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The management of international students has become a new challenge that China and most countries in the world must face in the context of the "Belt and Road Initiative" (BRI) and the globalization of higher education. In this paper, we conducted an empirical study on international students in China (ISC) based on a combination of spatial econometric models and analytical methods such as BCG, GeoDetector, and DDCAM, trying to provide a basis for "evidence-based decision-making" by the government in the management of international students and talents. Quantitative analyses revealed significant diversity and spatial effects in the spatio-temporal dynamics of ISCs, with the emergence of star, gazelle, cow, and dog types, suggesting that the scientific management of ISCs requires both local adaptation (spatial differentiation and heterogeneity) and regional collaboration (spatial correlation and autocorrelation). There were many factors affecting the spatio-temporal dynamics of ISCs, and the force of different factors varied considerably, with the key factor more than 0.5 but the auxiliary factor being less than 0.2. Moreover, the factors had significant interaction effects, and a large number of super-interacting factor pairs emerged, with a joint force of more than 0.9, twice the force of a single factor. Generally, the factors had a complex driving mechanism, suggesting that ISC management requires identifying the key and important factors, while attaching emphasis to the synergistic effects among different factors. The recommendation is that China should manage ISCs in spatial zones and introduce differentiated development strategies and policies in different zones. In conclusion, this paper proposes a technical route integrating "dynamic analysis-driving mechanism-policy design" for international students, which is applicable to China, while providing a reference for the management and spatial planning design of international students in other countries in the world. [ABSTRACT FROM AUTHOR]

In the context of globalization and the intensification of international competition, the construction of public cultural facilities has long been not limited to meeting the cultural needs of the people but has become an important initiative to shape the competitiveness of cities. This paper collected POI and socio-economic statistics from 2012 to 2020 from 285 Chinese cities and employed the coefficient of variation (CV), Gini index (GI), ESDA, and GeoDetector to analyze the spatial patterns and driving mechanisms of public cultural facilities. Findings: (1) Public cultural facilities in Chinese cities were featured by evident regional gradient differences and uneven spatial distributions, with a CV greater than 1.3 and a GI greater than 0.5 in both years. They also showed signs of aggregation at weak levels, with a Moran I of 0.15 in both years and a cluster pattern of "hot in the east and cold in the west". (2) Different types of public cultural facilities had differences in their differentiation, aggregation, and change trends. The CV changed from 1.39~2.69 to 1.06~1.92, and the GI changed from 0.53~0.80 to 0.47~0.62, with the differentiation of libraries, museums, theaters, art galleries, and cultural centers decreasing gradually, while that of exhibition halls increased day by day. As the Moran I increased from 0.08~0.20 to 0.12~0.24, libraries, museums, art galleries, and cultural centers showed weak aggregation with an increasingly strong trend. Theaters and exhibition halls also showed weak aggregation but in a declining trend, with the Moran I changing from 0.15~1.19 to 0.09~0.1. (3) The five driving variables exhibit significant differences in their strength across time and across regions, with the economic and infrastructure factors being the strongest and the urbanization factor the weakest. There are significant differences in the strength of the driving forces among the factors, with the total retail sales of consumers, the number of subscribers to internet services, regular higher education institutions, and undergraduates in regular HEIs playing both direct and interactive roles as the core factors. (4) The 285 cities in China are divided into four policy zonings of star, cow, question, and dog cities. Star cities should maintain their status quo without involving too much policy intervention, whereas the core and important factors should be the focus of policy in dog cities and cow cities, and the auxiliary factors should be the focus of policy in question cities. This paper contributes to the in-depth knowledge of the development pattern of public cultural facilities and provides a more refined basis for the formulation of public cultural facility promotion policies in China and similar countries. [ABSTRACT FROM AUTHOR]

Residential mobility serves as a pivotal determinant in reshaping urban social spaces and driving spatial differentiation and segregation within cities. This study harnesses a rich dataset from surveys and the housing market in Nanjing, China to dissect the spatial distribution patterns of its mobile population. Employing the Mantel Test—a novel approach in this context—we assess the interplay between spatial shifts in residential locations and the socio-demographic attributes of individuals, thereby shedding light on the socio-spatial dynamics across various migration categories. Our findings underscore a pronounced trend in the post-2000 era of China's housing marketization: residential migrations occur predominantly within a five-year cycle. The decay in migration distances aligns with the migration field formula, suggesting a systematic attenuation of mobility over spatial extents. The study identifies a strong congruence between the mobility rings—zones of frequent residential movement—and the micro-level characteristics of residents, reflecting the nuanced fabric of urban stratification. Furthermore, we unveil how macro-level institutional frameworks and the housing market milieu substantially shape and limit the migration frequency, hinting at the overarching impact of policy and economic landscapes on residential mobility patterns. The paper culminates by articulating the underlying dynamics of urban residential migration, providing a comprehensive account that contributes to the discourse on sustainable urban development and planning. [ABSTRACT FROM AUTHOR]

With the deepening of urbanization in China, the coordinated development of cities in different regions is an important part of the sustainable development of the country, and the reasonable quantification of the unbalanced development of cities in different regions is an important issue facing the society nowadays. Previous studies usually use population data to analyze the power-law distribution law to quantify the imbalance of urban development in different regions, but China's population data span a large number of years and numerous division criteria, and the results obtained from different population data are widely disparate and have obvious limitations. The paper starts from a fractal perspective and utilizes OpenStreetMap (OSM) data to extract national road intersections from 2015 to 2022, calculates critical distance thresholds for eight years using urban expansion curves, generates urban agglomerations in China, and quantifies the imbalance of urban development in different regions by calculating the urban agglomeration power-law index. The results indicate that (1) the critical distance threshold of urban expansion curves exhibits a slight overall increase and stabilizes within the range of 120–130 m, (2) the number of urban agglomerations in China has been increasing significantly year by year, but the power-law index has been decreasing from 1.49 in 2015 to 1.36 in 2022, and (3) the number of urban agglomerations and the power–law index of the Beijing–Tianjin–Hebei, Yangtze River Delta, Pearl River Delta, and Chengdu–Chongqing regions, which is consistent with the national scale trend, indicates that the scale distribution of urban agglomerations in China at this stage does not conform to Zipf's law, and there is a certain Matthew effect among cities in different geographic areas with a large unevenness. The results of the study can provide new ideas for assessing the coordinated development of cities in different regions. It compensates for the instability of population and economic data in traditional studies. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to provide new ideas and methods for the sustainable use of groundwater in areas with serious groundwater overexploitation and serious groundwater pollution. Geographic information systems (GIS) were combined with machine learning algorithms, water resources optimization technology, and groundwater numerical simulation to optimize the regulation of the groundwater table and quality beneath the Daxing District in the southern plain of Beijing. By collecting local consumption and supply data and observations of the groundwater table and quality in the connected aquifer beneath Daxing for the years 2006–2020, the corresponding water demands and groundwater impact were extrapolated for the years 2021–2025 based on the basis of the existing development model. Through the combination of GIS and machine learning algorithms, the NO3-N concentration of local groundwater monitoring points in wet years, normal years, and dry years were predicted. With respect to NO3-N pollution, three new groundwater exploitation regimes were devised, which we numbered 1 to 3. The optimal allocation of water resources was then calculated for wet year, typical year, and dry year scenarios for the year 2025. By comparing the water shortage, groundwater utilization rate, and NO3-N pollution under the new groundwater exploitation regimes, the optimal groundwater exploitation mode for the three different types of hydrological year was determined. The results indicate that NO3-N pollution was greatly reduced after the adoption of the optimal regimes and that the groundwater table demonstrated rapid recovery. These results can be of great help in realizing the management, supervision, and regulation of groundwater by combining GIS with machine learning algorithms. [ABSTRACT FROM AUTHOR]

A large body of recent studies--from both inside and outside of China--are devoted to the understanding of China's regional inequality. The current study introduces "the spatial field model" to achieve comprehensive evaluation and multi-scale analysis of regional inequality. The model is based on the growth pole theory, regional interaction theory, and energy space theory. The spatial field is an abstract concept that defines the potential energy difference that is formed in the process of a regional growth pole driving the economic development of peripheral areas through transportation and communication corridors. The model is able to provide potentially more precise regional inequality estimates and generates isarithmic maps that will provide highly intuitive and visualized presentations. The model is applied to evaluate the spatiotemporal pattern of economic inequality in China from 2000 to 2012 amongst internal eastern-central-western regions as well as north-south regions at three geographical scales--i.e., inter-province, inter-city, and inter-county. The results indicate that the spatial field model could comprehensively evaluate regional inequality, provide aesthetically pleasing and highly adaptable presentations based on a pixel-based raster, and realise the multi-scale analyses of the regional inequality. The paper also investigates the limitations and extensions of the spatial field model in future application. [ABSTRACT FROM AUTHOR]

The spatial pattern of rural poverty and its influencing factors are unique in regions located in the "double zone", overlaying the Loess Plateau landform and interprovincial border socioeconomic zone. Using Huining County, located in the interprovincial border area of the Loess Plateau, as a case study, this paper examines the spatial heterogeneity of rural poverty patterns and poverty-causing factors by using geographically weighted regression (GWR) modeling. The potential accessibility indicator is employed to identify the formative mechanism of rural poverty. The results show that rural poverty is significantly correlated with county-level accessibility, water resource accessibility, and town-level accessibility. County-level accessibility and town-level accessibility have significant border effects on rural poverty. The arid characteristics in certain areas of the Loess Plateau mean that the impact of water resource accessibility on the incidence of rural poverty is second only to that of county-level accessibility. Forestland resources have a positive correlation with the incidence of rural poverty in the region dominated by farming. Finally, targeted poverty reduction policies are proposed based on the results of the analysis of poverty-causing factors. The findings derived from this paper can help other developing countries in designing their own poverty reduction policies. [ABSTRACT FROM AUTHOR]

Geo-environmental factors are believed to be major determinants of rural poverty. However, few studies have quantified the effects of these factors on rural poverty in China. In this paper, we used county-level poverty incidence data and geo-environmental factors to explore spatial patterns of the incidence of poverty using global and local spatial autocorrelation analysis and to investigate the effect of geo-environment factors on rural poverty using a geo-detector model. Our results demonstrated that there was spatial clustering of the incidence of poverty in the study area. The incidence of poverty decreased from south to north and from the east and west to the central area. The incidence of high–high poverty areas was mainly distributed in the southeast of Guizhou Province and the incidence of low–low poverty areas was distributed in the northeast. The results also demonstrated that percentage of effective irrigation on arable land, slope, elevation and vegetation cover were the dominant factors explaining the spatial pattern of poverty. Interaction analysis demonstrated that the slope non-linearly enhanced the percentage of effective irrigation on arable land. Our findings suggested that geo-environment is the fundamental control factor explaining the spatial pattern of rural poverty in China. Through analysis of the impact of the geo-environment on the spatial pattern of poverty, this study provides a reference for effectively implementing targeted alleviation of poverty. [ABSTRACT FROM AUTHOR]

As an important carrier of expanded urban spatial growth, new towns have been a "policy tool" for spatial production in the new era and have received long-term and constant attention from circles such as geography, planning, and economics. National new districts constitute a new regional space for China to implement the national strategy and promote the transformation of urban development mode. They are mutually reinforcing with their mother cities and hinterland provinces. Based on the geodetector method, this paper reveals the key factors driving the development of national new districts by mother cities and hinterland provinces and their interaction effects, which provides a basis for municipal and provincial governments to accurately formulate policies to promote the development of new towns by classification. The study shows that, firstly, there are five types of driving factors, that is, all-round driving factors, scale-increasing factors, expansion and quality-improving factors, expertise driving factors, and non-driving factors. The strength and dimension of the driving factors are characterized by prominent heterogeneity; R&D personnel, export and import trade are the key factors to expand the increment, optimize the inventory, and improve the quality; the overall development driving forces are in the order of innovation > opening > industry > investment > population. Secondly, the pairwise interaction between different factors exhibits two-factor enhancement, and the population shows a nonlinear increase in the driving force of investment, openness, and innovation on a provincial scale. Thirdly, according to the driving force of the factors and the interaction between them, suggestions are put forward based on the development stage and key demands for city and provincial governments to make policies for the development of national new districts, to support the establishment of scientific competition and cooperation between new towns and mother cities or regions, and to build a long-term collaborative development mechanism. [ABSTRACT FROM AUTHOR]

Shoreline changes are of great importance for evaluating the interaction between humans and ecosystems in coastal areas. They serve as a useful metric for assessing the ecological costs of socioeconomic developmental activities along the coast. In this paper, we present an assessment of shoreline changes along the eastern coast of mainland China from ~1990 to 2019 by applying a novel method recently developed by us. This method which we call the Nearest Distance Method (NDM) is used to make a detailed assessment of shorelines delineated from Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images. The results indicate a dramatic decline in natural shorelines that correspond to the rapid increase in the construction of artificial shorelines, driven by China's economic growth. Of the entire coast of mainland China, the biggest change occurred along the Bohai Sea, where artificial shorelines expanded from 42.4% in ~1990 to 81.5% in 2019. Over this period, this study indicates that China lost > 60% of its biological shorelines, a trend that is especially worrisome because these include areas that were once biologically diverse and extremely rich. As anticipated, shoreline losses were greatest where regions of low economic value had been transformed to areas of higher economic value. Overall, this influence of human activities on shorelines in China is unprecedented. The repercussions of these changes on ecosystems, and the susceptibility of new shoreline developments to population growth and sea-level rise, need to be assessed urgently before additional changes are effected. [ABSTRACT FROM AUTHOR]

The street network is considered the skeleton of the city structure; it determines the efficiency and productivity of the city in that it acts like blood vessels transporting people, goods, and information. The relationship between street networks and economic development is an important research topic in urban geography. In recent years, complex network theory has been successfully used for understanding the characteristics of street network structure. However, researchers lack an analytical framework and methods for studying the relationship between the morphological structure of urban streets and the economic development level of cities. Accordingly, this paper proposes a methodological framework for first, quantitatively characterizing the urban morphological structure based on open street network data, and second, exploring the relationship between the morphological structure of the urban street and the urban economic development level. The proposed methodology was applied to 31 provincial capital cities in China. The results indicate that urban morphological structure can be quantitatively described by betweenness and closeness centrality extracted from street networks. Cities with similar structures have similar levels of economic development. Moreover, the results suggest a significant positive correlation between street network betweenness centrality Gini coefficients and cities' economic development levels, indicating that the street network may affect city productivity. This study makes two major contributions to the scholarly literature. Methodologically, the proposed framework provides technical and methodological support for a better understanding of the relationship between cities' economic development and urban street structure. Empirically, the demonstrated case study may guide decision-making involving regional development and the optimization of urban space. [ABSTRACT FROM AUTHOR]

Spatial distribution and population density are important parameters in studies on urban development, resource allocation, emergency management, and risk analysis. High-resolution height data can be used to estimate the total or spatial pattern of the urban population for small study areas, e.g., the downtown area of a city or a community. However, there has been no case of population estimation for large areas. This paper tries to estimate the urban population of prefectural cities in China using building height data. Building height in urban population settlement (Mdiffs) was first extracted using the digital surface model (DSM), digital elevation model (DEM), and land use data. Then, the relationships between the census-based urban population density (CPD) and the Mdiffs density (MDD) for different regions were regressed. Using these results, the urban population for prefectural cities of China was finally estimated. The results showed that a good linear correlation was found between Mdiffs and the census data in each type of region, as all the adjusted R2 values were above 0.9 and all the models passed the significance test (95% confidence level). The ratio of the estimated population to the census population (PER) was between 0.7 and 1.3 for 76% of the cities in China. This is the first attempt to estimate the urban population using building height data for prefectural cities in China. This method produced reasonable results and can be effectively used for spatial distribution estimates of the urban population in large scale areas. [ABSTRACT FROM AUTHOR]