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Understanding the characteristics of waterlogging in urban agglomeration is essential for effective waterlogging prevention and management, as well as for promoting sustainable urban development. Previous studies have predominantly focused on the driving mechanisms of waterlogging in urban agglomeration at a single scale, but urban agglomeration space has greater spatio-temporal heterogeneity, it is often difficult to fully reveal such characteristics at a single scale. Consequently, this study endeavors to explore the spatio-temporal evolution characteristics and underlying mechanisms of waterlogging incidents within urban agglomerations by adopting a multi-scale analytical approach. The results indicate that: (1) The waterlogging degree and high-density zones increase in the GBA, and the waterlogging points are spatially polycentric. However, the waterlogging point in Hong Kong is decreasing. (2) The influence of ISP and AI on waterlogging is dominant at all scales, followed by RE and Slope. ISP∩Slope and ISP∩RE are the key interactions for waterlogging. (3) The aggregation of waterlogging decreases with grid scale, and the influence of land cover factors on waterlogging increases with grid scale. Moreover, the findings at the grid scale outperformed those at the watershed scale, indicating that the grid scale is more conducive to the investigation of waterlogging in urban agglomerations. This research broadens our comprehension of the mechanisms behind waterlogging in urban agglomeration and provide references for policy decisions on waterlogging prevention and mitigation within urban agglomerations.

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In order to strengthen the overall planning and coordination of urban construction and ecological space in Baishan City, we should formulate a scientific land type planning of Production-Living-Ecological Space (PLES). In this paper, land use dynamic attitude model, land use transfer matrix, land use function center of gravity transfer model, eco-environmental quality index(EEQI) and geographic detector model are used to discuss the spatio-temporal evolution of PLES in Baishan City from 2000 to 2020. Spatio-temporal evolution of EEQI and its influencing factors. The results show that: (1) During the study period, Baishan City showed the characteristics that the production space first increased and then decreased, the ecological space decreased, and the living space continued to increase. Among them, the ecological space is the dominant space of Baishan City, covering an area of more than 80%. From the perspective of the transformation of PLES, from 2000 to 2020, Baishan City is mainly characterized by the transformation of ecological space into production space and living space. In the second type of space, green ecological space, forestry ecological space and other ecological space have decreased, while other types of space have increased in varying degrees. (2) During the study period, the overall EEQI of Baishan City remained in good condition, and the ecological environment quality(EEQ) of the three periods were 0.6571, 0.6412 and 0.6562 respectively. The higher EEQI is distributed in Changbai County and Linjiang City, while the areas with lower EEQI are concentrated in the north-central part of Hunjiang City, the middle part of Jiangyuan District and the northwest of Fusong County. (3) According to the analysis of the influencing factors of EEQ, the influence of the factors of spatial differentiation of EEQI in Baishan City changed significantly from 2000 to 2020, and the average annual rainfall was the core factor affecting the spatial differentiation of EEQ in Baishan City, the second is the urbanization rate and the distance from the county government, and the interaction between the average annual rainfall and the distance from the county government has a strong influence on the spatial differentiation of EEQ in Baishan City. This study reveals the evolution of spatial types and EEQ of PLES in Baishan City, and provides a scientific reference for the effective management and utilization of land resources in Baishan City.

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Studies on the spatio-temporal variation and coordinated development level of compactness with carbon emission intensity in the Chengdu-Chongqing urban agglomeration is of great significance to achieve green, low-carbon, and high-quality development of society. The spatial-temporal differentiation, coupling coordination, and driving factors of the compactness and carbon emission intensity of the Chengdu-Chongqing urban agglomeration from 2010 to 2020 were analyzed by using the methods of comprehensive evaluation, carbon emission identical equality, coupling coordination, and the Geo-detector model. The results showed that:① The compactness of the Chengdu-Chongqing urban agglomeration continued to rise from 0.18 in 2010 to 0.22 in 2020, with an overall increase of 22.22 %, in which the contribution rate of economic compactness to urban compactness increased yearly. There were significant regional differences in the compactness of cities. The high-value areas were concentrated in Chengdu, Deyang, and Mianyang in the northwest and Zigong, Neijiang, and Luzhou in the southwest, whereas the low-value areas were distributed in the middle. Ya'an was always the area with the lowest compactness of urban agglomeration. ② The carbon emission intensity of the Chengdu-Chongqing urban agglomeration was decreasing yearly, with a decrease rate of 39.57 % during the study period. The carbon emission intensity in the southern part of the urban agglomeration was higher than that in other regions as a whole, whereas Chengdu and Chongqing were low-value areas all the year round, and the regional differences of carbon emission intensity were gradually narrowing. ③ The coupling degree between urban compactness and carbon emission intensity changed from the antagonistic stage to the running-in stage; the coupling coordination degree increased notably, from 0.21 in 2010 to 0.69 in 2020; and the two systems gradually moved towards benign and coordinated development. The coupling coordination between the northwest of the urban agglomeration (Chengdu, Deyang, and Mianyang) and the south of Sichuan (Zigong and Luzhou) was generally high. Industrial structure, scientific and technological innovation, urbanization level, government intervention, and environmental livability all had significant effects on the coupling and coordination of the two systems. ④ Optimizing industrial structure, strengthening scientific and technological support, improving the quality of urbanization development, implementing active policy guidance, and building green barriers were effective ways to promote the coordinated development of compactness and low carbon in the Chengdu-Chongqing urban agglomeration.

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Green innovation in the tourism industry is a sustainable development concept for resource conservation and environmental optimization. The effective measurement of green innovation efficiency in the tourism industry and an accurate understanding of its spatial relationship was significantly important for promoting its sustainable development. Using the SBM-undesirable model, kernel density estimation, and a spatial Markov chain, we explored the spatio-temporal evolution characteristics and influencing mechanisms of urban tourism green innovation efficiency (TGIE) in China between 2000 and 2020. We found that (1) the temporal and spatial changes of TGIE were generally at a lower than medium level and fluctuated throughout country, with a transition in the east, collapse in the middle, and stagnation in the northeast. (2) The dynamic evolution of TGIE always exhibited polarization, but regional coordination was gradually enhanced with strong stability, although it was difficult to achieve leap-forward development. The cities with spatial upward transfer were concentrated mainly in the central and western region and while there were few cities with a downward adjustment, there were obvious asymmetrical spatial spillover effects. (3) The driving factors of TGIE were the overall economic level, industrial structure, government regulation, and education level. These factors had a significant positive relationship with TGIE, while the degree of opening up to the outside world has no significant effect, but the degree of influence, mechanism, and conditions of each factor were strongly regional.

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Exploring the nonlinear effect of urbanization on PM2.5 concentration and its driving mechanism is crucial for controlling urban air pollution. Based on remote sensing data and statistical data from 2002 to 2020, spatial autocorrelation, systematic dynamic panel regression, and spatio-temporal geographical weighted regression models were used to analyze the spatio-temporal evolution of PM2.5 concentration in the urban agglomeration of the middle reaches of the Yangtze River and explore the driving mechanism of urbanization on PM2.5 concentration at different spatial scales. The results showed that:① PM2.5 concentration in the middle reaches of the Yangtze River showed an overall decreasing trend from 2002 to 2020, with a spatial distribution pattern of "high in the north and low in the south." ② Hot spot cities expanded towards the western part of the urban agglomeration, whereas cold spot cities showed enhanced spatial correlation. ③ The relationship between PM2.5 concentration and economic, land, and population urbanization followed N-shaped, U-shaped, and U-shaped curves, respectively. Secondary industry and energy consumption significantly promoted the change in PM2.5 concentration, and precipitation and vegetation helped mitigate PM2.5 pollution. ④ The overall driving effects of all urbanization factors in local areas were transformed, and the main areas of influence were concentrated in the southeast, northwest, and southwest of the study area. Considering the current urban development status and regional characteristics of the urban agglomeration in the middle reaches of the Yangtze River, promoting green industrial transformation, rational planning of urban spatial distribution and population distribution, and enhancing infrastructure construction will facilitate the coordinated development between urbanization and environmental protection.

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The Yellow River Basin is one of the most important economic development belt and ecological management regions in China, it is of vital importance to study the coupling coordination between ecological protection and high-quality development. However, the systematic research from the perspective of ecological-production-living is still lacking. Therefore, a comprehensive evaluation index system including 29 indicators is constructed from ecological, production and living dimension. To evaluate the high-quality development level and coupling coordination degree of 61 cities in the Yellow River Basin, a comprehensive measurement model and coupling coordination model are established using the entropy weight TOPSIS method. With the help of ArcGIS, the spatial characteristics of high-quality development level and coupling coordination are visually illustrated. The results showed that: (1) From 2011 to 2020, the high-quality development of 61cities in the Yellow River Basin showed an increasing trend, and the level of upper and lower reaches was higher than that of the middle reaches. (2) According to the high-quality development level of 61 cities, it was divided into three types: sustainable growth type with 44 cities, the fierce fluctuation type with 11 cities and the other 6 cities was stable type. (3) The coupling coordination degree of ecology, production and living system also showed an increasing trend, while the degree was not high. (4)About the year-on-year growth rate of coupling coordination degree for 61 cities in 2020 compared with 2011, there are 19 cities more than 30 %, and 23 cities between 20% and 30 %, 11cities was 10%-20 %, the other 8 cities was less than 10 %. (5) There is a significant spatial difference in the level of high-quality development in the Yellow River Basin, while coupling coordination degree does not significant in spatial layout. Therefore, the development of different regions should adjust measures to local conditions, give full play to their advantages, and make up for their shortcomings to promote the overall development of the city.

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Inner Mongolia grassland is rich in natural vegetation and mineral resources. Based on Landsat5/7/8 NDVI data, we used pixel binary model to invert vegetation coverage of Inner Mongolia grassland area, investigated the stability, spatial distribution, and future evolution trend of vegetation coverage by using Sen+MK and Hurst index, and analyzed the driving factors of the spatial differentiation of vegetation coverage by the optimal parameters-based geographical detector. The results showed that vegetation coverage of Inner Mongolia grassland showed an increasing trend from 2006 to 2020, and the overall spatial pattern was high in the east and low in the west, mainly with great fluctuation. The regions with slight or obvious improvement characteristics (64.8%) were much more than those with slight or severe degradation characteristics (23.2%). Compared with that in the past 15 years, the proportion of degraded vegetation in the future is expected to increase to 36.6%. The central part of Xilin Gol League and Wulanqab in the central grassland area, the western part of Hulunbuir and Erdos in the eastern grassland area, and Wuhai in the western grassland area were at the risk of degradation, which should be paid more attention. Precipitation was the dominant factor of spatial differentiation in Inner Mongolia grassland, while soil type, land use, and air temperature had the most significant synergistic effect.

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BACKGROUND: Currently, the influenza epidemic in China is at a high level and mixed with other respiratory diseases. Current studies focus on regional influenza and the impact of environmental pollutants on time series, and lack of overall studies on the national influenza epidemic and the nonlinear correlation between environmental pollutants and influenza. The unclear spatial and temporal evolution patterns of influenza as well as the unclear correlation effect between environmental pollutants and influenza epidemic have greatly hindered the prevention and treatment of influenza epidemic by relevant departments, resulting in unnecessary economic and human losses. METHOD: This study used Chinese influenza incidence data for 2007-2017 released by the China CDC and air pollutant site monitoring data. Seasonal as well as inter monthly differences in influenza incidence across 31 provinces of China have been clarified through time series. Space-Time Cube model (STC) was used to investigate the spatio-temporal evolution of influenza incidence in 315 Chinese cities during 2007-2017. Then, based on the spatial heterogeneity of influenza incidence in China, Generalized additive model (GAM) was used to identify the correlation effect of environmental pollutants (PM2.5, PM10, CO, SO2, NO2, O3) and influenza incidence. RESULT: The influenza incidence in China had obvious seasonal changes, with frequent outbreaks in winter and spring. The influenza incidence decreased significantly after March, with only sporadic outbreaks occurring in some areas. In the past 11 years, the influenza epidemic had gradually worsened, and the clustering of influenza had gradually expanded, which had become a serious public health problem. The correlation between environmental pollutants and influenza incidence was nonlinear. Generally, PM2.5, CO and NO2 were positively correlated at high concentrations, while PM10 and SO2 were negatively correlated. O3 was not strongly correlated with the influenza incidence. CONCLUSION: The study found that the influenza epidemic in China was in a rapidly rising stage, and several regions had a multi-year outbreak trend and the hot spots continue to expand outward. The association between environmental pollutants and influenza incidence was nonlinear and spatially heterogeneous. Relevant departments should improve the monitoring of influenza epidemic, optimize the allocation of resources, reduce environmental pollution, and strengthen vaccination to effectively prevent the aggravation and spread of influenza epidemic in the high incidence season and areas.

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Urban morphology exhibits fractal characteristics, which can be described by multifractal scaling. Multifractal parameters under positive moment orders primarily capture information about central areas characterized by relatively stable growth, while those under negative moment orders mainly reflect information about marginal areas that experience more active growth. However, effectively utilizing multifractal spectra to uncover the spatio-temporal variations of urban growth remains a challenge. To addresses this issue, this paper proposes a multifractal measurement by combining theoretical principles and empirical analysis. To capture the difference between growth stability in central areas and growth activity in marginal areas, an index based on generalized correlation dimension Dq is defined. This index takes the growth rate of Dq at extreme negative moment order as the numerator and that at extreme positive moment order as the denominator. During the stable stage of urban growth, the index demonstrates a consistent pattern over time, while during the active stage, the index may exhibit abnormal fluctuations or even jumps. This indicates that the index can reveal spatio-temporal information about urban evolution that cannot be directly observed through multifractal spectra alone. By integrating this index with multifractal spectra, we can more comprehensively characterize the evolutionary characteristics of urban spatial structure.

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According to the dual carbon goal, urban development should adhere to the principle of low-carbon sustainable development in order to reach the "carbon peak" in 2030 and "carbon neutrality" in 2060. To achieve the dual carbon goal and sustainable land resource utilization, it is necessary to seek ways to improve land-use benefits and promote the coordinated development of economic, social, and ecological benefits. Therefore, the study analysed the coupling coordination and spatio-temporal evolution of land-use benefits in Anhui province, aiming to provide a reference for improving the level of regional land-use benefits. First, we developed a land-use benefit evaluation indicator system that took into account the dual carbon goal from three perspectives: economic benefits, social benefits, and ecological benefits or economic, social, and ecological benefits. Following that, we evaluated the spatial-temporal characteristics of land-use benefits using the coupling coordination model and coupling coordination gravity model. The results showed: (1) From the time scale, the comprehensive land-use benefits showed an increasing trend from 2011 to 2020, the coordination state changed from "moderately uncoordinated" to "on the verge of uncoordinated". (2) From the perspective of spatial differences, the coupling coordination level of land-use benefits in 16 prefecture-level cities increased year by year, but no prefecture-level cities reached the coordination stage. (3) As for the spatial linkage strength of coupling coordination of land-use benefits, 16 prefecture-level cities in 2011, 2015 and 2020 presented a similar spatial linkage pattern, and the coupling coordination of prefecture-level cities in southeast Anhui province was strongly influenced by regional factors.

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To achieve high-quality development, it is crucial to encourage the coordinated development of new infrastructure (NI) and regional sustainability (RS). Based on the coupling mechanism of NI and RS, this study develops a comprehensive evaluation index system, coupling coordination degree model (CCDM), spatial autocorrelation model, and standard deviation ellipse model used to estimate the coupling coordination degree (CCD) of NI-RS in China mainland between 2013 and 2020 and analyze the temporal and spatial evolution characteristic. The results showed the following: (1) The NI index fluctuated and increased, while the RS index increased year by year, showing a gradient-decreasing distribution pattern from east to west; (2) CCD increases year by year, but the spatial distribution is unbalanced, and CCD in eastern coastal areas is significantly higher than that in western areas. (3) The CCD of NI and RS showed a significant spatial positive correlation, showing that the high-value cluster in the east and the low-value cluster in the west. (4) The standard deviation ellipse of the national CCD showed a northeast-southwest distribution, the center of gravity migrated to the southwest, and the spatial clustering characteristics were significant. (5) Finally, the GM (1,1) model indicates a steady increase in CCD from 2021 to 2025. Based on the location distribution and evolution of the CCD between NI and RS in China, corresponding policy suggestions are put forward.

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Making hand movements in response to visual cues is common in daily life. It has been well known that this process activates multiple areas in the brain, but how these neural activations progress across space and time remains largely unknown. Taking advantage of intracranial electroencephalographic (iEEG) recordings using depth and subdural electrodes from 36 human subjects using the same task, we applied single-trial and cross-trial analyses to high-frequency iEEG activity. The results show that the neural activation was widely distributed across the human brain both within and on the surface of the brain, and focused specifically on certain areas in the parietal, frontal, and occipital lobes, where parietal lobes present significant left lateralization on the activation. We also demonstrate temporal differences across these brain regions. Finally, we evaluated the degree to which the timing of activity within these regions was related to sensory or motor function. The findings of this study promote the understanding of task-related neural processing of the human brain, and may provide important insights for translational applications.

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Industry is a core area to achieve the carbon neutrality target for most developing countries including China. Hence, it is of great practical significance to study the spatio-temporal characteristics of China's industrial carbon intensity and its evolution. The exploratory spatial data analysis methods were adopted to conduct global and local spatial correlation analysis in this paper. The result shows that (1) the industrial carbon emission intensity decreases year by year, with high industrial carbon emission intensity in the West and low in the East. (2) There is a correlation in the spatial distribution of industrial carbon intensity, with the Moran index experiencing the stage of descending first and then ascending. (3) The local spatial clustering of industrial carbon intensity is obvious. (4) Half of the provinces have experienced a leap, with the majority located in the western part of China. Based on these findings, it is concluded that industrial emission reduction policy synergy between provinces is particularly important, such as low-carbon industrial production policy and green industry development policy.

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Ensuring national food security is an eternal topic. We unified six categories of food with calorie content including grain, oil, sugar, fruits and vegetables, animal husbandry, and aquatic products on the basis of provincial-level data, and we dynamically evaluated caloric production capacity and the supply-demand equilibrium under the increase in feed-grain consumption as well as the food losses and waste in China from 1978 to 2020 at four different levels. The results show that: (1) From the perspective of food production, the total national calorie production showed a linear growth trend, with a growth rate of 31.7 × 1012 kcal/year, of which the proportion of grain crops has always exceeded 60%. Most provinces showed significant increasing trends in food calorific production, except for Beijing, Shanghai, and Zhejiang, which showed slightly decreasing trends. The distribution pattern of food calories and their growth rate were high in the east and low in the west. (2) From the perspective of the food supply-demand equilibrium, the national food calorie supply has been in surplus since 1992, but significant spatial heterogeneity is detected, with the Main Marketing Region changing from a tight balance to a short surplus, North China always remaining in calorie shortage, and 15 provinces still presenting supply and demand gaps up to 2020, necessitating the establishment of a more efficient and faster flow and trade system. (3) The national food caloric center has shifted 204.67 km to the northeast, and the population center has shifted to the southwest. The reverse migration of the centers of food supply and demand will further aggravate the pressure on water and soil resources and cause higher requirements for ensuring the circulation and trade system of food supply. The results are of great significance for the timely adjustment of agricultural development policies, making rational use of natural advantages and ensuring China's food security and sustainable agricultural development.

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The distribution characteristics of urbanisation level and per capita carbon emissions from 2006 to 2019 were investigated by the ranking scale rule, using 108 cities in the Yangtze River Economic Belt of China. A coupling coordination model was established to analyse the relative development relationship between the two, and exploratory spatial-temporal data analysis (ESTDA) was applied to reveal the spatial interaction characteristics and temporal evolution pattern of the coupling coordination degree. The results demonstrate that: (1) The urbanisation level and per capita carbon emissions of the Yangtze River Economic Belt show a stable spatial structure of 'high in the east and low in the west'. (2) The coupling and coordination degree of urbanisation level and carbon emissions show a trend of 'decreasing and then increasing', with a spatial distribution of 'high in the east and low in the west'. (3) The spatial structure exhibits strong stability, dependence, and integration. The stability is enhanced from west to east, the coupling coordination degree has strong transfer inertia, and the spatial pattern's path dependence and locking characteristics show a trend of weak fluctuation. Therefore, the coupling and coordination analysis is required for the coordinated development of urbanisation and carbon emission reduction.

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The concept of strong sustainability suggests that natural capital is irreplaceable and emphasizes that human natural consumption cannot exceed the carrying capacity of the ecological environment. In the realistic context of tightening resource constraints and ecological degradation, how to explore the optimal economic and well-being output under certain ecological constraints has become an important topic in sustainability research. Ecological well-being performance (EWP) can comprehensively reflect the efficiency of natural resources and ecological inputs into the well-being level and has become an important tool for sustainable development research. Based on strong sustainability, this paper constructs an EWP evaluation index system. It decomposes EWP into two stages: ecological economy and economic well-being, which opens the "black box" of the ecological well-being conversion process. PCA-DEA, the network super-efficiency Epsilon-based measure (Network SEBM) model, and Geodetector are used to dynamically measure the EWP in the Three Gorges Reservoir Area (TGRA) from 2010 to 2020 and analyze its spatial and temporal characteristics and influencing factors. The results show that (1) temporally, the EWP in the TGRA shows an increasing trend from 2010 to 2020, but the overall level is low. Spatially, it shows a high pattern in the east and low in the west, and spatial differences are gradually decreasing; (2) ecological economic efficiency is significantly lower than economic well-being efficiency, and ecological economic efficiency is the main reason limiting the improvement in EWP in the TGRA. The ecological well-being situation of the TGRA is not optimistic; (3) there is an overall problem of excessive ecological input and insufficient per-capita GDP and well-being output in the TGRA, and decisions should be made according to local conditions; (4) the level of economic development has an EWP level that plays a dominant role and also has a greater relationship with the policy system, socioeconomic conditions, and natural environment.

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With economic expansion having moderated to a "new normal" pace, the eastern coastal provinces have been given a new historical task of high-quality development and become a window and frontier of China's high-quality development. By designing and optimizing an index system of high-quality development levels and using the entropy-TOPSIS method, the study selected 21 indicators, include economic vitality, residents' living standards, innovation efficiency and green development, and took China's eastern coastal provinces as an example to study the characteristics of spatial-temporal variations in the high-quality development level from 2010 to 2020. Then, the study used the obstacle degree model to explore the factors that are obstacles to high-quality development. The results show that the high-quality development of the eastern coastal provinces presents an "up-down-up" fluctuation, with an increase of 40.1%. In particular, the development level of the residents' living standards dimension is higher, and the high-quality development level of each province shows different degrees of growth and gradually tends to balanced development, with the high-quality development of Shanghai, Jiangsu Province and Zhejiang Province in a dominant position. The spatial pattern of high-quality development in the study areas shows a spatial distribution pattern of "high in the east and low in the west, high in the north and low in the south", in which the bipolar spatial effect of the innovation efficiency dimension is becoming more and more prominent, while the regional synergistic development effect of the residents' living standard dimension is more obvious, and the high-quality development spatial pattern shows a "core-periphery" structure, and there is a path-dependent effect in time change, and agglomeration is produced by trickle-down effect in space. The obstacles to residents' living standards are high, and the main obstacle factor has gradually changed from insufficient output in innovation to a reduction in the scale of foreign trade. In addition, the problems of unreasonable industrial structure and shortage of per capita public cultural resources still exist. In provinces with a high-quality development level and a relatively developed economy, the biggest obstacle factors are economic vitality and residents' living standards. In provinces with a low level of high-quality development and a relatively backward economy, the biggest obstacle factors are green development and innovation efficiency, and there are both similarities and differences in the main obstacle factors among provinces.

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At present, China is in an important stage of transition into a global sports power where "outstanding competitive sports talents" are required to play an important role. Therefore, it is of great theoretical and practical significance to conduct in-depth research on domestic "outstanding competitive sports talents" to promote the sustainable development of China's competitive sports and enhance its comprehensive strength. In this study, WCA refers to "world-class athletes," indicating a group of talents who won medals in international sports events such as the Olympic Games during 2009-2019. In this regard, this study uses statistical and spatial analysis methods to reveal the spatial and temporal characteristics, evolutionary process, and migration mechanism of Chinese WCA. The conclusion shows that: In terms of temporal characteristics, the population numbers in general and among different genders are characterized by a "three peaks and two troughs" pattern. In contrast, the individual temporal pattern is characterized by an "inverted U″ and "inverted V″, with an average age of 23.18 years. In terms of space, a positive correlation is shown on the whole (Moran's I > 0), however, characteristics of geographical proximity and spatial heterogeneity are not prominent, illustrating the spatial form of random distribution with low aggregation which is primarily concentrated in the southeast of China and demonstrates a "northeast-southwest" trend. There are apparent differences between areas of origin and immigration areas: Liaoning and Shandong are the main areas of origin while destination areas are frequently located in the southeast and "People's Liberation Army of China" (PLA for short). Lastly, this paper discusses the causes and influences of the migration groups from three aspects: the migrating talents, the areas of origin and immigration areas, and Chinese sports, revealing the formation and influence mechanisms.

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Accurately identifying the degree of poverty and poverty-causing factors of poverty-stricken farmer households is the first key step to alleviating absolute and relative poverty. This paper introduces a multiobjective spatio-temporal evolution analysis method to examine poverty reduction of poverty-stricken farmer households under different development goals. A G-TOPSIS model was constructed to evaluate poverty-stricken households under short-, medium-, and long-term development goals. Then, GIS analysis methods were employed to reveal the spatio-temporal distribution of poverty-stricken households, and poverty causing factors were detected using the obstacle degree model. Taking Fugong County in Yunnan Province, China, as an example, the empirical results show that: (1) Great progress has been made in poverty reduction during the study period; however, some farmer households which have escaped absolute poverty are still in relative poverty and are still highly vulnerable. (2) Farmers with higher achievement rates under three different development goals are mainly distributed in the central and northern regions of study area, with a pattern of high-high agglomeration under the medium and low development goals, while low-low agglomeration mostly appears in central-southern regions. (3) Under the short-term development goals, the main poverty-causing factors are per capita net income, safe housing, sanitary toilets, years of education of labor force and family health. Under the medium- and long-term goals, per capita net income, labor force education and safe housing are the development limitations. (4) Infrastructure and public service are crucial to ending absolute poverty, and the endogenous force of regional development should be applied to alleviate the relative poverty through sustainable development industries and high-quality national education.

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Accelerating the promotion of high-quality development of tourism (HQDT) is of great significance to the sustainable development of tourism. This paper defined the concept of HQDT, and then built an evaluation system for HQDT measurement to analyze the spatio-temporal evolution characteristics of China's HQDT based on provincial panel data from 2010 to 2019, using Geodetector to explore the similarities and differences between driving factors of HQDT and tourism development scale (TDS). The results show that: 1) Taking the development concepts of innovation, coordination, green, openness and sharing as the guidance, and considering the organic unity of quantity and quality, the evaluation index system of the HQDT consists of six dimensions of economic stability, innovation driving, coordination and linkage, green and sustainability, openness and cooperation, and sharing and harmony, which respectively represent the basis, momentum, means, orientation, direction and purpose of the HQDT; 2) The level of China's HQDT shows an upward trend, presenting the characteristics of eastern region > central region > western region > northeastern region in 2019. The regional differences in China's HQDT show a downward trend, and the intra-regional differences have replaced the inter-regional differences as the main source of regional differences; 3) China's HQDT shows the characteristics of higher in the east and lower in the west along the Hu line, while the improvement speed of HQDT shows the characteristics of faster in the west and slower in the east, making the decline of east-west differentiation of China's HQDT and the movement of the gravity center towards southwest; 4) Both HQDT and TDS are obviously driven by tourism capital investment and regional consumption. In terms of differences, the HQDT is more driven by government guidance, innovation driving force, and opening up, while the main driving factors of TDS are more biased toward capital elements and hardware facilities, including informatization, tourism resource, traffic, and eco-environment.