Analysis on the evolution of city network based on the flow of highly cited Chinese scientists

doi:10.3969/j.issn.1004-9479.2023.09.2021594

Abstract

Abstract:

The geographical flow of talents is affecting innovation activities worldwide, so the research on the spatial structure evolution of talent flows is particularly important.This article uses the 2014-2015 global list of highly cited scientists as raw data to retrieve the study and work experience of scientists, uses cities as the research scale, and uses the P-space model to build a highly cited Chinese scientist flow network. At the same time, uses quantile regression analyzes the impact of whether scientists in core cities are more productive. We found that: (1) The city network of highly cited Chinese scientists has the characteristics of a small-world network, and the network space structure presents a "Y-shaped" structure. China-U.S., China-Europe, and China-Australia are the main sides. (2) This article analyzes the source cities and the target cities in which scientists move, and finds that Beijing, Cambridge(UStates), Shanghai are the city with a high degree.(3) The distribution of scientists shows a trend of spreading with the development of the stage.(4) Scientists in core cities have more high-quality paper output. The increase in the degree of centrality of scientists' target cities has a greater impact on the output of scientists.Key words: highly cited Chinese scientists; talent flow; P-space model; quantile regression

摘要：

人才的地理流动正在影响着世界范围内的创新活动，对人才流动的空间结构及演化研究显得尤为重要。以2014—2015年全球高被引科学家名单为原始数据，对科学家的学习和工作经历进行检索，以城市为研究尺度，借助P-space模型构建高被引华人科学家流动网络，并利用分位数回归分析城市是否位于核心城市对科学家产出的影响。发现：①高被引华人科学家流动城市联系网络具有小世界网络特征，网络空间结构呈现以中国-美国、中国-欧洲、中国-澳大利亚为主要框架的“Y形”结构；②本文对科学家流动的城市出入度进行分析，发现北京、剑桥（美）、上海为高入度高出度城市；③科学家的分布随着阶段的发展，呈现扩散趋势；④位于核心城市的科学家有更多高质量的论文产出，科学家的迁入城市度中心性的提升对科学家的产出影响更大。

关键词: 高被引华人科学家, 人才流动, P-space模型, 分位数回归

Kang SUN, Yuefang SI. Analysis on the evolution of city network based on the flow of highly cited Chinese scientists[J]. World Regional Studies, 2023, 32(9): 109-119.

孙康, 司月芳. 基于高被引华人科学家流动的城市网络空间演化分析[J]. 世界地理研究, 2023, 32(9): 109-119.

Figures/Tables 8

Fig.1 P-space model the conceptual diagram of the city network of talent mobility

Tab.1 Knowledge network indicators and meanings

指标	表达式	变量含义	解释
度中心性	$D = ∑ j = N x i j$	$D$ 表示度中心性，通过节点i与其他节点关联数量表示。 $x i j$ 表示节点i与节点j的是否关联，如果有联系为1，反之为0	指行为主体与其他主体直接相连的个数统计，表征行为主体的中心程度
平均路径长度	$L = 1 12 n (n - 1) ∑ i > j d i j$	$L$ 表示平均路径长度， $d i j$ 表示节点i与节点j之间的联系距离， $n$ 指总节点数量	定义网络中两节点之间的距离为连接这两个节点的最短路径的边数 (拓扑最短距离)，网络的平均路径长度定义为任意两个节点之间距离的平均值
聚类系数	$C = 1 n ∑ i = 1 n e i k i (k i - 1)$	C表示聚类系数，n指总节点数量， $k i$ 表示节点i的最大可能产生的邻接节点数量， $e i$ 表示节点i与邻接节点产生的实际联系数量	指网络中节点与其所有邻接点之间实际的边数与最大可能边数的比值

Tab.1 Knowledge network indicators and meanings

指标	表达式	变量含义	解释
度中心性	$D = ∑ j = N x i j$	$D$ 表示度中心性，通过节点i与其他节点关联数量表示。 $x i j$ 表示节点i与节点j的是否关联，如果有联系为1，反之为0	指行为主体与其他主体直接相连的个数统计，表征行为主体的中心程度
平均路径长度	$L = 1 12 n (n - 1) ∑ i > j d i j$	$L$ 表示平均路径长度， $d i j$ 表示节点i与节点j之间的联系距离， $n$ 指总节点数量	定义网络中两节点之间的距离为连接这两个节点的最短路径的边数 (拓扑最短距离)，网络的平均路径长度定义为任意两个节点之间距离的平均值
聚类系数	$C = 1 n ∑ i = 1 n e i k i (k i - 1)$	C表示聚类系数，n指总节点数量， $k i$ 表示节点i的最大可能产生的邻接节点数量， $e i$ 表示节点i与邻接节点产生的实际联系数量	指网络中节点与其所有邻接点之间实际的边数与最大可能边数的比值

Fig.2 P-space model city connection network "core-periphery" analysis

Fig.3 The spatial structure of the network of highly cited Chinese scientists in the mobile city

Fig.4 Statistics of city net in and out

Fig.5 Evolution of the scientist's mobile network

Fig.6 Display of quantile regression results

Tab.3 Quantile regression results

	所有城市	停留城市			迁入城市			迁出城市
分位点	模型1	模型2	模型3	模型4	模型5	模型6	模型7	模型8
分位点	degree	degree	国内	国外	degree	国内	国外	degree
q20	0.046^***	0.036^***	0.037^**	0.105	0.047^***	0.043^***	0.071^***	0.000
	(－0.006)	(－0.013)	(－0.015)	(－0.079)	(－0.010)	(－0.008)	(－0.027)	(－0.034)
q40	0.044^***	0.029^**	0.022	0.087^**	0.054^***	0.032^**	0.096^***	0.020
	(－0.010)	(－0.013)	(－0.015)	(－0.037)	(－0.014)	(－0.013)	(－0.025)	(－0.023)
q60	0.054^***	0.010	0.020	0.052	0.061^***	0.037^**	0.111^***	0.000
	(－0.010)	(－0.019)	(－0.015)	(－0.070)	(－0.013)	(－0.016)	(－0.017)	(－0.040)
q80	0.055^***	0.026	0.001	0.013	0.062^***	0.033^*	0.138^***	0.003
	(－0.013)	(－0.031)	(－0.020)	(－0.092)	(－0.023)	(－0.019)	(－0.031)	(－0.024)

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