | 重庆市 | 13 | 1.56 | 福州市 | 842 | 0.62 | 广州市 | 2148 | 0.62 | |||
西安市 | 9 | 1.42 | 福州市 | 9 | 1.08 | 长春市 | 731 | 0.54 | 西安市 | 1965 | 0.63 | |||
总和 | 414 | 65.30 | 总和 | 563 | 67.51 | 总和 | 126194 | 92.51 | 总和 | 182688 | 84.75 |
Tab. 3
Geographical concentration of branches in 2010 and 2020"
2010年 | 2020年 | 2010年 | 2020年 | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
地址 | 分支/个 | 占比/% | 地址 | 分支/个 | 占比/% | 地址 | 入度 | 占比/% | 地址 | 入度 | 占比/% | |||||
上海市 | 3328 | 3.17 | 上海市 | 4854 | 3.06 | 上海市 | 4285 | 3.14 | 上海市 | 6376 | 3.00 | |||||
北京市 | 3056 | 2.91 | 北京市 | 4715 | 2.97 | 北京市 | 3911 | 2.88 | 北京市 | 6046 | 2.85 | |||||
重庆市 | 2213 | 2.11 | 重庆市 | 2859 | 1.80 | 重庆市 | 2772 | 2.03 | 重庆市 | 4357 | 1.69 | |||||
广州市 | 2058 | 1.96 | 广州市 | 3384 | 2.13 | 广州市 | 2489 | 1.82 | 深圳市 | 3762 | 2.05 | |||||
天津市 | 1917 | 1.83 | 深圳市 | 2739 | 1.72 | 天津市 | 2301 | 1.69 | 成都市 | 3697 | 1.56 | |||||
深圳市 | 1648 | 1.57 | 成都市 | 2827 | 1.78 | 深圳市 | 2199 | 1.61 | 广州市 | 3590 | 1.74 | |||||
成都市 | 1621 | 1.54 | 天津市 | 2828 | 1.78 | 成都市 | 2041 | 1.50 | 天津市 | 3315 | 1.77 | |||||
杭州市 | 1416 | 1.35 | 杭州市 | 2074 | 1.31 | 杭州市 | 1822 | 1.34 | 杭州市 | 2901 | 1.19 | |||||
苏州市 | 1374 | 1.31 | 武汉市 | 2257 | 1.42 | 苏州市 | 1681 | 1.23 | 武汉市 | 2676 | 1.37 | |||||
武汉市 | 1271 | 1.21 | 苏州市 | 2127 | 1.34 | 武汉市 | 1562 | 1.15 | 西安市 | 2578 | 1.26 | |||||
总和 | 19902 | 18.96 | 总和 | 30664 | 19.31 | 总和 | 25063 | 18.37 | 总和 | 39298 | 18.48 |
Tab. 4
Geographical detection results of impact factors"
年份 | 区域 | X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 |
---|---|---|---|---|---|---|---|---|---|
2010 | 全国 | 0.294 | 0.083 | 0.206 | 0.683 | 0.683 | 0.255 | 0.212 | 0.347 |
东部 | 0.285 | 0.073 | 0.258 | 0.680 | 0.680 | 0.328 | 0.248 | 0.454 | |
中西部 | 0.718 | 0.652 | 0.571 | 0.865 | 0.560 | 0.789 | 0.796 | 0.146 | |
核心 | 0.259 | 0.077 | 0.169 | 0.668 | 0.670 | 0.219 | 0.174 | 0.319 | |
边缘 | 0.653 | 0.505 | 0.335 | 0.592 | 0.383 | 0.673 | 0.387 | 0.073 | |
2015 | 全国 | 0.290 | 0.093 | 0.493 | 0.704 | 0.381 | 0.381 | 0.132 | 0.362 |
东部 | 0.315 | 0.081 | 0.486 | 0.701 | 0.450 | 0.449 | 0.155 | 0.471 | |
中西部 | 0.771 | 0.656 | 0.628 | 0.839 | 0.636 | 0.875 | 0.666 | 0.176 | |
核心 | 0.248 | 0.083 | 0.465 | 0.688 | 0.346 | 0.345 | 0.091 | 0.330 | |
边缘 | 0.620 | 0.474 | 0.280 | 0.488 | 0.545 | 0.734 | 0.429 | 0.085 | |
2020 | 全国 | 0.401 | 0.083 | 0.375 | 0.962 | 0.718 | 0.347 | 0.194 | 0.292 |
东部 | 0.471 | 0.075 | 0.470 | 0.970 | 0.716 | 0.471 | 0.221 | 0.282 | |
中西部 | 0.779 | 0.591 | 0.616 | 0.680 | 0.613 | 0.845 | 0.831 | 0.297 | |
核心 | 0.361 | 0.089 | 0.469 | 0.962 | 0.701 | 0.304 | 0.141 | 0.301 | |
边缘 | 0.559 | 0.516 | 0.121 | 0.402 | 0.227 | 0.649 | 0.320 | 0.032 |
Tab. 5
Interactive detection results of impact factors in 2010"
影响因子 | X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 |
---|---|---|---|---|---|---|---|---|
X1 | 0.294 | |||||||
X2 | 0.340↗ | 0.083 | ||||||
X3 | 0.426↗ | 0.280↗ | 0.206 | |||||
X4 | 0.687↗ | 0.994↖ | 0.994↖ | 0.683 | ||||
X5 | 0.688↗ | 0.994↖ | 0.995↖ | 0.687↗ | 0.682 | |||
X6 | 0.422↗ | 0.295↗ | 0.300↗ | 0.686↗ | 0.688↗ | 0.255 | ||
X7 | 0.349↗ | 0.304↖ | 0.343↗ | 0.687↗ | 0.687↗ | 0.351↗ | 0.212 | |
X8 | 0.686↖ | 0.989↖ | 0.992↖ | 0.686↗ | 0.687↗ | 0.686↖ | 0.468↗ | 0.347 |
Tab. 6
Interactive detection results of impact factors in 2015"
影响因子 | X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 |
---|---|---|---|---|---|---|---|---|
X1 | 0.289 | |||||||
X2 | 0.315↗ | 0.093 | ||||||
X3 | 0.501↗ | 0.552↗ | 0.493 | |||||
X4 | 0.709↗ | 0.989↖ | 0.708↗ | 0.704 | ||||
X5 | 0.385↗ | 0.389↗ | 0.504↗ | 0.710↗ | 0.381 | |||
X6 | 0.384↗ | 0.389↗ | 0.709↗ | 0.710↗ | 0.460↗ | 0.381 | ||
X7 | 0.520↖ | 0.304↖ | 0.970↖ | 0.989↖ | 0.974↖ | 0.974↖ | 0.132 | |
X8 | 0.707↖ | 0.979↖ | 0.704↗ | 0.708↗ | 0.711↗ | 0.711↗ | 0.974↗ | 0.381 |
Tab. 7
Interactive detection results of impact factors in 2020"
影响因子 | X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 |
---|---|---|---|---|---|---|---|---|
X1 | 0.401 | |||||||
X2 | 0.995↖ | 0.083 | ||||||
X3 | 0.722↗ | 0.566↖ | 0.375 | |||||
X4 | 0.969↗ | 0.995↗ | 0.987↗ | 0.963 | ||||
X5 | 0.722↗ | 0.996↖ | 0.721↗ | 0.985↗ | 0.718 | |||
X6 | 0.405↗ | 0.566↖ | 0.511↗ | 0.969↗ | 0.722↗ | 0.347 | ||
X7 | 0.611↖ | 0.237↗ | 0.506↗ | 0.996↗ | 0.733↗ | 0.479↗ | 0.194 | |
X8 | 0.483↗ | 0.564↖ | 0.720↖ | 0.978↗ | 0.724↗ | 0.483↗ | 0.398↗ | 0.292 |
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