Evolution Of Spatial Patterns Research Articles

Exploring the Driving Factors of Land Use Change and Spatial Distribution in Coastal Cities: A Case Study of Xiamen City

This study focuses on the coastal city of Xiamen, examining the factors and driving mechanisms influencing land use changes and spatial patterns. Spatial logistic regression and Statistical Package for the Social Sciences (SPSS) software were employed using grid data with a resolution of 100m to analyze the spatial relationships between six driving factors (such as elevation and slope) and five land use types within the study area. Regression models were established for each factor, and all Relative Operating Characteristic (ROC) tests were passed. Based on the results of the logistic regression analysis, land use changes and spatial distribution were simulated using the updated Conversion of Land Use and its Effects (CLUE) model so as to validate the driving mechanisms. The findings indicate that the six driving factors effectively explain the spatial patterns of land use in the study area. The distance to the coastline is the primary influencing factor in the evolution of spatial patterns, particularly impacting built-up land and farmland, while for forest land, slope is the main factor affecting the spatial distribution. The simulation and accuracy analysis revealed an overall simulation accuracy ranging from 73% to 90.1%, demonstrating that the selected driving factors have effective explanatory power for the spatial distribution of land use. Thus, this study’s results provide valuable insights into the complexity of land use changes and serve as a reference for relevant departments in land use management and planning.

Unveiling geographical patterns of hierarchy in the Greek labor market network: toward a multilayer “status-polus” model

Abstract This article develops a comprehensive framework for understanding labor markets’ spatial configuration and development dynamics, across multiple spatial scales. It applies to the Greek Labor Market Network and delineates five geographical zones of topological similarity, proposing a “status-polus” model capturing the coexistence between spatial development patterns, structural units, administrative scale, intermodality, and their underlying economic geography theories, by distance. The analysis reveals hierarchical structural variations, a transformation from continuous to point spatial patterns; a reduction and escalation of structural units; a U-shaped rule describing the hubs participation; and the challenges faced by insular regions to integrate this labor market.

Exploring the coupling coordination relationship and obstacle factors of rural revitalization, new-type urbanization, and digital economy in China.

The digital economy injects new vitality into rural revitalization and new-type urbanization to achieve rural industrial transformation, while the countryside and the city provide the soil for the development of the digital economy. This research establishes the rural revitalization (RR), new-type urbanization (NU), and digital economy (DE) system and uses the coupled coordination degree (CCD) model and obstacle degree (OD) model to study the spatiotemporal evolution characteristics and obstacle factors of the composite system in China from 2011 to 2021. The result showed that: (1) the comprehensive development level of the composite systems is on an upward trend year by year, but still shows a low-quality state; (2) the CCD of China's provinces shows a spatial evolution pattern of high in the east and low in the west; (3) The obstacle factors of the RR, NU, DE subsystem are mainly involved the number of rural doctors and health workers, local financial income per capita and science and technology expenditure, and the digital finance coverage breadth index. These results suggested that Strengthening the synergy between China's urban-rural integrated development and digital construction in the future hinges upon providing valuable decision-making insights to facilitate the pursuit of regionally differentiated development and the achievement of sustainable development goals.

Research on the evolutionary characteristics and mechanism of production-living-ecological space in Shanxi Province, China

An investigation of the evolutionary characteristics and internal driving mechanisms of territorial space since the reform and opening up is essential. The study will guide the orderly development and rational layout of territorial space, as well as achievement transformation and high-quality development in Shanxi Province. We used land use data from 1980 to 2020, which was divided into four periods, to examine the changes in production-living-ecological spatial pattern in Shanxi Province. Various methods, including the territorial spatial transfer matrix, standard deviation ellipse and spatial autocorrelation, were employed to analyse the evolution of the territorial spatial pattern. Applying GeoDetector as the primary tool, we conduct research on the mechanisms underlying the evolution of this spatial pattern. The results indicated that Shanxi Province exhibits distinct differentiation characteristics in both the horizontal and vertical spatial dimensions. Over the 40-year period from 1980 to 2020, the territorial spatial pattern of Shanxi Province transitioned from gradual change to drastic change to moderate change. The production space (PS) and ecological space (ES) decreased, while the living space (LS) significantly increased. The territorial spatial pattern of Shanxi Province exhibited a northeast‒southwest distribution pattern, and the changes in the centre of gravity of the production-living-ecological spaces varied in direction. The spatial distribution of land in Shanxi Province is influenced by both natural factors and human activities, leading to changes in its territorial pattern over time. The primary catalyst for the development of production space (PS) is grain production, while the major determinants of the development of living space (LS) are the overall gross domestic product (GDP) and public financial expenditure. Thus, topography greatly influences ecological space (ES).

Особенности пространственного и экономико-демографического развития китайского города Хэйхэ

The trend for high-quality development in various areas of Chinese society is spreading to border areas under the modern border management model that combines security and economic empowerment. The leading role is given to the economic growth of cities on the border and attracting residents from internal Chinese regions to live in the border area. The purpose of the work is to determine the influence of spatial, economic and demographic factors on the development of the territory of the northeastern city of Heihe, bordering Russia, within the framework of the updated strategy for regional coordinated development and the strategy for transforming border areas. The study used a systematic method, which made it possible to identify patterns of spatial development of the border city of Heihe, and a statistical method, which contributed to the consideration of quantitative characteristics of the economic and demographic development of Heihe. The article draws attention to the fact that the spatial and economic development of border Heihe is based on the formation of an innovative industrial center, a modernized agricultural base, and a territory of ‘openness’ in Northeast Asia with a developed transport infrastructure. It is substantiated that the revival of the city of Heihe is due to transformations of the agricultural complex and mining industry with the introduction of high-tech innovations. The leading role is given to Chinese-Russian cooperation, which is expanding every year. All strategic projects of Heihe depend on the effective solution of such demographic problems as the reduction in the city's population, the outflow of population to other regions and the aging population. The materials of the article can be used by Russian authorities in the development of territories bordering China, and also serve as the basis for further research on this topic.

Spatial Pattern Evolution and Influencing Factors of Foreign Star-Rated Hotels in Chinese Cities

Spatial distribution is a critical factor influencing the success or failure of hotel management. This study examines the spatial distribution patterns of foreign star-rated hotels in China from 2000 to 2015 based on 27 typical city cases, using global and local spatial autocorrelation methods within GIS spatial analysis. The research explores the evolution of these patterns, analyzes key characteristics, and combines these insights with a stepwise regression method. Pearson correlation analysis is used to identify factors that influence the evolution of the spatial pattern. This study reveals that, first, the Z-value of global spatial autocorrelation of foreign star-rated hotels in China decreases from 2.38 to 1.63, indicating that the spatial distribution of foreign star-rated hotels in China has shifted from imbalanced to balanced, transitioning from economically developed regions such as areas with overseas Chinese populations, provincial capitals, and municipalities directly under central government control, toward tourist cities. Second, star-rated hotels hold a critical position within the spatial pattern, highlighting their central role in shaping the hospitality landscape. Third, the spatial distribution of foreign star-rated hotels is primarily influenced by the number of inbound tourists, followed by the presence of scenic spots rated 4A and above. The influence of other factors is found to be less significant. Fourth, the correlation coefficient between tourism demand and foreign star-rated hotels increased by 0.004, whereas the correlation coefficient between tourism supply and foreign star-rated hotels decreased by 0.036, indicating that market factors are playing an increasingly important role in shaping the evolution of foreign star-rated hotels in China, reflecting broader market dynamics. This study provides practical guidance for local Chinese hotels facing competition from foreign-funded establishments and offers theoretical insight into the strategic implementation of transnational operations. It points out the expansion direction of local Chinese hotels across different developmental stages.

Evolution of Spatial Patterns and Influencing Factors of Sports Tourism Development in Yangtze River Delta Region

The development of sports tourism is of great significance in promoting regional cultural exchanges, boosting economic development, accelerating the construction of national fitness, promoting the development of the sports industry, and advancing ecological environmental protection. With the integrated application of exploratory spatial data analysis and gray correlation analysis model, this article takes the Yangtze River Delta region as the research object and comprehensively explores the pattern evolution characteristics and influencing factors of its sports tourism development space. The study found that (1) the total amount of sports tourism resources in the Yangtze River Delta region has accumulated in fluctuation and iteration, and the types are constantly enriched; (2) the spatial pattern of sports tourism resources in the Yangtze River Delta region shows the evolution characteristics of “agglomeration–dispersion–agglomeration” over time; (3) the spatial evolution hot spots of sports tourism resources in the Yangtze River Delta region have experienced the following characteristics “unipolar-multipolar-area-wide-suburban”, and the center of gravity of spatial evolution has experienced the process of east–west linear development and north–south diffusion; and (4) the spatial development of sports tourism in the Yangtze River Delta region has experienced the process of “policy + sports + transportation” drive, “economic + social” drive, economic drive, and total-factor drive in different periods. The results of the study can help optimize the allocation of sports and tourism resources in the Yangtze River Delta region, further realize the in-depth integration and development of sports, culture, and tourism, and enhance the regional economy and public service level.

Characteristics of Spatial–Temporal Evolution of Sustainable Intensification of Cultivated Land Use and Analysis of Influencing Factors in China, 2001–2020

The rapid growth of the global population, the acceleration of the urbanization process, and the demands of economic development, place enormous pressure on scarce land resources. Cultivated land use presents a series of problems, hindering its socioeconomic and ecological sustainability. The sustainable intensification of cultivated land use (SICLU) is a development model designed to maximize land use efficiency, while minimizing environmental pollution. It is considered to be an efficient method to achieve three aspects of sustainable goals, namely in regard to society, the economy, and ecology, simultaneously. This approach has significant theoretical and practical implications for China’s food security and ecological safety. This study incorporates the “agricultural carbon emissions” indicator into the indicator evaluation system. Using the super-efficiency SBM model, we estimate the SICLU levels in China from 2001 to 2020. ArcGIS and the Dagum Gini coefficient decomposition model are employed to explore the temporal and spatial evolution characteristics and non-equilibrium spatial dynamics of SICLU in China. Finally, the Tobit regression model is used to reveal the driving factors. The results show the following: (1) Since 2003, China’s SICLU levels demonstrate an overall ascent amid fluctuations, sustaining a relatively high average annual level of 0.945. (2) In terms of spatial evolution patterns, China’s SICLU levels demonstrate significant spatial disparities, with distinct differences among the four major regions. Regions with similar SICLU levels show a certain degree of spatial adjacency. (3) There are significant regional disparities in China’s SICLU levels, which overall exhibit a declining trend. The differences between regions are the primary source of spatial variation, followed by hypervariable density and intra-regional disparities. (4) The regional industrial structure, the level of agricultural modernization, the agricultural cropping structure, and the per capita sown area, positively influence the enhancement of SICLU levels in China. Throughout the study period, the SICLU levels in China continuously improved and the overall regional disparities diminished. However, significant inter-regional imbalances persist, necessitating tailored optimization measures, based on local conditions. Establishing a coordinated mechanism for orderly and synergistic regional development is crucial, in order to provide references to decision-makers to promote the rational use of arable land in China.

The impact of agricultural production agglomeration on agricultural economic resilience: based on spatial spillover and threshold effect test

This study focuses on the role of agricultural production agglomeration in strengthening agricultural economic resilience, exploring the threshold effect of agricultural technological innovation level and the spatial spillover effect of agricultural production agglomeration on agricultural economic resilience. We conducted research across 31 provinces (including autonomous regions and municipalities) in China from 2007 to 2022. By constructing the evaluation index system of agricultural economic resilience, the entropy value method is used to measure the value of agricultural economic resilience, and then kernel density estimation and spatial econometrics model, threshold regression model are used to analyze the relationship between agricultural production agglomeration, agricultural technological innovation and agricultural economic resilience. (1) The analysis of the spatiotemporal evolution trend shows that the overall level of China’s agricultural economic resilience continued to rise, and presented a spatial development pattern of “high in the east and low in the west.” The overall level of agricultural production agglomeration in China shows a trend of first rising and then falling, among which the level of agricultural production agglomeration in the central region is significantly higher than that in the northwest and southeast regions. (2) The spatial Durbin model shows that agricultural production agglomeration can not only effectively improve the level of local agricultural economic resilience, but also have a positive impact on neighboring agriculture economic resilience produces positive spatial spillover effects. (3) Agricultural production agglomeration can improve the level of agricultural economic resilience by promoting agricultural social service. (4) The impact of agricultural production agglomeration on agricultural economic resilience shows great differences in different geographical regions. Among them, agricultural production agglomeration in the central region has a significant positive impact on the agricultural economic resilience of both local and adjacent areas. (5) The threshold effect model shows that the impact of agricultural production agglomeration on agricultural economic resilience has significant nonlinear characteristics, and its impact shows an increasing marginal effect as the level of agricultural technological innovation increases. To address this, policymakers should reinforce agricultural cluster construction, boost innovation capacity and treasure spillover effects between regions. These insights provide valuable direction for policymakers in crafting effective measures to enhance agricultural economic resilience.

A Physics-Informed Auto-Learning Framework for Developing Stochastic Conceptual Models for ENSO Diversity

Abstract Understanding El Niño–Southern Oscillation (ENSO) dynamics has tremendously improved over the past few decades. The ENSO diversity in spatial pattern, peak intensity, and temporal evolution is, however, still poorly represented in conceptual ENSO models. In this paper, a physics-informed auto-learning framework is applied to derive ENSO stochastic conceptual models with varying degrees of freedom. The framework is computationally efficient and easy to apply. Once the state vector of the target model is set, causal inference is exploited to build the right-hand side of the equations based on a mathematical function library. Fundamentally different from standard nonlinear regression, the auto-learning framework provides a parsimonious model by retaining only terms that improve the dynamical consistency with observations. It can also identify crucial latent variables and provide physical explanations. This methodology successfully reconstructs the equations of a realistic six-dimensional reference ENSO model based on the recharge oscillator theory from its data. A hierarchy of lower-dimensional models is derived, and their representation of ENSO (including its diversity) is systematically assessed. The minimum model that represents ENSO diversity is four-dimensional, with three interannual variables describing the western Pacific thermocline depth, the eastern and central Pacific sea surface temperatures (SSTs), and one intraseasonal variable for westerly wind events. Without the intraseasonal variable, the resulting three-dimensional model underestimates extreme events and is too regular. A limited number of weak nonlinearities in the model are essential in reproducing the observed extreme El Niño events and the observed nonlinear relationship between eastern and western Pacific SSTs. Significance Statement This study develops a physics-informed auto-learning approach to improve the modeling and understanding of El Niño–Southern Oscillation (ENSO), a major climate phenomenon influencing global weather and climate. The auto-learning framework explores the causality between key processes to systematically produce stochastic conceptual models with different climate factors that simulate the diversity of observed ENSO events. The key finding is that the minimal model for characterizing the ENSO diversity (with the least number of climate factors) is a four-variable model capturing thermocline depth, sea surface temperatures, and wind bursts that can reproduce intensity and spatial pattern variation. This advancement provides an interpretable tool to identify the minimum sufficient processes governing ENSO behavior for improved predictability.

Comprehensive correlation analysis enabled neural network prediction of heat and mass transfer during gas hydrate decomposition

A significant amount of natural gas is stored in a form of hydrate. Yet commercial exploitation of natural gas hydrate remains quite challenging due to limited comprehension of internal heat and mass transfer processes. In this work, a numerical model is developed to describe heat and mass transfer during methane hydrate decomposition and to provide sufficient data for neural network modeling. Based on the numerical model, the temporal and spatial evolution patterns of several decomposition characteristics, including multiphase saturation, temperature, gas pressure, and gas velocity, are elucidated. More importantly, the effects of 19 types of variables related to various boundary conditions, physical properties, and initial conditions are comprehensively investigated. A comprehensive correlation map between these variables and four key heat and mass transfer parameters reveals 41 positive and 35 negative correlations. Driven by abundant simulation data, an artificial neural network model is then developed to predict the heat and mass transfer parameters. As validated, the neural network model shows satisfactory efficiency and accuracy, achieving relative errors below 2% in the prediction of various heat and mass transfer parameters. This study provides a comprehensive theoretical guide and a useful method for understanding, regulating, and optimizing the natural gas hydrate exploitation.

Manufacturing sector spatial pattern evolution and its relationship with regional economic differences: Evidence from Jiangsu, China.

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.

Monitoring Activity of the Subglacial Lake Mercer Based on CryoSat-2 and ICESat-2 Altimetry Data from 2011 to 2023

Abstract. Active subglacial lakes are capable of exchanging and transporting water through water flow paths, thus having an important impact on ice sheet movement and even on the regional mass balance. The use of satellite altimetry can indirectly reveal internal subglacial hydrological activity through direct surface elevation observation, which plays an important role in the study of the evolution of active subglacial lakes. This paper monitors and analyzes the activity of the subglacial lake Mercer (SLM) in the Whillans and Mercer Ice Stream (WIS/MIS), using data from the CryoSat-2 satellite radar altimetry and ICESat-2 satellite laser altimetry. We adopt the differential DEM model and the repeat orbit model based on their respective data characteristics. Finally, we reveal the temporal and spatial evolution patterns of SLM during 2011-2023, and construct the elevation anomaly time series. The results show that the central and western parts of SLM were the areas with more significant elevation anomalies, and there were three significant fill-drain cycles during the time periods studied in this research, with the elevation anomaly range reaching about 8 m and 4 m for the first and second cycles, respectively, and the lake is currently in the third significant draining phase. Furthermore, the elevation anomalies obtained during the mission overlap time of CryoSat-2 and ICESat-2 data are in good agreement, which confirms the accuracy of our method.

Study on the Evolution of Spatial and Temporal Patterns of Carbon Emissions from Land Use in a River Basin

Land-use change significantly contributes to carbon emission. Analyzing this relationship fosters exploration of low-carbon, efficient land-use patterns at regional levels. Using ArcGIS 10.5 and the PLUS model, this study investigated land transfer trends across six counties and one district in the Malian River Basin between 2000 and 2020. It quantified carbon emissions from land use and performed spatial distribution analysis using land-use and socio-economic data. The study demonstrates the following: (1) Between 2010 and 2020, significant land-use changes occurred in the Malian River Basin with 72,919.49 km2 of land undergoing transformation. Notably, the farmland-to-forest and grassland conversion project in Qingyang City was a major factor contributing to the shift from arable land to forest and grassland. (2) Natural factors influencing land conversion in the Loess Plateau region primarily include precipitation and elevation. Conversely, social factors such as population density, road networks, and local government establishments in districts and counties are pivotal in driving land-use changes within the Malian River Basin. (3) Carbon emissions vary significantly among different land-use types, with building land, cropland, unutilized land, watershed, grassland, and forest land showing descending emissions. The rapid expansion of building land notably increases carbon emissions in the study region, while forest land, a significant carbon sink, absorbs approximately 88% of total carbon emissions. (4) Districts and counties in the study area exhibit varying levels of carbon emissions, with Ning County, Xifeng District, Huan County, Qingcheng County, Zhengning County, Heshui County, and Huachi County listed in descending order. Regions with higher carbon emissions typically host abundant energy resources and significant energy production and consumption activities. Variations in carbon emission levels are largely influenced by resource availability and development priorities. Variations in resource levels and developmental focus are pivotal in explaining differences in carbon emission levels. Thus, it is crucial to explore the dynamic interplay between land-use carbon emission efficiency and land evolution in the Malian River Basin. This research will support ecological management and sustainable economic development in the Yellow River Basin, while also contributing to the achievement of the “double carbon” goals.

Temporal and Spatial Evolution Patterns of 8-Day 30m Evapotranspiration in the Yellow River Basin of Inner Mongolia and Its Response to Land Cover Changes

Temporal and Spatial Evolution Patterns of 8-Day 30m Evapotranspiration in the Yellow River Basin of Inner Mongolia and Its Response to Land Cover Changes

Impact of Digital Inclusive Finance and Environmental Regulation on Eco‐Efficiency of Farmland Utilization in China

ABSTRACTThe farmland pollution has threatened the lifeblood of people's livelihoods through the soil‐to‐crop system, and the resulting food safety risks are becoming more and more pronounced. Digital inclusive finance (DIF) can provide convenient financial services for the eco‐transformation of farmland utilization. This study analyzed the spatial evolution pattern of eco‐efficiency of farmland utilization (EEFU) and DIF, empirically tested the impact of DIF on EEFU, and discussed the impact of DIF on EEFU with changes in the level of environmental regulatory intensity based on panel data from 2011 to 2022 in China. The results found that the development of EEFU and DIF is increasing, and the polarization phenomenon is constantly easing, but the absolute differences between different provinces in China have an expanding trend. DIF can improve the EEFU, and this promotion effect is more pronounced in eastern and central China, non‐grain‐producing areas, and areas with low levels of financial development. DIF will not be able to improve EEFU until environmental regulation has reached a certain level. China should fully leverage the positive role of DIF and environmental regulations in promoting the EEFU, provide more differentiated financial products, financial tools, and financial models for the eco‐transformation of farmland utilization, and explore a path for eco‐transformation of farmland utilization that complements environmental regulations and development of market‐inclusive finance.

A Coupled CA Urban Development Model (UDM) and Urban Fabric Generator (UFG) for the Assessment of Climate Hazards on Future Urban Development

Abstract. Cellular Automata (CA) spatial models have become a de facto means by which to simulate future scenarios of land use activity, particularly urban development. Increasingly, such models are being employed within large scale climate impact, adaption and resilience studies to provide future scenarios of land use change and urban development, facilitating the calculation of the economic impact of future climate hazards and development land use adaption options. However, many spatial CA models only provide information on whether land has undergone a transition from one use/activity to a new one. In the case of urban development, they are unable to provide information on the spatial pattern of new development at an intra-cell level of spatial fidelity. However, in many cases such information is important as the spatial impacts of hazards and the adaption and resilience of new urban development will be dependent on the precise spatial configuration of buildings, roads and urban green space. In this paper an Urban Fabric Generator (UFG) is presented that takes the outputs of an Urban Development Model (UDM) and simulates plausible spatial configurations of buildings, roads and urban green space. The utility of the UFG is demonstrated via two flooding case studies, where generated spatial patterns of urban form are used to parameterise a hydrodynamic pluvial flood model that evaluates the impact of future climate driven rainfall on property damage and the utility of infrastructure investment with regards to improving surface water flood resilience.

Analysis of Spatiotemporal Predictions and Drivers of Carbon Storage in the Pearl River Delta Urban Agglomeration via the PLUS-InVEST-GeoDetector Model

Land use and land cover change (LUCC) significantly influences the dynamics of carbon storage in thin terrestrial ecosystems. Investigating the interplay between land use alterations and carbon sequestration is crucial for refining regional land use configurations, sustaining the regional carbon balance, and augmenting regional carbon storage. Using land use data from the Pearl River Delta Urban Agglomeration (PRDUA) from 2010 to 2020, this study employed PLUS-InVEST models to analyze the spatiotemporal dynamics of land use and carbon storage. Projections for the years 2030, 2040, and 2050 were performed under three distinct developmental scenarios, namely, natural development (ND), city priority development (CPD), and ecological protection development (EPD), to forecast changes in land use and carbon storage. The geographic detector model was leveraged to dissect the determinants of the spatial and temporal variability of carbon storage, offering pertinent recommendations. The results showed that (1) during 2010–2020, the carbon storage in the PRDUA showed a decreasing trend, with a total decrease of 9.52 × 106 Mg, and the spatial distribution of carbon density in the urban agglomeration was imbalanced and showed an overall trend in increasing from the center to the periphery. (2) Clear differences in carbon storage were observed among the three development scenarios of the PRDUA between 2030 and 2050. Only the EPD scenario achieved an increase in carbon storage of 1.10 × 106 Mg, and it was the scenario with the greatest potential for carbon sequestration. (3) Among the drivers of the evolution of spatial land use patterns, population, the normalized difference vegetation index (NDVI), and distance to the railway had the greatest influence on LUCC. (4) The annual average temperature, annual average rainfall, and GDP exerted a significant influence on the spatiotemporal dynamics of carbon storage in the PRDUA, and the interactions between the 15 drivers and changes in carbon storage predominantly manifested as nonlinear and double-factor enhancements. The results provide a theoretical basis for future spatial planning and achieving carbon neutrality in the PRDUA.

Analysis of the spatial distribution patterns of education infrastructure development (A case study of 33 regencies/city in North Sumatra Province)

This study explores the spatial distribution pattern of educational infrastructure development across districts and cities in North Sumatra, identifying significant disparities between urban and rural areas. The study aims to: (1) determine the distribution of educational development across districts and cities, (2) analyze global spatial autocorrelation, and (3) identify priority locations for educational development policies in North Sumatra Province. The methodology includes quantile analysis, Moran’s Global Index, and Local Indicators of Spatial Autocorrelation (LISA) using GeoDa software to address spatial autocorrelation. The results indicate that there are nine areas with a low School Participation Rate Index (SPRI), eleven areas with a low School Facilities and Infrastructure Index (SFII), and eleven areas with a low Regional Education Index (REI). Spatial autocorrelation analysis reveals that SFII shows positive spatial autocorrelation, while SPRI and REI exhibit negative spatial autocorrelation, indicating a high level of inequality between regions. Labuhan Batu Selatan and Labuhan Batu are identified as priorities for the provincial government in overseeing educational development policies.

Detecting Driving Factors from Spatial Spectrums Perspective: A Multilevel Analysis of Homestay Industry in Heterogeneous Heritage Destinations

ABSTRACT Homestay represents a tourism product constrained by space and dependent on regional context. While academic attention has grown toward studying homestay distribution patterns in urban areas, heritage destinations have received limited focus. This study establishes a two-scale analytical framework to explore the spatial evolution patterns and formation mechanism of the homestay industry in diverse heritage destinations, identifying the main factors influencing spatial heterogeneity through geographic detectors. Our findings elucidate several key insights: (a) a consistent annual growth trend, alongside a distinct clustering and spatial autocorrelation in the distribution of homestays across the three types of heritage destinations – natural, cultural, and mixed World Heritage sites; (b) the predominant influence of topography, tourism development levels, and transportation infrastructure in shaping the spatial differentiation of homestays; and (c) socioeconomic and tourism development factors emerging as the primary drivers of this differentiation, while the transportation factor, particularly road mileage, exhibits a heightened significance in natural heritage sites. This study offers insights for planning, marketing, and managing tourism heritage destinations.