| 中国城市网络结构及空间关联性——来自“网络流”数据的分析 |
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| 引用本文：王耀中，黄选爱.中国城市网络结构及空间关联性——来自“网络流”数据的分析[J].财经理论与实践,2021,(2):112-118 |
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| 中文摘要:基于中国城市之间的关联特征和溢出效应，将人力资本变量纳入引力模型，运用社会网络分析和机器学习方法并依据主要节点城市的有向数据，考量城市网络结构及其关联性。结果显示：新引力模型可有效衡量样本城市的网络关联效应，金融状况、港口距离和高等教育资源丰裕度是决定城市引力流量重要原因，政府干预对城市引力流作用不明显；城市空间分布呈现明显的四级分层特征，但从人力资本对城市引力流影响看，作为中心城市的广州和深圳在网络拓扑中心的稳定性相对较弱。 |
| 中文关键词:社会网络分析 网络流 引力模型 城市结构 |
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| The Complex Network Structure and Spatial Correlation Effect of Chinese Cities—— Analysis of Network Flow Data |
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| Abstract:Based on the correlation characteristics and spillover effects between cities in China, this paper first brings the human capital into the gravity model, and uses social network analysis and machine learning methods to analyze the current situation of urban "network flow" and its influencing mechanism based on the directed data formed by 39 node cities. It is showed: first, the new gravity model is feasible to measure the network association effect of sample cities. With the improvement of the interaction of migration, logistics and capital flow, the more accurate the calculation results are. Secondly, financial financing, port distance and higher education resource abundance are the important sources of urban gravity flow, and government intervention has no obvious effect on urban gravity flow. Thirdly, the spatial distribution of cities in China is characterized by four levels: national central cities, regional central cities, regional geographic "intermediary" cities and marginal cities. Although Beijing, Shanghai, Guangzhou and Shenzhen are all in the center of the national urban spatial network, the stability of Guangzhou and Shenzhen in the network topology center is relatively weak compared with Beijing and Shanghai in terms of the impact of human capital on urban gravitational flow. |
| keywords:social networks flow Gravity Model urban structure |
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