"This paper uses data from the OPCS [Office of Population Censuses and Surveys] Longitudinal Study and the National Health Service Central Register to examine the contention that the South East region of England acts as a kind of 'upward social class escalator' within the British urban and regional system. To establish that this is so it is shown firstly, that the South East attracts to itself through inter-regional migration a more than proportional share of the potentially upwardly mobile young adults; secondly, that it promotes these young people along with its own young adults at rates which are higher than elsewhere in the country; and finally, that a significant proportion of those who achieve these higher levels of status and pay then 'step off' the escalator. They do this by migrating away from the South East at later stages of their working lives and at or near to retirement." (SUMMARY IN FRE AND GER)excerpt

"The role of location-specific amenities in human migration decisions, and subsequently regional development, is explored. A framework is developed which motivates a new assessment of existing alternative models of regional development, indicating the need for additional modeling efforts which focus upon amenities as critical elements in such analyses. The approach hinges upon the notion that amenity values are capitalized into wages, rents, or other local prices. This process of capitalization enables researchers to explore the implicit value that society places upon amenities, which can then be used in assessing future regional-development trends in a more comprehensive manner."excerpt

Cities make it easier for humans to interact, and one of the main advantages of dense, urban areas is that they facilitate social interactions. This paper provides evidence for the US suggesting that the resurgence of big cities in the 1990s is due, in part, to the increased demand for these interactions and due to the reduction in big city crime, which had made it difficult for urban residents to enjoy these social amenities. However, while density is correlated with consumer amenities, we show that it is not correlated with social capital and that there is no evidence that sprawl has hurt civic engagement.

以高端归国人才为对象,通过构建“归国人才三角”概念框架,设计基于高端移动性人才的城市关系构建方法,对人才迁移驱动的城市网络构建进行了初步探讨。研究认为：① 借助“行动者空间移动带来的空间动力”这一视角,运用高端归国人才在城市间的移动模拟城市间关系,可为构建城市网络提供一条新路径。② 基于人才流动的城市网络构建,有助于破解城市关系矩阵数据挖掘与案例城市选取的难题,是知识经济时代城市网络研究的新方向。③ “归国人才三角”概念框架将人才流动与城市网络两方面研究融为一体,既可以通过人才流动探讨城市创新空间组织特征,也可以通过城市网络方法分析人才流动网络,对城市网络研究和人才战略实施都具有一定参考价值。

As one of the important contents of urban geography, regional urban spatial organization is turning from the traditional city system research to the city networks research. There are two methods to construct city networks. One is using the relational data including the goods flow, capital flow, information flow, population flow among the city to construct city networks; the other one is building models to simulate the relationship between the city, such as interlocking network model and the gravity model. Inspired by the view of the "a spatial dynamic that changes whenever actors move in space" proposed by Menzel, this paper attempts to construct the city networks by using returnees' movement between cities. First, this paper discusses the theoretical relationship between talent mobility and city network. Relational economic geography uses the idea of "based on construction of individual relationship " into network analysis, evolutionary economic geography puts forward the thought of "dynamic proximity", and the research of early uses mobility of talents of high technology to measure the relationship between cities. These had laid a theoretical foundation for the construction of the city network based on the flow of talents movement. Then, we establish the conceptual framework of "triangle model of Chinese returnees". The returnees have the fellow villager's relationship resources in their native places, the classmates' relationship resources in overseas cities and the coworkers' relationship resources in the work city, and these are used in the analysis framework. By integrating the migration data of the returnees, we establish the cities' connections based on the flow of talent, and design a method to calculate the city connectivity. Finally, we select 6 high-end returnees of an innovative team in China to carry out a case analysis. Through the mining of the returnees' city resumes, we successfully construct the city network composed of six talents. As a result, it is suggested that: (1) city innovation network should be examined at national scale under global perspective, and the returnees are good carriers in such researches; (2) the "triangular model" reflects Chinese characteristics, which can explain the formation mechanism of city innovation network better; (3) the returnees team in Beta Pharma Co., Ltd is a typical case that it successfully facilitates inter-city innovation cooperation; and the "triangular model" framework provides a brand-new perspective and analytical method to further research on city innovation network. It should be noted that this paper focuses on theory construction and method exploration, and it needs to use a great quantity returnees' data to carry out systematic research on urban network in the next step, in order to make a greater contribution to urban network theory and the strategy of talents creative practice.

人文?经济地理学非常重视地理事物的区位规律研究，经典区位论已从产业区位转向城市区位，直至人类社会进步核心动力的创新活动区位。人是创新的主体，创新人才区位研究是地理学、区域科学与经济学研究热点与难点之一。为此，系统梳理创新人才的多尺度区位选择及其空间集聚文献，辨析创新人才流动、区位选择与城市（群）可持续发展的关联逻辑，构建城市群创新人才区位选择与空间集聚分析框架，尝试解析创新人才区位选择及其空间集聚机理，进而提出分工深化与人才异质性背景下城市群创新人才集聚的研究展望。

The human-economic geography attaches great importance to the study of the location law of geographical things, and the classical location theory has shifted from industrial location to urban location, and finally to the location of innovation activities. However, People are the main body of innovation, and the research on the location of innovative talents is one of the hotspots and difficulties in the field of geography, regional science and economics, the efficiency of the flow of innovative talents and the degree of their spatial agglomeration have become important indicators to measure the global competitiveness of a country or region. To this end, this study systematically sorts out the multi-scale location selection of innovative talents and their spatial agglomeration literature in urban agglomeration, summarizes the classification of innovative talents and the theoretical targets of their heterogeneous location selection, explores the flow of global talents and full picture of urban agglomeration, and analyses the logic related to the flow of innovative talents, location selection and sustainable development of urban agglomerations, and builds an exploratory framework for the location selection and spatial agglomeration analysis of innovative talents in urban agglomerations. It reveals the influence mechanism of the location selection of innovative talents and their spatial agglomeration, and then puts forward the research prospect of innovative talents aggregation in urban agglomeration under the background of deepening division of labor and heterogeneity of talents and attempts to provide reference for China's talents policy practice.

人才是推动地区创新发展的核心动力,也是未来城市竞争的关键。基于第六次全国人口普查和2015年全国1%人口抽样调查数据,运用集聚度、基尼系数等空间统计分析以及负二项回归模型方法,对2010—2015年中国城市高学历人才的空间演化特征及驱动因素展开研究。结果发现：① 人才呈现出高度集中且不平衡的空间分布格局,但不平衡的趋势有所缓解,分布方向为“东北—西南”走向。人才分布的空间溢出效应显著,城市群是人才集聚的高地。② 经济机会是影响中国高学历人才集聚的主导力量,其中工资是核心驱动因素,地区的发展水平、产业结构同样产生显著影响。③ 控制经济机会的影响后,以教育和医疗等基础公共服务、交通可达性和城市消费设施为核心的地方品质变量在高学历人才的空间集聚过程中发挥重要作用。④ 城市群与非城市群人才驱动因素存在显著差异：经济机会是城市群和非城市群人才集聚的主要驱动力;教育、消费、交通以及自然环境等地方品质变量对城市群人才集聚的影响更为显著。本研究为城市和区域人才政策和区域发展政策的制定提供借鉴与参考。

Talents are the core driving force for regional innovation and development, and they are also the key to urban competition in the future. Based on the data of the sixth population census and the national 1% population sampling survey in 2015, this paper uses the concentration index (CI), Gini index (GI) and several related spatial analysis methods to examine the spatial patterns of highly educated talents across China and the drivers behind these patterns from 2010 to 2015. The results are as follows: (1) Talents show highly concentrated and unbalanced spatial distribution patterns at the city level over the five years, but the trend of concentration and imbalance has gradually eased. Results from standard deviation ellipse (SDE) indicate that the distribution direction of highly educated people is "NE-SW". Furthermore, there is a significant spatial spillover effect in the distribution of talents, with three major urban agglomerations of China as the highlands. (2) Economic opportunities are the dominant drivers for the distribution of highly educated talents in China. Among them, wages are the core driver, and gross GDP and industrial structure of each city also exert a significant impact. (3) After controlling the impacts of economic opportunities, local quality variables represented by basic public services (e.g., education and medical care), transportation accessibility and urban consumption facilities play an important role in the distribution of highly educated talents. (4) There are significant differences between the driving factors for talents in urban agglomerations and non-urban agglomerations: economic opportunities are the main driving force for the distribution of talents in both urban agglomerations and non-urban agglomerations, while local qualities including education, consumption, transportation and natural environment have a more significant impact on the distribution of talents in urban agglomerations. This study provides references for the formulation of urban and regional talent policies and regional development policies.

基于2000和2010年全国人口普查分县数据和地级行政单元数据,采用不均衡指数和空间自相关分析等方法,刻画中国高技能劳动力和一般劳动力的空间分布格局及其变化,并采用空间滞后模型,识别高技能劳动力与一般劳动力空间分布的决定因素及其变化。结果表明：① 两类劳动力总体的空间特征均为东南密集,西北稀疏;② 2000—2010年,高技能劳动力在空间分布上呈集中化的趋势,而一般劳动力呈分散化的趋势;③ 十年间高技能劳动力集聚区虹吸作用加强,其分布越发集中于沿海特大城市群,而一般劳动力集聚区回波作用加强,其分布越发均衡;④ 空间回归分析结果表明,职工平均工资、是否直辖市或省会、每万人在校高校学生、是否沿海城市、常住人口数和空间溢出效应在十年中一直都是决定高技能劳动力和一般劳动力空间分布的主要因素,而失业率、中学生师比、二氧化硫排放量和绿地率在2010年也成为影响两类劳动力空间分布的主要因素。对比两类劳动力的模型结果可得,高技能劳动力的空间分布受行政因素、高校因素和地区生活舒适度的影响更大,而一般劳动力的空间分布受劳动力市场因素的影响更大。

Based on 2000 and 2010 county-level and city-level National Demographic Census data, we applied inequality index and spatial auto-correlation analysis to specify the spatial patterns of China's high-skilled and general labor force and its variation during the decade. We also use the SLM model to further analyze the main factors that contribute to the spatial patterns and the changes of them. The results are as follows: (1) The density of high-skilled labors and general laborers was high in southeastern China and low in northwestern regions. (2) From 2000 to 2010, the high-skilled labors had been accumulating spatially while the normal labors had been dispersing. (3) The siphon effects of the high-skilled-labor clusters, which were more inclined to accumulate in coastal metropolitan area compared with 10 years ago, also enhanced during the decade. On the contrary, the echo effect of general labor clusters strengthened and attained the uniform distribution. (4) Results of spatial regression analysis showed that the level of wages, urban administrative level, the number of ten thousand college students, the coastal cities, population and spatial spillover effects in the ten years were the main factors determining the spatial distribution of high-skilled and general labor force, while the unemployment rate, high school teacher ratio, concentration of sulfur dioxide and afforestation in 2010 became the main factors influencing the two types of labor distribution. Comparing the two types of labor model, we found that the spatial distribution of the high-skilled labors was more influenced by government forces, while the spatial distribution of general laborers was influenced by market forces.

基于2000年、2010年中国人口普查分地级及以上行政区数据和2015年中国各省份1%人口抽样调查数据，采用变异系数、泰尔系数、基尼系数测度中国高学历人才比重分布的空间不均衡程度，并采用面板数据Tobit随机效应模型，识别影响高学历人才比重空间分布的主要因素。结果表明：① 中国的人才比重分布表现出极大的不均衡性，人才比重的高低与城市等级密切相关，主要表现在直辖市、省会城市、计划单列市等行政区吸引了大量人才，而普通地级城市人才比重相对较低；② 10 a间人才比重的变异系数、基尼指数和泰尔指数均有所下降，表明人才比重分布的空间不均衡程度有所下降；③ 平均工资水平、生活成本、城市等级、每万人高校在校学生数、每万人医院卫生院床位数、人均科学事业、教育事业经费与人才比重呈正相关，中学生师比、万人互联网用户数与人才比重呈负相关，失业率、单位面积二氧化硫排放量、绿地率对人才比重没有影响。

Based on the data from the 2000 and 2010 China censuses by prefecture level and above, and the 2015 1% population sample survey data of Chinese provinces, this article studies the spatial pattern evolution of the distribution of highly educated talents and its influence mechanism in China. The coefficient of variation, Theil coefficient and Gini coefficient are used to measure the spatial imbalance of the highly educated talents proportion. The panel data Tobit model is used to identify the main factors affecting the spatial distribution of the proportion of the highly educated talents. The results show that: 1) The distribution of the highly educated talents proportion in China shows a great imbalance, closely related to the city level, which is highlighted in administrative regions such as municipality, provincial capitals, and city specifically designated in the state plan, to which a large number of talents are attracted, while the talents proportion in ordinary prefecture-level cities is relatively low; 2) In the past 15 years, the coefficient of variation, Gini coefficient and Theil coefficient of the talents proportion have all declined, indicating that the spatial imbalance of the talents proportion has decreased; 3) Average wage level, cost of living, city hierarchy, students enrollment of regular institutions of higher education per 10 000 people, the number of hospital beds per 10 000 people, expenditure for education per capita, expenditure for science and technology per capita are positively correlated with talents proportion; The ratio of middle school students to teachers and the number of internet users per 10 000 people are negatively correlated with talents proportion; Unemployment rate, green rate, sulfur dioxide emissions per unit area have no significant impact on the talents proportion.

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。

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.

基于2005~2015年全国统计年鉴数据,采用统计地图法和空间自相关分析,分析中国高校科研人才的时空分布特征（不包括港澳台数据）,并采用面板负二项回归模型,识别高校科研人才空间分布的决定因素。研究结果表明:①中国的高校科研人才分布表现出较强的空间不均衡性,总体趋势为东南密集,西北稀疏,科研人才高度集中在北京、天津、上海;②高校科研人才分布具有一定的空间集聚特征,其热点地区为北京、天津、上海和江苏等沿海省市,冷点地区集中分布在广大的西部地区和黑龙江省。2005~2015年,高级职称人才的空间集聚程度有所减弱,而中初级职称科研人才的空间集聚程度有所加强;③人才政策、高等教育规模和层次、公共服务水平和信息化水平是所有高校科研人才空间分布的主要影响因素。从职称差异的角度来看,薪酬和失业率等经济因素仅对初级职称科研人才的分布有影响,医疗、文化公共服务水平和人才政策仅对中高级职称科研人才的分布有影响,高等教育的规模与层次对各种职称人才的分布均有影响;从地区差异的角度来看,西部地区高校科研人才受到经济因素影响更为明显,而中东部高校科研人才受到公共服务水平影响的更为明显。

Based on the national statistical yearbook data from 2005 to 2015, this article studies the spatial pattern evolvement of the distribution of scientific research talents in China (limited to data, the study area not includes Hong Kong, Macao and Taiwan) and its influence mechanism by applying panel negative binomial regression and spatial auto-correlation analysis. The result shows that: 1）The distribution of scientific research talents in China showed great imbalance. Generally speaking, talents agglomerate in the southeast but sparse in the northwest. Scientific research talents are mainly agglomerate in Beijing, Tianjin and Shanghai; 2） The distribution of scientific research talents has some spatial agglomeration characteristics. Hot spots are the eastern coastal areas, especially Beijing, Tianjin, Shanghai and Jiangsu while the cold point area is concentrated in the western region and Heilongjiang Province. The area of cold points has decreased from 2005 to 2015; 3） Talent policy, the development of universities and the level of public service are the main factors that determines the overall spatial distribution of scientific research talents, but no evidence support that other factors have significant influence. With the increase of professional qualification, the influence of the economic factors on the scientific research talent gets weaker, but the influence of public service level gets stronger. On the other hand, from the perspective of regional differences, compared with the western regions, the scientific research talents working in middle and eastern universities are more likely to be affected by the public service.

知识经济时代人才是决定区域经济竞争力的重要资源，各地政府发起的人才争夺战日益白热化，中央政府也一再强调应当引导合理、公正、畅通、有序的人才社会性流动。基于南京市高校《毕业生就业质量报告》和一手问卷调查数据，进行毕业生择校迁移与择业迁移模式并采用多项逻辑斯蒂回归模型探究其影响因素研究。研究表明：① 南京市高校毕业生中近84%来自东部和中部地区，而择业迁移进一步向东集聚。② 毕业生整体迁移路径呈“东西非对称U型”格局，长三角城市群构成毕业生流动的核心区域；就业地经济发展水平明显高于生源地且往往与生源地邻近。③ 个体的教育背景、家庭和社会网络和对就业机会的评估都会显著影响毕业生的流动类型选择。

In the era of knowledge economy, talent are acknowledged as a key driver for innovation, and thereby determine the vitality and competitiveness of regional economy. The competition of attracting talent initiated by some second-tier cities is increasingly heating up. The central government has repeatedly stressed the importance of reasonable, fair, smooth and orderly mobility of talent to facilitate economic transformation. Against this background, based on the “Graduate Employment Quality Report” and first-hand questionnaire survey data of university graduates from Nanjing, this study adopted chord diagram and map visualization to depict the previous and subsequent migration patterns of university graduates. Furthermore, multinominal logistic regressions were employed to explore the factors underlying graduates’ migration choice. The results show that the the number of people flowing into Nanjing for higher education decreases with the increase in distance. Nearly 84% of the graduates originally come from the eastern and central regions. After graduation, their subsequent migration shows further concentration to the east. Using a measurement of regional circulation, the overall migration path of graduates presents an “east-west asymmetric U-shaped” pattern, with the Yangtze River Delta region constituting the core area for graduates to flow to. The economic development level of the destination of graduates’ subsequent migration is obviously higher than that of their domicile, and the destination is often in close proximity to their domicile. At the individual level, the rank of the university, discipline, social netweok, and graduates’ evaluation on job opportunities all have significant influence on graduates’ migration decisions.

自2017年以来,“人才争夺战”如火如荼,城市步入以人才为核心要素的高维竞争阶段。作为人才争夺战的主要目标和人力资本的重要承载者,本文聚焦中国“一流大学”毕业生,基于2018年《毕业生就业质量报告》,运用Cartogram地图呈现了毕业生的空间流动特征,并梳理政府人才政策文件构建了人才政策评价指标体系,运用有向迁移模型剖析毕业生流动的影响因素。结果表明：① 就学地存在粘滞性,但区域差异显著,毕业生向一线城市集聚。② 城市吸引力水平分异明显,部分城市通过发布人才政策以补充地方经济、舒适性吸引力的不足。③ 经济维度要素与城市舒适性均能有效吸引毕业生流入,较高房价收入比会引发“挤出效应”。人才政策中,落户政策对毕业生流入起激励作用。

Human capital is the key driver of urban innovation and development. In 2017, the "vying for talent" competition was initiated by some second-tier cities, since then cities have been competing each other fiercely for recruiting and retaining talent. This paper focuses on China′s "first-class" university graduates, who are regarded as the main target of the "vying for talent" competition and an important carrier of human capital. Based on 2018 Graduate Employment Reports, this paper demonstrates the spatial mobility of graduates using Cartogram. In addition, an evaluation system for assessing talent policy is constructed through analyses of the talent policies issued by different cities. With a directional migration model, the determinants of graduate mobility are explored. The results reveal that there are significant regional disparities in retention rates, with the geographical patterns of the Y-shaped low-value areas in the northeastern, northwestern, and central regions, and the U-shaped high-value areas covering east coast to southwest. Graduates have been accumulating spatially, and the T-shaped cluster along east coast and the Yangtze River Economic Belt has formed. The spatial patterns of "neighborhood interaction" and "long-distance unidirectional flow" are presented between the city of university and the city of employment. Moreover, the differences in city attractiveness are remarkable. First-tier cities are preferred by graduates, even though limited talent policies have been implemented there. Some second-tier cities have issued talent policies to make up for the lack of attractiveness in economy and amenities. The findings suggest that economic factors, such as income level and technological innovation, as well as amenities including natural and cultural environment, educational resources, and public transportation are positively associated with the volume of graduates inflow. It has been found that a higher ratio of house price to income has squeezed out university graduates. With regard to talent policies, only relaxed hukou policy has a strong effect on attracting graduates, whereas the effects of housing and employment policies are relatively limited. However, it needs to be noted that talent policies may take a longer time to show effects, which requires follow-up investigation. In addition, heterogeneity at the individual level in terms of place of origin, major, etc. should be explored in the future studies.

本文聚焦于高学历人才“逆向梯次迁移”现象，基于武汉市中小学教师招聘考试数据，运用二元Logistic回归模型分析正规就业驱动下北上广深至武汉的高学历人才逆向梯次迁移，结合粘性理论解释其机制。结果表明： ① 正规就业导向下，高学历人才逆向梯次迁移已成为人才迁移的重要趋势之一，以武汉为目的地的跨省城际迁移中近1/4呈现逆向梯次迁移意愿，非应届生占比高于应届生。② “逆向梯次迁移”受粘性因素、个人因素和城市因素影响。粘性因素即“户籍地粘性”与“就学地粘性”起到关键作用;包括“年龄”“毕业学校级别”“学历级别”和“户籍地所在城市级别”在内的个人因素和城市因素也存在影响。③ 与西方受宗教、种族、家庭和个人价值等因素影响不同，基于乡缘、学缘关系产生的户籍地、就学地粘性更深刻作用于中国的高学历人才择业迁移选择，引发逆向梯次迁移现象。

This study focuses on the trend and mechanism of highly educated population migrating down to urban hierarchy. Based on the recruitment data of Wuhan's primary and middle school teachers, we analyse the downward migration trend of candidates from first-tier cities (Beijing, Shanghai, Guangzhou, and Shenzhen) to the second-tier city (Wuhan), and explain its mechanism by drawing on the theory of stickiness. The results of binary logistic regression indicate that moving down the urban hierarchy has become one of the significant migration trends of the highly educated population driven by formal employment. In our case, nearly a quarter of inter-provincial migration samples show a willingness to move down the urban hierarchy, characterized by a higher proportion of non-fresh graduates than fresh graduates. For first-tier cities, Beijing has the highest proportion of highly educated population migrating down the urban hierarchy, followed by Shenzhen, Shanghai, and Guangzhou. Meanwhile, Beijing has the highest proportion of fresh graduates while Shenzhen has the highest proportion of non-fresh graduates in this downward migration. This downward trend is deeply influenced by the stickiness of place, the individual factors and the urban factors. The highly educated population is back to Wuhan (the capital city of Hubei) from first-tier cities. Additional factors including age, education background, and the city level of hukou place also have certain effects on the downward tendency. In particular, young people or graduates from "Double-First Class" universities are more likely to move down the urban hierarchy. Furthermore, unlike the return migration in the West, which is influenced by religion, race, family, and personal values, both the stickiness of the place of hukou based on regional affiliation and the stickiness of the place of study based on educational affiliation profoundly affect the choice of job-choosing migration of highly educated population in China, leading to the migration down the urban hierarchy. This study demonstrates the dynamic mechanism of the trend of highly educated talents moving down the urban hierarchy in depth, which provides both a theoretical and an empirical basis for policy-making for highly educated population migration.

从城市舒适性理论出发，系统梳理了创意人才及其空间集聚过程的相关文献，构建了驱动中国设计创意人才空间集聚的城市舒适性指标体系，通过大数据方法从国内设计师社交平台站酷网获取设计创意人才的属性数据，运用地理集中指数、区位熵、负二项回归方法，深入剖析了中国设计创意人才的空间集聚特征与机理。研究表明：① 中国设计创意人才及其创作在空间上呈现出显著的集聚现象，以北京为主的一线城市最为集中，但杭州、长沙、武汉、郑州这些新兴城市也表现良好，甚至超过部分一线城市；② 城市舒适性理论在解释中国设计创意人才空间集聚时具有很强的解释力，其中文化环境、教育环境和自然环境是影响设计创意人才空间集聚的核心因素；③ 证实了西方创意阶层理论的部分结论，发现文化基础设施、文化遗产、文化旅游、高素质人力资本、温暖的气候与良好的空气质量是提升城市对创意人才吸引力的重要因子，同时发现科研投入对设计创意人才集聚具有明显挤出效应，并且西方学者普遍强调的生活环境舒适性因子对设计创意人才影响有限。为深入理解创意人才的空间过程及其影响机理提供了详实的实证案例，为地方政府改善城市创意环境提供了参考依据。

Creative talents are the new dynamics to urban and regional economic development. However, limited attention was paid to the spatial agglomeration mechanism and driving factors of creative talents. Based on the theory of urban amenities, the paper establishes an index system of urban amenities to the spatial agglomeration of creative talents in design sectors in China. An extensive dataset with over 3 million designers registered in the online platform zcool.com was adopted as the sample for this study. It aims to examine the spatiality of China’s creative talents and its association with urban amenities for cultural production. Using the quantitative methods such as location quotient, geographic concentration index, and the negative binomial regression method, we investigate the characteristics and mechanism of the spatial concentration of Chinese creative designers. The results suggest that: First, the distribution of creative designers shows a significant spatial agglomeration. First-tier cities like Beijing are the most dominated, but emerging cities such as Hangzhou, Changsha, Wuhan, and Zhengzhou are also well-performed, even better than some first-tier cities. Second, amenity-based approach has a strong explanatory power in explaining the agglomeration of Chinese design creative talents. The cultural environment, educational environment, and natural environment are the core factors that affect the agglomeration of design creative talents. Third, this study echoes with some conclusions of the creative class theory. Cultural infrastructures, cultural heritages, cultural tourism, high-quality human capital, warm climate, and good air quality were essential to enhance the city’s attractiveness to creative talents. Simultaneously, it is found that scientific research investment has a significant crowding-out effect on the accumulation of creative talents. The amenity factors of the living environment that were emphasized by Western scholars have a limited impact on agglomeration of creative talents. In conclusion, this research adopted a more inclusive urban amenity theory, and proved that urban amenities matter in the process of concertation of creative designers. It also provides a reference for local governments to promote their brain gain policy for creative talents.

Previous analysis and modelling of interregional migration in China have treated migrants as a homogenous group. The flow of skilled migration is the focus of recent research. However, the skilled and less‐skilled migrations have not been systematically analysed and compared in terms of their determinants. Previous modelling of interregional migration in China does not take network autocorrelation into consideration. This paper attempts to fill this research gap by modelling skilled and less‐skilled migrations in China using the eigenvector spatially filtered method. It is found that both skilled and less‐skilled migrants tend to move away from the interior to the coastal region. Results from the eigenvector spatially filtered negative binomial regression model show that, compared with the migration of less‐skilled people, the migration of skilled people is less influenced by the friction of distance, the regional unemployment rate, and the concentration of foreign investment but is more affected by the regional wage disparity. With respect to the effect of amenities, climatic amenities exert a strong influence on skilled migration but have positive effect on less‐skilled migration at origin and no effect at destination. Quality medical services are influential for the migration of less‐skilled people to destinations but no effect on skilled people. Copyright © 2016 John Wiley & Sons, Ltd.

This paper examines the spatial patterns and determinants of China's interprovincial skilled migration by using data from 2005 one per cent population sample survey. While the coastal region benefits from the influx of skilled labour, the rest of China suffers from severe brain drain. Estimates from zero‐inflated negative binomial gravity models indicate that employment opportunities, especially interregional wage differentials, play a dominant role in attracting skilled labour, and that the impact of amenities on skilled migration turns out to be small and less clear. Findings suggest that China's skilled people prioritize their career prospects over the quality of life in the migration decision‐making process.

In consideration of the issue of network autocorrelation and zero‐inflated migration data, the study constructs an eigenvector spatial filtering (ESF) hurdle gravity model (ESF HGM) to examine the determinants of China's skilled and less‐skilled internal migrations between 2010 and 2015. In our case, the ESF technique effectively reduces network autocorrelation bias, while the hurdle approach enhances the model prediction on the probability of zeros. Results from the ESF HGM have illustrated significant differences in the determinants between skilled and less‐skilled migrations. It is found that the gravity factors (population sizes at origins and destinations; migration distance), regional industrial structure, unemployment rate, and education level are more related to the migration of less‐skilled people. However, the migration of skilled people is more affected by the wage disparity, natural comforts, and medical services of a region. Our results have also highlighted the differences in some factors (employment rate; education level at origins) between the probability of having migration and the migration volume.

依据教育部直属高校2015届本科毕业生生源与就业数据,采用指标评价与冷热点分析方法,分析升读大学与本科就业两个流动阶段的人才流动地域模式及省域空间分布格局。研究表明：① 人才流动具有明显的本地空间粘滞性特征,地域模式包括“本地-跃迁”型、“本地-半依附”型和“本地-依附”型。② 省际层面形成沿东南沿海与长江沿岸分布的“弓形”格局,显示了优势区域的整体粘滞性对人才高地形成的意义。研究指出,把握关键节点、依托城市群来发挥粘滞作用有助于城市推进引智工作。

By looking into the enrollment and employment data of graduates from universities directly administered by China Ministry of Education and with the method of index evaluation and hot-cold spot analysis, this article analyzed the graduates’ regional flowing patterns of two flowing stages of enrolled in the university and employed after graduation, and the resulting spatial distribution at the provincial level. The study found that the flow of talent from the university to study in different stages, can more clearly reveal the characteristics of the geographical space for talents. The local spatial viscosity in different regions dominates the flow of talent, and geopolitical and income factors in subsequent plays a role of regional adjustment. In the two stages, the flow of talent has significant spatial viscous characteristics. The geographical pattern of the flow includes “local-leapfrog” mode, “local-semi adherent” mode and “local-adherent” mode. Under the influence of different factors, the enrollment stage is dominated by “local-(semi) adherent” mode due to the adherence to the geo-social relations, while the employment stage is dominated by “local-leapfrog” type, which is adhered to the multiple possibilities of regional employment opportunities and benefits. From the perspective of the provincial pattern formed by talent flow, however, the spatial distribution of talents at the level of provincial level is more flat, while the phase of employment flow is more polarized in the longitudinal distribution. The “arch” pattern along the southeastern coast and the Yangtze River is characterized in both two stages, and the Yangtze River Delta region belongs to the hot spot of talent. However, because of the lack of provincial integration and linkage, the centralization of talent is not significant enough in the central and western regions, which highlight the important effect of the dominant area’s viscosity in the formation of the high ground of talent. It is suggested that different cities should bring into full play the role of local glutinosity to enhance the work of introducing university intelligence, from the two stages of talent generation and with the help of the strength of the urban agglomeration.

以中国科学院1192 名院士为典型群体, 运用基尼系数、集中化指数和空间自相关识别和剖析了高级科学人才成长的时空规律及其演化机制。结果表明:① 中科院院士国内籍贯或出生地、国内最高学历(位)获取地和国内长期工作地, 在东中西3 大地带均呈现出较强的不平衡性;② 自20 世纪50 年代以来, 中科院院士在上述3 地的大部分省域表现出地理空间上显著的正的空间自相关性, 其国内籍贯或出生地、国内最高学历(位)获取地和国内长期工作地均呈现出集聚逐渐弱化的空间演化态势;③ 中科院院士在上述3 地带的HH集聚省域数量随年代推进分别呈现出先增后减、变化不大和逐渐增加态势, 主要分布于中国东部沿海省域及其邻近省域;④ 从20 世纪50 年代至今, 中科院院士在上述3 地带时空格局演化跃迁较多的是类型Ⅳ(省域本身及其邻居均保持了相同水平), 近半省域表现出了空间上的连续稳定性, 而类型Ⅰ、Ⅱ和Ⅲ在数量上也有所差异, 且存在部分省域跃迁明显的现象;⑤自然环境、经济基础、政治环境和文化教育的差异和变化不同程度的影响着中科院院士的总体空间分布和时空格局演化。

Taking 1192 academicians of Chinese Academy of Sciences (CAS) as a case study, temporal and spatial pattern and evolution mechanism of the development of senior science talents are identified and analyzed by using regional Gini coefficient, centralization index and ESDA method. The results show that: (1) At the provincial level, the locations where the academicians of CAS were native or born, acquired his or her highest education (degree), and had long-term work experiences, showed a strong imbalance among the eastern, central and western China. (2) Most of the locations of the three categories have shown significant and positive geospatial autocorrelation since the 1950s. In terms of agglomerative growth, during the four time periods from 1950 to 2010, including 1950s, 1980s, 1990s and the first beginning of the 21st century, the locations of native and birth places presented a spatial trend of gradual increase followed by gradual decrease, while the locations of both highest education (degree) and long-term work experience presented a spatial trend of gradual decrease. (3) HH concentrated locations of the three categories are mainly distributed in the eastern and coastal regions and their neighboring provinces, and the number of three types of locations presented the trends of increase followed by decrease, little change, and gradual increase, respectively. (4) Most provinces among the type IV locations of the three categories with transitions in the evolution of temporal and spatial patterns are the ones that maintain the same level within themselves and with the neighboring provinces during the four periods from 1950 to 2010, and nearly half of provinces presented continuous stability. Type I, II and Ⅲ provinces varied in numbers and some of the provinces had significant transitional phenomenon. (5) Differences and changes of natural resources, economic conditions, political and cultural environment, and educational atmosphere all more or less have influences on the overall spatial distribution and the evolution of temporal and spatial pattern of academicians of CAS.

Interregional migration has become an important driver shaping the economic landscape in China. By using 2010 Chinese census data, this study investigates the determinants of interregional migration and their implications for regional growth, estimated using principal component analysis, and a negative binomial regression model. In particular, it measures the roles of region size, income and lifestyle, green spaces, and employment opportunities in the origin and destination regions, and distance between the two regions. In addition to confirming the continuing influence of economic indicators as well as distance (owing to high moving costs), the findings presented herein add to the policy debate by encouraging regional policymakers to consider the effects of income, lifestyle, and green spaces. A strategy based on cheap labour is no longer encouraging growth in China, while many regions are suffering from environmental degradation. To address these shortcomings, an amenities‐based growth strategy concerning income, lifestyle, and green spaces for regional development is discussed, and policy implications are suggested. Copyright © 2015 John Wiley & Sons, Ltd.

区域人才筛选是人力资源市场化配置中的马太效应,其空间表现为受区域之间经济梯度力的作用,高级人才从小城市向大城市或从落后地区向发达地区的逆向流动。对440份调查问卷的分析表明,强势地区对高级人才筛选力量十分强大,这种流动在人才年龄段上表现为双峰特征,与家庭状况密切相关,但与距离基本无关。由于区域人才筛选严重影响区域和谐发展,影响民族团结、社会安定及人口质量,必须致力于建立一种反区域人才筛选机制,将弱势区域的经济开发与人才开发有机结合起来;对弱势地区高级人才建立地区年功序列制度;稳定弱势地区初次就业的高校毕业生并为企业吸引高级人才提供制度支持。

Regional selection of talents is a kind of Matthew effect in the condition of market disposing human resources, which shows as talents’migration shifting from developing regions to developed regions.The analyses of 440 questionnaires show that the selective power from developed regions or large cities is very strong.From the angle of regional shift, among the 147 migrants, the horizontal migration (the West to the West, the Middle to the Middle, the East to the East) makes up 36%, the converse migration (the West to the Middle or the East, the Middle to the East) makes up 52.4%, while the direct migration (the East to the Middle or the West, the Middle to the West) makes up only 11.6%.From the angle of urban shift, the horizontal shift (between same level cities) makes up 67.3%, the converse migration (low level cities to high level cities) makes up 25.5%, while the direct migration (high level cities to low level cities) makes up only 7.5%.Among the 293 persons who haven’t migraten, the subjective desire of converse migration is even stronger.Those who hope to migrate to the East make up 83.5%, while those who hope to migrate to the West and the Middle in all make up only 16.7%.From the angle of cities, among the 293 persons, those who hope to migrate to metropolises and large cities in all make up 81.2%, while those who hope to migrate to medium-sized and small cities in all make up only 18.8%.The analyses also show that the migrant talents distribute in two peak age groups, one is between 25 to 30 years old, another is 45 to 50, that the migration is closely related to talents’families, but unrelated to moving distance.As regional selection of talents seriously affects poor regions, it is important to establish a kind of anti-selection mechanisms among regions.This kind of mechanisms is the important foundation of regional harmonious development, the important condition of the unity of various nationalities, the important avenues to improve the quality of population of poor districts.To establish the anti-selection mechanisms, the central government should combine talents development with economic development of poor districts, should establish a kind of award system according to work years in poor districts, should stabilize those graduating students who obtain their initial employment in poor districts by taking effective steps, should supply a powerful economic support for poor districts to draw high-level talents, and should create favorable conditions for poor districts’talents.It is important to realize that this kind of mechanisms is not planned measures which restrict the flow of trained personnel.On the contrary, even the economic measures, the policies should be based on market to realize rational distribution of high-level talents.

知识经济时代人才是建设粤港澳大湾区世界级城市群的重要生产要素。本研究采用2005年、2010年和2015年广东省人口普查和1%抽样调查数据,以及香港和澳门对应口径的统计数据,以县市为基本单元,提出从受教育程度和职业技能两个口径测度人才集聚水平,系统解析粤港大湾区高学历与高技能人才集聚的演化格局及影响因素。结果表明：① 粤港澳大湾区作为中国经济高度发达地区,人才集聚优势高度集中在香港、澳门,内地珠三角城市群的人才集聚水平低于京津冀城市群和长三角城市群。② 2005—2015年,粤港澳大湾区高学历人才集聚持续均衡化,而高技能人才集聚优势仍然体现在香港、澳门,内地因为发展教育提升的高学历人力资本尚未完全有效转化为高技能人力资本。③ 香港人才集聚水平处于绝对领先,澳门、广州、珠海和深圳次之,而外围县市相对处于人才洼地,特别是制造业发达的佛山、东莞人才集聚水平相对偏低。④ 面板模型表明,服务业对高技能人才集聚的拉动效应强于高学历人才,而制造业的拉动作用并不突出。高等教育对高技能人才集聚的带动作用要弱于高学历人才。高薪资待遇有利于促进高学历人才集聚,但对高技能人才集聚的促进作用有限。新时期,亟需推动粤港澳三地管理制度衔接、产业转型升级和优质高等教育建设,推动粤港澳大湾区建设国际创新中心。

In the era of knowledge economy, talents concentration plays a key role in the development of a world-class urban agglomeration of Guangdong-Hong Kong-Macao Greater Bay Area (GHM). Based on the population census of Guangdong Province in 2010, the 1% population sampling survey in 2005 and 2015, and employment statistics in Hong Kong and Macao, this study measures the level of talents concentration from two perspectives of educational attainment and occupation on the county scale, and analyzes the evolution pattern and motivations of talents concentration in the GHM. The results show that: (1) GHM is one of the highly developed economic areas in China, and there is the absolute advantage of talents concentration in Hong Kong and Macao, while the level of talents concentration in the Pearl River Delta urban agglomeration is lower than that in the Beijing-Tianjin-Hebei and Yangtze River Delta urban agglomerations. (2) From 2005 to 2015, the spatial distribution of highly-educated talents in the GHM tends to be balanced, and there is also the advantage of the concentration of highly-skilled talents in Hong Kong and Macao. The increasing human capital defined by educational attainment in the mainland, which is caused by the expansion of college enrollment in China, has not been fully and effectively transformed into the advantage of human capital defined by occupation. (3) The level of talents concentration in Hong Kong plays an absolute leading role, followed by Macao, Guangzhou, Zhuhai and Shenzhen, while the counties and cities on the periphery of GHM have a low level of talents concentration. In particular, although Foshan and Dongguan have some developed manufacturing industries, their talents concentration level is relatively low. (4) The panel model shows that the service industry has a greater promoting impact on the concentration of highly-skilled workers than that of highly-educated labors, and manufacturing industry does not influence the talents concentration. Higher education plays a less important role in promoting the agglomeration of highly-skilled workers than that of highly-educated labors. High salary helps promote the concentration of highly-educated workers, while it does not boost the concentration of highly-skilled labors. In the new era, it is urgent to promote the cohesion of management systems in Guangdong, Hong Kong and Macao, the industrial transformation and upgrading, and the establishment of high-quality higher education, ultimately, building GHM into an international innovation center.

随着新型城镇化建设的推进，“胡焕庸线”两侧人口发展出现了新态势。本文基于第七次人口普查数据，依据地级尺度划定的“准胡焕庸线”，分析2010—2020年“胡焕庸线”两侧人口集疏模式的新特征，并探讨影响因素。研究表明：① “胡焕庸线”依然稳定，2020年东南半壁与西北半壁的人口比为93.5∶6.5；② 西北半壁人口份额仍然微增，但是增幅较之前明显下降； ③ 2010—2020年西北半壁人口集中化指数由降转增，西北半壁和东南半壁的人口分布集中化程度均有所提升；④ 东南半壁人口集疏分化不断强化，2010—2020年人口负增长区的面积占比已达54.22%，超过人口正增长区；西北半壁人口集疏的马太效应特征愈发明显，人口负增长区占比也已升至26.15%；⑤ 省会首府人口增长优势愈发突出，且西北半壁比东南半壁更为明显；⑥ 经济发展水平、城区人口规模等级、行政区划等级等因素与区域人口变动显著相关；西北半壁行政区划等级对人口增长的影响效应尤为突出。综合来看，西北半壁已经出现与东南半壁相似的人口集疏过程，人口集疏模式已突破了“胡焕庸线”。中国即将迎来人口总量拐点，人口城镇化进程逐步减速，密切关注“胡焕庸线”两侧人口发展的新特征，对推动全国各区域协调发展具有重要的战略意义。

The "Hu Line" is a geographical line connecting Heihe City and Tengchong City in China. It is densely populated on the southeast side of the "Hu Line" but sparsely populated on the northwest side. Since the implementation of New-type Urbanization strategy in 2014, the spatial pattern of population distribution on both sides of the "Hu Line" has changed a lot. We put forward the "Quasi-Hu Line" based on the boundaries of prefecture-level administrative divisions. According to the seventh population census data in 2020, this study focuses on the new characteristics of population spatial patterns and population growth patterns on both sides of the "Quasi Hu Line" from 2010 to 2020. We analyze the driving factors combined with the development of New-type Urbanization. The main conclusions are as follows: (1) The human-land relationship reflected by the "Hu Line" was still stable, and the overall distributions of densely populated and sparsely populated areas were also steady. In 2020, the population ratio of the southeast side to the northwest side was 93.5∶6.5. (2) The population share of the northwest side still increased slightly, but the increment was significantly lower than that before 2010. (3) The centralization index of the population on the northwest side turned from decline to increase after 2010, which indicated the concentration of spatial population distribution in the northwest half was similar to that in the southeast half. (4) The spatial differentiation of population growth on the southeast side has been strengthened. The negative population growth area accounted for 54.22% of the southeast side from 2010 to 2020, exceeding the positive population growth area. The population growth on the northwest side had also changed from the "relative balance" model to the "Matthew effect" model, with the negative population growth area accounting for 26.14% of the northwest side. (5) The relative population growth advantage of provincial capitals was becoming much more prominent. The spatial polarization process of population growth slowing down or negative growth was formed in the surrounding areas of provincial capitals. In addition, such a process on the northwest side was more significant than on the southeast side. (6) The driving factors, including the level of economic development, the scale of urban population size, and the level of administrative divisions, significantly impacted population growth. For the northwest side, the level of administrative divisions had a particularly significant impact on population growth, while the impact of urban agglomerations was not noticeable. In general, the northwest side had begun to present similar population spatial distribution patterns and driving factors with the southeast side. In the context of population migration and urbanization, the novel patterns of spatial population distribution reflected a kind of "breakthrough" of the "Hu Line". With China's total population turning to decline and urbanization entering the mid-late stages, more attention should be paid to the regional differences and coordination of demographical structures on both sides of the "Hu Line".

准确认识和把握21世纪前20年中国人口迁移的地理格局和影响因素，是推动新型城镇化建设及区域均衡发展的关键科学问题。借助特征向量空间滤波泊松伪最大似然估计（ESF PPML）引力模型，基于第六次和第七次全国人口普查数据、2005年和2015年全国1%人口抽样调查数据，研究了2000—2020年中国省际人口迁移的时空演化特征及影响因素。主要结论为：① 2000—2020年间人口迁移格局总体稳定，具有较强的不平衡性和网络溢出效应，大量人口从中部、西部、东北地区迁移至东部地区；② 人口迁移格局稳中有变，不平衡程度和空间集聚程度逐渐减弱，人口迁移和人口分布显现出南北分异趋势，东北地区人口迁出强度不断增强； ③ 传统引力模型因素（人口规模、地理距离）、地区社会经济发展因素（工资差异、科技教育投入占比、医疗水平）、社会网络因素、环境舒适度（PM_2.5浓度）以及生活成本（住宅价格占比）共同影响人口迁移格局；④ 区域经济差异对省际人口迁移的影响作用逐步弱化，人口迁移逐渐转变为对工资收入、高质量公共服务、环境舒适度等多因素考虑的决策过程，生活成本逐渐对人口迁移产生显著影响。

Understanding the geographical pattern and driving factors of China's population migration in the first 20 years of the 21st century has become a pivotal issue in promoting the construction of the new-type urbanization and interaction development between regions during the 14th Five-Year Plan period (2021-2025). Supported by the data of the sixth and seventh national population censuses and 2005 and 2015 national 1% population sample surveys, this paper aims to investigate the spatiotemporal evolution characteristics and influencing factors of interprovincial migration in China from 2000 to 2020. Relevant population geospatial analysis and the eigenvector spatial filtering Poisson pseudo-maximum likelihood estimation (ESF PPML) gravity model are employed. The following conclusions are drawn: (1) China's population migration patterns were generally stable from 2000 to 2020, where notable imbalance and network spillover effects were also detected. A large number of migrants from central, western and northeast parts of China tended to move to the eastern region. (2) The overall migration patterns between 2000 and 2020 revealed several changing characteristics. The degree of imbalance and spatial agglomeration of migration patterns gradually weakened. Yet, a north-south differentiation emerged. It is also found that the out-migration trend of the northeast region increased with the elaspe of time. (3) The ESF PPML model indicated that gravity factors (population scale, geographical distance), regional socio-economic factors (wage disparity, proportion of investment in science and technology and education, medical care), social network factors, environmental comfort (PM2.5 concentration) and living costs (proportion of housing price) drove the interprovincial migration pattern between 2000 and 2020. (4) The influence of regional economic differences on interprovincial migration reflected a weakening trend. The pattern of population migration gradually turned into the decision-making of diversified demands for the economy, public services and urban amenities, while the living cost factor represented by housing price had played a gradually significant part. The conclusion of this paper provides policy references for the construction of the new-type urbanization and coordinated development between regions during the 14th Five-Year Plan period in China.

以江苏省为研究区，探索了2010—2020年间流动人口在城、镇和乡（即城区、镇区和乡村3类地域类型）分布的时空分异和影响因素。研究发现：① 江苏省城和镇流动人口占常住人口的比例普遍增加，但镇的增幅明显高于城，乡的流动人口占比却在近一半的市县出现减少。② 江苏省流动人口苏南—苏中—苏北分布的梯度格局尚未发生根本性改变，苏南仍是吸引流动人口的高地，但苏中和苏北的镇对省内流动人口的吸引显著增强。③ 苏南一些乡和苏中、苏北一些镇流动人口占比有明显提升，体现了乡镇对流动人口的集聚能力。④ 模型结果显示，政府社会投入的增加有利于增强城、镇、乡对流动人口的吸引力；城镇居民恩格尔系数在城和镇对两类流动人口占比的影响有所不同；产业的影响因城、镇和乡的不同而分异：工业在城、镇、乡均为正向影响；产业结构的高级化在城镇仅对省内流动人口占比具有正向作用；农林牧渔业在乡为负向影响。

Based on the data of the sixth and seventh national censuses (Jiangsu province), this paper seeks to explore the spatiotemporal differentiation and influencing mechanism of the distribution of floating population in cities (districts), towns, and rural areas from 2010 to 2020. The findings show that: (1) The floating-permanent population ratio in cities and towns in Jiangsu generally increased from 2010 to 2020, but the rate of increase in towns was significantly higher than that in cities; the floating-permanent population ratio decreased in nearly half of the units in rural areas. (2) The gradient pattern (south Jiangsu - central Jiangsu - north Jiangsu) of the distribution of the floating population remained stable. South Jiangsu is still a highland for attracting the floating population, but towns in central and north Jiangsu have become attractive to the floating population in the province. There are also differences in the distribution of the intra- and inter-floating population in cities, towns, and rural areas. (3) The share of the floating population in parts of rural areas in south Jiangsu and some towns in central and north Jiangsu increased significantly, reflecting the agglomeration capacity of the towns and rural areas for the floating population. (4) The results of the models show that the increase in government social investments is important to enhancing the attractiveness of cities, towns, and rural areas to the floating population; the Engel coefficient of urban residents has different effects on intra- and inter-floating population in cities and towns; the impacts of industry in cities, towns and rural areas are positive; the upgrading of the economic structure only has a positive effect on the ratio of intra-floating population in cities and towns; the effect of the farming industry is negative in rural areas.

基于第五、六、七次全国人口普查数据,本文分析了2000—2020年中国人口分布的空间格局、集散态势和影响因素。结果表明,中国人口分布依然保持东密西疏的基本格局,胡焕庸线两侧的自然地理环境差异性决定了这一格局的长期稳定性;人口空间集中化趋势在21世纪初的10年内主要发生在东南半壁,但在2010—2020年间成为各区域的共同特征。省会城市的人口集聚能力持续增强,2010—2020年的优势更加凸显,但这种行政力量主导的首位度提升效应将随着全省发展阶段的演进而逐渐弱化。人口变动的区域分化明显：沿海城市群的空间连绵化逐渐形成了强吸引力、高承载力的沿海人口增长带,东北地区几乎转向全面的人口收缩,中部各省依托省会展开的人口竞争依然激烈,川渝黔地区普遍实现了人口的止降回增,西北地区则面临人口收缩风险。此外,区域人口增长的驱动因素逐渐从经济要素主导转为经济要素和舒适性要素并重,各地区舒适性的差异可能将成为影响未来人口空间格局变化的重要因素。

Using data of the latest three decennial censuses, this paper systematically analyzes the overall spatial pattern, changing trends, and influence mechanisms of China's population distribution since 2000. The study reveals that the general population distribution pattern of "dense east and sparse west" in China has not changed, which is determined by the differences in natural geographic environments on the two sides of Hu Line. In the first decade of the century, population concentration was mainly found in the southeastern area, but it has been observed in all regions in the recent decade. Provincial capitals have become the winners of population competition and the advantages of population growth of provincial capitals have been further strengthened in the recent decade compared with 2000-2010. However, the administrative power-led effects of the increasing dominance of provincial capitals will gradually weaken with the development of their provinces. The regional differentiation of population changes is also apparent: coastal region gradually forms a dense belt of population and towns with solid population attraction based on the development of urban agglomerations; the northeast region almost turns to a complete population shrinking status; central provinces still compete fiercely for population based on their capitals; the population of Sichuan, Chongqing, and Guizhou has stopped the decline and returned to growth; and the northwest region faces the risk of population loss. In addition, this paper finds that the driving factors of regional population growth have gradually changed from economic factors to both economic and amenity factors, and the difference of amenities among regions may become an important factor for future changes in China's population geography.

城镇化是城镇和乡村人口变动共同作用的结果。本文从城乡人口变动的视角提出了城镇化地域类型的划分方法，基于历次全国人口普查数据，考察了中国城镇化地域类型的时空演变及其影响因素。研究发现中国的城镇化于1990年后进入快速阶段且当前仍在加速，但城镇化的实现路径存在较强的时空动态性：① 随着人口自然增长减缓和乡城迁移扩张，全国城镇化地域类型由城乡扩张型转变为城镇吸引型，乡村人口减少对城镇化的贡献持续增强；② 城镇吸引型是各地最主要的城镇化地域类型，但2010年以来大量地区因乡村人口加速外流而转变为乡村流失型，城乡扩张型地域的空间范围大幅收缩至西藏、上海等地，而城乡收缩型地域在东北地区的非省会城市大范围扩张；③ 经济性因素仍是当前中国城镇化的核心驱动因素，经济增长带动乡村人口外迁，工业化长期有利于城乡协调共进，而公共服务等社会性因素的作用仍然有限；④ 新型城镇化和乡村振兴的政策效应还有待发掘，西部城市群、中小城市的吸引力以及乡村地区的人口发展等问题仍需更多的研究关注。本文最后对中国城镇化的趋势性、动力机制和未来研究重点展开了讨论。

Urbanization is the outcome of population dynamics in both urban and rural areas, which has a profound influence on urban-rural development. This paper puts forward a method to identify regional types of urbanization from the perspective of urban-rural population change. Using this method and based on the census data, this paper analyzes the spatial pattern and influencing factors of China's urbanization. The results show that China has entered an era of rapid urbanization since 1990. Urbanization rates have risen in almost all regions, but with varied pathways over time and across regions. With the decline of natural population growth and the expansion of rural-urban migration, the process of urbanization depends more and more on rural population loss, leading to the transformation of urbanization regional type from "urban-rural growth" to "urban growth" in the country. Although the "urban growth" is the most common regional type of urbanization at both provincial and prefecture levels, the transformation of urbanization regional type from "urban growth" to "rural loss" has been witnessed in a large number of regions in the past decade. Besides, regions with the "urban-rural growth" type have shrunk to places such as Tibet and Shanghai while regions with the "urban-rural loss" type have expanded in non-provincial capital cities in Northeast China. At present, China's urbanization regional type is still driven by economic development: rural population declines faster in areas with rapid economic growth, leading to the "rural loss" urbanization; rapidly industrialized regions have undergone faster urban population growth and slower rural population loss than their counterparts; while the effect of social factors such as public services on urbanization regional type is limited. In addition, the impact of new urbanization strategy and rural revitalization strategy is not yet clear, given the limited attractiveness of urban agglomerations of the western region as well as small and medium-sized cities and the prevalence of rural population loss.