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This study utilized census data from Henan Province for the years 2000, 2010, and 2020 to investigate the spatiotemporal evolution of population aging, defined by the proportion of the population aged 65 and above. Employing spatial analysis techniques such as spatial autocorrelation and the standard deviation ellipse, the research mapped out the progression and distribution of aging demographics. Furthermore, the study delved into the influencing factors of aging using an optimal parameters-based geographical detector. Results indicate a deepening degree of population aging in Henan Province, transitioning from an adult type to an old type structure. There is a marked positive spatial correlation among counties, with high-value aging areas initially decreasing, then increasing, and notably spreading from the central to the central and southern regions of the province. The center of gravity for population aging, specifically around Changge City and Xuchang City, exhibits a trajectory moving southeast before shifting northwest. Factor detection reveals that in 2000, 2010, and 2020, the elderly dependency ratio predominantly influences the aging trend, with explanatory powers of 88.4%, 87.9%, and 90.9%, respectively. Interaction analysis indicates that the interaction between the old-child ratio and the elderly dependency ratio has a strong explanatory power for the aging patterns in Henan Province, reaching 97.3%, 97.0%, and 97.4%, respectively.

Under the influence of global climate change, the climatic conditions of China's major agricultural regions have changed significantly over the last half-century, affecting regional grain production levels. With its favorable conditions for agricultural activities, China's central region has been a strategic location for grain production since ancient times and has assumed an essential responsibility for maintaining national grain security. However, the key concerns of this study are whether the national grain security pattern is stable and whether it might be affected by global climate change (especially climate instability and increased risks in recent years). Therefore, the present study collected grain production data and used descriptive statistical and geospatial analyses to reveal the trend and spatiotemporal pattern of grain production in China's central region from 2010 to 2020. Then, a further analysis was conducted by combining meteorological data with a geographically weighted regression (GWR) model to investigate the relationship between spatial differences in the output per unit of the grain sown area (OPUGSA). The findings were as follows: (1) The overall development trend of grain production in China's central region from 2010 to 2020 revealed a positive overall trend in grain production, with notable differences in growth rates between northern and southern provinces. (2) Most regions in the southern part of the central region from 2015 to 2020 showed varying degrees of total output of grain (TOG) and OPUGSA reduction, possibly affected by the effects of the anomalies for global climate change and a strong El Niño effect in 2015. (3) Low-low (L-L) clusters of TOG and OPUGSA indicators were consistently in the northwest part (Shanxi) of the central region, and high-high (H-H) clusters of TOG were consistently in the central part (Henan and Anhui) of the central region, but H-H clusters of OPUGSA were not stably distributed. (4) The fitting results of the GWR model showed a better fit compared to the ordinary least squares (OLS) model; it was found that the annual average temperature (AAT) had the greatest impact on OPUGSA, followed by annual sunshine hours (ASH) and annual precipitation (AP) last. The spatiotemporal analysis identified distinct clusters of productivity indicators. It suggested an expanding range of climate impact possibilities, particularly in exploring climate-resilient models of grain production, emphasizing the need for targeted adaptation strategies to bolster resilience and ensure agricultural security.

This paper focuses on the scenario where relativistic electron nonlinear Thomson forward scattering is driven by circularly polarized laser pulses, using both theoretical analysis and numerical simulations. For the first time, we investigate how the magnetic flux density and beam waist radius jointly influence the spatiotemporal properties of radiation. For the superimposed field under different parameters, the forces exerted by the laser field and the applied magnetic field on electrons will show cooperative or competitive relationships at different moments of action. And the transformation of this relationship promotes the evolution of the electron radiation pattern in the superimposed field, which greatly changes the spatiotemporal properties of Thomson scattering. Based on this, completely new spatial distributions of radiation-ripple and lotus phenomena were discovered. In addition, the combination of parameters with the best radiation properties was selected through Big Data, and the quasi-periodicity of the azimuth angle ${{\phi }_{\bm{p}}}$ was discovered for the first time. These results will contribute to the understanding of the radiation mechanism of Thomson scattering in superimposed field and will be instructive for laboratory modulation of ultra-high-performance ${\bm{X}}/{\bm{\gamma }}$-rays.

As areas with high population and economic concentration, metropolitan areas are important geographical units for innovation transformation and radiation. However, studies on innovation space have mainly focused on the entire country, urban agglomerations, provinces, and cities, with less attention paid to metropolitan areas as important scales of scientific and technological innovation. In addition, existing studies often emphasize the overall characteristics of the innovation output space, neglecting diverse innovation subjects such as enterprises, scientific research institutions, and individuals. Based on the data of authorized invention patents in the Guangzhou metropolitan area, this study applied spatial research methods, such as spatial autocorrelation, kernel density analysis, and buffer analysis, to explore the overall evolution pattern of innovation output space in the metropolitan area as well as the evolution characteristics and influencing factors of the three types of innovation outputs, namely enterprises, scientific research institutions, and individuals, to provide a scientific basis for the development of innovation space and the optimization of innovation resources. The results show that: (1) the evolution of innovation output space generally follows the pattern of "gathering in the core area of the main district of the central city in a metropolitan region, generating innovation output growth points in the secondary cities and forming a number of innovation output cross-border integration areas." (2) The innovation output space of enterprises continues to cluster toward industrial parks and technology parks with clear boundaries, and the extensive construction of parks has driven the decentralized development of innovation output space of enterprises; The innovation output space of scientific research institutions, with strong stability, has always been highly concentrated in the core area of the main city of Guangzhou and has expanded with the construction of university towns within the metropolitan area; The individual innovation output has shifted from "explosive spatial dispersion" to "organized spatial convergence", and the adsorption effect of urban rail transit on individual innovation output is becoming increasingly significant. (3) The three types of innovation outputs always have spatial proximity characteristics, and enterprise innovation is the main driving force for innovation diffusion in the periphery of metropolitan areas. (4) Regional innovation output is influenced by both the innovation environment and innovation inputs, among which government support is the most critical driving force affecting the three types of innovation outputs. Economic base and location conditions are important pillars for the three types of innovation outputs, while the degree of openness has a greater contribution to enterprise innovation output by facilitating technological spillovers and knowledge flows. In general, the overall spatial evolution of innovation output in the metropolitan area follows the development pattern of "single core-expansion-multicore," with the spatial evolution of the three types of innovation outputs being closely related to the construction of regional industry-university-research centers and major transportation facilities.

[Objective] The increasingly complex international situations and frequent trade frictions have significantly intensified the supply risk of global industrial chains. The trade pattern evolution identifying and risk propagation modeling of global wind power industrial chains would provide essential implications for increasing the resilience of industrial chains and securing energy supplies. [Methods] This study depicted the spatial-temporal characteristics of major wind power industrial chains during 2000-2021 based on complex network analysis, revealed the resilience of the industrial networks by network resilience assessment, and tracked the impacts of supply risks from different countries (regions) based on cascading failure modeling. [Results] (1) The overall scale of global wind power industrial chains continued to expand with trade connections between countries (regions) intensifying, and the complexity and efficiency of industrial networks increased significantly. (2) China as a major exporter played an increasingly important role in the global wind power industrial chains, establishing intense trade relations with numerous countries (regions). (3) Supply risks originating from China may generate profound impacts on the tower and generator trade networks, while supply risks originating from the United States and Germany may generate profound impacts on the blade and gearbox trade networks, respectively. [Conclusion] Optimizing industrial layout, collaborating with emerging markets, and strengthening technological innovation are thus of great significance for systematically preventing potential risks within wind power industry chains worldwide.

The development of mining cities has led to changes in land use and the evolution of landscape patterns. Constructing an ecological security evaluation system can reflect the ecological security status of mining city areas and provide planning references for these cities. This study, based on Heihe City’s land use data from 1980 to 2020, systematically analyzes the spatial and temporal evolution characteristics of land use, landscape patterns, and landscape ecological security levels by constructing a land use transfer matrix and calculating landscape pattern indices and landscape ecological security indices. The results show that: 1) Forest land is the main type of land use in Heihe City, accounting for over 50% of the total area. Land use changes primarily occurred between 2000–2010 and 2010–2020, with the spatial pattern characterized by overall stability and localized dramatic shifts, mainly involving the conversion of forest land to farmland and unused land. 2) From 1980 to 2020, the landscape ecological security pattern in Heihe City improved. Landscape diversity and landscape contagion increased, while landscape fragmentation, the largest patch area, and the average patch area decreased. Land use was optimized overall, but the trend of forest fragmentation became noticeable. 3) Between 1980 and 2020, the landscape ecological security level in Heihe City improved significantly. Driven by ecological restoration policies, the ecological security level in the southern region greatly improved, while the spatial pattern continued to show a trend of lower security in the south and higher security in the north. Specifically, the area proportions of low-security and relatively low-security areas increased by 6.23% and 9.55%, respectively. The spatial clustering of landscape ecological security levels is evident, with high-high value clustering mainly in the north and low-low value clustering mainly in the south. It is necessary to further promote ecological protection in the north to ensure the ecological barrier function, while strengthening ecological restoration in the south to improve ecological security levels. Additionally, continuous macro-policy regulation is needed to maintain long-term ecological security in Heihe City. The ecological security level of mining city landscapes is influenced by economic dependence, policy constraints, and environmental issues. This study can provide guidance for planning improvements in the study area and other mining cities.

[Objective] The temporal and spatial variation of ecological environmental quality and its heterogeneity were analyzed in order to provide a scientific basis for the government and relevant departments to formulate ecological environmental protection policies. [Methods] The google earth engine (GEE) platform was used to select Landsat images of central Yunnan Province from 2000 to 2020, and the remote sensing ecological index (RSEI) was extracted year by year. Spatial analysis methods such as hotspot analysis, standard deviation ellipse, and ecological environment gravity center shift were used to explore the temporal and spatial evolution of ecological environmental quality. [Results] ① The overall ecological environmental quality of the central Yunnan urban agglomeration showed a declining trend, and the average RSEI dropped from 0.51 in 2000 to 0.46 in 2020. ② The ecological environmental quality in Central and Eastern Yunnan Province was obviously better than in the western region, except for a few years. Most of the areas with better ecological environmental quality had greater forest coverage. ③ In the past 20 years, the spatial distribution of the ecological environment of the Central Yunnan urban agglomeration showed a north-south direction, and the overall ecological environmental quality of the urban agglomeration showed a certain degree of aggregation. [Conclusion] The deterioration of the ecological environment of the Central Yunnan urban agglomeration was closely related to the rapid expansion of the built-up areas of the urban agglomeration, climatic anomalies, vegetation destruction, and aggravation of soil erosion.

Rapid socioeconomic growth has altered land use patterns, resulting in a surge in worldwide CO 2 emissions, triggering global climate challenges and adversely affecting human health, safety, and sustainable socioeconomic development. As a result, immediate action is required to undertake climate mitigation and adaptation strategies. This study, based on the causal logic of climate change, blue-green space patterns, and carbon emissions, uses the system dynamics (SD) model, patch-generating land use simulation (PLUS) model, and integrated valuation of ecosystem service and trade-offs (InVEST) models to simulate the evolution of blue-green space patterns and predict the spatial distribution of carbon storage in Wuhan to 2060 from 2030 under three SSP-RCP scenarios from CMIP6 and investigates their mechanisms. The findings show that across various SSP-RCP scenarios, the blue-green space patterns in Wuhan would decline over the next 30 years, with green spaces decreasing to some amount and blue spaces growing marginally. The carbon storage is also expected to decline due to the shrinking blue-green space patterns. The SSP126 scenario has the least shrinkage of blue-green spaces, resulting in a reduction of 7.18Tg in carbon storage. Under the SSP245 scenario, the expansion of non-blue-green spaces encroaches on blue-green spaces, resulting in an 8.13 Tg decrease in carbon storage. Across the SSP585 scenario, non-blue-green spaces expand the fastest, resulting in the highest loss of blue-green spaces and a considerable drop in carbon storage of 11.67 Tg. This research is extremely important for optimizing regional land use patterns, coordinating green and high-quality development in Wuhan, and assisting with the implementation of urban climate change adaptation plans., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

With the promotion of marine power strategies and marine ecological civilization construction, marine green development has become an important issue. This study utilizes the mixed distance function Epsilon-based measure and the global Malmquist–Luenberger index model, which accommodates both radial and non-radial models, to evaluate the marine green total factor productivity (GTFP) across 11 coastal regions of China. The findings of this study are as follows. First, the marine GTFP showed a gradual increase in China’s coastal regions during the study period, with notable differences observed among the three primary marine economic circles. Second, technological advancement was the primary driver of the transformation of marine GTFP. Third, the distribution across the space pattern of marine GTFP tended to be concentrated continuously, which shows that the overall spatial distribution pattern of marine GTFP remained relatively stable. Fourth, there is no obvious σ convergence in a northern, eastern marine economic circle. Both absolute and conditional β convergence exists, indicating that the marine GTFP converged to their respective steady-state levels during the study period. This study can provide decision-making reference for accelerating the green growth and sustainable development of China’s marine economy.

This study presents the case of China's Jiangsu Province. The spatial-temporal pattern evolution of different manufacturing sectors is discussed using spatial analysis technology (spatial autocorrelation and standard deviation ellipses). The Granger test is used to analyze the relationship between the change in the manufacturing industry spatial agglomeration and regional economic differences. The following conclusions are drawn: 1) The spatial agglomeration trend of most manufacturing sectors is weakening. Much of the manufacturing sector, like the rubber and plastic product industries, has been transferred from southern to northern Jiangsu. 2) From the scale, only a minority of these enterprises possess substantial registered capital. The capital injection scale of more manufacturing enterprises is insignificant. At the same time, manufacturing companies with substantial financial resources are increasingly inclined to choose less-concentrated areas when choosing new investment areas. 3) The reduction of regional economic differences is considered to be the Granger-cause for the decline of the spatial agglomeration degree of the manufacturing industry in Jiangsu Province. Analyzing the spatiotemporal pattern of the manufacturing industry in Jiangsu Province will provide specific policy reference values for the manufacturing industry and economic development of Jiangsu province. In addition, for companies of different sizes, the findings of this paper also provide valuable references on how they can choose suitable investment locations according to their size in the future., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Abstract The spatial differences of efficiency and development level of regional tourism are evident, and the dynamic grasp of their spatiotemporal evolution characteristics and coupling coordination relationship is important to promote high-quality sustainable tourism development. Herein, we measured the tourism development level of 31 provincial units in China during 2000–2020, introduced the data envelopment analysis-based Malmquist productivity index to determine tourism efficiency, used exploratory spatiotemporal data analysis methods to explore the spatiotemporal dynamic characteristics of regional tourism differences and spatial structure, constructed a coupled coordination model of tourism efficiency and development level and analyzed their coupling excellence and synergistic consistency. The results were as follows: (1) The spatial differences of comprehensive tourism efficiency from 31 provinces were evident, and the average situation showed high and low distribution characteristics in the east and west, respectively. The interannual changes showed a fluctuating downward trend, with the scale efficiency playing a supporting role for comprehensive efficiency and technical efficiency playing an influencing and restraining role. (2) Less fluctuation existed in the local spatial structure of tourism efficiency and development level; the direction of dependence was more stable, tourism development level was slightly more volatile, and the spatial dependency direction changes were similar. The tourism efficiency of local structure competition posture was stronger than that of collaboration, with the tourism development level collaboration integration being stronger. (3) The local spatial structure of tourism development level was more stable, the relative position of provincial units was more difficult to change, tourism efficiency of local spatial structure was unstable, and provincial units exhibited a greater possibility of change. (4) Tourism efficiency and the scale of the overall coupling degree and coupling coordination gradually improved, with similar spatiotemporal values. The overall coupling degree and coupling coordination degree of tourism efficiency and development level gradually improved, with similar spatiotemporal heterogeneity and volatility of local evolution. The area with a higher value coupling coordination degree spread slower and more widely.

In recent years, the tourism industry has developed rapidly. However, traditional tourism information has the disadvantages of slow response speed and limited information content, which cannot reflect the evolution trend of spatial and temporal patterns of tourism information in time. Here, based on the Baidu Index, we construct an evaluation framework to analyse the spatial and temporal flow of tourism information in the Chengdu–Chongqing urban cluster from 2011 to 2021. Then, we analyse the urban links between different network levels from the evolution pattern. Finally, we use the geodetector model to analyse its driving mechanism. The results show that Chengdu and Chongqing are the most active cities in the study area in terms of tourism information. The unbalanced development of tourism information between Chengdu and Chongqing and other cities in the region gradually deepens during the period 2011–2019 (polarization effect), but the unbalanced development moderates after 2019. On the other hand, cities in the middle of the Chengdu–Chongqing cluster always have weak agglomeration effects of tourism information. Cities with high tourism information outflow rates in the Chengdu–Chongqing city cluster are mainly concentrated around Chengdu. The average outflow rate of Deyang is the highest, at 27.8%. Cities with low tourist information outflow rates are primarily located in the west, central and south. Ya’an is the city with the lowest outflow rate, with an average of −62.2%. Specifically, Chengdu is the dominant and most radiantly influential city. The tourism information of the Chengdu–Chongqing urban cluster shows a radial network with Chengdu and Chongqing as the core. The driving force analysis shows that the push factor of tourist source, such as the number of people buying pension insurance, is the core driving mechanism, while the pull factor of destination, such as the park green area, and resistance factors such as psychological distance, are in the secondary position. In general, this paper uses Internet tourism data to expand the traditional tourism information research of the Chengdu–Chongqing urban cluster, which can better respond to the changes and needs of the tourism market and provide reference for the spatial optimization of tourism destinations.

Breakthrough innovation and incremental innovation have different impacts on economic development. For regional development, it is important to find a balance in dual innovation, which entails effective coordination of allocating innovation resources and managing risks. However, little attention has been given to the spatial relationship and differentiation mechanisms between breakthrough innovation and incremental innovation. Therefore, our research takes China’s three major urban agglomerations as examples, aiming to explore the spatial–temporal pattern evolution, influencing factors, spatial relationship, and spatial organizational patterns of breakthrough innovation and incremental innovation from 2000 to 2021. The research found that the spatial distribution of urban dual innovation is affected by the law of distance decay, and the spatial distribution of incremental innovation is more polycentric than that of breakthrough innovation. In terms of the differentiation mechanism, breakthrough innovation is more affected by the innovation atmosphere, while incremental innovation is more likely to be affected by the economic foundation and built environment. Our research effectively supplements the shortcomings in the spatial relationship research of breakthrough innovation and incremental innovation and provides references for formulating innovation policies.

Cotton is an important agricultural crop and strategic resource. China is currently the country with the largest global cotton production and consumption, but few studies have systematically analyzed the dynamic relationship between factor price change (FPC) and cotton production pattern evolution (CPPE). Based on provincial panel data from the main cotton planting areas from 1985 to 2021, this paper used spatial econometric models to empirically analyze the impact of FPC including labor price, production material cost, and mechanical cost on CPPE from the perspective of mechanical substitution difficulty. The findings are that (1) FPC significantly affected CPPE, specifically the rise of labor price induced the demand for mechanical substitution, resulting in a significant cotton agglomeration effect; (2) spatial econometric analysis found that FPC had a significant spatial spillover effect on CPPE in adjacent regions; and (3) the transfer analysis found that higher mechanical substitution difficulty exerted an inhibitory effect on cotton planting, leading to a gradual shift of the main cotton planting areas from the Yangtze River Basin and Yellow River Basin with high labor prices to the Northwest Inland region with lower labor prices. Updating the production technology and improving the efficiency of cotton specialization are effective strategies. The results are valuable for policy making related to the development of China’s cotton industry.

Warming weather has led to melting sea ice, and increasingly complex global geopolitics has drawn more countries’ attention to the Arctic. The Arctic Northeast Passage, as an emerging route connecting Eurasia, has seen a sharp increase in vessel activity. The period from 2015 to 2020, being a stable and undisturbed data period, is of significant theoretical importance for exploring the natural development of the Arctic Northeast Passage. The study found that the research period can be divided into three stages: from 2015 to 2017, the number of vessels grew slowly. In 2018 and 2019, the number of vessels and vessel activities saw significant growth, but an unexpected reverse growth occurred in 2020. Different types of vessels have unique activity characteristics and evolutionary patterns, influenced by the Arctic’s unique geographical environment, abundant natural resources, deepening Sino-Russian cooperation, and increasing global trade supply and demand. The results of this study aim to provide policymakers with analysis based on the initial development stage of the route, offering data support for future policy formulation, route planning, and research on the navigation safety of vessels on the Arctic Northeast Passage.

The capacity for urban innovation is a significant symbol of contemporary urban development. In order to promote sustainable urban innovation, it is crucial to match and optimize innovation spaces, actors, and their behavioral needs. Based on the data from patent inventions, which are commonly used to represent urban innovation, in this study, we investigated the formation mechanism of Guangzhou’s innovation pattern and its characteristics from 1990 to 2020 using Geographic Information System (GIS) technology. The results indicated that Guangzhou’s innovation spaces developed a center-radiation structure of “two districts and seven cores”. We investigated the path dependence of spaces, actors, and behavioral needs by examining the interaction between the innovation space layout and behavioral needs. The findings provide theoretical support for the city’s sustainable development in terms of innovation in the future.

The study of spatio-temporal evolution characteristics and factors affecting the coordinated development of population and green economy (CD_PGE) in Shandong province, China, has significant decision-making implications for promoting high-quality and sustainable regional development. Based on 2001 to 2020 panel data for each city and economic zone in Shandong province, this paper constructs an evaluation model for the CD_PGE systems. Using growth elasticity models, geographic concentration models, kernel density estimation models, spatial autocorrelation, analysis of population and regional green economy development in Shandong Province from the perspective of spatial agglomeration coupling, spatial and temporal coupling coordination patterns, and evolutionary characteristics. In addition, we use the fixed effect models and panel quantile models to empirically test the effects of coordinated demographic and green economy development. The results show that: (1) In terms of demo-graphic and economic development characteristics, Shandong's demographic and green economy development trends are good, but there are still many challenges. (2) According to the time series evolution and spatial distribution characteristics, the degree of CD_PGE in Shandong Province is on the rise, and the level of spatial distribution is distinct. (3) From the spatio-temporal dynamical grid evolution of the degree of CD_PGE, the CD_PGE is characterized by significant spatial clustering, but with large regional differences. (4) From an impact factor perspective, both market mechanisms and government intervention have a significant impact on the degree of CD_PGE, but the direction and extent of the impact vary.

Since the inception of China’s reform and opening-up policy, the rapidly advancing process of urbanization and the primacy accorded to urban development policies have imparted increasingly profound ramifications on rural domains. Nonetheless, antecedent research has predominantly fixated on urban sprawl, overlooking the spatial metamorphosis of rural settlements and the prospective developmental trajectories within the policy paradigm. Consequently, this inquiry endeavors to scrutinize the evolution of the spatial configuration of rural settlements in She County from the advent of reform and opening-up (1980–2020) utilizing remote sensing data. In tandem, through scenario delineation and the utilization of the CLUE-S model, it aspires to prognosticate the evolving trends in the spatial arrangements of rural settlements in She County by 2035. The empirical findings divulge that (1) The temporal progression of rural settlement spatial configurations in She County over the preceding four decades can be delineated into two discernible phases. From 1980 to 2000, alterations in the number, extent, and spatial morphological attributes of rural settlements remained circumscribed. While the count of rural settlements registered a diminution (by 3), the aggregate extent experienced a marginal augmentation (by 8.45%), concomitant with a gradual gravitation towards regular boundaries, manifesting a stochastic distribution throughout the investigation expanse. Conversely, from 2000 to 2020, the quantity and extent of rural settlements in She County underwent a precipitous augmentation (92 and 36.37%, respectively), characterized by irregular peripheries. (2) The CLUE-S model achieved an overall precision of 0.929, underscoring its applicability in emulating fluctuations in rural settlements. (3) Within the new-type urbanization scenario, the cumulative expanse of rural settlements witnessed a decline of 35.36% compared to the natural development scenario, marked by substantial conversions into grassland and urban land usage. Furthermore, orchestrated planning and directive measures have propelled the consolidation of rural settlements in She County, engendering a more equitable and standardized layout. Under the aegis of the ecological conservation scenario, the total rural settlement area recorded a 0.38% reduction vis-à-vis the natural development scenario, primarily entailing competitive coexistence with arable land, grassland, and urban land usage in spatial terms.

Featuring complex and fractured terrain, the Loess hilly and gully regions suffer poor grain production capacity. The behavior of farmers, the major users of agricultural production space, significantly influences the agricultural production space. Hence, it is essential to explore the evolution rules of the agricultural production space under the influence of farmer behavior and reveal the influencing mechanism of agricultural production space change, which will facilitate the promotion of ecological protection and high-quality development of the Yellow River Basin. Relying on six-stage remote sensing images of the Yanhe River Basin from 1995 to 2018, this study utilized a land use dynamic index, transfer matrix and landscape pattern index to analyze the spatial pattern evolution of agricultural production in the Yanhe River Basin. Furthermore, the geographic detector model was applied to quantitatively analyze the influencing factors of the spatial pattern evolution of agricultural production. The results demonstrated the following: (1) From 1995 to 2018, the overall area of cultivated land in the Yanhe River Basin decreased by 927.02 km2, with a change degree of 21.07%. The spatial structure of agricultural production changed, mainly transferring the cultivated land to woodland and grassland. (2) The spatial form of agricultural production has changed from fragmentation to regularity, and the complexity of the production space shows a trend of first increasing and then decreasing. (3) The evolution of the spatial pattern of agricultural production was affected by multiple factors of farmer behavior, where significant interactive enhancement effects existed. Specifically, labor input was the dominant factor affecting the overall scale of the production space, with an influence value of 0.202; fertilization input and cultivated land transfer were the key factors affecting the spatial distribution of production, with influence values of 0.264 and 0.242, respectively; income level and social interaction were the base factors affecting the spatial form of production, with influence values of 0.558 and 0.438, respectively. The research results provide scientific support for the improvement of agricultural production quality and the spatial evolution mechanism of agricultural production in the Yanhe River Basin.

【Objective】There is a large population and strong demand for vegetables in Guangdong-Hong KongMacao Greater Bay Area(hereinafter called "the Greater Bay Area"). The evolution of vegetable production pattern and its influencing factors are studied in order to provide references for the spatial layout optimization and high-quality development of vegetable industry in the Greater Bay Area.【Method】The global autocorrelation indexes of vegetable gross outputs of each year were calculated to judge the regional correlation of vegetable production by using the statistical data at counties of the Greater Bay Area from 2010 to 2020 and the administrative map of the same year. The aggregation area of vegetable production and its spatial pattern evolution in counties were analyzed by local autocorrelation index. Spatial regression model was used to analyze the socio-economic factors affecting vegetable production under spatial correlation.【Result】From 2012 to 2020, the global autocorrelation index passes the test and the value is greater than 0, indicating that the vegetable production at county level forms a positive spatial correlation pattern, and the vegetable production has a certain degree of aggregation in space and increases with inter-annual changes. Through local spatial autocorrelation test and analysis, it is found that vegetable production areas such as Longmen county, Boluo county and Huicheng district shows a high-high aggregation pattern, and gradually forms an aggregation pattern in the northeast of the Greater Bay Area over time. The high-low and low-high spatial patterns of vegetable production are gradually affected by the positive correlation of adjacent areas, and finally forms the same pattern with the surrounding areas. Through the analysis of spatial regression model, it is found that the main factors affecting vegetable production are land, economic level, labor force and agricultural technology input in 2012. After 2016, the main influencing factor is land, while the influence of other factors is not obvious or significant, indicating that the expansion of vegetable planting scale is the first factor of yield increase.【Conclusion】There is a positive spatial correlation in vegetable production in Guangdong-Hong Kong-Macao Greater Bay Area, and gradually forms an industrial core area. In the future, it is necessary to increase investment in agricultural science and technology in the core areas of vegetable production such as Boluo, Longmen, Huicheng and Conghua, to further improve the modernization level of production, continuously drive the surrounding areas to improve the comprehensive industrial benefits, and promote the high-quality development of the vegetable industry.

Astigmatic unitary transformations allow for the adiabatic connections of all feasible states of paraxial Gaussian beams on the same modal sphere, i.e., Hermite-Laguerre-Gaussian (HLG) modes. Here, we present a comprehensive investigation into the unitary modal evolution of complex structured Gaussian beams, comprised of HLG modes from disparate modal spheres, via astigmatic transformation. The non-synchronized higher-order geometric phases in cyclic transformations originate a Talbot-effect-like modal evolution in the superposition state of these HLG modes, resulting in pattern variations and revivals in transformations with specific geodesic loops. Using Ince-Gaussian modes as an illustrative example, we systematically analyze and experimentally corroborate the beamforming mechanism behind the pattern evolution. Our results outline a generic modal conversion theory of structured Gaussian beams via astigmatic unitary transformation, offering a new approach for shaping spatial modal structure. These findings may inspire a wide variety of applications based on structured light.