Suburbanization is a stage of urbanization process. It means that population, jobs and service trades decentralize from the inner city to suburban area. It has greatly changed the economic and urban development in the developed countries Peter Hall’s model of urban change offered a framework which can easily identify whether a city is in the process of suburbanization. How about it in China as a developing country? When did it start? And what is the present situation in process? Beijing region can be divided into three levels:the inner city(<i>cheng qu</i>),the inner suburban districts(<i>jin jiao qu</i>) and the outer suburban districts and counties(<i>yuan jiao qu xian</i>). The author discusses the situation of Beijing from the data of three population censuses(1964,1982 and 1990) and considers that the suburbanization in Beijing has been in process since 1982. During 1982-1990, population of all districts of the inner city lost 82,000 people and in fact the permanent residents holding the household registration lost 144,000, about 6.12% of permenant residents in the inner city. Like the West, suburbanization in China also happened against the macro backgrounds of improvements of transportation and living conditions, growth of urban population moving from rural areas. But in the cast of the forces that make decentralization possible, China is different from the western countries. In China, the levels of economy and urbanization are rather low; the real middle class is not rich; only very few Chinese families own private cars; and the inner cities still have great attraction for Chinese people. Investigating the causes of decentralization, the author considers three points:(1) reform of urban land use system brings about migration from the core area.(2) Construction of a large number of city roads reinforces this process.(3) Reform of housing system and renovation of delapidated houses encourage outward movement. In a word, the large scale renovation of the old city has led to suburbanization in Beijing under the open door policy and the socialist market system. It wasn’t spontaneity of any individual but under the leadership of government and “Dan Wei” (work-units). Finally, the author puts forward some thought provoking and distinctive phenomena and problems that should be seriously treated.

This study aims to examine the characteristics and changes of the spatial structure in the Beijing Metropolitan Area with the rapid urban growth and decentralization, through analyzing the spatial distributions of urban population and employment. To demonstrate the spatial evolution of population and employment distributions in the Beijing Metropolitan Area, we apply the nonparametric analysis in this study. Our study finds that the significant population and employment subcenters in the suburbs of the Beijing Metropolitan Area, characterized by the polycentric urban spatial structure. Since the 1980s, with the suburbanization of population, the number of population subcenters has increased in the Beijing Metropolitan Area, and the distribution of population subcenters has expanded from the inner suburbs to the outer suburbs. The overall trend toward the decentralization and polycentrification of population is evident, whereas the spatial extent of the decentralization of population is limited in the Beijing Metropolitan Area. Contrary to the decentralization of population, our study finds that the centralization of employment in the Beijing Metropolitan Area from 2004 to 2008 has led to the weakening influences of the outer suburban employment subcenters as well as the decline of the polycentricity of the spatial structure. This implies the spatial pattern of the Beijing Metropolitan Area may still be highly centralized, and the nature of the monocentric urban spatial structure may not be fundamentally changed. Meanwhile, the decentralization of population and the centralization of employment may lead to the overall jobs-housing imbalance. Therefore, to form the polycentric spatial structure, it is necessary to reinforce the agglomeration economies of suburban subcenters and improve the overall jobs-housing balance in the Beijing Metropolitan Area.

本研究应用非参数计量方法,实证刻画北京都市区人口—就业空间分布演化,揭示在快速城市化和城市增长背景下,北京都市区空间结构特征及发展趋势。研究发现,北京都市区人口和就业分布都呈现多中心空间结构。20 世纪80 年代以来,随着人口郊区化,北京都市区人口次中心数量不断增加,并由近郊向远郊扩展,人口分布呈现明显的分散化和多中心化趋势,但人口分散的空间范围还比较有限。与人口的分散化趋势不同,2004-2008 年,北京都市区就业仍呈现向心集聚的趋势,造成远郊就业次中心的影响不断被弱化,都市区空间结构的多中心性有所降低。这说明北京都市区的单中心或强中心结构可能并未从根本上改变,且人口的分散化和就业的向心集聚导致宏观面上人口—就业的空间失衡。北京都市区多中心空间结构的形成,需要强化郊区次中心的集聚能力,同时注重人口—就业的平衡布局,这是未来北京都市区空间结构调整的重点。

轨道交通的快速发展提高了通勤可达性,也被认为在重构城市职住空间格局进而影响职住关系方面产生重大影响。探究城市轨道交通站点周边地区（下称轨交站点周边地区）就地职住平衡与建成环境的关系,对于通过建成环境设计促进站点尺度职住&#x0201c;微平衡&#x0201d;,调节由于轨道交通所导致的区域性职住分离意义重大。本文基于武汉市189个轨交站点刷卡数据,首先识别了轨道交通通勤人员并分析了其出行特征,进而利用逐步回归与地理加权回归模型（GWR）探究武汉市轨交站点周边地区职住平衡与建成环境的关系。结果表明：① 从整体看,武汉市向心通勤显著,且汉口片区与武昌片区之间产生较大规模的跨江通勤,跨江交通压力较大;主城区职住平衡指数优于城市近郊区,就业集聚程度呈现&#x0201c;中心-外围&#x0201d;递减趋势,居住空间围绕二环线在外围城区分布,城市整体形成&#x0201c;中心就业,外围居住&#x0201d;的职住分离格局;② 土地利用混合度、公交站点数量对轨交站点周边地区职住平衡有正向促进作用,轨交站点出入口数量则有负向作用,且各因素的影响具有明显的空间异质性;③ 非首末站的职住状况较首末站更好,而是否为换乘站则差异不大。本研究可为轨交站点周边地区就地职住平衡的形成提供参考,促进轨道交通与城市功能协调发展。

Along with the social economic development, urbanization has speeded up in China. Suburbanization has been taking place in large and super-large cities. In Beijing, suburbanization (mainly led by housing suburbanization) started from the late 1980s and early 1990s. By now suburbanization in Beijing has experienced three stages: beginning, inner suburbanization and rapid development. Housing suburbanization has been characterized by concentric outward expansion along ring roads, leading to rapid urban sprawl. But urban sprawl in Beijing differs from low-density sprawl in Western countries. New housings are mainly located along arterial roads. Distinct regional variations exist in housing types, showing some similarities to housing segregation in Western cities. This paper argues that housing suburbanization in Beijing and that in Western cities share both similarities and differences. Suburbanization in both settings is a necessary result of improving urbanization and establishment of an urban land market, and guided by urban planning. In Beijing, however, housing suburbanization is "passive" suburbanization, as most residents do not really want to leave the central areas but urban renewal and extremely high housing prices force them to purchase housing in suburban areas. Along with housing suburbanization, the issue of spatial mismatch between housing and employment has emerged in Beijing. Despite all the differences, this spatial mismatch shares similarities to that in American cities in several aspects: spatial separation of residences from jobs, social segregation, leading to increasing costs (in terms of both time and money) for low-income commuters and many social problems such as traffic congestion and social segregation.

In the last 40 years, China has experienced large-scale migration, which has greatly contributed to its urban construction and economic growth. Meanwhile, migrants face residence and employment instability in the process of urban renewal. The existing literature mostly attributes this instability to individual factors, but ignores the influence of macro events (such as urban demolition and reconstruction) or the industrial restructuring on migrants' lives. Moreover, in most studies, migrants' residence and employment changes are regarded as independent behaviors of non-interference rather than as correlated behaviors. To fill these research gaps, this study builds a theoretical framework for the coordinative changes of migrant residences and employment. Based on a large-scale survey in Beijing in 2013, we use multilevel bivariate probit models to analyze how the background features impact migrants' residence and employment changes under urban renewal. In particular, this research focuses on two types of background features: the instability of residence, which is measured by the number of city villages in the sub-district, and the instability of employment, which is measured by the proportional area of the wholesale and retail industries in the sub-district. Moreover, we analyze the coordinative change between migrants' residences and employment. The results show that: (1) Compared with residents, migrants are more likely to change residence and employment, and the instability of their residence and employment are correlated more with urban renewal. Migrants living in the city center have higher stability in their residences and employment than those in outlying areas. (2) At the sub-district level, the difference in probability of employment change is significantly greater than that of residence changes. Residence and employment changes are correlated, and change in residence (or employment) is likely to lead to changes in employment (or residence) simultaneously. (3) Migrants living in the sub-districts of city villages have tended to report a higher probability of residence change in the last five years. (4) The young-generation migrants have higher coordination of residence-employment change than old-generation migrants, and the residence instability of the young generation is more related to the instability of the living environment in the sub-districts.

城市更新过程中流动人口面临居住与就业的不稳定。本文构建流动人口居住与就业协同变动的理论分析框架,使用北京大样本问卷调研数据,基于双层级双变量离散选择模型,分析城市更新背景下流动人口居住-就业变动协同性及其与背景环境的关系。结果发现：流动人口的居住/就业变动概率远高于本地人口,城市更新对流动人口居住和就业的不稳定性影响更大;流动人口的居住变动行为与就业变动行为是相互作用的协同过程,单项变动极有可能引发双变动的调整。居住在城中村数量较多的街道,流动人口发生居住变动的概率更大。青年流动人口的居住-就业变动协同性相比老一代更高,而且街道的居住不稳定性与青年流动人口的居住行为不稳定更相关。

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&#x0201c;一枝独秀&#x0201d;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%。

As the most important parts of urban systems, jobs and housing spaces and their balance directly affect the spatial structure of cities, the behavior and experience of the residents, and the harmony and livability of the society. This study used more than 100 million records of mobile phone signaling data, covering the whole city of Beijing and over a period of one month, to identify the jobs-housing spaces by targeting the origin-destination (OD) oriented connections applying the density-based spatial clustering of applications with noise (DBSCAN) method. Furthermore, this study explored the spatial distribution pattern and matching characteristics of Beijing's jobs and housing spaces from different spatial scales by using various measurement methods of jobs-housing balance. The spatial scales for analysis cover the whole city, ring roads, districts, and residential community and town and townships, and the methods applied include the spatial mismatch index, deviation degree, commuting flow rate, and so on. The results show that: 1) Mainly influenced by the spatial layout of the Beijing Master Plan, Beijing's housing space is characterized by dispersion at the large scale and agglomeration at the small scale, showing a pattern of scattered groupings. In contrast, its working space presents features of agglomeration at the large scale and dispersion at the small scale, retaining a significant single-centered layout. 2) Although working in local areas is the first choice for people at both the ring road scale and the district scale, there are still a great number of people works outside their residential areas. The degree of jobs-housing mismatch gradually decreases from the central city to the periphery no matter which method was adopted or at which scale. At the residential community and town and township scale, however, a more detailed feature of three-zones, with job agglomeration inside, housing agglomeration in between, and balanced distribution outside, was observed. 3) Both the general lack of jobs in certain areas caused by the high concentration of working space and the two-way commuting phenomenon in the majority of the areas caused by the high spatial concentration of jobs indicate the necessity of spatial reorganization of residential function and employment function. Specifically, for the regions with a high proportion of two-way commuting flow between them, such as Chaoyang-Changping, Tongzhou-Daxing, Haidian-Changping, Mentougou-Shijingshan, and Chaoyang-Tongzhou, further in-depth investigation should be conducted to find out the reasons for its formation and then possible industrial adjustment or functional reconstruction from the city level should be coordinated. Relying on big data, on the one hand, the job preference and demand of local residents can be identified, so the types and number of jobs in each region could be adjusted accordingly; on the other hand, the proper locations for new job and residential centers may be identified to help rearrange the land use of the whole city.

职住空间作为城市系统最重要的组成部分,直接影响了城市的形态结构、居民的行为体验以及社会的和谐宜居,长期以来受到城市研究者的关注和重视。论文利用覆盖北京全市域并持续1个月的1亿多条手机信令数据,基于DBSCAN的聚类方法,通过OD定向联系,识别出同时具备居住—就业关系特征的职住空间。在此基础上,针对北京市辖区、环路、街道乡镇等不同空间尺度,综合运用空间错位指数、职住偏离度、职住分离率、通勤流动率等计算方法,研究北京职住空间分布格局及匹配特征。研究发现：① 北京市居住空间呈现大分散、小集聚特征,就业空间呈现大集聚、小分散特征;② 基于各个空间尺度、不同测度方法的分析结果均表明,职住空间的不匹配程度呈现出由中心城区向外围逐渐降低的态势,但基于街道乡镇尺度呈现出由内向外更细化的就业集聚—居住集聚—二者均衡的三段式变化特征;③ 无论是就业空间高度集聚导致的非集聚区就业岗位数量不足,还是包括就业高集聚区在内大量区域出现的双向通勤现象,均说明居住功能和就业功能空间重组的必要性。

The rapid development of Chinese cities has accelerated the residential mobility of inner city. Precisely measuring residents' migration behavior and its spatial differentiation characteristics is of practical significance to analyze the evolution process of urban residential spatial structure from the perspective of human behavior. Taking Wuhan Metropolitan Area as an example, based on mobile signaling big data, this paper proposes the net activity index and the total activity index to quantitatively describe relocation activity degree, and classifies the residential places into six spatial types based on the classification and combination of the two indicators: high immigration and active area, stable and active area, high emigration and active area, high immigration and inactive area, stable and inactive area, and high emigration and inactive area, and then explores the spatial distribution characteristics of residents' migration. The results show that: (1) The population migration within Wuhan Metropolitan Area generally shows a trend of migrating from the main city to inner suburbs, and the total activity degree in the main city is higher than that in the suburbs. (2) The main city is dominated by high emigration and active areas, and high immigration and active areas, while the inner suburbs are dominated by stable and inactive areas. (3) There are differences in residential types among different spatial types: high immigration and active areas are mainly new residential areas, college residential areas, school district housing, and urban villages; stable and active areas mainly consist of rural residential areas and residential buildings around industrial parks; high emigration and active areas are mainly old residential areas, residential buildings around industrial parks, urban villages and rural residential areas; inactive areas are mainly rural residential areas. This paper proposes an evaluation index system of residents' relocation activity degree based on time-series mobile signaling big data, and proves its validity for the spatial classification of residents' migration places by empirical research. The results can provide data support for relevant planning decision-making departments to regulate population migration within a city, and provide reference for public resources allocation in different areas of the city.

中国城市的快速发展加速了城市内部人口的居住迁移,精细测度居民迁居行为及其空间分异特征,对于从人的行为视角来分析城市居住空间结构演进过程具有现实意义。本文以武汉都市发展区为例,基于手机信令大数据,提出净活跃度指标和总活跃度指标来量化描述迁居活跃度,并依据两个指标的分类及组合,将居民居住地划分为高迁入型活跃区、平稳型活跃区、高迁出型活跃区、高迁入型非活跃区、平稳型非活跃区与高迁出型非活跃区六种空间类型,进而探讨居民迁居的空间分布特征。结果表明：① 武汉都市发展区内部人口迁移总体呈现出从主城区向近郊区逐步迁移的趋势,且主城区人口总活跃度相较更高。② 主城区以高迁出型活跃区和高迁入型活跃区为主,近郊区则以平稳型非活跃区为主。③ 不同空间类型内的居住类型存在差异：高迁入型活跃区内以新建小区、高校住区、学区房、城中村为主;平稳型活跃区以农村居住地、园区周边住宅为主;高迁出型活跃区以老旧小区、园区周边住宅、城中村、农村居住地为主;非活跃区则以农村居住地为主。本文提出了一种基于时序手机信令大数据的居民迁居活跃度评价指标体系,并以实证研究证明其对于居民迁居地空间类型划分的有效性,研究结果可为相关规划决策部门掌控城市内部的人口迁移特征提供数据支撑、为城市不同区域针对性的进行公共资源配置提供参考依据。

Job-housing relationship in city has always been a concern of scholars. Excess commuting is a well-accepted concept for evaluating commuting efficiency and potential of optimizing it. By calculating difference between observed commute and theoretical minimum commute, excess commuting can measure to what extent the commute of a city is wasted. Studies on excess commuting are rather scarce in China, especially in perspective of different age groups. Using the mobile phone grid data in September 2017 in Shanghai, this paper analyzes the residential locations, job locations, and commuting distances of three age groups, namely, the young, the middle and the old. To calculate their excess commuting, a modified algorithm is devised, simulating individuals exchanging their residences based on the principle of Pareto Optimality, so that no one has to sacrifice their status quo. The new method also uses large-scale individual data instead of small-scale aggregate origin-destination data adopted in the traditional linear programming method. The job-housing relationships under the optimal commuting are estimated and compared between age groups. The results show that: (1) There is an evident inter-generational differentiation in the job-housing relationships. The young people are the disadvantaged group with the longest commute distances and the farthest residence locations from the city center; the elderly are the dominant group with the shortest commute distances and the closest residence locations from the city center; the middle-aged are in-between. (2) The excess commuting rate in Shanghai is 69%, indicating a large potential in optimizing the job-housing relationships. (3) The benefits of the optimization are immense: the average commuting distance declines, the inter-generational differentiation moderates, and the spatial structure of population age becomes more balanced. (4) The young group will benefit most from this process. It is suggested that the spatial planning and policy making of Shanghai should set a target for an ideal spatial structure of the urban population age, so as to increase the fluidity of the job and housing spaces, youthen the city center, and make the perimeter areas more livable for the middle-age and old people.

城市的职住关系一直为研究者广泛关注。过剩通勤概念以实际通勤与理论最短通勤的差距反映城市职住关系与通勤状态,是衡量通勤效率和优化潜力的常用指标。国内的过剩通勤研究数量有限,且鲜有从年龄视角考察。本文利用2017年9月上海市手机网格数据,分析不同年龄段居民的职住与通勤状态,采用模拟个人换居的方法测度过剩通勤,并探讨职住关系优化效应的代际差异。结果表明：上海居民的职住关系存在明显的代际差异,青年人是劣势群体,老年人是优势群体,中年人介于其间;上海全市过剩通勤率为69%,职住关系优化的潜力和效应巨大,可使得通勤距离显著减少,代际分异缓和,青年人的获得感最大,城市年龄空间结构更加均衡。提出上海的空间规划和政策应以理想的城市年龄空间结构为目标,以提高人的居住和就业空间的流动性、青年化城市中心、中老年化城市外围为途径。