利用2000 和2010 年中国人口普查分县数据，借助空间分析方法揭示近期中国常住人口城镇化格局特征及其动态变化，结合多元回归模型探讨城镇化空间分异的主要影响因素，基于主成分分析和聚类分析进行中国城镇化发展类型区划分。结果表明：① 2000-2010 年，中国常住人口城镇化率不断提高，年均增加1.3 个百分点，32%的县域城镇化水平年均增长超过1.5 个百分点；② 中国城镇化发展存在区域差异性和“低快高慢”的收敛性，东部、东北、中部、西部四大区域城镇化率年均分别增加1.4、0.5、1.4 和1.3 个百分点；③ 60%的中国县域城镇化发展集中在低城镇化率—高增长率、低城镇化率—中高增长率和低城镇化率—中增长率三种类型，且以中、西部地区分布最为密集；④ 中国县域城镇化格局基本未变，仍以珠三角、长三角、京津冀三大增长极的城镇化发展较为突出，东部地区及内陆省会城市周边的城镇化水平也相对较高；⑤ 经济发展水平、产业结构特征、人力资本状况、人口集聚能力、公共服务水平、地理区位特征对县域城镇化空间分异具有显著而稳健的影响；⑥ 中国城镇化格局可分为具有明显区类一致性和区外异质性的5 大类、20 个区域。新时期的城镇化发展应凸显地域功能、提升承载能力、夯实产业基础、强调节约集约。

本文总结了我国在城市的行政地域与景观地域严重背离的情况下，继续使用行政地域区分城乡的种种弊端，认为解决这一问题的关键在于建立适合中国特点又具有国际可比性的城市实体地域概念，以此作为城乡划分及统计的地域基础，文章提出以下限人口规模、非农化水平和人口密度三个指标定义城市实体地域，并在大量实证研究的基础上，通过对中国城市空间形态的分类，提出了一套完整的划分实体地域的方法和工作程序。

It is generally agreed that human settlements are classified either as urban places or as rural places,In urban studies,one has to deal with the relerant materials in the right way so as to reflect the reality of urban area.Urban areas may be conceived as administrative, functional and physical entities. This paper make a detailed analysis on the various drawbacks of the continued use of adiministrative area in the division of urban and rural area in spite of the serious discrepancies between urban administrative boundaries and the real’ urban space in China, Almost the administrative area of all cities and towns are much larger than the urban built-up area. As a result, vast rural areas and large agricultural population were included in the urban count. Here lies the principal difference with western countries. Obviously, it is difficult if not impossible for us to solve the contradiction by reducing the adiministrative area of a city or town. The authors believe that to solve this problem, it is essential to establish the concept of physical urban area with both adaptability to Chinese conditions and international comparability and make it the basis of urban-rural division and statistics.It is proposed that physical urban area in China can be delimitated in terms of three criteria,prescribed minimum population scale, degree of nonagricuturalization and population density, and the names of citied area or townized area can be given as the counterpart of urbanized area in the United States.The administrative area of a city or town in China consists of two parts: urban district and suburban area. The combination of the two parts is very conplex. For this reason, the key to put our scheme into effect is the classification of types according to the different relations between the two parts.Based on extensive research, the authors find that three factors bring about twelve types. AIa and AIb of them are the most basic types,All these types have their own quantitative standards.After understanding these types, the delineation of the physical urban areas become fairly easy according to the flow diagram(Fig.1).Eleven samples, except one type which is not find,are cited in this paper (Fig.2).

As the land use issue, caused by urban shrinkage in China, is becoming more and more prominent, research on urban shrinkage and expansion has become particularly challenging and urgent. Based on the points of interest (POI) data, this paper redefines the scope, quantity, and area of natural cities by using threshold methods, which accurately identify the shrinkage and expansion of cities in the Yellow River affected area using night light data in 2013 and 2018. The results show that: (1) there are 3130 natural cities (48,118.75 km2) in the Yellow River affected area, including 604 shrinking cities (8407.50 km2) and 2165 expanding cities (32,972.75 km2). (2) The spatial distributions of shrinking and expanding cities are quite different. The shrinking cities are mainly located in the upper Yellow River affected area, except for the administrative cities of Lanzhou and Yinchuan; the expanding cities are mainly distributed in the middle and lower Yellow River affected area, and the administrative cities of Lanzhou and Yinchuan. (3) Shrinking and expanding cities are typically smaller cities. The research results provide a quick data supported approach for regional urban planning and land use management, for when regional and central governments formulate the outlines of urban development monitoring and regional planning.

基于LandScan全球人口和ESA全球土地利用数据，构建2000—2015年中国省域人口空间结构数据库，刻画省域人口空间结构的时空格局，进而运用面板固定效应法、两阶段最小二乘法和差分GMM法探索空间结构对省域经济绩效的影响及这种影响对不同规模省域的差异，最后从省会城市集聚（不）经济的角度分析其内在机制。结果发现：① 2000—2015年，中国所有省会城市的人口规模都得到了显著增加，省域人口空间结构存在单中心化的演变趋势，且中西部地区省域人口空间结构相对偏单中心化；② 对所有省份尤其是人口规模相对较小的省份而言，人口空间结构单中心化倾向于提高省域经济效率，而对人口规模相对大的省份，人口空间结构单中心化很可能不利于省域经济效率的提高；③ 省会城市自身已经初显集聚不经济的迹象。

Based on the global population data from LandScan and land use data released by the European Space Agency, this paper identifies physical urban areas and extracts their populations. On this basis, we constructed a database of the population’s spatial structure of China’s provinces for 2000-2015 and analyzed the spatial and temporal patterns of provincial population spatial structure. Further, this paper uses fixed panel, two-stage least square method and difference GMM method to investigate the effect of population spatial structure on provincial economic performance, and how this effect varies across different sizes. We also probe the underlying mechanisms from the perspective of the agglomeration (dis) economy of the provincial capital. The following findings were reached. First, during the period 2000-2015, the population size of all provincial capitals in China has increased significantly. In terms of the spatial structure index, the temporal evolution of China’s provincial population spatial structure is relatively modest, with a tendency towards monocentricity; in the central and western regions, the provincial population spatial structure is relatively monocentric, while in the eastern coastal regions the population spatial structure tends to be polycentric. Second, for all provinces, especially those with relatively small populations, a monocentric population spatial structure facilitates economic efficiency throughout the province. Third, judging from the economic development of the provincial capital cities themselves, China’s capital cities have entered the stage of agglomeration diseconomy, while considering the economic development of the whole province, the provincial capital cities have a clear boosting effect.

Urban boundaries, an essential property of cities, are widely used in many urban studies. However, extracting urban boundaries from satellite images is still a great challenge, especially at a global scale and a fine resolution. In this study, we developed an automatic delineation framework to generate a multi-temporal dataset of global urban boundaries (GUB) using 30 m global artificial impervious area (GAIA) data. First, we delineated an initial urban boundary by filling inner non-urban areas of each city. A kernel density estimation approach and cellular-automata based urban growth modeling were jointly used in this step. Second, we improved the initial urban boundaries around urban fringe areas, using a morphological approach by dilating and eroding the derived urban extent. We implemented this delineation on the Google Earth Engine platform and generated a 30 m resolution global urban boundary dataset in seven representative years (i.e. 1990, 1995, 2000, 2005, 2010, 2015, and 2018). Our extracted urban boundaries show a good agreement with results derived from nighttime light data and human interpretation, and they can well delineate the urban extent of cities when compared with high-resolution Google Earth images. The total area of 65 582 GUBs, each of which exceeds 1 km2, is 809 664 km2 in 2018. The impervious surface areas account for approximately 60% of the total. From 1990 to 2018, the proportion of impervious areas in delineated boundaries increased from 53% to 60%, suggesting a compact urban growth over the past decades. We found that the United States has the highest per capita urban area (i.e. more than 900 m2) among the top 10 most urbanized nations in 2018. This dataset provides a physical boundary of urban areas that can be used to study the impact of urbanization on food security, biodiversity, climate change, and urban health. The GUB dataset can be accessed from http://data.ess.tsinghua.edu.cn.

夜晚灯光数据已被广泛应用于提取城市建成区的研究,由于灯光具有溢出特性,因此确定最佳灯光阈值成为研究热点。本研究中总结4种常用最佳阈值确定方法的特点,并使用Google Earth影像空间比较法,确定夜晚灯光数据提取城市建成区的最佳阈值。由于地形、交通等因素影响城市形态,城市形态影响夜晚灯光数据提取城市建成区的最佳阈值大小,因此,根据城市形态将阈值结果分为2类,并得到块状城市和带状城市建成区的最佳灯光阈值分别为43和 47。Google Earth影像空间比较法与其他方法相比具有不受统计数据限制,以及简易操作等优势,且从形态上对城市建成区提取结果进行空间比较;其次,根据城市形态对最佳阈值结果分类,使不同类型的城市建成区提取结果更准确。该方法适用于在地形复杂、形态多样的大尺度空间中提取城市建成区。

城市边界是城市演化认知和城市规划决策的空间依据。中国不少研究实践将地级市等行政地域范围视为城市,与物理上主要由连续人造不透水面构成的城市实体地域相比,存在较大的空间边界差异。然而,鲜有研究定量分析“实体”视角下长时间序列的城市用地演化,特别是对实体和行政两种统计单元视角下的城市扩张差异缺乏系统认识。本文基于30 m空间分辨率的多源遥感产品识别了1988—2018年8期的城市实体地域边界,分析其用地扩张过程,进一步与行政地域相对比,系统识别了两种统计视角下城市用地扩张的认知差异和空间模式。主要结论为：① 1988—2018年间,中国面积5 km²以上的城市实体地域单元数量增长了378%,以华东和华中地区的增长最为显著;城市实体地域中,88%为独立增长型,分布广泛但面积大都小于50 km²;12%为空间聚合型,主要分布在发达地区,面积普遍较大。② 相比行政地域,城市实体地域的位序规模更符合幂律分布,其Zipf指数30年来逐渐上升趋向于1,城镇体系发展愈发均衡;相比城市实体地域,行政地域单元在平原地区的城市扩张面积普遍偏高,在沿海地区的城市扩张强度普遍偏低。③ 和城市实体地域单元在拓扑关系上无法一一匹配的1327个行政单元大多分布在人口密集或发达的平原地区;结合实体和行政边界识别了“跨政区同城化”和“政区内集群化”两种扩张模式,前者多发生在省会区域,后者主要分布在华北平原,两者反映了城市化的空间重构。本文研究结果有助于深化中国土地城镇化时空过程的认识,并可对土地资源的科学配置、统一协调及行政区划的优化整合提供空间依据。

Urban boundaries are the spatial basis for the cognition of urban evolution and urban planning decision-making. In China, "city" usually refers to a legally defined administrative unit, whose spatial boundary does not coincide with that of physical entity consisting of a continuous artificial impervious surface. However, few studies quantitatively analyze the temporal and spatial evolution of urban physical areas over long time series, especially lacking a systematic understanding of the spatial differences in urban land expansion from the views of physical and administrative areas. Based on the multi-source remote sensing products with a spatial resolution of 30 meters, we identified the physical urban boundary from 1988 to 2018 and analyzed the land expansion process of the physical area. Further, we identified the cognitive differences and spatial patterns of urban land expansion from the above two views and found the following conclusions: (1) In the past 30 years, the number of urban physcial areas in China with an area of more than five square kilometers has increased by 378%, and the most significant growth is in East and Central China. Among the physical areas, 88% of them belong to the type of "independent growth" widely distributed but most of them are smaller than 50 km2, while the other 12% belongs to the type of "spatial aggregation", mainly distributed in developed areas and the area is generally large. (2) Compared to administrative units, the rank-size distribution of urban area is more complying with the power-law model. Its Zipf's exponent has gradually increased to 1 in the past 30 years, indicating development of the urban system has become more and more balanced. Compared to physical areas, administrative area have larger land expansion area in plain areas and lower land expansion intensity in coastal areas. (3) The 1327 county-level administrative units that cannot topologically match the urban physical area are mostly distributed in densely populated or developed plain areas. Combining two views, two types of spatial expansion have been further identified, namely "inter-districts co-urbanization" and "intra-district clustering". The former occurs in provincial capital area, while the latter is mainly distributed in the developed areas of the North China Plain. Both reflect the spatial reconstruction of urbanization. This research is helpful to deepen the understanding of the spatiotemporal process of China's land urbanization and provide decision support for scientific land resource management.

. Accurately mapping impervious-surface dynamics has great scientific\nsignificance and application value for research on urban sustainable development, the assessment of anthropogenic carbon emissions and global ecological-environment modeling. In this study, a novel and automatic method of\ncombining the advantages of spectral-generalization and automatic-sample-extraction strategies was proposed, and then an accurate global 30 m impervious-surface dynamic dataset (GISD30) for 1985 to 2020 was produced using\ntime-series Landsat imagery on the Google Earth Engine cloud computing\nplatform. Firstly, the global training samples and corresponding reflectance\nspectra were automatically derived from prior global 30 m land-cover\nproducts after employing the multitemporal compositing method and relative\nradiometric normalization. Then, spatiotemporal adaptive classification\nmodels, trained with the migrated reflectance spectra of impervious surfaces\nfrom 2020 and transferred pervious-surface samples in each epoch for every\n5∘×5∘ geographical tile, were applied to map\nthe impervious surface in each period. Furthermore, a spatiotemporal-consistency correction method was presented to minimize the effects of\nindependent classification errors and improve the spatiotemporal consistency\nof impervious-surface dynamics. Our global 30 m impervious-surface dynamic\nmodel achieved an overall accuracy of 90.1 % and a kappa coefficient of\n0.865 using 23 322 global time-series validation samples. Cross-comparisons\nwith five existing global 30 m impervious-surface products further indicated\nthat our GISD30 dynamic product achieved the best performance in capturing\nthe spatial distributions and spatiotemporal dynamics of impervious surfaces\nin various impervious landscapes. The statistical results indicated that the\nglobal impervious surface has doubled in the past 35 years, from\n5.116×105 km2 in 1985 to 10.871×105 km2 in 2020, and Asia saw the largest increase in impervious surface area compared to other continents, with a total increase of 2.946×105 km2. Therefore, it was concluded that our global 30 m\nimpervious-surface dynamic dataset is an accurate and promising product and\ncould provide vital support in monitoring regional or global urbanization as\nwell as in related applications. The global 30 m impervious-surface dynamic\ndataset from 1985 to 2020 generated in this paper is free to access at\nhttps://doi.org/10.5281/zenodo.5220816 (Liu et al.,\n2021b).\n

The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics describing the human presence on the planet that is based mainly on two quantitative factors: (i) the spatial distribution (density) of built-up structures and (ii) the spatial distribution (density) of resident people. Both of the factors are observed in the long-term temporal domain and per unit area, in order to support the analysis of the trends and indicators for monitoring the implementation of the 2030 Development Agenda and the related thematic agreements. The GHSL uses various input data, including global, multi-temporal archives of high-resolution satellite imagery, census data, and volunteered geographic information. In this paper, we present a global estimate for the Land Use Efficiency (LUE) indicator—SDG 11.3.1, for circa 10,000 urban centers, calculating the ratio of land consumption rate to population growth rate between 1990 and 2015. In addition, we analyze the characteristics of the GHSL information to demonstrate how the original frameworks of data (gridded GHSL data) and tools (GHSL tools suite), developed from Earth Observation and integrated with census information, could support Sustainable Development Goals monitoring. In particular, we demonstrate the potential of gridded, open and free, local yet globally consistent, multi-temporal data in filling the data gap for Sustainable Development Goal 11. The results of our research demonstrate that there is potential to raise SDG 11.3.1 from a Tier II classification (manifesting unavailability of data) to a Tier I, as GHSL provides a global baseline for the essential variables called by the SDG 11.3.1 metadata.

采用定基极差熵权法与定基等权赋值法相结合的综合评价方法、核密度估计、探索性空间数据分析等方法,分析2003—2018年中国284个地级以上城市公共健康水平的时空演化特征,并运用广义空间两阶段最小二乘法考察其影响因素。结果发现：① 研究期内中国城市公共健康水平整体呈上升趋势,但各城市间公共健康水平差异有所扩大,且始终存在多级分化现象;② 四大板块核密度曲线在重心位置、主峰高度、波峰数量、拖尾长度、拖尾厚度上均有各自演化形态,是一种时段特征与地区特色相互耦合叠加的结果;③ 中国城市公共健康水平存在正向空间溢出效应和空间“同群效应”,空间集聚类型实现了由LL型为主体向HH型为主体的转化;④ 收入水平、人口密度、科技进步、工业化程度、卫生体制改革对城市公共健康具有影响,但各因素对不同区域的影响存在异质性。

Based on the comprehensive evaluation method combining fixed base range entropy weight method and fixed base equal weight assignment method, kernel density estimation and exploratory spatial data analysis methods, this paper analyzes the spatio-temporal evolution characteristics of public health level of 284 cities in China from 2003 to 2018, and uses the generalized spatial two-stage least square method to investigate its influencing factors. The results show that: (1) During the study period, the urban public health level is on the rise as a whole in China, but the difference of public health level among cities has expanded, and there is always a phenomenon of multi-level differentiation. (2) The kernel density curves of the four plates have their own evolution forms in the position of gravity center, the height of main peak, the number of wave crests, the length of trailing and the thickness of trailing, which is the result of the coupling and superposition of time characteristics and regional characteristics. (3) There are positive spatial spillover effect and spatial “Peer Effects” in China's urban public health level, and the spatial agglomeration type has transformed from LL type to HH type. (4) Income level, population density, technological progress, industrialization and health system reform have an impact on urban public health, but the impact of various factors on different regions is heterogeneous.

通过对221 BC-1911 AD年间中国城市分布特征及其影响因素的研究,发现：① 在整个研究阶段,中国城市分布的重心位于中东部地区,秦—唐时期重心向西南地区大幅移动,唐—元时期重心先东北方向移动后转向西南方向,元—清时期重心主要向北移动。以腾冲—瑷珲一线为界线分区研究发现,在整个研究阶段西部重心在南北及东西方向呈现出较大的波动趋势,东部重心呈现出与全国类似的运动轨迹。② 标准差椭圆分析表明全国及东西部地区城市分布经历了明显的分散—集聚—分散的变化趋势,其中西部地区最为明显。从城市分布的平均方向看,全国及东部地区具有一定的相似性,均以东北—西南为主要分布特征,西部地区是以西北—东南为主要分布特征。③ 从城市密度分布特征看,其空间连续性和自组织性不断加强且由空间相关性引起的结构性变异处于显著状态。从方向上来看,全方向上的均质化程度呈下降趋势,西北—东南方向各时期城市密度均质化程度相对较好,空间差异相对较小,而东—西方向差异最为明显。④ 分析不同时期城市设置的相关因素发现,221 BC-1911 AD年间,中国城市设置相对集中在地形平坦、气候适中且靠近河流及中心城市的地区。

This paper aims to examine the distribution of cities in China during 221 BC-1911 AD and its influencing factors. Some conclusions can be drawn as follows: (1) the urban distribution gravity center of China during the entire study stage was located in the central and eastern regions and firstly, moved sharply southwestward from the Qin dynasty to the Tang dynasty. Secondly, the gravity center moved toward the northeast from the Tang dynasty to the Yuan dynasty, and then toward the southwest direction. Finally, the gravity center during the Yuan-Qing dynasty moved mainly towards the north direction. In this paper, we divided China into east and west parts by the population line of Tengchong-Aihui and found that during the study period, the gravity center path in the western region presented a large fluctuation trend in both the south-north and the east-west directions. However, the moving trajectory of the gravity center in the eastern region was similar to that of the whole country. (2) The analyses of the standard deviational ellipses showed that the urban distribution in the whole country, the eastern region, and the western region exhibited a distribution pattern of "decentralization, centralization and decentralization", and this trend in the western region was most obvious. From the perspective of the average direction, the urban distribution in the whole country and the eastern region were similar to a certain degree, with the northeast-southwest as the main characteristics. However, the urban distribution in the western region took the northwest-southeast as the main features. (3) Based on the urban density distribution and spatial correlation, we found that its spatial continuity and self-organization were continuously strengthened, and the structural variation was in a significant state. Viewing from the direction, we concluded that the degree of homogenization on the Omni-direction tended to decline. The urban density homogenization degree of each dynasty in the northwest and southeast directions was relatively good and the spatial differences were minimal, but the spatial differences in the east and west directions were most obvious. (4) The analysis of the related factors of the urban setting up in different periods revealed that the setting up of the cities of China during 221 BC-1911 AD mainly focused on the flat terrain near the river and the central city with moderate climate.

小城镇镇区土地利用结构是小城镇社会经济发展的基本空间映射,是小城镇规划管理的关键问题之一,合理的空间结构能够促进小城镇功能的发挥和用地效益的提升。当前该领域的研究主要侧重某一区域或者某一类型的研究,全国层面的调查研究较少,造成当前理论研究结论的普适性和可推广性较低。采用2016年住房和城乡建设部全国121个镇的调查数据,对中国小城镇镇区土地利用结构特征进行深入分析并揭示其特征形成机制。研究发现：① 由于镇区人口少、规模差异小,不同人口规模小城镇之间的土地利用结构有差异,但是不突出。② 多数小城镇受镇区产业空心化影响,用地结构的居住服务功能较为显著。③ 小城镇镇区用地结构与区域经济发展水平、工业化发展水平密切相关,经济发展水平高的地区,工业用地占比高。④ 受大城市辐射和虹吸作用影响,近郊小城镇土地利用结构的城市化现象明显。⑤ 土地所有制与用地结构密切相关,国有土地占比高的镇,公共设施用地比例高。本研究对于丰富和完善中国小城镇理论研究具有重要的理论价值,对于指导小城镇规划建设具有重要的实践价值。

<p id="C3">Land use structure is one of the fundamental spatial features of socioeconomic development. It is also a key issue of urban planning and management in small towns because proper land use structure is related to the improvement of urban function and land use efficiency. The existing literature on land use structure in townships of China is dominated by the studies which either focus on a given region or one certain type of townships, while a nation-wide study is missed. As a result, a generalized knowledge of land use structure in townships remain scarce. This paper aims to fill the research gap. Using the data of 121 townships from a survey conducted under the leadership of the Ministry of Housing and Urban & Rural Development, the paper analyzes the characteristics of land use structure and its mechanisms in townships in China. The results of analysis show that: (1) There is a small variation in land use structure between the townships due to a small size of total population and slight difference in population size between them; (2) Residential land use occupies a high proportion of the total area of land use in most townships due to industrial hollowing-out; (3) Land use structure is significantly related with industrialization and economic growth, and the higher level of economic growth, the higher proportion of industrial land use; (4) Land use structure in the townships which are located near to the large cities shows a clear trend of urbanization-featured land use due to the spillover effects and siphonic effects from these large cities; (5) Land use structure in the townships is obviously affected by land ownership and a higher proportion of state-owned land is related with a higher level of land use for public service facilities. The findings of this paper would have important theoretical values of enriching the existing literature and practical values of guiding urban planning and construction in townships of China.</p>