Externalities, Floating Population and Spatial Agglomeration

With the further development of socialist market economy, the mobility of factor markets in China, especially the labor market, is strengthened. Externalities interacts with the agglomeration of productive factors. Under the framework of new economic geography, this article presents a theoretical model involving the endogenous population density affected by urban externalities. Results show that the population density is more concentrated around the center because the degree and extent of interaction between individuals intensifies when the distance from the center decreases. When there are several externalities resources, the aggregation of externalities changes the configuration of spatial factor allocation. These results fit well with the empirical facts about the decreasing density of floating population along the cities of Guangzhou, Dongguan and Shenzhen in Guangdong Province which is situated in the eastern coast of the Pearl River Delta. We fred that under the impacts of externalities released from Hong Kong into the coast, floating population was more concentrated around Shenzhen and Dongguan, which are more adjacent to Hong Kong compared with Guangzhou City.

Migration Networks Pattern of China’s Floating Population from the Perspective of Complex Network

Since China’s reform and opening-up,the growing disparity between urban and rural areas and regions has led to massive migration.With China’s Rural Revitalization Strategy and the industrial transfer from the eastern coastal areas to the inland,the migration direction and pattern of the floating population have undergone certain changes.Using the 2017 China Migrants Dynamic Survey(CMDS),excluding Hong Kong,Macao,and Taiwan regions of China,organized by China’s National Health Commission,the relationship matrix of the floating population is constructed according to the inflow place of the interviewees and their outflow place(the location of the registered residence)in the questionnaire survey.We then apply the complex network model to analyze the migration direction and network pattern of China’s floating population from the city scale.The migration network shows an obvious hierarchical agglomeration.The first-,second-,third-and fourth-tier distribution cities are municipalities directly under the central government,provincial capital cities,major cities in the central and western regions and ordinary cities in all provinces,respectively.The migration trend is from the central and western regions to the eastern coastal areas.The migration network has‘small world’characteristics,forming nine communities.It shows that most node cities in the same community are closely linked and geographically close,indicating that the migration network of floating population is still affected by geographical proximity.Narrowing the urban-rural and regional differences will promote the rational distribution this population.It is necessary to strengthen the reform of the registered residence system,so that the floating population can enjoy urban public services comparable to other populations,and allow migrants to live and work in peace.

Spatial Pattern of Long-term Residence in the Urban Floating Population of China and its Influencing Factors

Exploring long-term residence among the urban floating population is crucial to understanding urban growth in China,particularly since the 2008 financial crisis.By using China Migrants Dynamic Survey data for 2012–2014,China Labor-force Dynamics Survey data for 2014–2016,and macroscale urban matched data,we analyzed the spatial pattern of long-term residential behavior in China’s urban floating population in 2012–2016 and developed an urban spatial utility equilibrium model containing‘macro’urban factors and‘micro’individual and household factors to explain the pattern.The results first revealed that long-term residence is defined as≥6 yr for the urban floating population in China.Second,members of this population are more likely to be long-term residents of the megacities in the three urban agglomerations in eastern China as well as of small and medium-sized cities in western and northeastern China,whereas short-term residence is more likely in cities in central China and near the three urban agglomerations.Third,urban population density and housing prices,both have a significant U-shaped effect,are main factors affecting the spatial pattern of long-term residence.

Spatial and Temporal Changes of Floating Population in China Between 1990 and 2000

By studying the county-level census data of 1990 and 2000, we analyzed the spatial and temporal changes in the floating population in China between 1990 and 2000. The results of the analysis revealed the following characteris- tics. First, the spatial distribution of the migrants (referred to as 'floaters' in this paper) became increasingly concentrated in the cities during the 1990s. Second, the number of floaters increased rapidly during this period, and the area in which the floaters settled expanded quickly into four population explosion belts: the coast, the Changjiang River Delta, the Beijing-Guangzhou Railway and national border belts. Third, the number of inter-province floaters increased rapidly and exceeded that of intra-province floaters in the 1990s. In addition, to obtain a quantitative relationship between the number of floaters and 10 socio-economic variables by using statistical methods and also to find the chiefly important pulling factors of the migration destination, the authors selected approximately 100 cities with the largest population of floaters. Consequently, we found that four factors-GDP, passenger trips per 10,000 persons, per capita GDP and foreign direct investment-could provide an explanation for 83.7% of the number of floaters in 2000. The GDP showed the highest correlation with the number of floaters, suggesting that a highly developed economy is the most important factor that attracts floaters. Furthermore, a fairly close relationship between the number of floaters and the GDP was also found in 2000 for all the counties.

Influencing Factors and Urbanization Effects of the Spatial Pattern of Floating Population in Anhui Province

Based on the population census data,this paper analyzed the influencing factors and urbanization effects of the floating population in Anhui Province using ArcGIS spatial analysis,factor analysis,multiple liner regression,and spatial autocorrelation,and reached the following conclusions:① From 2000 to 2010,the floating population in Anhui Province was concentrated in cities dotted the Huai River and the Yangtze River,and Hefei City absorbed the most inter-provincial floating population and intra-provincial floating population.② The overall economic strength had the greatest impact on attracting floating population,while the income level factor has less impact on attracting floating population.The overall economic strength and the strength of science,education,culture,and health of prefecture-level cities in Anhui Province were more attractive to the intra-provincial floating population and less attractive to the inter-provincial floating population.③ Population mobility promoted urbanization.Large cities could attract more migrants from counties.The urban population system in Anhui Province was generally developing towards the concentration of large cities,while the proportion of the county population in the total population was decreased.

Origin Distribution Patterns and Floating Population Modeling:Yiwu City as a Destination

Existing quantitative migration studies are mainly at the inter-region or inter-province level for lacking of detailed geo-referenced migration data.Meanwhile,few of them integrate explorative spatial data analysis and spatial regression model into migration analysis.Based on aggregated registered floating population data from 2005 to 2008,the phenomena that population floating to Yiwu City in Zhejiang Province is analyzed at the provincial and county levels.The spatial layout of Yiwu's pull forces is proved as a V-shaped pattern excluding Sichuan Province based on map visualization method.Using the migration ratio in 2007 as an explanatory variable,two models are compared using ordinary least square,spatial error model and spatial lag model methods for county-level data in Jiangxi and Anhui provinces.The model with migration stock provides an improved fitting over the model without migration stock according to the model fitting results.The floating population flocking into Yiwu City from Jiangxi is determined mostly by migration stock while the determinant factors are migration stock and distance to Yiwu City for Anhui.The distance-decay effect is true for migration flow from Anhui to Yiwu City while the distance rule is not confirmed in Jiangxi with the best fitting model.The correlation between per capita net income of rural labor forces and migration ratio is not significant in Jiangxi and significant but at the 0.1 level only in Anhui.Further analysis shows that the distance,income and man-land ratio are important factors to explain population floating at earlier stage.However,as the dynamic population floating process evolves,the determinant factor would be migration stock.

Evolution and Determinants of Population Agglomeration in Less Developed Metropolitan Areas:A Case Study of the Taiyuan Metropolitan Area,China

It is of importance to enhance the urban areas'capacity for population aggregation in underdeveloped regions,aiming to rectify the imbalanced and insufficient pattern of economic development in China.Taking the Taiyuan Metropolitan Area(TMA)in central China as a case study,this paper examines the evolutionary process and characteristics of population agglomeration from 2000 to 2020,and identifies factors associated with agglomeration and their spatial effects.The findings indicated that:1)against the background of sustained population shrinkage in the provincial area,the TMA showed a demographic trend of steady increase,albeit with a decelerated growth rate.In the metropolitan area,urban population size continued to grow rapidly,whereas the rural areas endured sustained losses.Disparities in city size continued to widen,and the polarization of concentrated population in the core cities kept increasing.2)Agglomerations in both secondary and service industries had significant positive effects on local population agglomeration,with the former effect being stronger.Regional economic development,government fiscal expenditure,and financial advancement all contributed to facilitating local population clustering.From a spatial spillover perspective,service agglomeration and financial development promoted population agglomeration in surrounding areas.Conversely,fiscal expenditure inhibited such agglomeration.As for industrial agglomeration and regional economic development,their spatial spillover effects were non-significant.The results obtained reveal several policy implications aimed at enhancing the population agglomeration capacity of the metropolitan area in underdeveloped regions during the new era.

The regional types of China＇s floating population： Identification methods and spatial patterns

With the rapid increase of the number and influence of floating population in China,it is urgently needed to understand the regional types of China＇s floating population and their spatial characteristics. After reviewing the current methods for identifying regional types of floating population,this paper puts forward a new composite-index identification method and its modification version which is consisted of two indexes of the net migration rate and gross migration rate. Then,the traditional single-index and the new composite-index identification methods are empirically tested to explore their spatial patterns and characteristics by using China＇s 2000 census data at county level. The results show：（1） The composite-index identification method is much better than traditional single-index method because it can measure the migration direction and scale of floating simultaneously,and in particular it can identify the unique regional types of floating population with large scale of immigration and emigration. （2） The modified composite-index identification method,by using the share of a region＇s certain type of floating population to the total in China as weights,can effectively correct the over-or under-estimated errors due to the rather large or small total population of a region. （3） The spatial patterns of different regional types of China＇s floating population are closely related to the regional differentiation of their natural environment,population density and socio-economic development level. The three active regional types of floating population are mainly located in the eastern part of China with lower elevation,more than 800 mm precipitation,rather higher population densities and economic development levels.

Urban Agglomerations in China: Characteristics and Influencing Factors of Population Agglomeration

Urban agglomeration(UA)is an advanced spatial economic form formed and developed in the process of rapid industrialization and urbanization,and an important carrier of urbanization and economic development.The economy has developed rapidly in the recent decades of China,and the UAs have also developed rapidly.However,as a large population country,the population distribution and changes of UAs in China has unique characteristics.Using the fifth,sixth and seventh population census data,spatial auto-correlation and spatial econometric models,we analyzed the spatial-temporal evolution characteristics and influencing factors of population agglomeration in China’s UAs.Results revealed that:1)from 2000 to 2020,the population gradually converged into UAs,and the characteristics of population agglomeration in different development degree of UAs differ.The higher the development degree of UA,the higher the population agglomeration degree.Besides,UAs are the main area with the most significant population agglomeration degree,and the spatial autocorrelation show that the cities with similar degree tend to be concentrated in space.The urban population gathering in UAs has a certain positive spillover effect on population size of neighboring cities.2)Economic development and social conditions factors are important factors affecting population agglomeration degree in UAs.The main factors of population gather into UAs are similar with the outside UAs,but the positive promotion of urbanization rate and proportion of tertiary industry in GDP on population agglomeration of UAs in China are enhancing from 2000 to 2020.Meanwhile,the other factors,such as high-quality public services,good urban living environment conditions,high-quality medical and educational resources,are also important factors to promote urban population gather into UAs.This study provides a basis for formulating the development planning of UAs in China,and enriches the relevant theoretical research of population evolution and influencing factors of UAs.

国家中心城市人口协同发展模式及效能类型探究——基于七次全国人口普查数据的分析

基于协同理论的分析框架,在七次全国人口普查数据的基础上,采用改进引力模型、社会网络分析方法,从关系协同、地位协同和圈层协同的视角对城市群人口协同发展与国家中心城市效能类型的关系进行研究,发现竞合力、主导力和聚合力共同构成城市群人口高效协同的三维支撑体系。非中心城市的竞合力是城市群提升人口潜力的关键。尽管国家中心城市在城市群人口系统从旧结构向新结构演化的过程中发挥着决定性作用,但京津冀、长三角、珠三角城市群的其他城市对国家中心城市的人口引力作用相对更强,非中心城市对城市群活力的影响值得重视。中心城市的主导力是城市群实现人口跃迁的关键。国家中心城市在城市群发展过程中的集聚作用突出且呈现持续增强态势,在从弱主导走向强主导的演化过程中,国家中心城市经济实力是其重要支撑。城市网络的聚合力是城市群优化人口格局的关键。在与城市群其他城市之间的圈层关系中,国家中心城市保持较高的子群内部密度、形成稳定的凝聚态势是城市群辐射能力的重要体现。基于“三力”研究,建议“任务面向型”城市积极引导产业和人口有序分流、避免人口过度集中,提升城市群人口发展的协同效能;“成长面向型”城市加快培育非中心城市的发展能力,提升城市群整体的伺服效能;“关系面向型”城市加强与其他中心城市之间的分工协作,改善城市群整体的自组织机制效能。

高质量发展视域下城市群空间网络联系与人口长期均衡发展——基于成渝城市群的实证分析

在人口规模巨大的背景下,促进人口长期均衡发展、深入实施区域协调发展是我国高质量发展的必由之路。采用2006—2020年16个城市的面板数据,使用耦合协调度模型和修正引力模型分别测度成渝城市群16个城市在2006—2020年间的空间网络联系度与人口长期均衡发展水平,运用空间杜宾模型实证分析城市群空间网络联系对人口长期均衡发展的影响及其时空异质性和网络外部效应,发现:(1)城市群空间网络联系可促进本地人口长期均衡发展,但将在周边形成“集聚阴影”,产生不利于周边城市人口长期均衡发展的网络负外部效应。(2)成渝城市群空间网络联系对成都市、重庆市双核心城市以及绵阳市、宜宾市和泸州市三个副中心城市的人口长期均衡发展水平产生促进作用。随着行政级别的提升,城市网络联系逐渐由不利于周边城市人口长期均衡发展的负外部效应转变为促进周边城市人口长期均衡发展的正外部效应。(3)城市空间网络联系所产生的外部效应呈现先负后正,最后趋于收敛的倒S型,且在0-100公里的范围内形成“集聚阴影”,150-200公里范围内为城市网络联系发挥正外部效应的最优半径。(4)科技创新和创业活跃度的提升是城市空间网络联系促进人口长期均衡发展的关键渠道。

城市群流动人口居留意愿网络特征分析及空间模式划分

基于2017年CMDS数据,运用社会网络分析、地理空间分析方法,解析中国京津冀、长三角、珠三角、成渝、长江中游及哈长城市群的流动人口居留意愿网络空间结构差异,划分其空间模式。研究发现,城市群居留网络整体上呈不同等级的“圈层”结构,距离衰减效应显著。居留网络结构特征迥异,沿海城市群流动人口来源腹地广,京津冀和长三角集聚效应明显,流动人口居留意愿更高;珠三角城市群居留强度低,结构松散。内陆城市群吸引范围有限,居留意愿低,成渝城市群居留联系弱,内外部流动相对均衡,长江中游城市群呈现弱集聚与轻均衡特征,哈长城市群内部结构紧凑,外部联系松散。不同区位、不同规模、不同发展阶段的城市群其居留意愿网络空间格局差异明显,流动人口居留选择具有明晰的地域指向。沿海城市群应加大区域低中心性城市发展,创造更多的就业机会,以吸引人口流入;内陆城市群应优化产业结构布局,平衡人口流出与流入,避免区域人口过度流失。

中国流动人口城市认同度与感知接受度空间格局及影响因素研究

基于2017年中国流动人口动态监测数据,采用空间自相关分析、多尺度地理加权回归(MGWR)模型对中国城镇流动人口城市认同度与感知接受度影响因素的空间异质性进行分析。研究发现:①流动人口的城市认同度和感知接受度空间格局相似,均呈现出明显的空间集聚性,但总体上流动人口的感知接受度低于其城市认同度。②城市认同度和感知接受度空间异质性影响因素差异明显,城市认同度的影响因素较少,高学历人才占比、在婚比重、跨省流动比例具有全局显著影响,感知接受度的影响因素更多,除主要流动因素(跨省流动比例、来源城市群比例)的影响具有全局性外,其他变量均呈现出明显的局部异质性。③城市认同度受“个体成长—来源城市—劳力水平”三重影响,而感知接受度受“个体内部特征—流动状态—外部经济产业”三重影响。中国流动人口的城市认同度与感知接受度的影响因素存在明显的空间异质性,需要因地制宜地制定流动人口管理与服务政策。

卫生服务空间可及性评估模型的比较研究

目的比较供需比例法、重力模型、传统两步移动搜索法及改进两步移动搜索法四种模型在卫生服务空间可及性评估中的原理方法和应用价值,为卫生服务空间可及性评估模型的科学选用提供参考。方法以南宁市妇幼卫生服务为例,收集南宁市行政区划矢量地图、道路交通网络矢量图、妇幼卫生资源及需求人口等资料,采用供需比例法、重力模型、传统两步移动搜索法及改进两步移动搜索法分别从县区、乡镇/街道、行政村多尺度上综合评估南宁市妇幼卫生服务的空间可及性。结果四种模型均发现南宁市妇幼卫生服务空间可及性呈明显的空间分异性,可及性从市中心向周边地区递减。但不同模型和不同空间尺度下探测到的可及性高值/低值区域,可及性递减趋势,可及性中位数和四分位数间距等存在较大差异。结论供需比例法、重力模型、传统两步移动搜索法及改进两步移动搜索法在卫生服务空间可及性评估中的应用价值不同,测算出的空间可及性强度亦不同。应用时需参考四种模型结果从多维空间尺度上仿真现实情景,综合评估卫生服务的空间可及性。

中国流动人口自评健康状况影响因素及公平性

目的:了解中国流动人口健康状况及其空间分布和影响因素,评价健康公平性。方法:使用2017年国家流动人口动态监测数据,采用二分类Logistic回归分析流动人口健康状况影响因素,采用集中指数法分析流动人口健康公平性,采用空间自相关法分析健康状况和健康公平性的空间聚集。结果:中国流动人口不健康率为2.71%。年龄和性别对自评健康有显著影响,即随着年龄的增长,流动人口的自评健康逐渐变差,同时女性更容易认为自己不健康。公平性分析显示流动人口的集中指数为0.021 7,城市户籍流动人口为0.021 6,农村户籍流动人口为0.021 9,表明流动人口健康状况公平性偏向高收入阶层,农村户籍流动人口健康不公平程度大于城市户籍流动人口。自评健康全局Moran’s i=0.211,不健康率全局Moran’s i=0.291,表明自评健康有空间聚集趋势。流动人口两周患病率公平性Moran’s i=0.136,显示出空间聚集特征,且以北部和东南沿海地区为主。结论:总体上中国流动人口健康状况较好,健康影响因素包括性别、年龄,健康不公平的集中趋势体现在东南沿海和北部地区,具有倾贫性特征。

主体功能区视角下的滇中城市群人口-经济-资源环境时空耦合分析

[目的]探析人口、经济和资源环境三者之间协调及发展水平状况,阐明各县耦合协调关系及影响因素,为主体功能区规划实施提供有力支撑。[方法]基于2010年、2015年、2020年滇中城市群的自然、经济及人文数据,从主体功能区视角建立人口-经济-资源环境耦合协调发展评价指标体系。运用序关系分析法(G1)与熵权法(EW)确定综合权重,通过耦合协调度模型度量各县耦合协调关系,定量分析了2010年、2015年、2020年滇中城市群人口-经济-资源环境耦合协调演变状况,探析其发展规律。[结果](1)3个时段内,研究区人口在县域尺度呈空间正相关关系,且空间聚集性逐渐增强,呈现西部低人口密度聚集和中部高人口密度聚集态势且常年稳定;(2)2010—2020年,人口、经济和资源环境三者之间的耦合协调度综合发展水平有微弱提升,严重失调的协调类型逐渐消除,濒临失调占比46.94%成为最主要的协调类型;(3)人口、经济和资源环境两两耦合协调分布不均,其中2020年人口-经济综合耦合协调指数最低为0.4543,协调度水平时空分布与三系统协调评价结果吻合度是两两耦合协调评价中最高的,对人口-经济-资源环境耦合协调评价结果影响最大;(4)在主体功能区划实施前后,国家重点开发区两两耦合协调水平变化显著,人口-经济-资源环境整体耦合协调水平较低且有倒退趋势,省级重点开发区长期稳定可控;(5)研究区的人口、经济和资源环境三者之间存在较强的相互作用关系,但协调性在一定程度上处于制约状况。[结论]高耦合协调性主要依赖于人口对市县区经济的带动作用。重点开发区的有效实施必须抓好人口与经济协同发展,人口和经济协调是未来优化主体功能区的重点方向。

基于人口流动的中国城市群多中心结构演化

利用2015—2018年腾讯人口迁徙大数据和社会经济统计数据,采用多中心结构测度等方法,对中国19个城市群的人口规模、流动特征和空间结构演化进行分析.结果表明:(1)从整体上看,中国城市群的形态多中心结构指数差异明显,且有微弱向单中心空间结构发展的态势,而功能多中心结构指数大多较低,但表现出不断上升趋势.(2)在多中心结构类型匹配方面,优化提升类城市群的功能多中心结构指数较高但形态多中心结构指数普遍偏低,发展壮大类城市群的形态多中心结构指数较高但功能多中心结构指数大多偏低,而培育发展类城市群内部存在不同匹配类型.(3)在多中心结构类型演替方面,优化提升类和发展壮大类城市群多中心化发展的趋势较为明显,而培育发展类城市群大多呈现单中心化发展趋势.

中国人口发展的空间格局研究——基于“七普”数据的实证

文章基于2020年第七次全国人口普查最新公布的全国和各省份数据资料,运用空间数据分析方法初步探讨了“七普”数据中人口分布和人口增量的格局变化。研究发现:尽管“七普”数据仍然显示出“东南高、西北低”的人口分布特征,但是内在空间结构产生了新现象,在京津冀城市群、长三角城市群和粤港澳大湾区三大城市群基础上正在形成包括成渝城市群、西咸城市群、长江中游城市群等新生城市群,中心城市也在快速成长;通过对比人口普查数据发现,东北地区出现了较为普遍的人口收缩型城市,而广州市、深圳市和成都市人口增量迁入势头迅猛;粤港澳大湾区城市群人口辐射带动作用显著,内陆城市群则需要持续关注人口“虹吸效应”潜在问题。

西部“小个专”法人互嵌式发展的武汉实践——基于空间正义视角的研究

正义的城市空间是流动人口互嵌式发展的客观条件,城市空间正义的持续发展也是互嵌式发展的结果。以武汉市西部“小个专”法人的互嵌式发展实践为例,城市为流动人口提供了开放、接纳、支持其发展的城市空间,同时也随着群众发展的需求而逐渐完善着城市的发展规划。在正义的城市空间,西部“小个专”法人的发展能够互嵌于社区生活、居民结构与城市现代化发展之中。然而,流动人口所派生出的深化互嵌式发展需求,对城市规划与服务提出了更高的要求,二者间发展节奏的差距形成张力,解构着流动人口对城市的认同。夯实群众互嵌式发展的客观基础,发挥流动人口互嵌式发展的主体能动性,在完善城市空间正义中将满足群众更高的互嵌式发展需求。

山东半岛城市群人口老龄化空间分异特征及其驱动因素研究

基于第六次和第七次人口普查数据,运用老龄化系数等指标、空间自相关等方法,分析了山东半岛城市群县域人口老龄化空间分异特征,利用地理探测器揭示其驱动因素。结果表明:①人口老龄化空间分异显著,呈现“东北高西南低”的分布格局,且老龄化程度呈全面升级态势,县级市和县的老龄化内部差异小于市辖区。②整体集聚格局较为稳定,呈现“中心—外围”的空间梯度差异,老龄化变动趋势差异显著。③老龄化存在较强的空间自相关性,热点区自鲁东地区向西迁移,冷点区由西部向东部和南部迁移。④人口老龄化的空间分异是自然环境、人口结构和社会经济等多种因素共同作用的结果,人口年龄结构惯性是其主导因素。