Spatio-temporal evolution of urban network in the Guangdong-Hong Kong-Macao Greater Bay Area ——Based on listed company organization relations

doi:10.3969/j.issn.1004-9479.2018186

Abstract

Abstract:

Urban network evolution is an important basis for urban regional planning and construction. This paper presents the shifting urban network created by listed companies in Guangdong-Hong Kong-Macao Greater Bay Area between 2000 and 2016. The methodology used to examine cities' connection, local group and overall network structure is based on the interlocking network model and the Social Network Analysis. The results show that the headquarters of listed companies are highly agglomerated, and its radiation scope is gradually expanding. Meanwhile, the strong connection between Hong Kong, Shenzhen and Guangzhou constitutes a solid contact corridor. Along with the development of network structure, the dual core status of Shenzhen and Hong kong is becoming more and more highlighted. As for flow direction, Hong Kong is a strong export-oriented city, while the others are input-oriented cities, but the imbalance of output and input is gradually weakening. In addition, there is an evident phenomenon of subgroups in the studied area, we find that the subgroups built on geographical proximity is gradually disappearing, cross regional integration is becoming prominent. Finally, the overall urban network presents a relatively high density and a tendency of decentration. At the same time, the urban network has changed from formal polycentricity to functional polycentricity, and it has entered the centrifugal stage.

Key words: urban network, interlocking network model, Social Network Analysis, corporate organization, Guangdong-Hong Kong-Macao Greater Bay Area

摘要：

城市网络演化是城市-区域规划及建设的重要依据。通过链锁网络模型及社会网络分析法，利用上市企业总部-分支机构数据，分析粤港澳大湾区城市网络的演化特征。结果显示：粤港澳大湾区上市企业总部呈高度集聚分布，总部经济的辐射范围逐步扩大。港深广之间高强度联系构筑一条联通廊道，香港、深圳的双核心地位凸显。城市网络单向流动特征明显，除香港为输出型城市，其他城市均为输入型，流入-流出不对称性逐渐减弱。粤港澳大湾区城际联系由地理邻近的片区化组团向跨片区融合发展转变。整体城市网络呈现高密度、去中心化趋势，并由形式多中心向功能多中心转变，进入离心化的功能多中心发展阶段。

关键词: 城市网络, 链锁网络模型, 社会网络分析, 企业组织, 粤港澳大湾区

Yao XIONG, Li HUANG. Spatio-temporal evolution of urban network in the Guangdong-Hong Kong-Macao Greater Bay Area ——Based on listed company organization relations[J]. World Regional Studies, 2019, 28(5): 83-94.

熊瑶, 黄丽. 粤港澳大湾区城市网络的时空演化[J]. 世界地理研究, 2019, 28(5): 83-94.

Figures/Tables 8

Tab.1 Service value assignment criteria

标准	服务值
总部	5分
总投资额≥1亿元	4分
1000万≤总投资额＜1亿元	3分
100万≤总投资额＜1000万	2分
总投资额＜100万	1分
未设分支机构	0分

Tab.2 The number of listed companies' headquarters and branches in 2000, 2008 and 2016

地区	总部			分支机构
地区	2000	2008	2016	2000	2008	2016
总计	568	879	1017	527	1542	3470
香港	300	434	528	64	205	602
深圳	143	237	253	132	364	889
广州	49	86	94	130	297	551
佛山	18	27	31	34	86	181
东莞	12	23	26	46	153	320
中山	13	19	23	27	69	116
珠海	19	23	23	18	83	203
澳门	4	6	13	25	116	258
惠州	1	9	10	31	108	238
江门	5	9	10	15	50	79
肇庆	4	6	6	5	11	33

Tab.3 Changing ranks and proportion of listed companies' headquarters in 2000, 2008 and 2016

地区	2000年		2008年		2016年
地区	排序	占比	排序	占比	排序	占比
香港	1	52.8%	1	49.4%	1	51.9%
深圳	2	25.2%	2	27.0%	2	24.9%
广州	3	8.6%	3	9.8%	3	9.2%
佛山	5	3.2%	4	3.1%	4	3.0%
东莞	7	2.1%	5	2.6%	5	2.6%
中山	6	2.3%	7	2.2%	6	2.3%
珠海	4	3.3%	5	2.6%	6	2.3%
澳门	9	0.7%	10	0.7%	8	1.3%
惠州	11	0.2%	8	1.0%	9	1.0%
江门	8	0.9%	8	1.0%	9	1.0%
肇庆	9	0.7%	10	0.7%	11	0.6%

Tab.4 Relative connectivity of top fifteen city-dyads in 2000, 2008 and 2016

2000年			2008年			2016年
城市对	排序	P_ab	城市对	排序	P_ab	城市对	排序	P_ab
香港-深圳	1	1.00	香港-深圳	1	1.00	香港-深圳	1	1.00
香港-广州	2	0.76	香港-广州	2	0.57	香港-广州	2	0.53
深圳-广州	3	0.34	深圳-广州	3	0.33	深圳-广州	3	0.37
香港-东莞	4	0.33	香港-东莞	4	0.31	香港-东莞	4	0.31
香港-佛山	5	0.20	深圳-东莞	5	0.27	深圳-东莞	5	0.30
深圳-东莞	6	0.18	深圳-惠州	6	0.19	深圳-惠州	6	0.24
香港-中山	7	0.17	香港-佛山	7	0.19	香港-珠海	7	0.16
广州-佛山	8	0.16	香港-珠海	8	0.16	香港-佛山	8	0.16
香港-惠州	9	0.15	香港-中山	9	0.15	香港-惠州	9	0.16
香港-江门	10	0.12	广州-佛山	10	0.15	深圳-佛山	10	0.15
深圳-惠州	11	0.12	香港-惠州	11	0.13	广州-佛山	11	0.14
香港-珠海	12	0.11	香港-澳门	12	0.13	广州-珠海	12	0.14
香港-澳门	13	0.11	广州-东莞	13	0.11	深圳-珠海	13	0.13
深圳-佛山	14	0.10	香港-江门	14	0.11	香港-澳门	14	0.13
广州-东莞	15	0.07	深圳-珠海	15	0.11	广州-东莞	15	0.13

Tab.5 Relative connectivity in 2000, 2008 and 2016

地区	2000年		2008年		2016年
地区	排序	RC_a	排序	RC_a	排序	RC_a
香港	1	1.00	1	1.00	1	1.00
深圳	2	0.63	2	0.79	2	0.91
广州	3	0.52	3	0.55	3	0.59
东莞	4	0.23	4	0.31	4	0.36
惠州	6	0.12	6	0.19	5	0.24
佛山	5	0.17	5	0.21	6	0.24
珠海	8	0.10	8	0.18	7	0.23
中山	7	0.10	7	0.15	8	0.17
江门	9	0.07	9	0.12	9	0.13
澳门	10	0.06	10	0.08	10	0.10
肇庆	11	0.04	11	0.05	11	0.06

Tab.6 Relative out-degrees, relative in-degrees and node symmetry of cities in 2000, 2008 and 2016

地区	2000年			2008年			2016年
地区	$C a o$	$C a I$	$N S I a$	$C a o$	$C a I$	$N S I a$	$C a o$	$C a I$	$N S I a$
香港	32.9	6.1	-0.69	30.6	6.9	-0.63	26.9	9.3	-0.49
深圳	7.9	12.6	0.23	10.6	12.3	0.07	11.7	13.8	0.08
广州	4.6	12.4	0.46	5.5	10.0	0.29	6.8	8.5	0.11
东莞	0.5	4.4	0.80	0.7	5.1	0.76	1.6	5.0	0.52
佛山	0.5	3.2	0.73	0.9	2.9	0.53	1.5	2.8	0.30
珠海	0.4	1.7	0.62	0.9	2.8	0.51	1.4	3.1	0.38
中山	0.7	2.6	0.58	0.6	2.3	0.59	1.3	1.8	0.16
澳门	2.3	2.4	0.02	1.3	3.9	0.50	1.2	4.0	0.54
惠州	0.3	3.0	0.82	0.4	3.6	0.80	0.7	3.7	0.68
江门	0.1	1.4	0.87	0.2	1.7	0.79	0.4	1.2	0.50
肇庆	0.2	0.5	0.43	0.2	0.4	0.33	0.3	0.5	0.25

Tab.6 Relative out-degrees, relative in-degrees and node symmetry of cities in 2000, 2008 and 2016

地区	2000年			2008年			2016年
地区	$C a o$	$C a I$	$N S I a$	$C a o$	$C a I$	$N S I a$	$C a o$	$C a I$	$N S I a$
香港	32.9	6.1	-0.69	30.6	6.9	-0.63	26.9	9.3	-0.49
深圳	7.9	12.6	0.23	10.6	12.3	0.07	11.7	13.8	0.08
广州	4.6	12.4	0.46	5.5	10.0	0.29	6.8	8.5	0.11
东莞	0.5	4.4	0.80	0.7	5.1	0.76	1.6	5.0	0.52
佛山	0.5	3.2	0.73	0.9	2.9	0.53	1.5	2.8	0.30
珠海	0.4	1.7	0.62	0.9	2.8	0.51	1.4	3.1	0.38
中山	0.7	2.6	0.58	0.6	2.3	0.59	1.3	1.8	0.16
澳门	2.3	2.4	0.02	1.3	3.9	0.50	1.2	4.0	0.54
惠州	0.3	3.0	0.82	0.4	3.6	0.80	0.7	3.7	0.68
江门	0.1	1.4	0.87	0.2	1.7	0.79	0.4	1.2	0.50
肇庆	0.2	0.5	0.43	0.2	0.4	0.33	0.3	0.5	0.25

Tab.7 Density of cohesive subgroups of urban network in 2000, 2008 and 2016

子群	2000年			2008年			2016年
子群	1	2	3	1	2	3	1	2	3	4
1	20.5	25.8	14.4	71.5	71.8	31.6	-	255.8	65.0	4.0
2	4.8	5.3	1.7	20	21.3	5.5	85.5	21.1	15.1	4.2
3	1.6	1.2	0.8	4.8	3.8	3.9	28.7	8.7	4.8	1.3
4							3	1.7	1.7	-

Tab.8 The polycentric degree of urban network in 2000, 2008 and 2016

多中心度	2000年	2008年	2016年
$P F i n$	0.17	0.20	0.27
$P F o u t$	0.16	0.16	0.16
$P$	0.17	0.18	0.22

Tab.8 The polycentric degree of urban network in 2000, 2008 and 2016

多中心度	2000年	2008年	2016年
$P F i n$	0.17	0.20	0.27
$P F o u t$	0.16	0.16	0.16
$P$	0.17	0.18	0.22

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