Spatial Agglomeration Research Articles

Where are the cruise ships? mobility and immobility of cruises under COVID-19.

The aim of this study is to reveal the spatial-temporal characteristics and influencing factors of the static and dynamic distribution of global cruise ships, against the backdrop of the COVID-19 pandemic, which has transformed the global cruise network from "lines" to "points". This study collected trajectory data for 292 cruise ships and data for 789 cruise ports worldwide from March 2020 to July 2021. Based on the relationship between port and navigation, port management rights research, and port geography theory, we analyzed the spatial distribution and spatiotemporal migration of cruise ships with ArcGIS tools. It was found that, compared with normal times, the distribution of cruise ships in regional markets, countries, and ports showed stronger spatial agglomeration characteristics and formed four types, which were mainly influenced by differences in cruise operators' fleet scale, positioning, and itinerary. With the improvement of the epidemic, cruise ships trended to gather at the cruise home port. During the COVID-19 pandemic, there was an obvious separation between the epidemic prevention country, flag country, and operator country. Operators were inclined to berth their cruise ships in the countries where cruise ships were registered, countries of operators, and ports with high integration. Rather than simply emphasizing the static state of the cruise shipping network, the global cruise geography under the COVID-19 pandemic reflects the right relationship between ports, cruise ships, and companies. This study provides a methodological framework for analyzing the cruise shipping network at the port level and has practical implications for micro-interpretation of the dotted cruise shipping network during the COVID-19 pandemic.

How Does the Three‐North Shelterbelt Engineering of China Improve Its Habitat Quality? A Study of 40 Years of Change Tracking and Driving Factors

ABSTRACTThe Three‐North Shelterbelt Engineering of China (TNSEC) is a large‐scale ecological restoration project that was initiated in 1978 to enhance the ecological environment of the Three‐North Shelterbelt Engineering Region (TNSER). However, the macro long‐term data and driving mechanisms behind habitat quality in the TNSER remain inadequately understood. Therefore, multi‐source observational data, the InVEST model, and spatial analysis techniques were employed to quantify the spatiotemporal evolution of habitat quality in the TNSER since 1980. Additionally, the Optimal Parameters‐based Geographical Detector (OPGD) model was utilized to quantitatively elucidate the driving mechanisms behind changes in habitat quality. The habitat quality of TNSER has remained stable from 1980 to 2020, exhibiting significant spatial agglomeration characteristics. Areas with higher habitat quality are demonstrating a trend of expansion, while regions with poor habitat quality are experiencing fragmentation and expansion. It is anticipated that the habitat quality of TNSER will continue to remain stable in the future; however, areas with poor habitat quality and their adjacent zones are showing signs of deterioration. Changes in habitat quality within TNSER are influenced by multiple factors, primarily socio‐economic factors in nature, with GDP accounting for 55% of the variations in habitat quality. This study offers valuable recommendations for policymakers from a sustainable development perspective, highlighting the critical role of large‐scale ecological projects in enhancing environmental quality amidst ongoing ecological challenges.

Significant negative impact of human activities on carbon storage in the Yellow River Delta over the past three decades

With the increasing intensification of human activities, significant changes in land use and land cover (LULC) have posed a severe threat to the carbon storage capacity of wetland ecosystems. A deep understanding of this impact is crucial for protecting regional ecosystems and promoting sustainable development. This study utilized the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and the human activity intensity (HAI) index to conduct detailed grid analysis and global analysis of carbon storage through creating fishnet system and explored the complex relationship between carbon storage and HAI in the Yellow River Delta (YRD), China. The results indicated that over the past 30 years, natural wetlands such as meadow wetlands and salt marshes in the study area had undergone significant degradation due to escalating human activities, while artificial wetlands and non-wetland areas expanded. Concurrently, the total regional carbon storage had declined by 2.08 Tg, representing a significant drop of 8.22 % in the YRD from 1990 to 2020. Among them, dry land, as the primary land type, served as the most crucial carbon pool. Additionally, the human activity intensity of land surface (HAILS) increased significantly, with a growth rate of 37.27 %. HAI mapping revealed a continuous expansion of areas with high HAI. In contrast, the Yellow River Delta National Nature Reserve (YRDNNR) maintained relatively low HAI. Correlation analysis further showed the significant negative correlation (p < 0.01) between carbon storage and HAI, with r values of grid analysis ranging from −0.1395 to −0.0344, while that for global analysis was −0.9643, respectively. This reflected the spatial heterogeneity and agglomeration effects of data analysis across different scales. This study provides valuable insights for achieving the “dual carbon” goals and supporting the conservation and management of wetland ecosystems.

Spatiotemporal Relationship Between Land Subsidence and Ecological Environmental Quality in Shenfu Mining Area, Loess Plateau, China

The exploitation of coal resources has caused problems such as ground deformation, affecting the ecological environment. Spatiotemporal varying characteristics between land subsidence and ecological environmental quality (EEQ) are an important research hotspot. Using the SBAS-InSAR method, 64 Sentinel-1 images were utilized to monitor land subsidence in the Shenfu mining area, one of China’s largest coal source regions. And the remote sensing ecological index (RSEI) was used to monitor and evaluate EEQ of the Shenfu mining area. Global and local spatial autocorrelation methods were used to assess the spatial aggregation degree and change patterns over time. Spatial Econometric Models were employed to explore the impacts of land subsidence on EEQ. The results showed the following: (1) The average RSEI values in the Shenfu mining area were 0.531, 0.488, and 0.523 in 2016, 2018, and 2020, respectively; there was a slight downward trend in EEQ. The permanent scatter (PS) point deformation rate ranged from −353.40 mm/year to +246.24 mm/year, with average deformation rates of 0.1642, 0.2181, and 0.2490 mm/year, respectively. (2) There was a significant correlation and spatial agglomeration effect between land surface subsidence and EEQ. Low–high, high–low, and low–low clusters were the main types of relationships, indicating that land subsidence primarily has a negative spatial impact on the ecological environment. (3) The relationship between land subsidence and EEQ varied spatially in the Shenfu mining area at 500 × 500 grid units. This research can provide scientific guidance for disaster prevention and sustainable development in mining areas by considering long-term differences in ecological environmental quality and its correlation with land subsidence.

Spatial–Temporal Characteristics and Driving Factors of Visible and Invisible Non-Grain Production of Cultivated Land in Hebei Province Based on GlobeLand 30 and MODIS-EVI

The growing problem of non-grain production of cultivated land (NGPOCL) has increased food security risk, garnering attention from China and other nations worldwide. Current research predominantly focuses on the internal planting structure of cultivated land. To more comprehensively measure the level of NGPOCL, we categorized NGPOCL into two types: visible non-grain production of cultivated land (VNGPOCL) and invisible non-grain production of cultivated land (INGPOCL). VNGPOCL and INGPOCL scopes were extracted utilizing land use and vegetation index data, exploring their spatial–temporal characteristics and driving factors through spatial feature analysis and multiple linear regression methods. The findings are as follows: (1) The degree of VNGPOCL shifted from mild to moderate, with its rate increasing from 5.16% in 2000–2010 to 10.82% in 2010–2020. Furthermore, the spatial variation in VNGPOCL indicated a growing east–west disparity while showing a reduction in north–south differences, reflecting significant spatial agglomeration effects. (2) There was a dramatic increase in areas classified as having moderate to severe INGPOCL, with the rate rising from 14.24% in 2000 to 41.47% by 2020. The east–west and north–south disparities concerning INGPOCL diminished rapidly, also indicating strong spatial agglomeration effects. (3) The driving factors for VNGPOCL and INGPOCL differed significantly depending on developmental stages. The results contribute valuable insights into accurately characterizing the spatial–temporal features associated with NGPOCL in Hebei Province while enhancing risk management strategies related to NGPOCL.

Analysis of Spatial Agglomeration Characteristics of Logistics Enterprises in Henan Province based on Poi

As an important industry connecting urban and rural areas and serving production and life, logistics enterprises, as the micro-foundation of the development of logistics industry, have a profound impact on the spatial reconstruction of regional economic and social development. National Development and Reform Commission issued the "Fourteenth Five-Year Plan" modern logistics development and "Fourteenth Five-Year Plan" modern circulation system construction planning "put forward new requirements for the development of logistics industry, emphasizing the optimization of logistics spatial layout and speed up the construction of national logistics hub, especially pointed out that to make up for the shortcomings of the central and western regions. As an important base for the development of China's logistics industry, the spatial layout of logistics enterprises in Henan province not only affects the high-quality development of the logistics industry in the province, but also plays an important role in the improvement of the national logistics network. In this paper, based on POI data, the mean nearest neighbor method, local Moran scatter plot analysis and kernel density visualization technology are used to analyze the spatial agglomeration characteristics of logistics enterprises in 157 counties in Henan Province. It is found that the spatial distribution of logistics enterprises in Henan Province has gradually evolved from multi-point dispersion to a single-core and multi-core collaborative model with Zhengzhou as the core, and presents a significant spatial positive correlation and agglomeration trend. This change has improved the logistics efficiency and promoted the strengthening of regional economic ties. At the same time, the agglomeration trend of logistics enterprises in Henan Province continues to strengthen, from the initial agglomeration in the core area to the diversified diffusion pattern, showing the continuous optimization and reconstruction of logistics network in the province.

A Study on the Impact and Mechanisms of Digital Economy Development on the Formation of New Productive Forces

This study investigates the impact of digital economy development on the formation of new productive forces in Guangdong Province. Utilizing key indicators such as GDP, education, and technology data, the research employs data cleaning and spline interpolation for preprocessing. Hypotheses P1 through P4 were proposed based on literature and regional policies. An evaluation system for new productive forces was established using the entropy weight method. Empirical analysis via STATA software confirmed a significant positive effect of the digital economy on new productive forces, supporting hypothesis P1. Mechanisms including technological breakthroughs, human capital, and industrial transformation were validated, confirming hypotheses P2, P3, and P4. The study highlights the spatial agglomeration effect between digital economy and new productive forces, suggesting mutual enhancement and significant spillover effects across regions.

The Impact of Digitalization on Agricultural Green Development: Evidence from China’s Provinces

Agricultural green development is crucial for achieving the United Nations 2030 Sustainable Development Goals, with the digital technology revolution acting as a catalyst for both China’s green agricultural transformation and global sustainable development efforts. This study utilizes panel data from 30 Chinese provinces (including autonomous regions and municipalities) from 2012 to 2022 to assess the digitalization level and agricultural green development through a combined entropy weight and TOPSIS method. It also investigates the spatial agglomeration of agricultural green development using Moran’s I index and empirically evaluates the impact of digitalization on agricultural green development through OLS and spatial Durbin models. The findings reveal that, while China’s agricultural green development has progressed slowly yet steadily during the study period, it demonstrates significant spatial agglomeration, driven primarily by agricultural production efficiency and resource recycling. Notably, a non-linear U-shaped relationship exists between digitalization and agricultural green development, suggesting that digitalization fosters agricultural green development only after surpassing a certain threshold. Additionally, digitalization has spatial spillover effects: advancements in neighboring provinces correlate with local agricultural green development in a U-shaped manner, with an initial “siphon effect” followed by a “trickle-down effect.” These insights inform policy recommendations aimed at optimizing the use of digital technology to facilitate green agricultural transformation, offering valuable guidance for policymakers.

What influences the development of wine-producing regions? New economic opportunities in the eastern foothills of the Helan Mountains in China

The formation and development of wine regions require unique natural conditions and hundreds of years of technical and cultural accumulation. The development of the wine industry at the eastern foot of Helan Mountain in China, in addition to the appropriate geographical environment, the technical migration, and talent flow supported by consistent policies, have improved the allocation ability of resource factors and conversion efficiency, making it a world-renowned new wine-producing region in just 40 years. This particular mechanism deserves attention as new industries develop in other parts of the world. Using the methods of spatial autocorrelation, hot spot analysis, and geographic detector, this paper comprehensively analyzes the growth and spatial evolution characteristics of wineries in the eastern foot of Helan Mountain in China and deeply discusses the mechanism of the interaction between the localization of global floating capital and China's unique regional economic policies. It is found that the local selection of global floating capital has a spatial locking effect on the formation of CHMWR, and the rolling support policies of China's central and local governments promote the rapid expansion and regional spread of the number and scale of wineries, with significant spatial agglomeration and spatial diffusion. The profound mechanism of such changes lies in the dual interaction of the local selection of global liquid capital and the Chinese government's wine-producing policies adapted to the geographical environment. In this process, a series of internal and external factors such as the innovation of grape planting and wine-making technology, consumer market, and government behavior have accelerated the continuous growth of the wine-producing region at the eastern foot of Helan Mountain in Ningxia. This paper adds a new understanding and deep understanding of the interaction between the local implantation of economic globalization and the macro-control policies of the Chinese government.

Knowledge Spillovers and Integrated Circuit Innovation Ecosystem Resilience: Evidence from China

A resilient innovation ecosystem is an important guarantee for enhancing industrial competitiveness. Knowledge spillover is the key driving force to enhance system resilience. Firstly, we use the MEREC-CoCoSo method to calculate the resilience level of the integrated circuit (IC) innovation ecosystem and analyze the evolution trajectory of the resilience before and after the emergence of the “stuck-neck” problem. Secondly, based on the panel data of 30 provinces (autonomous regions and municipalities directly under the central government) in China from 2011 to 2021, this paper analyzes the mechanism of the impact of intra-regional knowledge spillovers on the resilience of IC innovation ecosystems using the fixed-effect model and analyzes the spatial effect of inter-regional knowledge spillovers on the resilience of innovation ecosystems using the spatial Durbin model under the human capital matrix. Finally, we analyze the regulating role of contractual and relational governance mechanisms and try to open the “black box” of governance. The result shows the following: (1) The polarization of innovative ecosystem resilience of integrated circuits is gradually increasing, with strong spatial agglomeration, high–high agglomeration, low–low agglomeration, and low–high dispersion, and there is an obvious “matthew effect” and “siphon effect”. (2) Both intra- and inter-regional knowledge spillovers contribute significantly to the resilience of IC innovation ecosystems. The contractual governance mechanism can effectively enhance the impact of knowledge spillovers on the resilience of innovation ecosystems in the region, and the relational governance mechanism has a positive impact on the resilience of innovation ecosystems in neighboring regions. (3) Heterogeneity results show that knowledge spillovers within the Pan-PRD region have a significant positive impact on innovation ecosystem resilience. Knowledge spillovers between regions with low innovation capacity have a double effect on innovation ecosystem resilience, and knowledge spillovers between regions with “talent highlands” have a facilitating effect on innovation ecosystem resilience. Accordingly, policy recommendations are put forward to open up channels for innovation knowledge spillover, realize effective allocation of innovation resources, and optimize the system of innovation talents.

Online bilateral matching methodology for anti-epidemic resources based on spatial transmission risk

This paper focuses on the online anti-epidemic resource allocation between demanders and suppliers considering the epidemic spatial spread. The spatial crowdsourcing with sharing platform is an effective way for anti-epidemic resource allocation, and a reasonable online matching strategy can improve the efficiency of resource utilization. The paper proposes online matching heuristic strategy (HSTRF-LRLUF strategy) and designs an online batch bilateral matching algorithm for anti-epidemic resources, which considers the impact of grid spatial aggregation and diffusion risk of emerging infectious diseases. The population distribution within grids and the commuting patterns between grids can provide decision support for selecting online matching strategies of anti-epidemic resources. A larger matching time window focusing on the spatial transmission risk (TR) of the epidemic can obtain better matching results. However, with a smaller matching time window, the decision makers can focus on spatial agglomeration risk (OR) or spatial diffusion risk (IR). The paper effectively combines the spatial crowdsourcing model with the anti-epidemic resource allocation to achieve the allocation of emergency resources to individuals. A combined anti-epidemic resource online matching heuristic strategies is designed from the spatial agglomeration risk and the spatial diffusion risk. Decision makers can dynamically adjust the online matching strategies of anti-epidemic resources by evaluating the spatial agglomeration risk, the spatial diffusion risk, and the overall spatial transmission risk based on the real-time spread of the epidemic and the supply of anti-epidemic resources.

Has Environmental Regulation Promoted the Spatial Agglomeration of Hog Production in Administrative Border Regions? Evidence from the Yangtze River Basin in China

This study finds that the livestock environmental regulation (LER) imposed by China has promoted the spatial agglomeration of hog production in the administrative border areas, thereby posing a threat to ecological sustainability. We apply the difference-in-differences-in-differences (DDD) method to a unique dataset in counties along the main stem and 38 important tributaries of the Yangtze River basin in China from 2008 to 2019. Specifically, the slaughter volume and density of hogs in the most downstream border counties of a city are 7.87% and 9.56% higher, respectively, than those of otherwise identical counties within the same municipal jurisdiction. Conversely, in the most upstream border counties, these figures decreased by 12.02% and 15.35%, respectively. Moreover, we find that the fixed capital investment in the most downstream border counties is 22.54% less than that in otherwise identical counties. These findings imply that LER has driven the agglomeration of hog production in downstream administrative border areas, inadvertently shifting the pollution burden to these regions and posing potential challenges to the effectiveness of environmental policies.

Coupling relationship and development patterns of agricultural emission reduction, carbon sequestration, and food security

In-depth exploration of the coupling relationship between agricultural emission reduction and carbon sequestration (ERCS) and food security provides an important basis for promoting sustainable low-carbon development in agriculture. This research investigates the coupling mechanisms and the current state of coordinated development of agricultural ERCS and food security using provincial panel data from 2001 to 2022 in China. The agricultural ERCS level shows an upward trend, with higher levels in the north and lower in the south; externalities are positive in the north but negative in the south. Significant dynamic interactions and spatial correlations between the agricultural ERCS and food security exist, with a local spatial agglomeration pattern of “north–south opposition”. Areas of high-high agglomeration are mainly concentrated in the north, while low-low agglomeration areas are primarily in the south. High-high agglomeration areas drive growth in transitional and low-growth areas through diffusion effects. The average coupling coordination degree of provinces increased from 0.432 to 0.473, indicating more coordinated development, and with a decreasing polarization trend. The spatial distributions of the coupling coordination degree and the relative development index are higher in the north and lower in the south, with many areas showing high adjustment, low adjustment, and high antagonism, particularly in the south where the number of high antagonistic areas has decreased. Implementing differentiated development strategies between the north and the south, using spatial agglomeration characteristics to optimize regional policies, focusing on the diffusion effects of high-coupling coordination areas to drive the development of low-growth and transitional areas, and enhancing the lagged terms can promote sustainable coordinated agricultural development.

The Impact of Rapid Urbanization on the Efficiency of Industrial Green Water Use in Urban Agglomerations around Poyang Lake

The construction of urban agglomerations around Poyang Lake is an important starting point of the strategy for the improvement of central China, but the spatial agglomeration of industry and population brings great pressure to the ecological environment. It is of great practical value to explore the impact of rapid urbanization on the water use efficiency of important ecological functional areas. Considering the undesired output of industrial production, this paper adopts the SE-SBM model to measure industrial green water use efficiency, comprehensively considers different aspects of urbanization of the urban agglomeration around Poyang Lake, empirically tests its inhibiting or boosting effect on industrial green water use and explores its spatial spillover effect with the help of a spatial metrology model. The results show that (1) the industrial green water use efficiency of urban agglomerations shows an overall upward trend, and the efficiency value of central cities is significantly higher than that of non-central cities and continues to show a state of diffusion; (2) social urbanization, environmental urbanization, and balanced urbanization can significantly improve industrial green water use efficiency, while industrial urbanization or industrialization inhibits the improvement in water use efficiency; (3) considering the spatial spillover factor, there are significant positive local effects between population urbanization and balanced urbanization, and significant positive spatial spillover effects between industrial urbanization and environmental urbanization; (4) the original model can pass the significance test by replacing the output-oriented water use efficiency with the input-oriented or non-oriented water use efficiency; the study area is extended to Jiangxi Province, and the impact of urbanization on industrial water use efficiency is basically consistent. We should adhere to the new type of urbanization that improves well-being and is friendly to the environment, rationally plan the industrial spatial pattern of urban agglomerations, adhere to the ecological and environmental threshold on undertaking industrial transfer, and promote the flow and sharing of green production factors.

Spatio-temporal Situation, Regional Differences, and Dynamic Evolution of the Distribution of Ecological Compensation in the Yellow River Basin

In the context of regional coordinated development and modernization of ecological and environmental governance capacity, the spatial collaborative governance of transboundary river basins has received extensive attention, but the ecological compensation of river basins is faced with the intertwined relationship of rights and responsibilities, and the horizontal ecological compensation mechanism has not yet been perfected. Based on the emergy ecological footprint model, the ecological compensation amount of 90 cities in the Yellow River Basin in 2007-2021 was measured, and the spatiotemporal pattern, regional differences, and dynamic evolution characteristics of distribution were explored by combining exploratory spatial data analysis, Dagum Gini coefficient, kernel density estimation, and spatial Markov chain analysis. The results showed that： ① The spatiotemporal pattern of ecological compensation in the Yellow River Basin was different, the ecological compensation amount decreased slightly in the fluctuation and increased from the northwest to southeast gradient in general, and the high-value areas were concentrated in the areas with a superior ecological background or developed economy, and some upstream cities had given up many development opportunities to maintain the ecological security of the Yellow River Basin, however, failed to obtain reasonable ecological compensation. ② A significant positive spatial agglomeration phenomenon was observed in ecological compensation and the overall spatial distribution trend was "cold in the north and hot in the south." The number of low-payment areas and low-compensation areas gradually decreased, showing a gradually shrinking agglomeration layout from the periphery to the center, whereas the number of high-payment areas and high-compensation areas continued to increase and formed a spatial evolution characteristic of the coexistence of scattered distribution and group distribution. ③ The overall regional differences in ecological compensation expanded. The over-variation density was the main source of the overall differences, and the focus on alleviating the intra-regional and inter-regional differences was located in the downstream areas. ④ A slight multi-level differentiation phenomenon was present in ecological compensation. The influence of different neighbors on the horizontal transfer of ecological compensation was quite different. This spatial spillover effect easily formed a "space club convergence" phenomenon within a certain geographical spatial range, but with the expansion of time, the probability of maintaining the original level of ecological compensation in each city decreased, and mobility gradually increased. Therefore, increasing the investment in ecological compensation in different fields, strengthening the coordinated development between regions, and giving full play to the spatial spillover effect are important ways to solve the current spatial imbalance of ecological compensation in the Yellow River Basin.

A global assemblage of tax haven clusters: profit shifting, tax dodging and money laundering

In this paper we explore a selection of tax haven clusters in the US and Europe as diverse but recognisable species of some fifty of the phenomena worldwide that have been seldom studied from an economic geography or spatial planning viewpoint. We treat the objects of study as being almost perfect expressions of the classical, canonical cluster configuration. They are: spatial agglomeration, untraded as well as market dependencies, small and medium-sized enterprise status, associative or agentic relationships, interaction with large firms inside or outside the cluster and knowledge acquisition, advisory, consultancy and knowledge translation services from research and innovation intermediaries - public and/or private. However, we think their characteristics demand further recognition of the interactive and often innovative communication networks by means of which these cluster incumbents interact globally. This is because they are set within a worldwide system that can be conceptualised as an exemplar of the well-known construct known as an ‘assemblage’ (Deleuze and Guattari, 2004a). Unlike more recent treatments from De Landa (2019) and others, we try to concentrate on the motive power of the ‘agentic’ impulses of ‘desire’ that motivate human action (e.g. regarding ‘God, Gold or Glory’ as commonly-rehearsed motivators for colonialism of various kinds). It has been more common to use ‘assemblage theory’ to analyse the interlocking ‘machinic’ elements of complex systems like power networks or laboratory interactions. To unlock the mysteries of system interlocking in our selected tax havens, which comprise Wilmington, Delaware (US), Ireland (EU) and Gibraltar (former EU, now British Overseas Territory) – post-Brexit - occupying a ‘quantum’ space ‘in which both the colonial and post-colonial find expression’ (Perera, 2021). Methodologically, we seek ‘pattern recognition’ to identify the lineaments that explain these clusters and the ‘agentic’ actors that motivate them.

A Geographically Weighted Regression–Compute Unified Device Architecture Approach to Explore the Spatial Agglomeration and Heterogeneity in Arable Land Consumption in Southwest China

Arable land loss has become a critical issue in China because of rapid urbanization, industrial expansion, and unsustainable agricultural practices. While previous studies have explored the factors contributing to this loss, they often fall short in addressing the challenges of spatial heterogeneity and large-scale dataset analysis. This research introduces an innovative approach to geographically weighted regression (GWR) for assessing arable land loss in China, effectively addressing these challenges. Focusing on Chongqing, Guizhou, and Yunnan Provinces over the past two decades, it examines spatial autocorrelation with R-squared values exceeding 0.6 and residuals. Eight factors, including environmental elements (rain, evaporation, slope, digital elevation model) and human activities (distance to city, distance to roads, population, GDP), were analyzed. By visualizing and analyzing R² spatial patterns, the results reveal a clear spatial agglomeration distribution, primarily in urban areas with industries, highly urbanized cities, and flat terrains near rivers, influenced by GDP, population, rain, and slope. The novelty of this study is that it significantly enhances GWR computational capabilities for handling extensive datasets by utilizing Compute Unified Device Architecture (CUDA) on a high-performance GPU cloud server. Simultaneously, it conducts comprehensive analyses of the GWR model’s local results through visualization and spatial autocorrelation tools, enhancing the interpretability of the GWR model. Through spatial clustering analysis of local results, this study enables targeted exploration of factors influencing arable land changes in various temporal and spatial dimensions while also evaluating the reliability of the model results.

A framework integrating affinity propagation algorithm and spatial bivariate analysis for enhanced identification and localisation of soil heavy metals pollution sources.

The accurate identification of pollutant sources and their spatial distribution is crucial for mitigating soil heavy metals (SHMs) pollution. However, the receptor model struggles to effectively categorize pollutant sources and pinpoint their locations and dispersion trends. We propose a novel comprehensive framework that combines a receptor model, random forest (RF), affinity propagation (AP) algorithm, and bivariate local indicator of spatial association (BLISA), to optimize the traditional approach for tracing SHMs sources in industrial regions. We apportioned SHMs sources using a receptor model combined with RF, while BLISA combined with AP methods were employed to accurately locate the source areas and identify their dispersion tendencies. The results revealed that SHMs originated from mixed sources of equipment manufacturing agglomeration and agricultural activities (59.0%), geological background (30.5%), and emissions from heavily-polluting industries (10.5%). The pollution sources of soil Cd and Pb were located near specific industries, showing characteristics of multi-site concurrent pollution diffusion influenced by their proximity to industrial sites. The spatial distribution of Cr, Cu, and Zn sources was concentrated in high-density urban industrial areas, transitioning from point to nonpoint sources, with diffusion patterns influenced by the spatial agglomeration effect of industries. Our enhanced framework accurately identifies the location of SHMs sources and their dispersion tendencies, thereby improving regional soil pollution management.

Spatial–Temporal Development Trends and Influencing Factors of Government Environmental Information Disclosure: Empirical Evidence Based on China’s Provincial Panel Data

Government environmental information disclosure (GEID) plays an important role in promoting the digital transformation of environmental governance, leading the concept of sustainable development, enhancing public oversight capacity, and promoting democratic decision-making governance. Using provincial panel data from China spanning from 2009 to 2021, we conducted spatial data exploratory analysis and used the dynamic spatial panel model to investigate the spatial–temporal development trends and influencing factors of GEID. The results show that (1) GEID in China exhibits significant spatial agglomeration characteristics, with an “H-H” (High-High aggregation type) agglomeration characteristic observed in three national strategic development regions: Yangtze River Delta, southeast coastal areas, and Beijing–Tianjin–Hebei region. (2) The spillover effect from the southeast coastal provinces gradually radiates to the northwest, resulting in an overall westward movement of GEID. (3) GEID exhibits a significant path-dependency feature in the temporal dimension and a “peer effect” in the spatial dimension. (4) Population size has the greatest impact on GEID. Population size, public participation, and the industrial and transportation sectors positively influence GEID improvement at the local level. However, they generate negative spillover effects to neighbouring provinces. Environmental status and the size of the Real Estate sector have no significant effect. Therefore, China should strengthen regional cooperation, narrow regional disparities, cultivate new quality productive forces, establish a government-led proactive disclosure mechanism under public supervision, and improve the level of GEID at the national level.

Spatiotemporal Evolution and Driving Forces of Tourism Economic Resilience in Chinese Provinces

This study focuses on the resilience of tourism economies in Chinese provinces, exploring their spatiotemporal evolution and driving forces as a crucial prerequisite for promoting the high-quality development of China’s tourism industry. We construct a resilience evaluation index system from four dimensions: resistance resilience, recovery resilience, reshaping resilience, and development resilience, using provincial tourism data from 2012 to 2022. The study employs Moran’s Index, kernel density estimation, and GIS technology to investigate the differentiation characteristics, spatial evolution processes, and spatial agglomeration characteristics of provincial tourism economic resilience in China. Finally, the GeoDetector model is used to analyze the driving factors. The findings are as follows: (1) Over time, most provinces and cities in China have shown varying degrees of improvement in tourism economic resilience, with different changes observed across the four dimensions. (2) Spatially, significant differences exist between provinces, with better resilience in the east than in the west and in the south than in the north. (3) Regionally, while no polarization is observed, there is a distinct differentiation between high and low-value areas. (4) Regional linkages indicate the presence of interregional associations in China’s tourism economic resilience, with non-uniform distribution of cold and hot spots. (5) Key driving factors include per capita railway mileage, domestic tourism revenue, the number of travel agencies, and the number of employees in accommodation and catering. Under the backdrop of rapid tourism economic development, improving infrastructure construction and enhancing the comprehensive strength of the tourism industry is vital for boosting tourism economic resilience.