Cities function as complex systems where subsystems interact to form higher-level urban complexes characterized by intricate nonlinear coupling relationships among various networks. Although a substantial amount of research on urban networks from the perspective of single-factor flow exists both domestically and internationally, such studies have limitations, as they do not fully capture the multifaceted nature of urban systems. Research on multi-city networks in China primarily examines the structural characteristics and dynamic mechanisms of networks with distinct attributes. While international studies have explored interactions and coupling effects among subsystems within complex systems, focusing on aspects like network robustness and cascading failures, relevant studies on urban networks remain relatively limited. This study proposes a comprehensive research framework called Correlation-Multiplex Coupling Networks-Coupling Linkage Effect Evaluation (CMC). Focusing on the coupling of enterprise and information networks in the Chengdu-Chongqing twin-city economic circle, this study further analyzes the coupling linkage effect between these networks. The results indicate that: First, there exists a complex nonlinear coupling relationship between information and enterprise networks, with a correlation coefficient exceeding 0.625 and different degrees of interactive coupling between nodes; second, from 2011 to 2020, there were notable differences in the core-edge structures of the three networks within the twin-city economic circle. Interactions between cities in core areas were more frequent than those in peripheral areas, displaying a prominent "rich man clubs" phenomenon and preferential links. The hierarchical structure and "Matthew effect" of the urban network were evident. The enterprise network evolved from a dual-core, single-strong link structure to a dual-core, multiple-strong-link structure, achieving a more balanced network over time. The information network transitioned from a single-centered structure around Chengdu to a weaker dual-core structure, with Chongqing as a secondary core. Third, from 2011 to 2020, differences in coupling and linkage effects between enterprises and information networks were significant, with node coupling and linkage primarily at medium to low levels. Link coupling and linkage were mainly at medium to high levels, and interactive linkage weakened over time. The "rich club" phenomenon in coupled networks was stronger than in information networks but weaker than in enterprise networks. Compared to enterprise networks, the "Matthew effect" of coupled networks was less pronounced. In 2020, due to COVID-19 impacts, coupling and linkage between enterprise networks and information networks were significantly weakened, and urban comprehensive capacity did not markedly improve. This study expands the research perspective on urban networks, enriching the field by using a multiplex network approach and coupling coordination model, providing a methodological reference for similar research in other regions and enhancing understanding of the linkage effects among urban subsystems.

The New Energy Vehicle (NEV) industry is key for the transformative development of China's automotive industry, with policy chains, market scale, and technological innovation serving as critical drivers of its innovation network. To provide scientific support for enhancing independent innovation capabilities and achieving sustainable development in China's NEV industry, this study analyzes policy portfolios and patent collaboration data using Social Network Analysis (SNA) and Vector AutoRegression (VAR) models and investigates the mechanisms through which policy chains shape the innovation network, with a focus on how policy interventions influence collaborative innovation dynamics and spatiotemporal evolution patterns. The results indicate that: (1) China's existing policy chain system exhibits short-term positive effects on enhancing the innovation capabilities of the NEV industry, primarily driven by fiscal subsidies, tax incentives, and pilot demonstration projects. However, these effects diminish over time. (2) Significant spatiotemporal disparities in innovation development have emerged due to the influence of policy chains and regional differences in resource allocation, economic foundations, and innovation environments. Developed eastern regions, supported by mature industrial clusters, were found to demonstrate higher policy responsiveness, whereas the central and western regions lagged in translating policy inputs into innovation outputs. (3) The spatial structure of the innovation network transitioned from a triangular pattern to a diamond-shaped framework with multiple core regions, reflecting the holistic evolution of collaborative relationships under policy guidance. This study makes the following key contributions: (1) systematically categorizing the major direct policies of China's NEV industry into vertical and horizontal policy chains and constructing a comprehensive policy chain framework applied to the analysis of the development of the NEV industry's innovation collaboration network; (2) contributing to theoretical discussions on innovation network research regarding knowledge flows, inter-entity collaboration, and innovation environment shaping by proposing a "policy chain–innovation network" coupling framework that links macro-level governance with micro-level collaborative behaviors. Consequently, future policy design for China's NEV policies must undergo a paradigm shift. Policymakers should prioritize a systematic, adaptive, and forward-looking approach to construct a multilevel policy chain system that balances short-term stimulation with long-term capacity building. To stimulate endogenous innovation vitality within enterprises, it is essential to accelerate the transition from government-led subsidy policies to market-oriented competition. Simultaneously, spatially differentiated policies should be implemented and tailored to local innovation environments to address regional development disparities. This includes increasing support for infrastructure investments and talent mobility programs in the central and western regions to narrow the innovation capability gap between the regions. By promoting a shift from industry-supportive to competition-driven policies, a market-friendly policy environment can be established that enhances the market's role in selecting technologies, products, and services. This approach ultimately fosters the synergistic development of policy chains and sustainable innovation ecosystems.

Megacities serve as primary spatial carriers of industrialization and sources of innovation and upgrading. Existing research predominantly relies on business registration data to identify the micro-locations of industrial activities; however, this approach suffers from limitations such as low identification accuracy and difficulties in subdividing the types of industrial activities. This study utilizes Shenzhen as a case and constructs a new method for identifying the locations of industrial activities based on the "Land-Building" perspective. By integrating land use and building data and by employing kernel density estimation, spatial autocorrelation analysis, and spatial econometric models, this study systematically examined the spatial distribution characteristics and factors influencing industrial production and R&D activities. The findings reveal that 1) the overall spatial distribution of industrial activities in Shenzhen exhibits a pattern of "dense in the north and sparse in the south," with 95.06% of industrial activity buildings located in the six districts outside the "Guan" area and 4.94% in the four districts inside the "Guan" area. There are significant differences between the spatial locations of industrial and industrial production activities. Industrial production activities display a "multicenter contiguous" layout, primarily distributed in the northeastern "Longgang-Pingshan" industrial production zone, the northwestern "Bao'an-Guangming" production zone, and the cross-city production zone at the border between Shenzhen and Dongguan. In contrast, industrial R&D activities exhibit a "single-center scattered" spatial distribution, mainly concentrated in central urban areas such as Nanshan District and Futian District, with key clusters in streets such as Yuehai, Xili, and Xixiang. 2) Both urban industrial R&D and production activities exhibited positive spatial autocorrelation and spatial spillover effects. Moran's I index for urban industrial R&D is 0.518, while that for production activities is 0.123, indicating that the spatial autocorrelation and spillover effects of industrial production activities are stronger than those of industrial R&D activities. Industrial production activities can exert economic agglomeration effects over a broad geographical range, whereas industrial R&D activities generate agglomeration effects only in smaller areas. The spatial clustering of industrial production activities centers around Fubao Street, with low-low clustering dominating the fan-shaped area from Xixiang Street to Yantian Street and high-high clustering prevailing outside this area. The spatial clustering of industrial R&D activities exhibits an "interlaced and embedded" pattern, forming a nested spatial distribution. 3) The spatial layout of industrial activities in megacities is the result of the combined effects of multiple factors including socioeconomic development, transportation infrastructure, production and living facilities, and geographical location. However, the driving factors for the microlocation distribution of the two types of activities differ. Factors such as population density, industrial development, highways, railway stations, commercial facilities and administrative area size positively influenced the spatial agglomeration of industrial production activities. In contrast, the economic level, industrial development, bus stops, subway stations, and financial facilities positively affect the spatial agglomeration of industrial R&D activities. This study suggests that governments should consider the regularity and heterogeneity of the spatial agglomeration of different industrial activities when conducting urban spatial and industrial planning, promote reasonable functional zoning of urban industrial activities, and enhance the supply of industrial development factors to foster the agglomeration and development of industrial activities.

New urbanization is an important for narrowing the urban-rural income gap and providing powerful support for promoting regional cooperative development. Based on panel data of 71 county units in the Wuling Mountain Area from 2014 to 2021, the spatial effect and influence mechanism of new urbanization on the urban-rural income gap were investigated using the spatial Durbin, mediation effect, and panel threshold models. The results revealed the following: 1) A positive spatial correlation existed between new urbanization and urban-rural income gap in the Wuling Mountain Area. The local spatial clustering was dominated by the "low-high", and new urbanization had a significant spatial spillover effect on the urban-rural income gap. 2) New urbanization in the Wuling Mountain Area reduced the urban-rural income gap by increasing the level of digitally inclusive financial development and promoting upgrading the industrial structure. 3) The impact of new urbanization on the urban-rural income gap in the Wuling Mountain Area was constrained by itself and the level of economic development, and there was a single-threshold effect, which showed the law of diminishing margins and the inverted "U"-shaped change trend, respectively. Based on the spatial perspective and non-linear perspective, it investigated the spatial effect and influence mechanism of the new urbanization on the urban-rural income gap in the Wuling Mountain Area, which not only made up for the limitations of the existing research in exploring the relationship between the two from the perspective of localization, but also expanded the existing theoretical research system, and provided the theoretical basis and practical guidance for the Wuling Mountain Area and other underdeveloped areas to accelerate the two-way flow of urban and rural factors, to promote the organic integration of new urbanization and rural revitalization strategy, and to promote the realization of common prosperity.

As economic globalization and informatization continue to advance and regional integration develops, interregional urban relations are being reconfigured by various mobile elements. Based on complex network theory, academics have conducted numerous studies on the network structure of urban agglomerations using relational and flow data, such as economy, trade, human mobility, and enterprise branching. However, there are fewer urban networks based on the perspective of government and enterprise linkage, and an in-depth portrayal of government-enterprise linkage networks helps to understand the urban economic interactions implied by the government-enterprise linkage and promote the development of regional economic integration. The study of government-business networks can help elucidate the economic interactions between cities implied by government and business linkages and promote regional economic integration. The pattern characteristics of city networks of government-enterprise linkages in urban agglomerations are explored using social network analysis based on government procurement data of the city agglomerations in the Middle Yangtze Urban Agglomeration, and the influencing factors are explored from the perspectives of proximity and city attributes through a negative binomial regression model. We found that: the procurement method of the city cluster in the Middle Yangtze Urban Agglomeration is dominated by public tendering, and the blocking effect of geographical distance affects the procurement method; the provincial capital city is the dominant city in the government procurement network of the Middle Yangtze Urban Agglomeration, with Wuhan and Changsha pointing to the obvious most wanted link, and Jiujiang playing a more prominent role as a bridge in the network; the linkage of the Middle Yangtze Urban Agglomeration is dominated by the linkage within the subcities, the linkage across the provinces and regions is weak, and Jiujiang plays the role of "broker." The "broker" role of the plate four and take the role of intermediary to promote the connection and transmission of the relationship in the overall network; the Middle Yangtze Urban Agglomeration government-enterprise linkage network is affected by the proximity and city attributes, the economic proximity, organizational proximity, financial expenditures, and the market environment pre-promotional role. Geographic distance and economic scale play negative roles in city networks. This study enriches the perspective of urban network research and contributes to a deeper understanding of urban economic interactions in the context of governmental and business connections.

The dynamic pulse of urban life has captivated scholars, policymakers, and city planners. Understanding the intricate mechanisms that shape urban vibrancy is pivotal for optimizing urban vibrancy patterns and fostering sustainable livable cities. However, previous studies have predominantly emphasized the relationship between objective neighborhood attributes and urban vibrancy. Systematically incorporating a multidimensional set of neighborhood factors, including human vision, functionality, morphology, location, and street configuration, and utilizing spatial regression models in resonance with advanced algorithms facilitates a more profound comprehension of the spatiotemporal differentiation of urban vibrancy within urban spaces and the driving or inhibiting factors involved. This work focuses on the Nanshan District of Shenzhen City as the study area, and has three objectives: (i) one-week passenger flow data that characterized the spatiotemporal distribution of urban vibrancy were provided; (ii) broadly collected Street View Images (SVI) were incorporated as a visional environmental factor, together with functional and morphological factors, into understanding the influencing mechanism of urban vibrancy; and finally, (iii) machine learning tools were employed to apply the Random Forest Regression (RFR) algorithm in exploring the independent driving role of the characteristic factors behind the temporal distribution of urban vibrancy, and the Geographically Weighted Regression (GWR) model was used to probe the influence of the characteristic factors on the spatial distribution of this dynamic concept. The results are as follows: 1) Nanshan District emerges a mosaic of multi-vibrancy centers, where urban functions and business forms emerge as the dominant causative factors that shape the urban vibrancy and human perceptions, exhibiting unignorable effects. Evidently, employment and commuting activities have a ripple effect on economic urban vibrancy throughout the center of the administrative district, making weekday vibrancy surpass that of weekend; 2) Variance of the spatiotemporal distribution of urban vibrancy is improved or inhibited by the characteristic factors, among which, the entertainment function plays a major role as the driving force; additionally, enclosure, Point Of Interest (POI) diversity, road density, and building density have promoting effects on the overall urban vibrancy. The effects of greenness, openness, business function, traffic function, residence function, and living function variate depends on space changes as well as the inhibiting effects of casual function; 3) Neighborhood with compact buildings, composite functions, and green surroundings motivate the surge of urban vibrancy; therefore, the creation of spaces with mixed functionality and compact structure, routes with diverse street view interface elements and open sky views, places with multi-level and highly accessible blue-green landscapes, and cultivation of new urban functions through urban renewal are encouraged during urban construction. This study reveals the importance of human vision in the relationship with urban vibrancy, provides insights into the dynamic evolution of urban vibrancy, and makes recommendations for stock planning for a sustainable and lively city, creating opportunities for people to enjoy various daily life activities by providing convenience, services, and novel cultural experiences, especially in the areas of shopping, catering, infrastructure, and transportation facilities. This study helps urban planners deepen their understanding of urban vibrancy, thereby promoting the implementation of people-oriented urban planning and management.

China imports more than 80% of its iron via sea. The spatiotemporal patterns of iron ore shipping, as well as its changes, are linked to the China's national economic security. The COVID-19 pandemic has profoundly affected the spatiotemporal patterns of global shipping. By studying the changing trend of China's iron ore import pattern during the pandemic, this study attempts to provide a basis and experience to prevent similar crises and improve supply chain resilience in the future. This study employs Automatic Identification System data to estimate China's iron ore imports during the COVID-19 pandemic from January to June 2020. Furthermore, this study integrates the standard deviation ellipse and origin-destination, flow analysis methods to examine changes in the iron ore trade pattern. The findings reveal the following. 1) The import pattern of iron ore in China exhibits pronounced geographical concentration. In terms of port distribution, the Bohai Rim serves as a core hub, hosting the majority of the country's major ore-receiving ports. Among them, the Caofeidian Port, Zhoushan Port, and Jinggang Port constitute three strategic fulcrums. 2) From the perspective of trade source countries, China's iron ore imports face substantial market concentration risks. Australia (accounting for over 60%) and Brazil (accounting for over 20%) contributed more than 80% of the total import volume, forming a highly dependent supply system. The main ports for iron ore exports from Australia are located on the west coast, including Ports Hedland, Dampier, and Walcott, while Port Itaqui in Brazil is also a major source of China's iron ore imports. 3) COVID-19 had the greatest impact on China's iron ore imports in February 2020. Imports rebounded in March as production resumed in China. In May, a higher import share in the Yangtze and Pearl River Delta regions shifted the import center slightly southward, although it remained along the southern boundary of the Bohai Rim port cluster. Despite the pandemic, the Bohai Rim ports retained their status as the primary import hubs. 4) Compared with 2019, iron ore exports from major exporters, including Australia, Brazil, South Africa, India, and Ukraine, increased from January to June 2020. China's dependence on iron ore from Australia and Brazil has decreased annually, while its dependence on Ukraine and India has increased. This has led to a westward shift in overseas iron ore supply centers. 5) Among ports with a monthly throughput exceeding 5 million tons, the iron ore supply to China from Australia's Ports Hedland, Dampier, and Walcott generally increased, whereas Brazilian Itaqui Port experienced a continuous decline in its supply to China starting in February. Among significant ports with a monthly throughput below 5 million tons, Peru's San Nicolas Port and South Africa's Saldanha Bay Port were the most severely impacted by the pandemic, with a notable reduction in their iron ore supply to China.

Power perspective is important for understanding how urban road networks operate, promote public participation, and improve operational efficiency. Taking Nanjing (1368—1978) as an example, a six-century historical geographic information dataset of the urban road network was constructed using local chronicles, old maps, and other historical materials. Spatial syntactic theory was used to explore the process of historical changes and morphological evolution of road networks under power interactions. The conclusions are as follows: 1) The interaction between government and public power has undergone a temporal process from absolute control and antagonistic resistance to government dominance. The structure of the road network reflects the ruling philosophy and national consciousness of different regimes. The expression of government power in the road network shows an evolution from "symbolic symbol" to "conceptual penetration." During the feudal period, to the east of the Imperial City along the north―south axis was the Qianbu Corridor Street, with both sides of the street arranged symmetrically to symbolize the status of the imperial power. During the capitalist period, the government and public experienced repeated confrontations, and the costs of road construction, routes, and road widths aroused strong public opposition. During the early socialist period, government-led planning was based on the full communal ownership of the land. The power of the residents to intervene in the construction of the road network was curtailed. The interaction between government and public power influenced the direction of growth and accessibility of the road network. The formation of the Republic of China is the time demarcation point when both the degree of integration and core of penetration of the road network moved from the south to north of the city. During the feudal period, the degree of normalized angular integration (NAIN) and normalized angular choice (NACH) of the core was stable in the "Ming Tile Gallery—Pingshi Street—Caixia Street" and "Red Paper Gallery—Xiajie Kou—Jewelry Gallery" in the south of the city. During the period of national capitalism, in response to pressure from residents, the focus of road network construction shifted to the sparsely populated northern part of the city, and the core of the road network's capacity moved from the south of the city to Gulou in the north. During the early stages of socialism, influenced by the Soviet model, the construction of factories and workers' villages led to the integration of the road network structure in the north and east of the city and the emergence of a new road network structure. The NAIN index of the road network in the north and east of the city further increased, and the foreground network of NACH ran through the entire city. Our study demonstrates that ancient Chinese historical data have great potential for use in quantitative studies of urban street networks. The power perspective helps reveal changes in urban social organization and lifestyle, constructs history and social life with a spatial dimension, and provides a new research perspective for urban history.

Songjiang is often referred to as the "Cultural Root of Shanghai," with its rich history and cultural heritage playing a notable role in the region. Among its natural resources, Jiufeng stands out as the only mountain and forest resource in Songjiang, celebrating its unique natural beauty since the Yuan Dynasty. Over the centuries, Jiufeng has become one of the most renowned scenic spots in the Jiangnan region. Even today, it remains a crucial landmark for showcasing the local mountain and forest landscapes. The primary objective of this study was to conduct an in-depth exploration of the spatial imagery associated with the Jiufeng area. By identifying and understanding the unique landscape features of Jiufeng, this study aimed to reveal its historical appearance, thereby contributing to the preservation and development of traditional mountain and forest landscape culture of China. This study leverages rich historical and geographical information as well as spatial imagery embedded in classical Chinese poetry. Specifically, this study focuses on 639 poems on the Jiufeng area from the Yuan, Ming, and Qing dynasties. Utilizing digital humanities techniques, this study extracted landscape elements and emotional expressions from these poems, which were then synthesized into the overall spatial imagery of the Jiufeng landscape. The research findings reveal three key insights regarding the landscape imagery of the Jiufeng area in Songjiang, Shanghai. 1) The landscape features of Jiufeng can be categorized into two primary aspects: natural and cultural elements. Natural landscape elements, such as mountains, rivers, and forests dominate both in quantity and variety, indicating their notable role in the minds of local poets. These natural elements are not only abundant, but also prominently featured in poetic descriptions, far surpassing the depictions of cultural landscapes. Among cultural elements, architectural features are the most frequently portrayed. These structures, often closely connected to the historical and cultural heritage of the region, serve as important vessels for the emotional expressions of poets. 2) The emotional tone of the poems about Jiufeng is predominantly characterized by neutral and positive sentiments. This study identified a diverse range of emotions within the texts, with "joy," "ease," "nostalgia," and "sorrow" being the most commonly expressed emotions. Notably, the emotions of "nostalgia," "serenity," and "regret" are the most frequently mentioned, suggesting that poets often employed neutral and positive feelings to convey their experiences and reflections on the Jiufeng landscape. 3) The research culminates in the identification of five distinctive landscape imagery categories associated with the Jiufeng area. These categories are: "Autumn Moon Boating," which captures the dynamic and vibrant water scenes, depicting how people in ancient times enjoyed boating and frolicking under the moonlight; "Leisurely Seclusion," which reflects the role of Jiufeng as a place of retreat, where individuals could enjoy a tranquil and peaceful lifestyle; "Reflections on Song Mountain" and "Mountain Recollections," which highlight the cultural significance of Jiufeng as a site where scholars engaged in literary and artistic pursuits; and "Huating Scenery," which encapsulates the natural beauty of Jiufeng, expressing the deep admiration of poets for its picturesque landscapes. This study offers new perspectives for deepening the understanding of the Jiangnan Mountain and forest landscapes in the Jiufeng area of Songjiang through its methodologies and findings. First, the application of digital humanities techniques to analyze a large volume of text enhances research efficiency and provides more accurate results. Second, by analyzing the compositional elements of landscape imagery in the Jiufeng area, this study identified unique aspects of the landscapes of the region that serve as valuable reference materials for contemporary landscape designers. In summary, this research not only enriches the theoretical knowledge in this field, but also provides valuable guidance for practical applications, contributing to the advancement of high-quality landscape development.

In the context of science and technological innovation, the spatial distribution and evolutionary patterns of cultural and creative enterprises tend to diversify, and their migration characteristics and mechanisms also change. Although traditional regional factors are difficult to explain the migration characteristics and mechanisms of cultural and creative enterprises effectively, amenity theory provides a new perspective for interpreting these characteristics. However, there is still relatively little research on amenities in China, and most of it focuses on national and regional scales, emphasizing conceptual interpretation, indicator construction, and evaluation. Few scholars have focused on the correlation between amenities and the spatial agglomeration of cultural and creative industries, and there have been few quantitative studies on the interaction between amenities and the relocation of cultural and creative enterprises. This study is based on the micro subjects of enterprises using UCINET social network analysis and geographic detectors to explore the migration characteristics and impact mechanisms of cultural and creative enterprises in Guangzhou from an amenity perspective. Research has shown the following: (1) Unlike the early evolution pattern of cultural and creative enterprises mainly gathering in the central area, and also different from the one-way migration path of manufacturing enterprises mainly transferring from the central area to the periphery, cultural and creative enterprises present more complex migration network relationships. The specific manifestation is that the urban center is not only the main place for enterprises to move in, but also the main place for them to move out. Both the migration and relocation modes dominate the central area, showing balanced diffusion and hierarchical decay, and the amenity preferences and migration characteristics of different types of enterprises tend to be diversified. (2) The influence of amenity factors on the migration of enterprises at different stages and types is heterogeneous, with factors such as leisure, social places, and transportation facilities having significant impacts. In contrast, the influence of landscape amenity factors is relatively low. The applicability of amenity factors varies regionally, domestically, and internationally. (3) The impact of amenities on the decision-making process of enterprise migration is mainly achieved through two types of entities: the enterprise and practitioners. The impact mechanism can be summarized into three aspects: the flow of enterprises in the central area under policy guidance, the return of enterprises under demand for supporting facilities, and the outward migration of enterprises under the assistance of policies and facilities. Specifically, the migration of large and medium-sized enterprises within the central area is often driven by their own development considerations. For enterprises that relocate from peripheral urban areas to central areas, the demand for amenity facilities from employees has gradually become a concern. Small- and medium-sized enterprises that relocate from the central to peripheral urban areas have higher autonomy and local mobility among their employees, and are more sensitive to environmental and policy amenities. Finally, based on the migration characteristics and impact mechanisms of cultural and creative enterprises, this study proposes strategic recommendations for creating cultural and creative spaces, developing cultural and creative industries, and shaping these amenities. The main contribution of this study to the existing literature is the exploration of the spatiotemporal migration characteristics of cultural and creative enterprises in Guangzhou from the perspective of new urban economic theory and summarization and improvement of existing research on the construction of amenity index systems. This study systematically reveals the impact mechanism of cultural and creative enterprise migration. Enriching and improving theoretical research on the interaction between amenities and creative enterprises can provide decision-making support for the high-quality development of Guangzhou's cultural and creative industries, as well as a reference for the spatial allocation and management of cultural and creative industries in other regions of the country.

In the early 16th century, Portugal was the first country to colonize Southeast Asia. In the 19th century, colonizers introduced a system of forced cultivation in the archipelagic countries of Southeast Asia, establishing plantations to grow specific crops for the needs of the host countries and the international market, with the intention of turning Southeast Asia into a raw material source and market for product dumping. The huge profits from the plantations were channeled back to the host countries, reinforcing the colonial system. With the deepening of colonization, plantations expanded from archipelagic to peninsular countries, and the sovereign state not only controlled the lifeblood of the colonial agricultural economy but also had a sustained and far-reaching impact on the formation and development of the local agricultural pattern. Post-World War II, Southeast Asia formed a pattern of agricultural types in which gatherers, fishers, hunters, nomadic farmers, small farmers, plantations and farms, settlement farmers, and agricultural cooperatives (groups) coexisted. Compared with the other types, plantations created by external forces developed extremely quickly, with high production levels and economic value, but had a relatively short history. In the 1930s, the global colonial system collapsed, plantations lost the support of the colonial regime and declined, and Southeast Asian agriculture was transformed into a smallholder economy. Plantations were an indelible part of Southeast Asia's colonial history. In the post-colonial period, scholarly attention continued to focus on the long-term effects of colonialism on formerly colonized countries. Therefore, it is necessary to analyze and reflect on the historical legacy as well as the potential risks in contemporary international cooperation. To promote the construction of China's overseas agricultural cooperation zones in the post-colonial period, this study begins with a human geography perspective, takes the critical theory of capital as a guide, establishes the theoretical framework of the spatial contestation of colonialism, first-combs through the phases of the rise and fall of Southeast Asia's plantations against the background of colonialism, and finally analyzes in detail the mode of construction of plantations. It was found that 1) with the deepening and expansion of colonial activities, the focus of the colonizers' spatial competition in Southeast Asia shifted from a single material resource to a broader and more complex material and non-material resource, and the establishment of plantations was an effective means for colonizers to maintain their dominant position; 2) the construction of plantations was filled with the colonial regime's competition for colonial land, labor, and resources of the international market, and each kind of resource competition formed the corresponding power spatial relations. 3) The plantation promoted the colonizer's penetration into the local society, and its shaping effect on Southeast Asian countries made the operation of the colonial society fully serve the capital accumulation of the colonial regime and consolidate the colonizer's dominant position.

Over the past 40 years, significant changes have occurred along the coastlines of Qinzhou Bay in China and Manila Bay in the Philippines. Understanding the patterns of these changes is important for the management and planning of coastal zones. This study is based on the Google Earth Engine platform, using the modified normalized difference water index, combined with the Otsu algorithm and the Digital Shoreline Analysis System, to extract the coastlines of Qinzhou Bay in China and Manila Bay in the Philippines over the past 40 years, and then analyze the spatiotemporal variation characteristics of the two coastlines and the situation of reclamation. The results show that in the past 40 years, the coastline of Qinzhou Bay has generally advanced towards the sea, with a coastline length increase of 44.78 km, an average End Point Rate(EPR)of 6.81 m/a, and average Linear Regression Rate(LRR)of 6.16 m/a. Natural coastline length continued to decrease, whereas the proportion of artificial coastlines continued to increase. The Index of Coastline Utilization degree (ICUD) values show an upward trend, whereas the Index of Coastline Type Diversity (ICTD) values show a continuously decreasing trend. The Manila Bay coastline first increased and then decreased, with an overall decrease of 1.05 km—a relatively small change. The coastline also shows a trend of advancing towards the sea with an average EPR of 2.36 m/a and average LRR of 2.32 m/a. The proportion of natural coastlines continued to decline, whereas that of artificial coastlines gradually increased. The ICUD values showed a steadily increasing trend, whereas the ICTD values showed a downward trend. The cumulative area of reclamation in Qinzhou Bay has reached 6,674.27 hm², with an average annual expansion rate of 196.30 hm²/a. Reclamation activities were significantly active and large-scale. However, the cumulative reclamation area of Manila Bay is only 1,718.59 hm², with an average annual expansion rate of 50.55 hm²/a, indicating relatively limited reclamation activities. The reclamation intensity index and annual spatial expansion rate of Qinzhou Bay were higher than those of Manila Bay. Overall, compared to Manila Bay, the Qinzhou Bay coastline exhibited more significant characteristics in terms of change amplitude, change speed, and reclamation intensity. Reclamation activities have a significant impact on coastline changes, and port and dock construction and aquaculture are the main driving factors for reclamation.

River terraces are important geomorphic indicators that reveal the evolution of rivers, and their climatic and tectonic responses. Dating is key to studying terrace evolution. In the past decade, electron spin resonance (ESR) dating has been widely applied in Quaternary geology and environmental research, and has solved a series of Quaternary chronology problems. This study selected well-exposed river terrace sections of the Zhenjiang River in the upper reaches of the Beijiang River as the research object and used ESR dating to analyze the ages and formation mechanisms of the terraces of the Zhenjiang River system. The experimental data show that the Ti-Li core dose-response curve of quartz in the sediment samples of the Zhenjiang River terraces fits well with the ESR signal strength, indicating that the terrace samples are stable and meet the requirements of ESR dating. Two age data were obtained from the bottom and the top of the ZJ-P₁ profile, with ages of 654 ± 79 ka and 231 ± 29 ka respectively; three age data (576 ± 38 ka, 523 ± 55 ka and 256 ± 26 ka) were obtained from the bottom to the top of the ZJ-P₂ profile, and three age data (392 ± 56 ka, 132 ± 15 ka and 41 ± 6 ka) were obtained from the bottom to the top of the ZJ-P₃ profile, being respectively. These results reflect the continuous sedimentation of the strata. Through the comparative analysis with the existing thermoluminescence age data of the Zhenjiang River and adjacent basins, it is determined that there are two distinct river terraces on the left bank of the Zhenjiang River in the upper reaches of the Beijiang River, and the final formation times of T₂ and T₁ are approximately 231 ± 29 and 41 ± 6 ka, respectively. Similarly, the ages of the river terraces in the main basins of northern Guangdong obtained by different dating methods were similar, indicating that the rivers in northern Guangdong were generally incised during the Middle and Late Middle Pleistocene and that the main rivers in northern Guangdong have synchronous evolution characteristics. On the basis of sedimentary characteristics of the river terraces in the Zhenjiang section of the upper reaches of the Beijiang River, terrace dating data, and previous research, it is shown that the formation of the second terrace in the Zhenjiang section was mainly influenced by tectonic uplift movements; the final formation time was in the Middle and Late Pleistocene, and the first terrace was formed under the combined action of climate change and tectonic activity during the late Pleistocene. Based on the ages of the samples at the top of the T₂ and T₁ gravel layers and the incision heights, the corresponding incision rates were calculated to be 0.056 and 0.524 mm/a, respectively. Finally, a comparison with the downcutting rates of other river terraces in neighboring areas showed that the Jinjiang and Zhenjiang Rivers exhibited higher downcutting rates since the Middle to Late Pleistocene, indicating the presence of tectonic uplift in northern Guangdong during this period. This study determined the ages and formation mechanisms of low-level river terraces in the Zhenjiang River section using ESR dating and provides an important reference for the study of climatic and tectonic responses in northern Guangdong.

Leaf Chlorophyll Content (LCC) is vital for both direct and indirect plant growth and development. Accurate monitoring of LCC in ginseng fruits provides essential data for assessing their photosynthetic and nutritional status, which is beneficial for the development of precision agriculture. Traditional chemical analyses in laboratories require a large number of samples, which are not only time-consuming and destructive, but also fail to meet the precise management needs of extensive fields. Although some handheld devices can measure the leaf LCC accurately and quickly without causing damage, they cannot provide large-scale information. Hyperspectral remote sensing is widely applied for rapid and non-destructive LCC monitoring because of its strong continuity and abundant data. In this study, we used ginseng fruit leaf hyperspectral data and the corresponding LCCs as datasets. We applied the Discrete Wavelet Transform (DWT) to extract the low-frequency coefficients from the 0-10 layers of the hyperspectral data. We then conducted a Pearson correlation analysis on the 0-10 layer spectral datasets and their corresponding LCCs. We combined Variable Combination Pattern Analysis (VCPA) with Genetic Algorithm (GA), employing the combined VCPA-GA algorithm to extract sensitive bands from the full spectrum and each decomposed layer of the ginseng fruit leaf. Finally, we established estimation models for the ginseng fruit LCC using the Back Propagation Neural Network (BPNN), GA-BPNN, Particle Swarm Optimization (PSO)-BPNN, and BP-AdaBoost neural network models. Among the four machine-learning models, the BP-AdaBoost neural network exhibited the best overall predictive performance. The predictive performance of the PSO-BPNN model was similar to that of the BPNN model, whereas the GA-BPNN model exhibited the lowest predictive performance. This study shows: (1) The 1-5 layer DWT spectra accurately reflect the overall characteristics of the original spectrum, with a decrease in correlation at each layer beyond the fifth layer, and the spectra beyond the seventh layer no longer represent the overall features of the original spectrum. This is because the wavelet transform process has some errors that increase with the number of decomposition layers. (2) The VCPA-GA hybrid variable selection algorithm merges the strengths of the VCPA and GA, addressing the tendency of the VCPA to select fewer variables and overcoming GA's limitations in handling many variables which can lead to overfitting, providing a theoretical basis for estimating ginseng fruit LCC using hyperspectral remote sensing. (3) Among the four machine-learning models, predictions from to 1-2 and 6-7 layers were generally lower than those of the 0 layer, while predictions from the 3–5 layers are higher, showing an overall trend of initial increase followed by a decrease as the number of wavelet decomposition layers increased. (4) Ginseng fruit leaf hyperspectral data processed by the DWT-VCPA-GA algorithm with a 4-layer DWT spectrum yielded the best predictive performance in the BP-AdaBoost regression model, with R ²=0.919, mean absolute percentage error = 2.090%, and relative percentage difference = 3.900. (5) After optimizing the BPNN regression model with various algorithms, only some optimized models improved their predictive performance and accuracy to a certain extent, making the choice of the right optimization algorithm crucial for model improvement.