Large regional differences and uneven regional development is fundamental of China. Regional balanced development is an important topic in the field of development geography. This study reviews the course of regional balanced development in China and summarize the characteristics of regional balanced development in each period. This study suggests that inter- regional development of China shows a state of succession between balanced development and non-balanced development. Each succession brings the quality of social development to a new level and gradually make social development move towards the state of high- quality development and balanced regional development. Then, this study discusses the scientific connotation of regional balanced development. Under the guidance of sustainable development theory, we should pay attention to the resource endowment difference in different area, solve the problem among economy, human and nature and promote spatial balance of regional development and green development of ecological economic coordination. The balanced promotion of regional people's well-being is the ultimate goal of regional balanced development. In the end, based on the thinking of development geography, this study discusses the path of regional balanced development in China from three aspects of society, economy and ecology. Suggestions are put forward for the balanced development of China's regions and the improvement of people's well-being.

区域差异大、发展不平衡是中国的基本国情,研究区域均衡发展问题是发展地理学领域的重大课题。本文首先回顾了中国的区域均衡发展历程,总结了各时期区域均衡发展的特征,研究指出,中国区域间发展呈现出均衡发展与非均衡发展演替的状态,每次演进使得社会的发展质量迈进新的台阶,逐渐走向高质量发展与区域均衡发展的状态。其次,本文探讨了当前区域均衡发展的科学内涵,强调要以可持续发展理论为指引,关注不同地区资源禀赋差异,解决经济、人、自然三者间的矛盾,促进区域发展的空间均衡与生态经济协调的绿色发展,最终落脚到区域人民生活福祉的均衡提升为区域均衡发展的最终目标。最后,本文以发展地理学的思维从社会、经济、生态三个方面探讨中国区域均衡发展的路径,为中国区域均衡发展和国民福祉的提升提出若干建议。

In remote sensing research, night light remote sensing as an important branch of active development has attracted increasingly more attention from the fields of natural and social science research in recent decades. Different from traditional daytime optical satellite remote sensing that obtains ground object radiation information, nighttime light remote sensing is the acquisition of visible-near-infrared electromagnetic information transmitted from the Earth's surface under cloud-free conditions at night. Compared to the ordinary satellite remote sensing images, ground light intensity information recorded by the night light images directly reflects the difference of human activity. Therefore, it is widely used in urbanization research, extraction of impervious surfaces, estimation of socioeconomic indicators, major events assessment, ecological environment assessment, and other fields. At present, although research results based on nighttime light data are continuously accumulating, there is a general lack of systematic review of these results and the current reviews suffer from incomplete coverage and poor timeliness. Therefore, this article summarizes the research results of nighttime light data in recent decades and examines them from the aspects of data processing and technical approaches, urbanization application research, and parameterization. Finally, we identify the following three research hotspots: multi-source data fusion, application expansion, and short-period surface light monitoring.

在遥感应用研究中,夜间灯光遥感作为发展活跃的一个重要分支,近年来受到越来越多来自自然科学领域和社会经济领域的关注。与传统的光学遥感卫星获取地物辐射信息不同,夜间灯光遥感是获取夜间无云条件下地表发射的可见光-近红外电磁波信息。相比于普通的遥感卫星影像,夜间灯光遥感所使用的夜间灯光影像记录的地表灯光强度信息更直接反映人类活动差异,因而被广泛应用于城市化进程研究、不透水面提取、社会经济指标空间化估算、重大事件评估、生态环境评估等领域。目前,虽然基于夜间灯光数据的应用研究成果正在不断积累,但对成果的系统性总结、整理的研究目前存在着覆盖面不全、时效性不强等不足。基于此,论文通过对近几十年来有关夜间灯光数据的研究成果的详细梳理,从数据处理与技术方法、应用研究等方面进行归纳总结。最后,文章从多源数据融合、应用领域拓展和短周期地表灯光监测3个方面指出了未来的研究热点。

Earth’s artificially lit area is expanding at 2.2% per year, with existing lit areas brightening by 2.2% per year.

A pervasive issue in social and environmental research has been how to improve the quality of socioeconomic data in developing countries. Given the shortcomings of standard sources, the present study examines luminosity (measures of nighttime lights visible from space) as a proxy for standard measures of output (gross domestic product). We compare output and luminosity at the country level and at the 1° latitude × 1° longitude grid-cell level for the period 1992-2008. We find that luminosity has informational value for countries with low-quality statistical systems, particularly for those countries with no recent population or economic censuses.

Economic inequality at the local level has been shown to be an important predictor of people’s political perceptions and preferences. However, research on these questions is hampered by the fact that local inequality is difficult to measure and systematic data collections are rare, in particular in countries of the Global South. We propose a new measure of local inequality derived from nighttime light (NTL) emissions data. Our measure corresponds to the local inequality in per capita nighttime light emissions, using VIIRS-derived nighttime light emissions data and spatial population data from WorldPop. We validate our estimates using local inequality estimates from the Demographic and Health Surveys (DHS) for a sample of African countries. Our results show that nightlight-based inequality estimates correspond well to those derived from survey data, and that the relationship is not due to structural factors such as differences between urban and rural regions. We also present predictive results, where we approximate the (survey-based) level of local inequality with our nighttime light indicator. This illustrates how our approach can be used for new cases where no other data are available.

Reliable data on economic inequality are largely limited to North America and Western Europe. As a result, we know the least about areas where inequality presents the most serious developmental policy challenge. We demonstrate that spatial variation in night-light emitted per person can reflect the distribution of income. This allows us to map global patterns and trends in economic inequality using remote sensing.

Night-time lights satellite images provide a new opportunity to measure regional inequality in real-time by developing the Night Light Development Index (NLDI). The NLDI was extracted using the Gini coefficient approach based on population and night light spatial distribution in Romania. Night-time light data were calculated using a grid with a 0.15 km2 area, based on Defense Meteorological Satellite Program (DMSP) /Operational Linescan System (OLS satellite imagery for the 1992–2013 period and based on the National Polar-orbiting Partnership–Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) satellite imagery for the 2014–2018 period. Two population density grids were created at the level of equal cells (0.15 km2) using ArcGIS and PostgreSQL software, and census data from 1992 and 2011. Subsequently, based on this data and using the Gini index approach, the Night Light Development Index (NLDI) was calculated within the MATLAB software. The NLDI was obtained for 42 administrative counties (nomenclature of territorial units for statistics level 3 (NUTS-3 units)) for the 1992–2018 period. The statistical relationship between the NLDI and the socio-economic, demographic, and geographic variables highlighted a strong indirect relationship with local tax income and gross domestic product (GDP) per capita. The polynomial model proved to be better in estimating income based on the NLDI and R2 coefficients showed a significant improvement in total variation explained compared to the linear regression model. The NLDI calculated on the basis of night-time lights satellite images proved to be a good proxy for measuring regional inequalities. Therefore, it can play a crucial role in monitoring the progress made in the implementation of Sustainable Development Goal 10 (reduced inequalities).

Through specific analysis of the three dimensions of night light development index (NLDI), cultural diversity and the rank-size rule, the human development level of the Qinghai-Tibet Plateau in different periods is evaluated. The results show that: 1) Since the 21st century, human development in the Qinghai-Tibetan Plateau has been improved year by year, but the overall level of human development is still below average level. There are some spatial differences in the level of human development within the region. The night light intensity in the southeast is relatively high, showing the concentrated population distribution. 2) The areas with high ethnic diversity in the Qinghai-Tibetan Plateau mainly extend and expand along the Tibetan-Yi Corridor and Hexi Corridor. The night light intensity in the multicultural blending area is high, and the overall level of human development is on the rise. 3) The rank-size rule is used to analyze the urban scale constructed by night light intensity. A prominent problem in the urban system of the Qinghai-Tibet Plateau lies is that the maximum urban scale is still too small.

通过夜光发展指数（NLDI）、文化多样性和城市位序规模法则3个维度进行分析，评估不同时期青藏高原地区的人类发展水平。研究表明：① 21世纪以来，青藏高原地区人类发展水平在逐年提升，但是人类发展水平整体上还处于中等偏下水平，区域内部人类发展水平存在一定的空间差异，东南部夜间灯光强度比较高，人口分布集中；② 青藏高原民族多样性较高区域主要沿着藏彝走廊和河西走廊延伸和扩展，多元文化交融地区的夜间灯光强度高，人类发展水平总体呈上升趋势；③ 采用位序规模法则分析由夜光强度构建的城市规模，最大城市规模太小仍然是青藏高原地区城镇体系的突出问题。

Cities and villages are components of a specific organism. Only the sustainable development of two parts can support the prosperous development as a whole. According to the theory of man-earth areal system, urban-rural integrated system and rural regional system are the theoretical bases for entirely recognizing and understanding urban-rural relationship. To handle the increasingly severe problems of "rural disease" in rapid urbanization, accelerating rural revitalization in an all-round way is not only a major strategic plan for promoting the urban-rural integration and rural sustainable development, but also a necessary requirement for solving the issues related to agriculture, rural areas, and rural people in the new era and securing a decisive victory in building a moderately prosperous society in all respects. This study explores the basic theories of urban-rural integration and rural revitalization and analyzes the main problems and causes of rural development in the new era, proposing problem-oriented scientific approaches and frontier research fields of urban-rural integration and rural revitalization in China. Results show that the objects of urban-rural integration and rural revitalization is a regional multi-body system, which mainly includes urban-rural integration, rural complex, village-town organism, and housing-industry symbiosis. Rural revitalization focuses on promoting the reconstruction of urban-rural integration system and constructs a multi-level goal system including urban-rural infrastructure networks, zones of rural development, fields of village-town space and poles of rural revitalization. Currently, the rural development is facing the five problems: high-speed non-agricultural transformation of agriculture production factors, over-fast aging and weakening of rural subjects, increasingly hollowing and abandoning of rural construction land, severe fouling of rural soil and water environment and deep pauperization of rural poverty-stricken areas. The countryside is an important basis for the socioeconomic development in China, and the strategies of urban-rural integration and rural revitalization are complementary. The rural revitalization focuses on establishing the institutional mechanism for integrated urban-rural development and constructs the comprehensive development system of rural regional system, which includes transformation, reconstruction and innovation in accordance with the requirements of thriving businesses, pleasant living environments, social etiquette and civility, effective governance, and prosperity. Geographical research on rural revitalization should focus on the complexity and dynamics of rural regional system and explore new schemes, models and scientific approaches for the construction of villages and towns, which are guided by radical cure of "rural disease", implement the strategy of rural revitalization polarization, construct the evaluation index system and planning system of rural revitalization, thus providing advanced theoretical references for realizing the revitalization of China's rural areas in the new era.

城市与乡村是一个有机体,只有二者可持续发展,才能相互支撑。依据人地关系地域系统学说,城乡融合系统、乡村地域系统是全新认知和理解城乡关系的理论依据。针对日益严峻的“乡村病”问题,全面实施乡村振兴,既是推进城乡融合与乡村持续发展的重大战略,也是破解“三农”问题,决胜全面建成小康社会的必然要求。本文探讨了新时代城乡融合与乡村振兴的基础理论,剖析了乡村发展面临的主要问题,提出了问题导向的中国城乡融合与乡村振兴科学途径及研究前沿领域。结果表明：① 城乡融合与乡村振兴的对象是一个乡村地域多体系统,包括城乡融合体、乡村综合体、村镇有机体、居业协同体,乡村振兴重在推进城乡融合系统优化重构,加快建设城乡基础网、乡村发展区、村镇空间场、乡村振兴极等所构成的多级目标体系。② 中国“三农”问题本质上是一个乡村地域系统可持续发展问题,当前乡村发展正面临主要农业生产要素高速非农化、农村社会主体过快老弱化、村庄建设用地日益空废化、农村水土环境严重污损化和乡村贫困片区深度贫困化等“五化”难题。③ 乡村是经济社会发展的重要基础,城乡融合与乡村振兴战略相辅相成,乡村振兴应致力于创建城乡融合体制机制,推进乡村极化发展,按照产业兴旺、生态宜居、乡风文明、治理有效、生活富裕的要求,构建乡村地域系统转型—重构—创新发展综合体系。④ 乡村振兴地理学研究应着眼于乡村地域系统的复杂性、综合性、动态性,探究以根治“乡村病”为导向的新型村镇建设方案、模式和科学途径,为实现新时代中国乡村振兴战略提供理论参考。

美国国防气象卫星搭载的业务型线扫描传感器（DMSP/OLS）获取的夜间灯光影像,可客观地反映人类开发建设活动强度,其广泛应用于城市遥感的多个领域。但该数据缺少星上的辐射校正,下载的原始影像数据集不能直接用于研究,需进行区域校正。长时间序列的DMSP/OLS夜间灯光影像数据集主要存在2个问题需在校正过程中解决：（1）原始影像数据集中的影像是非连续性的;（2）数据集中的每一期影像都存在着像元DN值饱和的现象。针对这2个问题,本文提出了一种不变目标区域法的影像校正方法,对提取出来的每一期中国区域的夜间灯光影像进行了校正,该校正方法包括相互校正、饱和校正和影像间的连续性校正。最后,为了检验校正方法的合理性与可靠性,本文将校正前后中国夜间灯光影像与GDP和电力消耗值,分别进行回归分析评价表明,校正后的影像更客观合理地反映区域经济发展的差异。

. The nighttime light (NTL) satellite data have been widely\nused to investigate the urbanization process. The Defense Meteorological\nSatellite Program Operational Linescan System (DMSP-OLS) stable nighttime\nlight data and Suomi National Polar-orbiting Partnership Visible Infrared\nImaging Radiometer Suite (NPP-VIIRS) nighttime light data are two widely\nused NTL datasets. However, the difference in their spatial resolutions and\nsensor design requires a cross-sensor calibration of these two datasets for\nanalyzing a long-term urbanization process. Different from the traditional\ncross-sensor calibration of NTL data by converting NPP-VIIRS to\nDMSP-OLS-like NTL data, this study built an extended time series (2000–2018)\nof NPP-VIIRS-like NTL data through a new cross-sensor calibration from\nDMSP-OLS NTL data (2000–2012) and a composition of monthly NPP-VIIRS NTL\ndata (2013–2018). The proposed cross-sensor calibration is unique due to the\nimage enhancement by using a vegetation index and an auto-encoder model.\nCompared with the annual composited NPP-VIIRS NTL data in 2012, our product\nof extended NPP-VIIRS-like NTL data shows a good consistency at the pixel\nand city levels with R2 of 0.87 and 0.95, respectively. We also found\nthat our product has great accuracy by comparing it with DMSP-OLS radiance-calibrated NTL (RNTL) data in 2000, 2004, 2006, and 2010. Generally, our\nextended NPP-VIIRS-like NTL data (2000–2018) have an excellent spatial\npattern and temporal consistency which are similar to the composited\nNPP-VIIRS NTL data. In addition, the resulting product could be easily\nupdated and provide a useful proxy to monitor the dynamics of demographic\nand socioeconomic activities for a longer time period compared to existing\nproducts. The extended time series (2000–2018) of nighttime light data is\nfreely accessible at https://doi.org/10.7910/DVN/YGIVCD (Chen et\nal., 2020).\n

Readily available and accurate data on spatial population distribution is essential for understanding, and responding to, many social, political, economical and environmental issues, such as humanitarian relief, disaster response planning, environment impact assessment, and development assistance. Research on spatialization of demographic data plays an important role in grid transformation of social-economical data. Furthermore, as gridded population data can be effectively interoperate with geospatial data and remote sensing images, it is a useful supplement to census data. This paper reviewed spatialization methodologies, predictive modeling factors and typical datasets in the literature of population data spatialization research. Shortcomings of demographic data and advantages of spatial population distribution data are compared and summarized firstly. The spatialization methodologies are grouped into three categories, i.e., population distribution models from urban geography, areal interpolation methods and spatialization methods based on remote sensing and GIS. Population models from urban geography include the Clark's model and allometric growth model. The areal interpolation methods had been distinguished by point based method and area based method. Spatialization methods based on remote sensing and GIS are most widely used in nowadays, which can be further grouped into three categories for two reasons: one is the relationship between population and land use, urban area, traffic network, settlement density, image pixel characteristics, or other physical or socioeconomic characteristics, and the other is the calculation strategy. Various methods mentioned above have their own application environment and limitations. We reviewed the principles and applicability of every method in detail. After that, we generalized the frequently used factors in the spatialization process, involving land use/land cover, traffic network, topography, settlements density, night light, texture variable, and spectral reflectance. In the meantime, some typical research cases about the factors also were exemplified and analyzed. In addition, we introduced a few widely used spatial population distribution datasets or influential population spatialization projects. They consisted of China km grid population datasets, UNEP/GRID, GPW/GRUMP, LandScan, AfriPop & AsiaPop & AmriPop. The producers, resolution, characterization year and generation method of each one were presented exhaustively. Based on the above review, we discussed the current research problems and outlined research priorities in the future. The problems include the temporal inconsistency of input data, coarse resolution of demographic data, lack of in-depth study on scale effect, the scarcity of time series products and few validation works. To deal with these issues, more studies should be conducted to the following aspects: comprehension of population distribution mechanism, calculation of consistency and validation of existing datasets, application of multi-sources remote sensing data and volunteered geographic information, continuous space-time simulation of population distribution in the typical areas, sub-block-level population estimation, self-adaptive spatialization method which integrates multiple elements and multiple models. In summary, the research on spatialization of demographic data has made breakthroughs in the past two decades. Meanwhile, there are a few problems that need to be solved immediately. Since these two aspects had been reviewed as comprehensively as possible, we hope issues discussed in this paper could enlighten and promote the future study in this field.

人口数据空间化研究旨在发掘和展现人口统计数据中隐含的空间信息，并以地理格网或其他区域划分的形式再现客观世界的人口分布，具有重要的科学意义。人口空间分布数据有助于从不同地理尺度和地理维度对人口统计数据形成有益补充，其应用广泛，相关研究方兴未艾。主要从以下3 个方面对人口数据空间化研究进行综述：① 主要空间化方法的原理及其适用性；② 空间化中用到的建模参考因素，并结合具体应用案例分析其作用机理；③ 典型人口空间化数据集。在此基础上，分析了现阶段人口数据空间化所运用的输入数据的质量和详细程度、尺度效应及时空分辨率、长时间序列数据集和精度检验等方面存在的问题；并探讨了人口数据空间化未来的研究方向。

China's per capita GDP has reached 64520 yuan (about 10,000 US dollars). The development process of countries around the world and the inverted U-shaped curve of regional development gaps indicate that the evolution of China's regional development pattern will step into an inflection point segment and that the development gap between regions has witnessed a switch from continuing widening to narrowing. In this paper, we discuss the evolution process of China's regional development pattern over the 40 years since 1978 before the inflection point: (1) The gap in per capita GDP between the upstream areas (the top 9 most developed provinces) and the downstream (the last 8 underdeveloped provinces) increased from 407 yuan in 1978 to 53817 yuan in 2018. (2) Under the pull of export-oriented economy and non-state-owned economy, the mean center of urbanization and economic development rotates in a clockwise direction. Southeast China has become an area with accelerated economic agglomeration. (3) China's regional development pattern has presented a diamond structure with the four core regions of Beijing-Tianjin-Hebei, the Yangtze River Delta, the Pearl River Delta and Chengdu-Chongqing as the apex and the connection of adjacent regions as the boundary. The GDP in these regions accounts for about 75% of the national total. In 2020, China's regional development pattern will enter an inflection point of evolution. The form is characterized by the narrowing of the regional economic gap, which is essentially a transition from non-high-quality regional development to high-quality regional development. The high-quality development of the region is a process of growth in which the ecological and social benefits will be basically synchronized in the process of sustained economic growth, and a regional high-quality development model that is differentiated by geographical functions is presented. In the next 30 years, the regulation and optimization of China's regional development pattern should focus on three types of regions: key urbanization regions, relatively underdeveloped regions, and key areas for security, as well as inter-regional interactions according to the dual objectives of promoting regional balanced (coordinated) development and high-quality development. Establishing a space governance system with the major function zoning-strategy-planning-system as the mainstay will become the basic guarantee for regulating and optimizing the regional development pattern.

2018年底中国人均GDP达到64520元（将近10000美元）。世界各国发展历程和区域发展差距倒“U”字型曲线表明,中国区域发展格局演变将步入从区域间经济发展差距持续扩大转向缩小的拐点区段。本文讨论了1978年以来拐点发生前40年间中国区域发展格局演变过程,① 处于发展上游（前8位最发达的省区）和下游（后8位欠发达省区）省区人均GDP的差距从1978年的407元增加到2018年的53817元;② 城市化和经济发展的重心在外向型经济、非公有经济的拉动下,呈顺时针方向发生旋转,中国东南地区成为经济加速集聚的区域;③ 中国区域发展格局形成以京津冀、长三角、珠三角、成渝地区四大核心区域为顶点、相邻区域的连线为边界合围成的菱形结构,其GDP占全国比重稳定在75%左右。2020年中国区域发展格局应进入演变的拐点,一方面表现为区域经济差距的缩小,另一方面则是从非高质量的区域发展向高质量区域发展的转变。区域高质量发展是在经济效益持续增长过程中,生态效益和社会效益将相向基本同步的增长过程,并呈现出因地域功能不同而分异的区域高质量发展模式。未来30年,按照促进区域均衡（协调）发展和高质量发展的双重目标要求,应把重点城市化地区、相对欠发达地区、重要安全保障区等三类区域以及区域间互动关系等为调控和优化中国区域发展格局的重点,而建立以主体功能区划—战略—规划—制度为统领的空间治理体系,将成为调控和优化区域发展格局的基本保障。

Based on the social-economic data from 1952 to 2000 in China, the question of regional disparity in China was reconsidered by using spatial analysis methods. Spatial analysis needed in social and economic science for the observed spatial values are not independent each other, i.e., (or) they don't follow the same distribution, and (or) there is a trend along different directions. In this paper, the centrographic statistic was used to estimate basic parameters about the spatial distribution. The geometric center of Chinese Mainland with the centers of arable land area, population and GDP based on provincial level data were compared. The results show that the center of population and GDP had a significant offset with the center of geometry and land use. These are the key to understand the spatial disparity in China. The centers of population have an obviously trend of shift from the east to the west of China Mainland. This may be caused by the family planning and the other polices. But the shifts of the population center will help to improve the development of the west of China. As comparison with population, the shifts of the center of GDP had a different direction. It moved to south since the 1978 while the opening-up policy was adopt in china. In other words, the south of China had more rapid increase than north since 1978. This is not consistent with the opinion that the disparity of regional economic is great from east to west in China. The different shifts of centers of population and GDP also indicates that the economy of west did not increase with the increasing of population. The shifts of population and GDP centers indicate the change of the social and economic pattern in China. The difference of them also indicates that the imbalance of development in China. The Exploratory Spatial Data Analysis (ESDA), which based on the computing spatial autocorelation and spatial heterogeneity, was also used to detect the geographical dynamics of Chinese regional disparity patterns. There are significant positive spatial autocorrelation (Moran's I)of per capita GDP in China. That is, the relatively high (low) developed province tends to be located nearby other high (low) developed provinces more often than expected due to random chance, and then each province should not be viewed as an independent observation. The econometric estimations based on geographical data (i.e. localized data) have to take into account the fact that economic phenomena do not be randomly spatially distributed. We also compared the temporal change of the spatial autocorrelation in China, and found that there is an obviously temporal increase of Moran's I since 1952 to 1995. This means that the disparity is increased in the same periods. But this trend does not keep on since 1990s because we found that the Moran's I soothed with a little fluctuating. Moran's I Scatterplots and LISA (Local Indicators of Spatial Association, LISA) cluster maps were used to test the local pattern of the Chinese economic development. The results of local statistic show that the two types of clusters (High-High and Low-Low) are increasing which means that the heterogeneous is increasing too. And this is the other indicator of the regional disparity in China.