中国城市网络关联格局的影响因素分析——基于电子信息企业网络的视角

doi:10.11821/dlyj020171009

摘要/Abstract

摘要：

城市网络关联格局影响因素的测度及其作用机理的解析是建立城市网络理论模型的关键环节。基于2017年中国电子信息100强企业网络视角构建城市网络,采用指数随机图模型定量测度了中国城市网络的影响因素,解析了城市网络生长发育的微观过程,并探索性的提出了理解中国城市网络发育机理的概念框架。研究发现：偏好依附效应和接收者（GDP）效应构成了中国城市网络中心性格局的微观基础,中国城市网络生长发育表现为择优选择的地理过程;互惠性链接深刻影响着城市间的关系格局,网络闭合机制逐步成为城市间链接关系的重要影响因素;空间距离对基于电子信息企业网络的城市网络约束作用并不明显,城市网络表现出在“流动空间”中生长发育的特征。

关键词: 城市网络, 指数随机图模型, 偏好依附, 流动空间

Abstract:

The identification of factors underlying the spatial structure of urban network and the analysis of its mechanism is the key to establishing theoretical models of urban network. Based on the ownership linkage model, the urban network in China is specified through the lens of top 100 corporate networks within the electronic information industry in the years of 2005 and 2017, and its structural characteristics are described from three aspects of centrality, linkages and triad census. Then, by using the exponential random graph models (ERGMs), an econometric analysis is conducted to identify the influencing factors, and the micro processes in the spatial growth of urban network are examined. Finally, by combining theories of resource dependence and transaction cost, a conceptual framework for comprehensively understanding the mechanisms driving urban network growth in China is suggested for further discussion. Three main findings are concluded. First, the preference attachment effect and the receiver (GDP) effect constitute the micro basis of centrality pattern of the urban network in China. Outdegree centrality is mainly affected by expansionary effect, and indegree centrality is affected by both convergent effect and receiver (GDP) effect, which has caused urban network growth to be a process of preferential selection. The economic mechanism of this process can be interpreted as the dependence of enterprise on specific assets, market thickness and other specific resources. Second, the influencing factors of city linkages tend to be diversified. While reciprocity remains the important mechanism in the linkages of urban network, network closure mechanism has gradually become an important factor in the relationship between cities. The reciprocity effect and the network closure mechanism constitute the micro basis of coherent subgroups in the urban network in China, which can be explained by the restrictive function of transaction cost. Third, geographical distance does not have a significant effect on the urban structure within the network of electronic information industry. The location of electronic information industry is more flexible, which makes the urban network grow in a “space of flows”. Cities between long distance established linkages from the first beginning of urban network development, promoting the expansion of urban network over a large spatial scale.

Key words: urban network, exponential random graph model, preference attachment, space of flows

盛科荣, 张红霞, 赵超越. 中国城市网络关联格局的影响因素分析——基于电子信息企业网络的视角[J]. 地理研究, 2019, 38(5): 1030-1044.

Kerong SHENG, Hongxia ZHANG, Chaoyue ZHAO. Determinants of the urban spatial network in China: An analysis through the lens of corporate networks within electronic information industry[J]. GEOGRAPHICAL RESEARCH, 2019, 38(5): 1030-1044.

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序号	2005年		2017年		2005—2017年出度变化	2005—2017年入度变化
序号	出度	入度	出度	入度	2005—2017年出度变化	2005—2017年入度变化
1	北京（25.15）	北京（15.27）	北京（19.62）	上海（11.11）	深圳（20.46）	上海（9.59）
2	深圳（9.28）	上海（14.67）	深圳（17.11）	北京（10.13）	北京（17.26）	北京（7.92）
3	杭州（9.27）	深圳（11.37）	杭州（5.56）	深圳（7.52）	福州（5.75）	深圳（5.89）
4	青岛（8.08）	长沙（2.99）	沈阳（4.48）	南京（3.49）	沈阳（4.60）	南京（3.96）
5	许昌（4.79）	西安（2.69）	福州（4.39）	苏州（3.05）	惠州（4.60）	苏州（3.45）
6	沈阳（4.19）	大连（2.68）	惠州（3.94）	成都（3.05）	上海（4.21）	成都（3.24）
7	苏州（3.58）	杭州（2.39）	上海（3.76）	广州（2.78）	南京（3.09）	广州（3.07）
8	厦门（2.99）	沈阳（2.39）	苏州（3.76）	杭州（2.59）	杭州（3.96）	南宁（2.94）
9	上海（2.69）	南京（2.39）	南京（3.49）	西安（2.41）	石家庄（3.83）	重庆（2.81）
10	绵阳（2.38）	武汉（2.39）	青岛（2.86）	武汉（2.41）	苏州（3.83）	杭州（2.68）

三方组类型	2005年	2017年	三方组类型	2005年	2017年
A, B, C	497062	517033	A→B←C, A→C	23	186
A→B, C	17450	44429	A←B←C, A→C	0	14
A?B, C	2521	4490	A?B?C	83	184
A←B→C	432	3286	A←B→C, A?C	16	51
A→B←C	165	412	A→B←C, A?C	22	183
A→B→C	323	1455	A→B→C, A?C	14	48
A?B←C	164	356	A→B?C, A?C	28	125
A?B→C	355	1520	A?B?C, A?C	7	28

参数[PNet中的代码]	2005年			2017年
参数[PNet中的代码]	估计值	标准误	t-比率	估计值	标准误	t-比率
弧[Arc]	-4.43*	0.28	-0.01	-4.16*	0.31	0.01
互惠性[Reciprocity]	2.63*	0.68	-0.04	1.58*	0.20	-0.07
交替-出-星[AoutS]	1.33*	0.16	0.07	1.67*	0.15	-0.03
交替-入-星[AinS]	0.83*	0.18	-0.02	0.63*	0.18	0.02
循环三方组[T10]	0.68	0.40	-0.08	0.54*	0.23	0.01
发送者（GDP）[sender]	1.11	0.69	-0.02	0.22	0.44	-0.04
接收者（GDP）[receiver]	1.90*	0.73	-0.02	1.95*	0.47	0.06
趋异性（GDP）[difference]	-0.44	0.81	-0.04	-0.81	0.56	0.01
空间距离[Covariate Arc]	-0.20	0.15	-0.03	-0.02	0.03	-0.01

参数[PNet代码]	2005年			2017年
参数[PNet代码]	观测值	预测值	t-比率	观测值	预测值	t-比率
弧[Arc]	149.000	149.833	0.096	342.000	341.992	-0.000
互惠性[Reciprocity]	23.000	22.748	0.148	49.000	49.077	-0.036
2-路径[2-path]	1079.000	1015.896	2.083	3576.000	3541.574	0.873
2-入-星[in-2-star]	571.000	520.065	0.600	1685.000	1313.611	0.112
2-出-星[out-2-star]	691.000	684.714	0.740	3013.000	3089.823	-0.449
交替-入-星[AinS]	178.953	177.798	0.060	486.270	485.872	0.013
交替-出-星[AoutS]	195.782	190.481	0.065	536.852	515.453	0.869
传递三方组[T9]	232.000	215.354	1.660	1181.000	1135.781	3.276
循环三方组[T10]	56.000	53.612	0.137	243.000	245.574	0.059
循环闭合[AT-C]	101.500	100.256	0.162	341.984	342.938	-0.052
发送者（GDP）[sender]	17.530	17.170	0.086	40.750	40.289	0.038
接收者（GDP）[receiver]	15.410	14.882	0.149	29.300	28.079	0.270
趋异性（GDP）[difference]	19.040	16.624	0.104	36.510	36.385	0.024
空间距离[Covariate Arc]	231.000	227.504	0.085	548.000	546.337	0.024
入度标准差	3.978	3.763	0.968	5.205	5.113	0.466
入度分布偏度	3.097	3.015	2.690	2.748	2.504	0.874
出度标准差	4.382	4.985	0.945	8.367	7.909	0.564
出度分布偏度	3.309	3.202	0.847	2.348	2.873	0.737