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The central region of Inner Mongolia is the northern ecological safety barrier of Beijing and even the whole country. It is one of the main sources of dust in North China, and air pollution control is the top priority in this region. In this study, the central region of Inner Mongolia was selected as the study area, multiple auxiliary variables were used to estimate the spatial distribution of PM2.5 concentration from 2016 to 2018 by geographically weighted regression, and the socioeconomic determinants of PM2.5 concentration were analyzed by geographic detectors. The results show that: 1) the established model can better estimate the spatial distribution of PM2.5 concentration in the study area, and the monthly mean correlation coefficient R of the verification parameters is stable, ranging from 0.58 to 0.66. 2) PM2.5 concentration in central Inner Mongolia showed significant temporal and spatial variation. The mean annual PM2.5 concentration along the Yellow River basin is the highest in the study area. PM2.5 concentration first increased and then decreased from 2016 to 2018. 3) Urban built-up area, permanent population and per capita GDP are the key factors affecting the spatial and temporal distribution of PM2.5 concentration in the study area. The results of this study provide theoretical basis and technical support for air pollution monitoring, management and prevention in central Inner Mongolia.

Although the literature has studied the direction and extent of the effect of green finance on industrial-structure optimization, quantitative analysis of the coupling coordination and spatial–temporal differences between green finance and industrial structure is relatively scarce. Therefore, in this paper, we built the theoretical framework of the coupling coordination relationship between green finance and industrial-structure optimization, and then we used the coupling coordination degree and geographic detector model to investigate the spatial–temporal evolution characteristics and influencing factors of the coupling coordination between the two based on the panel data of 31 provinces from 2012 to 2019. The results show that China’s green finance and industrial-structure optimization have basically reached the primary coupling, and the coupling coordination degree is from 0.40 to 0.43, which shows a “W”-type fluctuation trend of recovery. The regional gap of the coupling coordination degree firstly decreased and then increased, showing a change law of “agglomeration, equilibrium and agglomeration”. In the spatial dimension, the high-level coordination region showed an increasing trend of “fragmentation” fluctuation, while the low-level coordination region concentrated in the central and western regions with a tendency of “low value locking”. The trend surface showed a spatial characteristic of “high in the north and low in the south–high in the east and west and low in the middle”. We also found that the dependence of foreign trade and technological innovation are the main factors affecting the coupling coordination degree, and the interaction between government support and human capital synergistic is the crucial channel for the coevolution of green finance and industrial structure to promote green and low-carbon development.

The development of rural tourism (RT) has great significance in reducing poverty and achieving rural vitalization. Qinghai-Tibetan Plateau (QTP) is a depressed area with rich RT resources due to its unspoiled nature and diverse culture. For future sustainable development of RT in QTP, this paper analyzes the spatial distribution characteristics and its influencing factors of RT villages using various spatial analysis methods, such as nearest neighbor index, kernel density estimation, vector buffer analysis, and geographic detectors. The results show the following. First, the RT villages present an agglomeration distribution tendency dense in the southeast and spare in the northwest. The inter-county imbalance distribution feature is obvious and four relatively high-density zones have been formed. Second, the RT villages have significant positive spatial autocorrelation, and the area of cold spots is larger and of hot spots is smaller. Third, the RT villages are mainly distributed with favorable topographic and climate conditions, near the road and water, around the city, and close to tourism resources. Fourth, the spatial distribution is the result of multifactor interactions. Socio-economic and tourism resource are the dominant factor in the mechanism network. Fifth, based on the above conclusions this study provides scientific suggestions for the sustainable development of the RT industry.

Forests play an irreplaceable role in preserving soil and water, as well as realizing carbon neutrality. However, logging and urban expansion have caused widespread forest fragmentation globally, resulting in biodiversity loss and carbon emissions. Therefore, it is a prerequisite to develop a comprehensive index for evaluating the degree of forest fragmentation to propose effective policies for forest protection and restoration. In this study, a forest fragmentation comprehensive index (FFCI) was constructed through principal component analysis (PCA) based on land-use data from 2000 to 2020 in Fujian Province, composed of five commonly used landscape metrics: patch density (PD), largest patch index (LPI), mean patch area (MPA), aggregation index (AI), and division. Then, the semivariogram function and moving windows method were employed to explore the scale effect and spatiotemporal variations of FFCI. The spatial autocorrelation analysis was used to distinguish the spatial relationship of forest fragmentation, while the driving mechanisms were explored using the geographic detector (GD). The results show that the optimal scale to reflect forest fragmentation based on the semivariogram and moving window method was 3500 m. The proposed FFCI could explain more than 85% of the information for all landscape metrics, and the effectivity of FFCI was validated by urban–rural gradient and transect analysis. We also found that, despite having the highest forest coverage in China, Fujian Province has experienced severe forest fragmentation. High and medium fragmentation accounted for over 50% of all types of fragmentation, with decreasing trends in low and very low fragmentation and increasing trends in high fragmentation over time, indicating that the degree of forest fragmentation in the study area was aggravated over time. Moreover, the spatial distribution pattern of FFCI was mainly high–high clusters and low–low clusters, showing a decreasing trend year by year. The areas with high fragmentation were mainly distributed in the urban center of coastal cities, while the internal cities in western and central regions had a relatively low degree of fragmentation. Additionally, the spatial differentiation in the variation in FFCI was mainly influenced by elevation, slope, and nighttime light intensity. The superimposed impact of two factors on the variation in FFCI was greater than the impact of individual factors. These results provide an effective approach for assessing the degree of forest fragmentation and offer scientific support for mitigating forest fragmentation.

Urbanization profoundly impacts the global carbon cycle and climate change. Many studies have shown that both urban vitality and urban carbon emissions are deeply affected by spatial planning and city structure. However, the specific relationship between urban vitality and urban carbon emissions is rarely studied. An index system of urban vitality was established from four aspects: social, economic, cultural, and environmental. After analyzing the spatial distribution characteristics of urban vitality combined with spatial syntax and the TOPSIS model, this paper further investigated the influence of urban vitality-building factors on the distribution of urban carbon emissions based on the Geodetector method. The research results show that: (1) Xuzhou shows obvious spatial differences in urban vitality, mainly decreasing from the center to the surrounding areas, with a small vitality center in the northeast. (2) The impact of different dimensions of vitality on urban carbon emissions is apparently different. (3) Facilities’ aggregation has the weakest explanatory power for urban carbon emissions, while the NDVI has the highest explanatory power. This study helps to clarify the spatial correlation and influence mechanism between urban vitality and urban carbon emissions. Finally, some suggestions are proposed to construct low-carbon and high-vitality cities.

Promoting research on urban park use is important for developing the ecological and environmental health benefits of parks. This study proposes uniquely integrated methods combined with big data to measure urban park use. It combines comprehensive geographic detectors and multiscale geographically weighted regression from a geospatial perspective to quantify the individual and interactive effects of the parks’ characteristics, accessibility, and surrounding environment features on weekday and weekend park use. The study also explores the degree of influence of spatial changes. The results indicate that the park-surrounding facilities and services factor contributed most to use, while its interaction effect with park service capacity had the greatest impact on park use. The interaction effects showed binary or nonlinear enhancement. This suggests that park use should be promoted within multiple dimensions. Many influencing factors had significant changes in the geographic space, suggesting that city-level park zoning construction should be adopted. Finally, park use was found to be affected by users’ subjective preference on weekends and convenience factors on weekdays. These findings provide a theoretical basis for the influencing mechanisms of urban park use, which can help urban planners and policymakers formulate more specific policies to successfully manage and plan urban parks.

The regions near the Tropic of Cancer are a latitudinal geographical zone with typical climatic, topographic, and human landscape features. It is necessary to explore the region’s net primary productivity (NPP) dynamics as it combines complex topography, various vegetation types, and intense human activities. The study sets the transect near the Tropic of Cancer (TCT) and uses the Carnegie–Ames–Stanford (CASA) model to estimate the NPP from 2000 to 2020. After using the RESTREND method, the paper calculates and compares the relative contributions of climate variability and anthropogenic activities to NPP changes. Finally, the geographical detector (Geodetector) model is applied to evaluate how anthropogenic and natural factors affect spatial distribution patterns and NPP changes. The results indicated that the average annual NPP is 820.39 gC·m−2·yr−1 during the 21 years. In addition, when the NPP varies, it increases over the entire study area, with a slope of 4.81 gC·m−2·yr−1, particularly in the western region. Across the entire research area, 63.39% and 77.44% of the total pixels positively contribute to climate variability and human activities in NPP, with a contribution of 0.90 and 3.91 gC·m−2·yr−1, respectively. Within the western, central, and eastern regions, anthropogenic activities have a stronger impact on NPP than climate variability, particularly pronounced in the eastern region. Furthermore, vegetation cover is the dominant factor in the spatial patterns and NPP trends across the TCT and the three regions. In contrast, climate factors are shown to be less influential in NPP distribution than in the western region. The results also demonstrated that the effect of population density and the GDP on NPP gradually rises. Two-factor interaction is much larger than any individual factor, with the dominant interaction factor being vegetation cover with climatic factors. Lastly, the findings revealed that anthropogenic activities positively promote NPP accumulation across the TCT, thus highlighting the importance of human activity-led ecological restoration and ecological protection measures that contribute to regional carbon sequestration and carbon balance.

The impact of the built environment on the ridership of ride-hailing results depends on the spatial grid scale. The existing research on the demand model of ride-hailing ignores the modifiable areal unit problem (MAUP). Taking Chengdu as an example, and taking the density of pick-ups and drop-offs as dependent variables, 12 explanatory variables were selected as independent variables according to the “5D” built environment theory. The nugget–sill ratio (NSR) method and optimal parameter-based geographical detector (OPGD) model were used to determine the optimal grid scale for the aggregation of the built environment variables and the ridership of ride-hailing. Based on the optimal grid scale, the optimal data discretization method of the explanatory variables was determined by comparing the results of the geographic detector under different discretization methods (such as the natural break method, k-means clustering method, equidistant method, and quantile method); we utilized the geographic detector model to explore the relative importance and the interactive impacts of the explanatory variables on the ridership of ride-hailing under the optimal grid scale and optimal data discretization method. The results indicated that: (1) the suggested grid scale for the aggregation of the built environment and ride-hailing ridership in Chengdu is 1100 m; (2) the optimal data discretization method is the quantile method; (3) the floor area ratio (FAR), distance from the nearest subway station, and residential POI (point of interest) density resulted in a relatively high importance of the explanatory variable that affects the ridership of ride-hailing; and (4) the interactions of the diversity index of mixed land use ∩ FAR, distance to the nearest subway station ∩ FAR, transportation POI density ∩ FAR, and distance to the central business district (CBD) ∩ FAR made a higher contribution to ride-hailing ridership than the single-factor effect of FAR, which had the highest contribution compared with the other explanatory variables. The proposed grid scale can provide the basis for the partitioning management and scheduling optimization of ride-hailing. In the process of adjusting the ride-hailing demand, the ranking results of the importance and interaction of the built-environment explanatory variables offer valuable references for formulating the priority renewal order and proposing a scientific combination scheme of the built-environment factors.

As one of the three major black soil regions in the world, northeastern China has an important strategic position there. Since the 20th century, the local environment has undergone great changes under the influence of the natural economy, and it is particularly important to quantitatively assess the degree of change. However, there have been few long-term quantitative studies of environmental spatial-temporal variances in the three northeastern provinces. Therefore, in this study, four typical remote sensing indices of the normalized difference vegetation index (NDVI), land surface temperature (LST), normalized differential building–soil index (NDBSI) and wetness (WET) were employed to construct the remote sensing ecological index (RSEI) using a principal component analysis (PCA) method based on the Google Earth Engine (GEE) platform in northeastern China. The spatiotemporal variations in the eco-environmental quality were detected using linear slope and M–K test, and the direct and interactive effects of different influencing factors on the RSEI changes during 2000–2020 were explored based on geographic detection. The results show that the interannual variations in the RSEI show a fluctuating upward trend, with an increase percentage of 12.45% in the last two decades, indicating that the ecological quality of northeast China has gradually improved. Furthermore, that the western and eastern Heilongjiang provinces and western Jilin provinces contributed substantially to the improvement of environmental quality, while the environmental quality of Jilin provinces and central Liaoning provinces decreased to varying degrees. Compared with 2000, the area with a fair environmental quality grade had the greatest change, and had decreased by 60.69%. This was followed by the area with an excellent quality grade, which increased by 117%. Land-use type had the greatest impact on environmental changes in northeastern China, but the impact degree gradually decreased, while the impact of socioeconomic factors such as the gross production of agriculture, forestry, animal husbandry and fishery and population density on environmental quality gradually increased. The major reason for the decline of environmental quality in central Jilin and central Liaoning is that urbanization development had occupied a large amount of cropland. This shows that taking into account the virtuous cycle of an ecological environment while promoting urban and rural development may be an important task for northeastern China in the future.

This paper aims to contribute to the effectiveness of pro-poor tourism in rural areas. We use 5770 pro-poor tourism villages in China as the research objects; the spatial distribution characteristics of pro-poor tourism villages in China are analyzed using a combination of disequilibrium index, kernel density analysis, and spatial autocorrelation; their influencing factors are detected using a geographical detector and overlay analysis. The study results show the following: (1) The distribution of pro-poor tourism villages is exceptionally uneven in three zones, eight regions, and inter-provincial levels, forming a high-density cluster belt that includes Hebei, Henan, Anhui, and Hubei, and five high-density cluster cores that include southern Gansu, Sichuan–Gansu–Shaanxi border area; Guizhou, Hunan, and Chongqing border area; southern Sichuan; and southwest Guizhou. (2) Regarding spatial correlations, the pro-poor tourism villages in central and western regions are in hot spots, while those in eastern regions are in cold spots. The hot spots gradually increase, while cold spots gradually decrease, and the clustering trend of the distribution of the pro-poor tourism villages is increasingly apparent. (3) Pro-poor tourism villages are affected by social, economic, industrial, and other human factors as well as natural geographical factors such as terrain, precipitation, river, and climate, among which the industrial factors have a more significant impact. Pro-poor tourism villages are concentrated in humid mountainous areas with an altitude of about 1000 m and an annual precipitation of more than 800 mm, and they are mostly distributed in the subtropical monsoon climate zone closer to the river and more suitable climate. (4) It is suggested that pro-poor tourism villages can be divided into four types: the resource underutilization type, mountain environment restriction type, traffic location non-optimization type, and industrial development lagging type, and the sustainable development strategies of different types of pro-poor tourism villages are proposed.

The temporal and spatial evolution of landscape pattern is the most intuitive form of land use transition. Analyzing the change of landscape pattern and its driving factors is of great significance to land use management and water quality protection in the basin. Based on the land use data obtained from the remote sensing image interpretation of the Yellow River Basin (Henan section) in 1990, 2000, 2010, and 2020, the landscape pattern evolution characteristics of the Yellow River Basin (Henan section) were quantitatively studied using the methods of multi-angle land use transfer matrix, land use information atlas, and landscape pattern index, and the influencing factors of landscape pattern evolution of the Yellow River Basin (Henan section) were revealed using the geographic detectors (a new statistical method to measure the explanatory power of independent variables to dependent variables mainly by analyzing the overall differences among various types of geographical spaces). The results show that: (1) From 1990 to 2020, the mutual transformation of land use types in the Yellow River Basin (Henan section) was frequent, and the transformation tracks were diversified. Among them, the outflow behavior of land use types is mainly manifested in the transformation from cultivated land to construction land, and the inflow behavior of land use types is mainly manifested in the transformation from grassland and water to cultivated land. (2) In the information map of land use change in the Yellow River Basin (Henan section) from 1990 to 2020, the stable type had the widest distribution range, accounting for 94.60% of the total area of the study area, with two main change patterns: “cultivated land-cultivated land-cultivated land-cultivated land” and “woodland-woodland-woodland-woodland”, which indicates that the landscape pattern of the basin dominated by cultivated land and woodland has not changed fundamentally. The four land use change structure types, repeated change, early change, intermediate change and continuous change, account for a relatively small proportion and are concentrated in the vicinity of the Yellow River. (3) At the landscape level, the watershed generally shows the trend of decreasing landscape fragmentation, increasing landscape heterogeneity and constantly balancing landscape patch types. At the level of patch type, the landscape dominance of cultivated land decreases, while that of construction land increases. The occupation of construction land is the main reason for the fragmentation and homogenization of cultivated land. (4) From the perspective of landscape scale and patch type scale, through the geographical exploration of various natural factors and socio-economic factors that potentially affect the landscape pattern evolution, it is found that the spatial differences of natural factors such as slope, elevation, temperature, and precipitation can better reflect the spatial heterogeneity of the landscape pattern in the Yellow River Basin (Henan section) than those of socio-economic factors such as GDP and population density, and the interaction of any two driving factors has a greater influence on the spatial distribution characteristics of landscape pattern than any single factor, indicating that the formation of spatial heterogeneity in the Yellow River Basin (Henan section) is the result of the interaction of various influencing factors. The results of this study can provide ideas for exploring the trend and influencing mechanism of landscape pattern change in the basin, and have important reference significance for ecological environment management, ecosystem protection, and land use planning in the Yellow River Basin (Henan section).

Basic education resources are basic urban and rural social public security resources, and their spatial distribution is an important issue related to people’s livelihoods and social justice. Taking Wuhan as a case study, this paper analyzed the spatial distribution characteristics of rural basic education resources based on the methods of the average nearest neighbor index, imbalance index, kernel density analysis and two-step floating catchment area and then used geographic detector analysis to detect its influencing factors. The following findings were obtained: (1) Rural kindergartens and elementary schools in Wuhan City’s new urban districts showed a clustered distribution pattern, while secondary schools showed a uniform distribution trend. The spatial distribution of rural basic education resources is poorly balanced, with a tendency to cluster in Huangpi District, Xinzhou District and Caidian District; the overall spatial distribution density of rural basic education resources showed the distribution characteristics of “block-like clustering and multicenter development”. (2) The spatial accessibility of kindergartens showed a spatial pattern of “large dispersion and small clustering”, with multiple high-value clustering areas; and the accessibility of elementary and secondary schools showed a spatial pattern of high in the south and low in the north. (3) The population, economy and education development level are the main factors affecting the spatial distribution of rural basic education resources, while the influence of infrastructure construction is weak. The core influencing factors of the spatial distribution of each type of basic education resource are both consistent and different. According to the interaction factor detection, the spatial distribution of rural basic education resources in Wuhan City’s new urban districts is the result of the combined effect of multiple factors.

Based on the pressure–state–response (PSR) framework, in this paper, we construct an assessment system for sustainable water utilization (SWU) in 60 prefecture-level cities along the Yellow River Basin. Then, a spatial autocorrelation model is employed to analyze the spatial distribution characteristics of SWU. Further, nine related variables are selected to explore the key factors affecting the spatial differentiation of SWU with a geographic detector model. The results are as follows: (1) The overall level of SWU in the Yellow River Basin is not high, and the level of SWU in five cities is much higher than that in other cities. (2) Overall, the SWU shows noticeable spatial autocorrelation characteristics along the Yellow River Basin. Additionally, locally, it shows high–high agglomeration, low–high agglomeration, and low–low agglomeration. (3) The most important factors affecting the spatial differentiation of SWU in the Yellow River Basin are precipitation, population density, and the proportion of tertiary industry in regional GDP.

Analyses of landscape patterns, analyses of land-use/land-cover evolution characteristics, and a driving force analysis during the expansion of urban agglomerations can assist urban agglomerations in solving ecological and environmental problems; moreover, these tools can provide a reference for urban land-use structure optimization and urban landscape planning. In this study, the rapid urbanization and urban agglomeration of small- and medium-sized cities were evaluated; specifically, the urban agglomeration of the northern slopes of the Tianshan Mountains (UANSTM) was assessed. Using multi-period (1995, 2000, 2005, 2010, 2015, 2018) land-use data and statistical yearbook data, we calculated the landscape index and comprehensive land-use degree index using the Moran index and geographic probe methods. We studied the expansion patterns and spatial distribution patterns of urban land and quantitatively explored the influence mechanisms of natural and socioeconomic factors on the degree of land use to clarify the characteristics and driving forces of land-use evolution. The results show the following: the area of urban land continued to increase between 1995 and 2018; the dominance of the landscape indexes within each time period changed with urban development; and intra-urban landscape heterogeneity is gradually increasing. In terms of the spatial and temporal distribution of the degree of land use, the expansion of urban agglomeration is centered on the city of Urumqi, spreading towards the cities of Changji and Shihezi; the rest of the counties and cities are fast urban-expansion zones. Under the influence of multiple source factors, the expansion of the UANSTM depends on the distribution of oases, which is mainly influenced by the distribution of vegetation and the density of the river network and can only rely on the ecological carrying capacity of oases for production and life. The results of the study can provide a basis for decision-making processes surrounding the future layout of the UANSTM ecological environment.

Using the Google Earth Engine (GEE) platform, Moderate-resolution image spectroradiometer (MODIS) data of the Weihe River Basin from 2001 to 2021 were acquired, four ecological indicators, namely, greenness, wetness, heat, and dryness, were extracted, and the remote sensing ecological index (RSEI) was constructed through principal component analysis. In addition, the geographic detectors and a multi-scale geographic weighted regression model (MGWR) were used to identify the main driving factors of RSEI changes and capture the differences in spatial changes from different perspectives using multiple indicators. The results show that (1) the quality of the eco-environment in the Weihe River basin improved as a whole from 2001 to 2021, and the RSEI increased from 0.376 to 0.414. In terms of the RSEI grade, the medium RSEI and high RSEI areas increased significantly and the growth rate increased significantly, reaching 26.42% and 27.70%, respectively. (2) Spatially, the quality of the eco-environment in the Weihe River Basin exhibited a spatial distribution pattern that was high in the south and low in the north, among which the quality of the eco-environment in the Weihe River Basin in northern Shaanxi and northwestern Ningxia and Gansu was relatively low. In addition, during the study period, the RSEI of the Qinling Mountains in the southern part of the Weihe River Basin and the Jinghe River and Luohe River areas improved significantly. The urban area on the Guanzhong Plain in the Weihe River Basin experienced rapid economic growth, and urban expansion led to a significant decrease in the quality of the eco-environment. (3) The eco-environment quality in the Weihe River Basin is the result of the interaction of natural, anthropogenic, and landscape pattern factors. All of the interactions between the influencing factors had a stronger influence than those of the individual factors. There were significant differences between the individual drivers and the spatial variation in RSEI, suggesting that different factors dominate the variation in RSEI in different regions, and zonal management is crucial to achieving sustainable management of RSEI. The study shows that to improve the eco-environment quality of the Weihe River Basin, it is necessary to further strengthen ecological protection projects, reasonably allocate landscape elements, and strengthen the resistance and resilience of the ecosystem.

Scientific analysis of the spatial features and driving factors of homestead agglomeration in different landform types of mountainous and hilly areas in Southwest China is of great significance for the optimization of national spatial patterns and high-quality rural development. This paper selects 22 villages in 3 towns with different landform types in Chongqing and examines the spatial features and driving factors of homestead agglomeration in different landform types from the perspectives of terrain gradient, kernel density estimation, farmer household agglomeration status, and landscape index. We analyzed the agglomeration spatial features of different landform types and explored their driving factors and mechanisms. It was found that (1) the distribution of homesteads in mountainous and hilly areas has obvious terrain gradient characteristics, and the layout of platform area homesteads is not constrained by terrain conditions. (2) Chongqing homesteads show a spatially dispersed pattern, but the degree of homestead spatial agglomeration varies significantly among different landform types, with those in mountainous and hilly areas being mainly dispersed and those in platform areas being mainly clustered. (3) Homestead spatial agglomeration is characterized by a combination of factors inside and outside the rural system and farmers’ willingness. There are differences in the driving factors of homestead agglomeration in different landform types. Homestead agglomeration in mountainous and hilly areas is mainly driven externally, while homestead agglomeration in platform areas is mainly driven internally. In both situations, farmers’ willingness must be fully considered. The results of this study can provide a scientific basis for the spatial planning and optimal allocation of land resources in the southwestern hilly area.

By utilizing the tourism development data of Beijing for the period from 2010 to 2019, this study examined the spatial pattern distribution of tourism development in Beijing using the coefficient of variation and Moran’s I index. In addition, the geographic detector method was employed to explore the impact of tourism resource investment, tourism reception facilities, and urban development level on the spatial pattern of tourism development. The results indicate that the spatial differences in tourism development in various Beijing districts are gradually expanding, mainly focusing on the differences between urban function expansion regions. The number of tourists shows a spatial distribution pattern including a core area, urban function expansion area, ecological conservation area, and new urban development area. The spatial correlation of tourism development increases gradually, and some parts show the spatial correlation form of low–high aggregation. Tourism resource investment, tourism reception facilities, and urban development level all play a significant role in promoting the spatial pattern of tourism development, among which the most obvious role is the interactive effect of tourism reception facilities, star-rated hotels, and openness. Therefore, to improve the development of Beijing’s tourism industry, the government needs to pay attention to the differences in the expansion of urban functions, the degree of contact between regions, the number of tourism reception facilities, and the level of regional openness. The significance of this research is in promoting spatial governance, coordinated development among regions, and the high-quality development of tourism in Beijing, and laying down a foundation for the introduction of spatial collaborative governance policies in other megacities in China.

The urban surface temperature is a complex integrated natural-human geographic phenomena; with the development of geostatistical methods and the application of multisource data, its research has gradually shifted from a single perspective to a study that integrates multiple factors such as nature and humanity. However, based on the context of the integration of natural and human factors and mutual constraints of each factor, the research on the mechanism of influence on urban habitat thermal environment needs to be further deepened. Therefore, this paper explores the spatial and temporal heterogeneity of urban surface temperature in Zhengzhou City during the summer of 2013–2020 from the perspective of multi-source data fusion, and uses the Geodetector model to quantitatively reveal the main influencing factors of urban surface temperature and the impact of superimposed factors on the compound effect of surface temperature. The results show that: (1) the urban thermal environment in the central of Zhengzhou city (region within the first ring) is obvious, and it is mainly concentrated in commercial and densely populated areas. (2) According to trend analysis, the northwest-southeast direction of the city continues to increase in temperature from 2013–2020, coupled with the direction of urban development. (3) Among the factors affecting urban surface temperature, normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), tasseled cap wetness (TCW), and human elements are particularly typical. NDVI and TCW are strongly negatively correlated with the urban thermal environment, while NDBI and human elements are strongly positively correlated. (4) Mitigation of the urban thermal environment can start with the interaction mechanism of positive and negative factors. This study provides new ideas for the mechanism analysis of spatial and temporal evolution patterns of the urban thermal environment under multifactorial constraints, and provides suggestions and decisions for promoting green and sustainable urban development.

Vegetation phenology and its spatiotemporal driving factors are essential to reflect global climate change, the surface carbon cycle and regional ecology, and further quantitative studies on spatiotemporal heterogeneity and its two-way driving are needed. Based on MODIS phenology, meteorology, land cover and other data from 2001 to 2019, this paper analyzes the phenology change characteristics of the Yangtze River Delta from three dimensions: time, plane space and elevation. Then, the spatiotemporal heterogeneity of phenology and its driving factors are explored with random forest and geographic detector methods. The results show that (1) the advance of start of season (SOS) is insignificant—with 0.17 days per year; the end of season (EOS) shows a significant delay—0.48 days per year. The preseason temperature has a greater contribution to SOS, while preseason precipitation is main factor in determining EOS. (2) Spatial differences of the phenological index do not strictly obey the change rules of latitude at a provincial scale. The SOS of Jiangsu and Anhui is earlier than that of Zhejiang and Shanghai, and EOS shows an obvious double-clustering phenomenon. In addition, a divergent response of EOS with elevation grades is found; the most significant changes are observed at grades below 100 m. (3) Land cover (LC) type is a major factor of the spatial heterogeneity of phenology, and its change may also be one of the insignificant factors driving the interannual change of phenology. Furthermore, nighttime land surface temperature (NLST) has a relatively larger contribution to the spatial heterogeneity in non-core urban areas, but population density (PD) contributes little. These findings could provide a new perspective on phenology and its complex interactions between natural or anthropogenic factors.

Analyzing the distribution characteristics and influencing factors of cultivated land in complex geomorphic areas is significant in evaluating the agricultural farming environment and formulating cultivated land protection measures. This study extracted cultivated land information based on multi-source remote sensing data, and analyzed the geomorphic differentiation of cultivated land distribution and climate response from 2000 to 2020 using the Gini coefficient, spatial autocorrelation analysis and geographic detector. The results show that cultivated land is mainly distributed in low-altitude hills and low-altitude small undulating mountains, and secondarily in low-altitude alluvial and proluvial plains and platforms. Moreover, from 2000 to 2020, the cultivated land in the high-altitude and high-altitude undulating mountains and medium and high-altitude undulating mountains in the Northwest Plateau of Sichuan showed an upward trend, while the cultivated land in the Sichuan Basin mainly increased from the north and south to the middle of the basin. In addition, the highest temperature has the strongest ability to explain the spatial heterogeneity of cultivated land. From the calculation results of the influence coefficient of a single climatic factor and the combined effect of multiple climatic factors, the main factors that affect the distribution of cultivated land are different in different geomorphological regions. Finally, it is proposed to formulate a long-term strategy for agricultural production to adapt to climate change in complex geomorphic areas in order to reduce the negative impact of environmental change on agricultural production.

The booming population and accelerating urbanization in the Huaihe River Basin have sped up the land use transformation and the cultivated land fragmentation (CLF), seriously impeded the advancement of agricultural modernization, and threatened regional stability and national food security as well. The analysis of CLF degree and its spatiotemporal distribution characteristics, along with the influencing factors in the Huaihe River Basin, is of great significance for promoting the intensive and efficient utilization of cultivated land resources and maintaining food security. Previous studies lack the measurement and cause analysis of CLF in Huaihe River Basin. To bridge the gap, this study introduces Fragstats4.2 and ArcGIS10.3 to analyze the spatiotemporal characteristics of CLF in county units in the Huaihe River Basin from 2000 to 2018 through the Lorentz curve, entropy method, and spatial auto-correlation method while the causes of the spatiotemporal differentiation of CLF in the basin were explored with the help of a geographic detector. The results show that the spatial distribution of cultivated land in the Huaihe River Basin is relatively balanced, and the Gini coefficients of cultivated land from 2000 to 2018 were 0.105, 0.108, and 0.113, respectively. More than 56% of the counties in the basin have a location entropy greater than 1. the percentage of landscape, area-weighted mean patch area, patch cohesion index, and aggregation index decrease year by year while the patch density and splitting index show an upward trend. The landscape pattern of cultivated land is highly complex, and the overall fragmentation degree is increasing. The county distribution pattern of the CLF degree with random and agglomeration is generally stable. The spatiotemporal differentiation of CLF in the Huaihe River Basin is affected by multiple factors, among which the influences of the normalized difference vegetation index, per capita cultivated land area, and intensity of human activity obviously stronger than other factors, and the contribution rate of the factors reached more than 0.4. The interaction effect among the factors is stronger than that of single factor, with dual-factor enhancement and nonlinear enhancement dominating. The results of this study have important implications for optimizing the agricultural structure in the Huaihe River Basin and alleviating the CLF in important grain production areas.

Extreme weather events have led to significant changes in vegetation over the past few decades. Extreme climate indices and net primary productivity (NPP) of vegetation on Yunnan from 1982 to 2019 were calculated based on Rclimdex 1.0 software and the Carnegie-Ames-Stanford approach (CASA) model, respectively. The relationship between them was studied on annual and monthly scales. The interaction detector was used to calculate the contribution rate of extreme climate factors and synergistic effect on NPP. The results showed that：(1) Annual NPP showed a significant increasing trend in southeast Yunnan, while it showed a decreasing trend in central and northwest Yunnan. (2) Extreme temperature indices (TXx, TXn, TNx, TNn) showed significant increasing trends (P

The arid region of northwest China is a hotspot for global desertification research, because it is affected by the combined effects of climate change and human activities, and it has had a huge impact on human society in recent years. Based on multiple meteorological and spatial attribute data sets, NDVI-Albedo feature space principle and geographic detectors were used to analyze the main drivers to the spatiotemporal changes of desertification in the arid region of northwest China from 2003 to 2018. The results are shown that: (1) The area of severe desertification in the arid region of northwestern China shows a downward trend, and the area of high desertification and non-desertification regions increases correspondingly, the area of medium and low desertification remains basically unchanged. The desertification divided index (DDI) is the highest in the mid-altitude region. The DDI of middle and lower reaches of the Tarim River has the fastest rising rate, due to the impact of human agricultural activities and ecological water conveyance. (2) According to the analysis results of the geographic detector model, precipitation is the main factor driving the spatial distribution of desertification, especially the interpretive ability of the interaction between land use and precipitation is more than 50% for the spatial distribution of DDI. Land use change and its interaction with other factors have the highest degree of explanation for desertification changes. Therefore, human activities, especially land use and ecological water conservancy projects, are the most important factors to improve desertification in the arid area of northwest China. This study provides a scientific basis for agricultural management and ecological engineering construction in arid region.

Wetlands are an important transitional ecosystem, and they play an important role in maintaining ecological balance. However, human activities and climate change have led to a decrease in wetlands. Therefore, to explore the degree of damage and assess the future trends of Guangxi wetlands, this study used the Google Earth Engine (GEE) cloud platform, and the support vector machine algorithm was selected for comparison and analysis to simulate the accuracy of land cover. GIS was used to analyse the evolution and degree of damage of nearly 30 Guangxi wetlands. The geographic detector model was used to explore the driving mechanism, and finally, the CA-Markov model and multi-scenario simulation were used to predict the wetland evolution from 2018 to 2035 to reveal the future direction of development. The results showed that (1) From 1990 to 2018, paddy fields accounted for the largest proportion of wetlands in Guangxi. In the past 30 years, the total area of wetlands in Guangxi has been degraded, with a total decrease of 983.33 km2. (2) The degree of wetland damage results showed that the total damaged area was greater than the total restored area, The wetland damage in Nanning city was the most serious, with an area difference of 503.22 km2 between the damaged and restored areas. (3) The analysis of the driving mechanism of wetland damage showed that distance from cities and towns, average precipitation and population density were the main driving factors. (4) The spatial distribution of natural development and economic construction in 2035 will be slightly damaged; additionally, the spatial distribution of ecological protection will expand as a whole. From 2025 to 2035, wetlands will be basically stable under Natural Development Scenario (NDS), degraded each year under the Economic Construction Scenario (ECS), and steadily increased each year under Ecological Protection Scenario (EPS).

Investigating regional connections and their influencing factors from the perspective of &ldquo, flow&rdquo, space is one of the foundations of promoting regional development. In this article, the data we used includes actual highway passenger flow data, National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) nighttime light remote sensing data, and socioeconomic data. We analyzed the spatial distribution pattern, connection intensity and spatial autocorrelation of highway passenger flow in Chongqing during the working day, weekend and May Day and revealed the influencing factors by means of a geographic detector. Three key conclusions resulted from this research. First, highway passenger flow in Chongqing districts exhibits spatial agglomeration that is clearly higher in western Chongqing than in eastern Chongqing and forms an obviously dual-core &ldquo, star&rdquo, structure, with the main urban area and Wanzhou serving as the core. Second, a factor detector notes that the nighttime light area index (0.9251, 0.9512, 0.9541) has the strongest explanatory power for the spatial differentiation of passenger traffic in Chongqing districts, which is the key factor. Third, interaction detection shows that the interaction between the two factors displays an enhancement effect at different times. The nighttime light area index shows the strongest explanatory power under the synergy of tourist attractions, which are 0.9850, 0.9903 and 0.9908. But the per capita GDP and highway mileage have the most obvious enhancement effect after interaction (0.9544, 0.9661, 0.9652). Therefore, in future planning and development, we should pay attention to cooperation and exchanges between districts and use the nighttime light area index as an important reference factor to provide a scientific basis for the construction of public transport and economic construction in Chongqing.