Internal migration in China has presented a series of new characteristics recently. The secondary migration and spatial redistribution of existing migrants become increasingly important in determining the future patterns of urbanization. Urban agglomerations (UAs) have long been the major destination of China's internal migration. They are also appointed as the main form of future urbanization in the recently released national planning of new-type urbanization. Five major UAs were selected as a case study, including three coastal ones, namely the Yangtze River Delta (YRD), the Pearl River Delta (PRD), and the Beijing-Tianjin-Hebei region (BTH), and two inland ones, namely the Middle Yangtze River (MYR) and the Chengdu-Chongqing (CC) region. Based on data of the latest population census and the dynamic monitoring survey of floating population in the five major UAs, this paper first examined the spatial patterns of in-migrants from multiple dimensions of destination, origin, and distance of migration. The trends and urbanization effects of migration on the destination and origin were then assessed by comparing the settlement and hukou transfer intentions of migrants with different origins and destinations. The results showed the coexistence of common and distinct features in these mega regions. Although the continuous attractiveness of central cities for migrants was observed in all regions, peripheral cities in the YRD and PRD have become increasingly attractive as well, leading to a moderately dispersing trend in these two pioneering coastal UAs. Moreover, the concentration level and spatial distribution of migrants among cities were generally stable in the YRD and PRD but continuously adjusting in the BTH and two inland UAs. The fastest growth was found in inter-county migration within province and the slowest in intra-county migration. The coastal UAs were strongly preferred by inter-provincial migrants, while the inland ones could only attract migrants from the same or surrounding provinces. Despite this, significant distance attenuation was found in all of them. In terms of the origins of migrants, those from central provinces had flowed mainly to the YRD and PRD, whereas those from the northeast showed a high preference for the BTH region. We can anticipate the future patterns of migration and urbanization from the settlement intentions of migrants from and to different cities. From the destination view, the advantage in public services made central cities considerably more attractive than other cities. Hence, they are expected to be continuously faced with severe contradiction between supply and demand of public services. In the inland UAs, however, central cities and ordinary ones are able to share the pressure of public service provision. From the original view, the high-quality and equally accessible public services are important for inland regions to attract return migrants, and providing high possibility for the return-migration-induced urbanization. However, the population loss in the northeast may become a long-term trend that can hardly be reversed in the visible future.

城市群在中国城镇化格局中占有重要地位,也是快速城市化时期的主要人口流入地。本文关注京津冀、长三角、珠三角、长江中游和成渝五大城市群,利用人口普查和流动人口动态监测调查数据,从流入人口分布格局、流动范围和来源地等多维度剖析城市群人口流入的空间模式,并从居留和落户意愿空间差异的视角探讨空间模式的发展趋势及其对流入地和流出地的影响。研究发现,各城市群流入人口向中心城市持续集中,等级和空间分布格局总体稳定;流动范围有所扩大,省内流动增速普遍高于省际;沿海城市群人口吸引范围大但仍服从距离衰减律,不同来源地流入人口的城市群偏好存在差异。在流入地,沿海城市群中心城市面临流动人口管理服务的持续挑战,内陆城市群中心城市和一般城市吸引力并存;在流出地,平等开放的高质量公共服务供给是吸引人口回流的重要途径,少数地区的人口流失可能成为较长期的现象。

According to previous studies, not only does the conditional gravity model based on ordinary least squares often bring about poor fitting of migration flows in reality, but also there exists overdispersion in the extended Poisson gravity model. Simultaneously, network autocorrelation usually exists in population migration data （e.g., the spatial interaction among migration flows). The problems mentioned above result in biased estimation. In order to capture network autocorrelation and deal with the issue of overdispersion, we build an eigenvector spatial filtering negative binomial gravity model (ESF NBGM) based on the data of 1% national population sample survey in 2015, to analyze the driving mechanism of interprovincial population migration flows in China. The results are as follows: (1) Positive spatial spillover effect exists in interprovincial population migration flows, and ESF can capture network autocorrelation in data, so as to reduce the estimated deviation of the model. Furthermore, eigenvectors ranking top 1.4% can properly interpret the spatial pattern of high network autocorrelation in data. (2) There exists overdispersion in China's interprovincial migration flows. Considering this problem, a negative binomial regression model is more suitable for the estimation of driving mechanism for population migration, together with statistical enhancement. (3) Network autocorrelation leads to overestimation of distance variables and underestimation of non-distance variables. The results of the improved model reveal that: chief factors the affect driving mechanism are regional population characters, social network, economic development and education level. Meanwhile, living environment and road network gradually become one of the most crucial pulling factors that influence migration flows. (4) Compared to previous studies, social network (i.e. migration stock) plays a more significant role in population migration flows, while the impact of spatial distance keeps weakening.

人口迁移数据中往往存在较强的网络自相关性,以往基于最小二乘估计的重力模型与迁移数据的拟合度较低,而改进后的泊松重力模型仍存在过度离散的缺陷,以上问题均导致既有人口迁移模型中的估计偏差。本文构建了特征向量空间滤波（ESF）负二项重力模型,基于2015年全国1%人口抽样调查数据,研究2010-2015年中国省际人口迁移的驱动因素。结果表明：① 省际人口迁移流间存在显著的空间溢出效应,ESF能有效地提取数据中的网络自相关性以降低模型的估计偏差,排序在前1.4%的特征向量即可提取较强的网络自相关信息。② 省际人口迁移流之间存在明显的过度离散现象,考虑到数据离散的负二项重力模型更适用于人口迁移驱动因素的估计。③ 网络自相关性会导致模型对距离相关变量估计的上偏与大部分非距离变量估计的下偏,修正后的模型揭示出以下驱动因素：区域人口特征、社会网络、经济发展、教育水平等因素是引发省际人口迁移的重要原因,而居住环境与公路网络等因素也逐渐成为影响人口迁移重要的“拉力”因素。④ 与既有研究相比,社会网络因素(迁移存量、流动链指数)对人口迁移的影响日益增强,而空间距离对人口迁移的影响进一步呈现弱化趋势。

Population migration flows between different regions are related to not only the origin- and destination-specific characteristics, but also to the migration flows to and from neighborhoods. Intuitively, changes in the characteristics of a single region will impact both inflows and outflows to and from other regions. In order to explore the spatial interaction mechanism driving the increasing population migration in China, this paper builds the spatial OD model of interprovincial migration flows based on the sixth national population census data and related social-economic data. The findings are as follows: (1) Migration flows show significant autocorrelation effects among origin and destination regions, which means that the migration behavior of migrants in some region is influenced by that of migrants in other places. The positive effects indicate the outflows from an origin or the inflows to a destination tend to cluster in a similar way. Simultaneously, the negative effects suggest the flows from the neighborhood of an origin to the neighborhood of a destination tend to disperse in a dissimilar way. (2) Multilateral effects of the regional economic and social factors through the spatial network system lead to the clustering migration flows across interrelated regions. Distance decay effect plays the most influential force in shaping the patterns of migration flows among all the factors and the negative spillover effect further aggravates the friction of distance. As for destinations, the influence of wage level and migration stocks is beyond that of GDP and the positive spillover effects of these factors enhance the attraction of neighborhood regions. The spillover effects of unemployment rate and college enrollment of higher education are significantly negative while the effect of population in a destination is not significant. As for origins, population and migration stocks lead to positive spillover effects on the neighborhoods while the effects of other factors are negative. (3) Changes in the regional characteristics will potentially lead to a series of events to the whole migration system, and the flows to and from the center of oscillation and its neighborhoods vibrate greatly compared with other regions. The simulation results of 5% GDP increase in Jiangsu province indicate that the outflows to other regions decrease while the inflows from all others increase to some different extent. Comparatively, the influence on the flows to and from the regions neighboring Jiangsu is significant while that of remote regions is much less, which cannot be explained by the traditional gravity model.

区际人口迁移不仅与迁出地和目的地的要素特征以及距离有关,而且还受到周边迁移流的影响.基于网络自相关理论,利用"六普"省际人口迁移数据和相关统计资料,在重力模型的基础上考虑迁移流之间可能存在的几种空间依赖形式,构建中国省际迁移流的空间OD模型,初步揭示区域经济社会等因素及其空间溢出效应对省际人口迁移的影响,并就区域要素变化对整个省际人口迁移系统产生的"连锁反应"进行了模拟.结果表明:① 中国省际迁移流之间存在显著的网络自相关效应.目的地和迁出地的自相关效应皆为正,导致迁入和迁出流的空间效仿行为;迁出地和目的地周边则出现负的自相关效应,导致迁移流的空间竞争行为;② 区域经济社会等因素通过网络空间关系对周边地区产生的多边溢出效应导致迁移流在空间上集聚.其中,距离衰减效应位居各要素之首,其溢出效应进一步加剧距离的摩擦作用;对目的地而言,区域工资水平和迁移存量超过GDP的影响并产生正的溢出效应,促进周边地区吸引更多的外来人口;对迁出地而言,人口规模和迁移存量产生正的溢出效应,推动周边地区人口外迁;③ 区域要素变化潜在地对整个省际人口迁移系统产生一系列"连锁反应",震荡中心及其周边区域的迁移流波动较大.江苏省GDP增长5%的模拟结果表明,江苏迁往全国其他省份的人口数量都有不同程度地减少,而其他省份入迁人口均有所增加.相对而言,江苏周边省份的迁入或迁出流受到的波动较大,偏远省份波及较小,这是传统的重力模型所无法解释的.

"In this paper, [the authors] suggest an alternative method for fitting the gravity model. In this method, the interaction variable is treated as the outcome of a discrete probability process, whose mean is a function of the size and distance variables. This treatment seems appropriate when the dependent variable represents a count of the number of items (people, vehicles, shipments) moving from one place to another. It would seem to have special advantages where there are some pairs of places between which few items move. The argument will be illustrated with reference to data on the numbers of migrants moving in 1970-1971 between pairs of the 126 labor market areas defined for Great Britain...."excerpt

Since China's reform and opening up in 1978, the scale of inter-provincial migration increased sharply, especially from 2000 to 2010. The redistribution of inter-provincial migration has had significant influences on China's urbanization and socio-economic development. Using data from China's 2000 and 2010 censuses, this paper investigates inter-provincial migration by describing its spatial patterns and estimating its impacts on urbanization development with various indictors, such as geographic concentration index, gravity center of migration, composite index of migration, network analysis, and coefficient of variation. The results are as follows: 1) There have been escalating dispersion trends for the distribution of in- and out-migration, especially the out-migration. At the same time, the gravity center of in- and out-migration both move towards north and east. Besides, the destinations of migration has shifted from one single polar (Guangdong) in 2000 to multi-polar (Guangdong, Zhejiang, Shanghai, Jiangsu, Beijing, Fujian) in 2010; while Anhui, Sichuan, Henan and Hunan become the new top four sources of migration in 2010. 2) With the rapid growth of floating population, the network trend of inter-provincial migration is more prominent. Overall, the migration network of 2010 becomes more compacted, connective and balanced than that of 2000. At the regional scale, the migration of population is mainly from central and western to eastern China. The eastern region become the main destinations, while the central and western regions of China have become the main sources of inter-provincial migration, especially the "mid-belt subsiding" is more prominent. At the provincial scale, the increased migration flows mainly to Shanghai, Zhejiang, Jiangsu, Beijing, Tianjin, Fujian, etc. Besides, the migration flows formulate different models due to the effects of regional development disparities, migration distance and migration stocks. 3) The regional types of inter-provincial migration could be divided into four kinds: active regions have a larger floating population and their in-migration is much more than out-migration, which is mainly located in the eastern coastal provinces and Xinjiang; active regions have a larger floating population and their out-migration is much more than in-migration, which is mainly located in the central and western China; active regions have a larger floating population and their in-migration is nearly equal to out-migration; inactive regions have a smaller floating population, such as some provinces with a large ethnic minorities population. 4) Inter-provincial migration has a positive effect on the development of urbanization. It contributes to 18.13% of the increment of urbanization rate and it also narrows the disparities of urbanization rate among provinces, during 2000-2010. However, migrant workers account for most of the inter-provincial migration, and make great contributions to the development of urban socio-economy, but they cannot share the same identity with the citizens due to the household registration system. Therefore, it is necessary to accelerate the reform of household registration system and promote the citizenization of migrant workers.

基于2000年和2010年人口普查数据,利用多种指标和方法研究了中国省际人口迁移的格局演变及其对城镇化的影响。研究发现：省际迁入和迁出人口在空间分布上均呈分散化态势,迁入迁出重心均向北向东偏移,迁入地由广东省“一枝独秀”向多极化演变,安徽、四川、河南、湖南成为新的四大迁出地。利用净迁移流构建的省际人口迁移网络,表现出紧凑化和均衡化趋势;迁移流仍然是从中西部地区指向东部地区,但新增加迁移流集中指向长三角、京津以及福建。综合考虑省际人口迁移强度和方向,可将全国31个地区划分为净流入型活跃区、平衡型活跃区、净流出型活跃区以及非活跃区四种类型。省际迁移改变了迁入地和迁出地的城乡人口结构,通过不同模式促进了城镇化率的提高和省际差异的缩小,对2000-2010年全国城镇化率增加的贡献占到了18.13%。

Migration plays an increasing role in economy since mobility rose and economic restructuring has proceeded during the last three decades in China. Given the background of most studies focusing on migration in a particular period, it is critical to analyze the spatial-temporal patterns of migration. Using bicomponent trend mapping technique and interprovincial migration data during the periods 1985-1990, 1990-1995, 1995-2000, 2000-2005, and 2005-2010, we analyze net-, in-, out-migration intensity, and their changes over time in this study. Strong spatial variations in migration intensity were found in China's interprovincial migration, and substantial increase in migration intensity was also detected in Eastern China during 1985-2010. Eight key destinations are mostly located within the three rapidly growing economic regions of Eastern China (Pearl River Delta, Yangtze River Delta and Jing-Jin-Ji Metropolitan Area), while most key origins are relatively undeveloped central and western provinces, which are exactly in accordance with economic development patterns in China. The results of bicomponent trend mapping show that most provinces with an increasing trend in in-migration are located in South China, and those with a decreasing trend are in North China. Therefore, in a sense, the migration in the south was more active than that in the north over the last three decades, which might largely be the results of cultural differences between the south and north. These results on spatial-temporal patterns reveal that migration has a strong relationship with regional development, and economic-cultural factors may be of increased relevance to interprovincial migration.

改革开放以来,伴随着快速城镇化进程的推进以及20世纪80年代户籍制度出现松动,大规模跨区域的人口迁移流动已呈现不可阻挡之势,人口迁移时空格局亦发生着剧烈的变化。目前大多数相关研究只关注某一特定时期的人口迁移,故而人口迁移的时空格局分析显得尤其重要。使用双组分趋势制图法和1985-1990年、1990-1995年、1995-2000年、2000-2005年以及2005-2010年五个时期的人口迁移数据,分析人口迁入、迁出和净迁移的强度及其变化特征。在研究时期内,中国省际人口迁移表现出强烈的空间差异,迁移强度也有大幅的增加。八个主要人口迁入地全部位于东部三个经济快速增长的发展区域（珠江三角洲、长江三角洲和京津冀都市圈）内,而主要的人口来源地都是相对欠发达的中西部省份,这和全国经济发展空间格局完全吻合。双组分趋势地图结果显示南方省份的人口迁移强度及变化趋势都强于北方省份,因此从某种意义上说,在1985-2010年间,南方省份的人口迁移较北方省份更活跃,这可能主要受到南北方自然地理环境以及文化差异的影响。人口迁移时空格局分析结果表明,中国的人口迁移规律正在逐渐形成新特色,东部和西部地区之间的人口流动主要是受到经济因素和区域发展差异的影响,而南方和北方地区的人口迁移活跃程度则主要是由自然地理环境以及文化差异所致。

当一个人作出迁移的决定之后,还需要作出向何处迁移的决定。在以往的研究中,对于人口迁移的迁入地选择过程缺乏研究。本文利用 1990年人口普查资料,通过建立人口迁移分析矩阵,探讨了对人口迁移迁入地选择过程进行分析的实现方法

With the loosing of the household registration (hukou) system, China has witnessed massive volumes of interregional migration. Among all kinds of migration, skilled migration and less-skilled migration present differential spatial patterns and network characteristics over time and may exert distinguished impacts on regional development and innovation. However, due to limited data availability, prior studies only adopted cross-sectional data analysis and examined the patterns of skilled and less-skilled migrations before 2005, whereas few have been done on related topics after 2005. Drawing on previous studies, the present paper uses micro-level datasets from population censuses and sample surveys to assess the spatial evolution patterns of China's skilled and less-skilled internal migrations between 1995 and 2015. Besides, several complex network analysis methods have been employed, together with the network visualization technique. The results of the paper are listed as follows. First, there were spatial clustering and uneven patterns in both skilled and less-skilled internal migrations over the 20 years of 1995-2015, where migration flows containing a large percentage of skilled and less-skilled migrants originated from inland western and central regions to the eastern coastal areas of China. However, the degree of spatial unevenness in the migration patterns has been slightly weakened. In addition, the average migration distance of skilled migration was farther than that of less-skilled migration in the same period. Second, both of the two migration networks have presented a “small-world” property over the 20 years. In comparison, the intensity and connection degree of less-skilled migration networks have prevailed over that of the skilled migration networks. Third, results from the community detection have revealed that both skilled and less-skilled migrations exhibited a network structure where Beijing, Shanghai, and Guangdong served as the core nodes. Skilled migration presented a more stable network structure during the 20 years, while less-skilled migrations showed a persistently changing pattern. Fourth, the paper suggested that regional economic disparity, path dependence effect, the heterogeneity of different labour forces in the job market, and the differences in labour market demand for different labour forces were underpinning factors shaping the spatial evolution pattern and network structure of the two types of migration from 1995 to 2015.

自户籍制度放宽以来,中国经历了大规模的人口迁移。其中,异质性劳动力（高技能劳动力、普通劳动力）在迁移过程中表征出不同的空间格局和网络组织特征,对地区经济发展产生各异的影响。本研究基于人口普查和抽样调查微观抽样数据,使用复杂网络理论对1995—2015年中国异质性劳动力迁移的时空格局和网络演化展开分析,并分析了格局背后的可能成因。研究发现：① 省际高技能和普通劳动力迁移均呈现出持续高度不平衡的空间集聚特征,承载大量人口的迁移流主要由中国中西部地区指向东部沿海地区,但这种空间不平衡特征呈现出一定的减弱趋势。高技能劳动力的平均迁移距离比普通劳动力更长;② 两类劳动力迁移网络中均呈现明显的“小世界”特性,但普通劳动力迁移网络的迁移强度和关联程度均高于高技能劳动力迁移网络;③ 两类劳动力迁移网络均呈现出以北京、上海、广东为主要核心节点的网络结构。高技能劳动力网络结构呈现相对稳定的特征,而普通劳动力网络结构则呈现出变化的趋势;④ 地区经济差异、路径依赖效应、异质性劳动力在就业市场上的差异性以及劳动力市场对异质性劳动力需求的差异是导致两类劳动力迁移格局差异性的重要成因。

This article reviews international literature on the changing trends of population migration and mobility and their mechanisms from the following perspectives: the changing trends of different forms of population mobility，the international comparison of the overall intensity of China’s internal mi- gration，the micro life course of population migration and mobility，and the mechanism and recent evolution of circular migration．The authors derive the following views on the future trends of China’s population migration and mobility from the above review and its implications: First，the recent decreasing trends of regional and rural-urban migration in China indicate changes in the forms of China’s population mobility rather than a real decline of its overall volume and intensity，which will still be on the rise; Second，great attention still needs to be paid to the portable issues of various public services provided to the floating population; and third，the management of and services to the floating population need to be improved in accordance with the changes in the forms of population mobility，especially the rise of urban to urban and intra-urban migration and mobility．

文章从人口迁移流动的不同形式及其演变趋势、中国国内人口迁移整体强度的国际对比、人口迁移流动的微观生命过程、以及人口循环流动的发生发展机制和近期演变等4个方面回顾了国际上对人口迁移流动演变趋势及其机制的相关研究,并根据从中获得的借鉴和启示对中国人口迁移流动的未来走势做出判断,认为目前我国人口在区域和乡城间迁移流动的减缓更多预示的是人口迁移流动形式的改变,而不是其整体规模和强度的下降,后者还有着相当大的上升空间;今后仍不可忽视针对迁移流动人口的各种公共服务的转移、接续问题,并要根据人口迁移流动形式的变化,尤其是人口在城市间和城市内部迁移流动上升的新形势,与时俱进地改进流动人口的管理和服务工作。