中国顶尖学术型人才空间分布特征及其流动趋势——以中国科学院院士为例

doi:10.11821/dlyj020180304

摘要/Abstract

摘要：

院士作为国家高端的科研领军人才,在创新发展、人才培养,科研引领,国家重大战略和核心技术突破方面挥着重要作用。以中国科学院755名院士为例,运用核密度分析与流动指数等空间分析方法,系统分析了中国顶尖学术型人才的空间分布特征及求学、就业与工作地变更三种空间流动规律。结果表明：① 院士出生地或籍贯集聚分布在经济条件良好、文化底蕴深厚的东部沿海地区;学历取得地主要集中在北京、上海与江苏等985、211高校聚集省区;初次工作地点在不同时代表现各异;最终工作地呈现向北京、上海等特大城市聚集的特征。② 求学（出生地—大学）流动由东部多个省份向北京、上海等国家中心城市近距离流动。毕业首次就业流动空间聚集程度小,且流向随院士所处时代的不同而变化。③ 46.23%的院士有工作地变更流动趋势。变更一次的院士其流动呈现“孔雀东南飞”的特点,具有明显的向东指向性和空间聚集性;变更两次及两次以上的院士,除最后一次流动外无明显空间指向性。④ 流动指数分析表明,北京与上海作为经济发达区既是强流入中心也是强流出中心,双向流动量大;而西藏、海南、澳门、内蒙古及广西等省份流动指数为0,属于附属型省份。

关键词: 学术人才, 创新驱动, 空间分布, 人才流动, 聚集特征

Abstract:

Academicians, as national high-end research leaders, play an important role in innovative development, talent training, scientific research guidance, national major strategies and core technology breakthroughs. Based on the survey of 755 academicians of the Chinese Academy of Sciences (CAS), the spatial analysis methods such as nuclear density analysis and mobility index are used in this paper to systematically analyze the characteristics of spatial distribution and three flow patterns of study, employment and changing workplaces of China's top academic talents. The results indicate that: (1) The birthplaces or native places of the academicians are mainly located in the eastern coastal regions with good economic conditions and profound cultural heritages; The academic qualifications of the academicians are mainly obtained in regions such as Beijing, Shanghai and Jiangsu Province, where top universities (also known as 985 and 211 universities) are located. The first workplaces of the academicians present different manifestations in different periods. The final workplaces are mainly distributed in megacities such as Beijing and Shanghai. (2) The flow of education (birthplace-university) moves from some provinces in the east to the regional center cities, such as Beijing and Shanghai. After graduation, the employment flow shows the characteristics of a large number of destinations which vary with different periods of different academicians, and the degree of spatial aggregation is small. (3) Some 46.23% of the academicians have a tendency of changing their workplaces. The flow of academicians who changed their workplaces one time presented the agglomeration characteristics of “peacocks fly to southeast”, which comes from a long narrative poem with the same name in ancient China, this article uses it to describe the phenomenon that many China's top academic talents moves to the southeast coastal zones, with obvious spatial orientation; the academicians who changed their workplaces two or more times have no apparent spatial aggregation feature except for the last flow by the cause of changing workplace. (4) The analysis of flow index shows that Beijing and Shanghai, as rapidly developed economic zones, are both strong inflow and strong outflow centers with large two-way flow; while Tibet, Hainan, Macao, Inner Mongolia, and Guangxi provincial-level regions have a zero mobility index, and they are subordinate provinces.

Key words: academic talents, innovation-driven, spatial distribution, talent flow, aggregation characteristics

周亮, 张亚. 中国顶尖学术型人才空间分布特征及其流动趋势——以中国科学院院士为例[J]. 地理研究, 2019, 38(7): 1749-1763.

Liang ZHOU, Ya ZHANG. The spatial distribution and flow trends of Chinese top academic talents: Based on the case study of academicians of Chinese Academy of Sciences[J]. GEOGRAPHICAL RESEARCH, 2019, 38(7): 1749-1763.

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	地学部		化学部		技术科学部		生命科学和医学学部		数学物理学部		信息技术学部
	人流量	占比	人流量	占比	人流量	占比	人流量	占比	人流量	占比	人流量	占比
求学人员流动	115	92.00	107	86.29	118	89.39	114	81.43	119	82.07	79	88.76
就业人员流动	72	57.60	75	60.48	82	62.12	91	65.00	85	58.62	51	57.30
一次工作变更	24	19.04	29	23.39	22	16.67	22	15.71	20	13.79	7	7.87
二次工作变更	12	9.60	14	11.29	20	15.15	16	11.43	8	5.52	9	10.11
学部总人数	125		124		132		140		145		89

流动类型	省份类型	省份	数量（人）	比重（%）
流入地	强中心	北京、上海	2	5.88
	次强中心	广东、江苏、浙江、安徽、陕西、四川、天津	7	20.59
	弱中心	辽宁、湖北、山东、福建、湖南、江西、河南、贵州、山西、吉林、重庆、青海、河北、	13	38.24
	附属型	甘肃、香港、黑龙江、云南、台湾、宁夏、新疆、澳门、西藏、广西、内蒙古,海南	12	35.88
流出地	强中心	北京、上海、吉林	3	8.82
	次强中心	江苏、浙江、陕西、辽宁、湖北、甘肃、香港、黑龙江、	8	23.53
	弱中心	广东、安徽、四川、天津、山东、福建、湖南、江西、河南、贵州、山西、重庆、青海、河北、云南、台湾、宁夏、新疆	18	52.94
	附属型	澳门、西藏、广西、内蒙古、海南	5	14.71