Search Results (762)

Understanding the forces that shape farmland rental prices in major agricultural regions such as the U.S. Corn Belt is essential for evaluating the economic and environmental resilience of agricultural regions. This study develops an integrated framework that combines spatial modelling with uncertainty-aware spatial analysis to examine how macroeconomic conditions influence rental dynamics across the core Corn Belt. Using geographically weighted regression, the analysis captures spatial variation in the sensitivity of rental prices to oil prices, interest rates, and economic activity, revealing substantial geographic heterogeneity in macroeconomic exposure. The results reveal pronounced spatial heterogeneity in rental price responses, with geographically weighted models consistently outperforming global linear specifications. Despite strong spatial variation in rental sensitivities, neither prediction uncertainty nor maize yield volatility displays a clear regional pattern, indicating that production stability and model reliability are highly localised. By linking spatially varying rent sensitivities with indicators of economic pressure and production instability, this study provides new insights into agricultural sustainability risk. The findings highlight the importance of place-based policy and region-specific risk management under increasing macroeconomic volatility. Full article

The built environment, serving as the core spatial vehicle for human production and daily activities, constitutes a vital foundation for achieving sustainable urban development and high-quality renewal. However, amidst rapid urbanisation, certain areas continue to grapple with issues such as ageing infrastructure, inefficient land use, and imbalanced spatial structures, hindering the establishment of sustainable urban forms. Consequently, identifying the evolutionary characteristics and influencing mechanisms of the built environment from the perspective of spatial heterogeneity holds critical significance for advancing refined governance and sustainable planning. Taking Kwun Tong District in Hong Kong as a case study, this research constructs an Adaptive-Entropy Multi-Scale Geographically Weighted Regression (MGWR) analytical framework. This systematically reveals the spatial distribution patterns of built environment elements and their multi-scale spatial heterogeneity characteristics. The findings indicate the following: (1) The built environment exhibits significant spatial differentiation and clustering structures across different scales, reflecting complex spatial processes driven by multiple interacting factors (2) Compared with the OLS model at a 1000 m scale and the GWR model at a 500 m scale, the Adaptive-Entropy MGWR model at a 100 m scale demonstrated superior fitting accuracy and explanatory power. It more effectively captured local structural variations and scale effects, thereby offering greater guidance value for sustainable planning. Building upon these findings, this study further proposes pathway recommendations for urban renewal and built environment optimisation in Kwun Tong District, offering an analytical approach and technical framework that may serve as a reference for sustainable development in high-density cities. Full article

Understanding extreme precipitation (EP) evolution is crucial for global climate adaptation and hazardous disasters prevention. However, spatial non-stationarity of urbanization relationships with EP variations has been rarely discussed in a complex topographic context. Taking the city Liuzhou in China as the example, this study separately quantified the evolution of EP intensity, magnitude, duration, and frequency on different temporal scales with Innovative Trend Analysis (ITA). Based on a finer spatial (5 km grid) scale and multiple temporal (daily, daytime, nighttime, and 14 h) scale analyses, it innovatively identified spatially varying urbanization effects on EP with more details in different elevations. Our results indicate that: (1) from 2009 to 2023, EP events became more intense, persistent, and frequent, particularly for higher-grade EPs and in the steeper north of Liuzhou; (2) despite the globally negative correlations, spatial correlations between comprehensive urbanization (CUB) and each EP index on individual temporal scales were still explicitly categorized into four types using LISA maps—high-high, high-low, low-low, and low-high; (3) Geographically Weighted Regression (GWR) was demonstrated to precisely explain the response of most EP characteristics to multiple manifestation of urbanization with respect to population (POP), economy (GDP), and urban area (URP) expansion (adjusted R²: 0.5–0.8). The predictive accuracy of GWR on urbanization and EPs was spatially non-stationary and variable with temporal scales. The local influential strength and direction varied significantly with elevations. The most significant and positive influences of three urbanization predictors on EPs occurred at different elevation grades, respectively. Compared with POP and GDP, urban area percent (URP) was indicated to positively relate to EP changes in more areas of Liuzhou. The spatial and quantitative relationships between urbanization and EPs can help to guide effective urban planning and location-specific management of flood risks. Full article

Research on the characteristics and functions of ancient Juntun (military tillage) has paid limited attention to the distribution patterns and influencing factors of Juntun in specific regions. This study employs a comprehensive approach integrating GIS technology and the multi-scale geographically weighted regression (MGWR) model to quantitatively analyze the spatial distribution characteristics and influencing factors of Ming Dynasty Juntun in Fujian. The study reveals that Juntun were primarily located in flat areas near water systems, while exhibiting a U-shaped distribution pattern away from garrison forts, reflecting a synergy between agricultural foundations and military defense. MGWR analysis further indicates that fiscal and taxation factors had a stronger influence on their distribution than arable land resources, highlighting their non-purely agriculturally driven nature. This research provides a quantitative basis for understanding the organizational logic and spatial strategy of ancient military settlements, offering valuable insights for the conservation and study of military heritage. Full article

The Songliao River Basin, as a core area of multicultural integration in Northeast China, still lacks systematic research on the spatial distribution of religious sites and their influencing factors. This study integrates spatial pattern analysis methods (kernel density, standard deviation ellipse, imbalance index) and spatial econometric models (spatial error model, geographically weighted regression model) to explore the spatial distribution characteristics of 1288 religious sites in the basin and the influencing mechanisms of natural, socio-economic, and cultural factors. Results: (1) Religious sites in the basin show a clustered distribution of “higher density in the south than the north, one main cluster and two sub-cores”, with a northeast–southwest trend and poor balance at the prefectural-city scale. (2) Cultural factors are the core driver; cultural memory and social capital in traditional villages promote the agglomeration of religious sites and shape the “one village, multiple temples” pattern. Intangible Cultural Heritage, Major Historical and Cultural Sites Protected at the National Level, and religious sites form a tripartite symbiotic spatial relationship of “cultural practice—spatial carrier—institutional identity”; natural factors lay the basic pattern of spatial distribution. (3) Policy factors have a significant impact: A-rated Tourist Attractions and Performing Arts Venues show a positive effect, while museums exhibit spatial inhibition due to functional competition. (4) Economic, Population, and Transportation factors had no statistically significant effects, indicating that their spatial distribution is driven primarily by endogenous cultural mechanisms rather than external economic drivers. This study fills the gap in research on the spatial distribution of religious sites in Northeast China. By integrating multiple methods, a quantitative demonstration of the coupling mechanism of multiple factors was conducted, providing scientific support for religious cultural heritage protection policies and sustainable development strategies amid rapid urbanization. Full article

The impact of the built environment on urban vitality is multifaceted, yet a holistic understanding that simultaneously considers its scale dependence, spatial heterogeneity, and interactive mechanisms remains limited. To unravel these multi-scalar mechanisms, this study develops an integrated analytical framework. Taking Xi’an, China, as a case study, we first construct a multidimensional built environment indicator system grounded in Jane Jacobs’ theory of vitality. Empirically, we employ the Optimal Parameters-based GeoDetector (OPGD) to objectively identify the optimal spatial scale and detect non-linear and interaction effects. Meanwhile, the Multiscale Geographically Weighted Regression (MGWR) model is used to delineate spatial heterogeneity. Our findings systematically unravel the complex mechanisms: (1) The optimal analysis scale is identified as a 2 km grid; (2) All elements significantly influence vitality, but through distinct linear or non-linear pathways; (3) The effects of attraction density, road network structure, and bus stop density exhibit significant spatial heterogeneity; and (4) Third place density and population density act as key catalysts, non-linearly enhancing the effects of other elements. This research presents a synthesized perspective and nuanced evidence for precision urban regeneration, demonstrating the necessity of integrating scale, heterogeneity, and interaction to understand the drivers of urban vitality. Full article

The last kilometer connection problem of metro transit stations is the core factor to measure the connection efficiency and service quality. Establishing the spatiotemporal distribution pattern of the connection distance is conducive to clarifying the interaction mechanism between bike-sharing connections and urban space. This study focuses on the travel behavior of shared bicycle users accessing metro stations, aiming to reveal the access distance decay patterns and their relationship with influence factors. Finally, the random forest algorithm was used to explore the nonlinear relationship between the influencing factors and the connection decay distance, and to clarify the importance of the factors. Multiple linear regression was applied to examine the linear correlation between the distance decay coefficient and the factors influence. The geographically weighted regression was further employed to explore spatial variations in their effects. Finally, the random forest algorithm was used to rank the importance of the impact factors. The results indicate that proximity distance to metro stations, proximity distance to bus stops, and the number of bus routes serving the station area have significant negative correlations with the distance decay coefficient. Significant spatial heterogeneity was observed in the influence of each factor on the distance decay coefficient, based on the geographically weighted regression analysis. With a high goodness-of-fit (R² = 0.8032), the Random Forest regression model furthermore quantified the relative importance of each factor influencing the distance decay coefficient. The findings can be directly applied to optimize the layout of shared bicycle parking, metro access facilities planning, and multi-modal transportation system design. Full article

China’s higher education system is the largest globally but faces significant spatial imbalance issues. While studies have examined the spatial distribution of universities, long-term dynamic analysis, quantitative exploration of influencing factors, and investigation of spatial heterogeneity are lacking. This study investigates the spatiotemporal evolution of China’s regular higher education institutions (HEIs) from 1952 to 2023 by using ArcGIS spatial analysis and integrating the Geographical Detector and Multi-scale Geographically Weighted Regression (MGWR) models. Findings reveal that (1) the spatial distribution of China’s HEIs has become increasingly clustered, transitioning from a “point-like” to a “network-like” and finally to a “surface-like” pattern, with its center shifting southwestward—this evolution reflects the gradual formation of a spatially sustainable layout that adapts to regional development needs. (2) Multiple interacting factors influence distribution—including the number of full-time faculty, regional GDP, national universities’ presence during the Republic of China era, and fiscal expenditure on education—with significant variations in their explanatory power. Regional population size also exerts a notable influence. (3) The impact of these factors exhibits significant spatial heterogeneity, with pronounced local imbalances. Thus, multi-scale processes operating at different geographical levels have shaped HEIs’ spatial pattern and addressing this heterogeneity is a key prerequisite for achieving sustainable and equitable development of higher education. These findings provide critical insights for optimizing higher education resource allocation, promoting balanced regional development, and advancing the construction of a high-quality education system in China. Full article

The spatiotemporal evolution of ecosystem services has a profound influence on the fragile eco-environment in Inner Mongolia and the arid/semi-arid and the ecological barrier regions of Northern China; in particular, the small-scale and high-value land variables may lead to large eco-environment effects through altering the ecosystem services, which is still unclear in this vulnerable area. The differential driving mechanism of both human activities and natural factors on ecosystem services also needs to be revealed. To solve this scientific issue, the synergistic methodology of spatial analysis technology, the improved ecosystem service assessment method, flow gain/loss model, global/local Moran’s I approach, and the Geographically and Temporally Weighted Regression (GTWR) model were applied. Our main results are as follows: remote sensing monitoring showed that the land changes featured a persistent expansion of cropland and built-up areas, with a decline in grassland and wetland, along the east–west gradient from forests, grasslands, and unused-lands, to become the dominant cover type. According to our improved model, the ecosystem services considering the internal structure of build-up lands were first investigated in this ecologically fragile area of China, and the evaluated ecosystem service value (ESV) reduced from CNY 5515.316 billion to CNY 5425.188 billion, with an average annual decrease of CNY 3.004 billion from 1990 to 2020. Another finding was that the small-scale land variables with large ecological service impacts were quantified; namely, the proportion of grassland, woodland, wetland, and water body decreased from 62.71% to 61.34%, with only a relatively minor fluctuation of −1.37%, but this decline resulted in a large ESV loss of CNY 116.141 billion from 1990 to 2020. From the driving perspective, the temperature, digital elevation model (DEM), and slope exhibited negative effects on ESV changes, whereas a positive association was analyzed in terms of the precipitation and human footprint during the studied period. This study provides important support for optimizing land resource allocation, guiding the development of agriculture and animal husbandry, and protecting the ecological environment in arid/semi-arid and ecological barrier regions. Full article

Urban vitality is an important evaluation indicator for enhancing urban spatial efficiency and promoting sustainable development. However, few studies have systematically integrated steady-state and dynamic vitality perspectives. To address this gap, we integrate steady-state vitality and dynamic vitality indicators, and use geographically weighted regression (GWR) and geographically weighted logistic regression (GWLR) to quantify how the built environment, natural elements, and travel purposes influence urban vitality. The results reveal that: (1) From the steady-state perspective, urban vitality exhibits a distinct polycentric structure, with transportation POI and catering facilities serving as core driving factors; (2) From the dynamic perspective, areas where citizens are always highly concentrated are mainly influenced by floor area ratio and transportation POI. Green space coverage and building density significantly correspond to patterns of persistently low vitality, while periodic population fluctuations are associated with subway accessibility and proximity to waterfronts. This study provides a comprehensive analysis of the stable spatial distribution and dynamic changes in population aggregation, offering a theoretical and empirical basis for optimizing urban spatial layout and meeting citizens’ activity needs. Full article

As critical ecosystems, coastal zones necessitate the identification of their ecosystem service values, trade-off/synergy patterns, spatiotemporal evolution, and driving factors to inform scientific decision-making for sustainable ecosystem management. This study selected the coastal zone of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as the research region. By incorporating land-use types such as mangroves, tidal flats, and aquaculture areas, we analyzed land-use changes in 1990, 2000, 2010, and 2020. The InVEST model was employed to quantify six key ecosystem services (ESs): annual water yield, urban stormwater retention, urban flood risk mitigation, soil conservation, coastal blue carbon storage, and habitat quality, while spatial correlations among them were examined. Furthermore, Spearman’s rank correlation coefficient was used to assess trade-offs and synergies between ecosystem services, and redundancy analysis (RDA) combined with the geographically and temporally weighted regression (GTWR) model were applied to identify driving factors and their spatial heterogeneity. The results indicate that: (1) Cultivated land, forest land, impervious surfaces, and water bodies exhibited the most significant changes over the 30-year period; (2) Synergies predominated among most ecosystem services, whereas habitat quality showed trade-offs with others; (3) Among natural drivers, the normalized difference vegetation index (NDVI, positive effect) and evapotranspiration were critical factors. The proportion of impervious surfaces served as a key land-use change driver, and the nighttime light index emerged as a primary socioeconomic factor (negative effect). The impacts of drivers on ecosystem services displayed notable spatial heterogeneity. These findings provide scientific support for managing the supply-demand balance of coastal ecosystem services, rational land development, and sustainable development. Full article

The metro system is a key driver of urban land use development; however, its spatiotemporal impact mechanisms remain insufficiently understood. This study investigates the effects of metro development on land use types and development intensity in Wuhan, China, from 2014 to 2019, and employs a Geographically and Temporally Weighted Regression (GTWR) model to capture the spatiotemporal heterogeneity of these impacts. Results show that metro construction significantly promotes land use transformation along metro lines, especially from non-construction land to residential and commercial uses, while also increasing development intensity. GTWR analysis further reveals that metro network characteristics, station attributes, and built environment features surrounding stations strongly influence land development. These impacts exhibit pronounced spatiotemporal heterogeneity, becoming more pronounced over time as the metro network extends into suburban areas. The findings provide valuable insights for urban and transportation planners, supporting the formulation of strategies for integrated land use development and metro network expansion. Full article

Ecological Carrying Capacity (ECC) represents an ecosystem’s ability to sustain human activities without compromising its ecological integrity, yet fine-scale quantification of its spatiotemporal dynamics remains limited. Focusing on the ecologically vital Dongting Lake Region (DTLR) in China, this study established a finer resolution ECC assessment framework by integrating multi-source remote sensing data within the Driving Forces–Pressure–State–Impact–Response–Management (DPSIRM) model. ECC and its driving mechanisms were examined across the DTLR and its surrounding buffer zone from 2000 to 2020. Results revealed a pronounced U-shaped trajectory of ECC in the DTLR, with an initial decline followed by a sustained recovery after 2010, while the buffer zone consistently maintained a 15–20% higher ECC throughout. Subsystem analysis indicated steady improvements in Management capacity, whereas Pressure, State, and Impact subsystems peaked mid-period before declining. Projections suggest continued ECC enhancement, reflecting rising regional resilience. Spatially, ECC patterns jointly emerged from the interaction of anthropogenic stressors and ecological restoration. Geographically weighted regression (GWR) identified proximity to green spaces as the strongest positive driver, while impervious surface (−27%) and infrastructure density (−19%) exerted significant negative effects. These findings offer a scalable, remote sensing-based framework for ecosystem-oriented spatial planning, highlighting the strategic role of green infrastructure in sustaining ecological resilience. Full article

Underground gas storage (UGS) facilities may cause ground displacements as a result of the cavern convergence or regular gas injection (alternate ground uplift and subsidence). The occurrence and scale of displacements are strongly dependent on the storage time and cavern capacity. At an early stage of facility operation, displacements can be difficult to detect in the presence of wetlands. The main objective of this study was to describe the global and local relationships between vertical ground displacements observed over a small and relatively new Kosakowo UGS facility (Poland) from 2014 to 2024 (dependent variable) and selected topographic, hydrological, and mining factors (independent variables). The dependent variable was determined through SBAS-InSAR analysis of Sentinel-1 SAR data, while the independent variables were developed using passive Sentinel-2 imagery and open geospatial data. The global relationships between variables were described using Ordinary Least Squares (OLS) and Generalized Linear Regression (GLR) models, while the Geographically Weighted Regression (GWR) model was utilized to analyze local relations. The results obtained indicate that ground displacements were characterized by seasonal fluctuations between 4 mm and 10 mm. The factors that had, both globally and locally, the strongest influence were soil moisture, vegetation water content, and the flora condition, indicating that the environmental hydrogeology had the greatest impact on the phenomenon under study. None of the considered models identified underground gas storage as a significant contributing factor to the observed ground displacements. The results confirm that the presence of wetlands can be a significant obstacle to an accurate description of the impact of gas storage on the ground movements, especially in UGS areas at an early stage of operation. Full article

Groundwater depletion causes a critical risk for the sustainability of urban and agricultural resilience in semi-arid regions such as Tangshan city. This study deployed an integrated framework that combines understandable machine learning (Shapley Additive exPlanations (SHAP), Geographically Weighted Regression (GWR), spatial autocorrelation (Local Indicators of Spatial Association or LISA), and scenario-based recharge forecasting to evaluate the spatial drivers and patterns of groundwater stress and project potential future risks. Using spatial groundwater table data from 2022 and key environmental and anthropogenic variables such as evapotranspiration (ET), population, temperature, precipitation, and land use and land cover changes, an XGBoost (Extreme Gradient Boosting) regression model was trained to capture nonlinear spatial patterns. SHAP analysis found that ET and population density are prominent contributors to groundwater depletion in agricultural and urban zones. To capture spatial heterogeneity, GWR was utilized to estimate localized coefficients and construct a Vulnerability and Resilience Index (VRI) from normalized coefficients and residuals. LISA validated vulnerability zones and revealed transitional stress regions. Future risks are also projected using Coupled Model Intercomparison Project Phase 6 (CMIP6) climate data and land-use data to run recharge modeling from 2023 to 2049 for both representative concentration pathway (RCP) 4.5 and RCP 8.5. Results show that RCP 8.5 demonstrates highly unstable recharge with frequent negative episodes (ET > P), while RCP 4.5 shows relatively stable patterns of groundwater table. Furthermore, coupled with urban and agricultural expansion, RCP 8.5 intensifies depletion risks. This combined framework provides analytical understandings of spatial driver patterns and scenario-based risk assessments under climate and land use change. The findings of the study recommend priority zones for intervention and underline the importance of adaptive, scenario-sensitive groundwater governance in semi-arid, urbanizing regions. Full article