Search Results (242)

Water erosion is a core issue that threatens the ecological integrity of the highland ecosystems in the Andes Mountains and the agricultural sustainability of the Ramis River basin. This study uses the Revised Universal Soil Loss Equation (RUSLE), which integrates five factors, rainfall erosivity (R), soil erodibility (K), topography (LS), cover and management (C), and support practices (P), to estimate the spatial distribution of potential water erosion rates in this basin. The results show that the very low and low erosion classes together cover 73.21% of the basin, while the high, very high, and extreme erosion classes account for 17.29% of the total area. Among these, the extreme erosion class, with an annual erosion volume exceeding 250 tons per hectare, covers 8.07% of the basin, equivalent to 1190.13 square kilometers. This extreme erosion is concentrated in steep headwater areas and five sub-basins including Cuenca Grande. Comparative model verification shows that the Ordinary Least Squares (OLS) model only identifies a positive correlation between slope gradient and potential soil loss, with an extremely low explanatory power (R² = 0.045). Its residuals exhibit significant spatial autocorrelation (Moran’s I = 0.204, p < 0.001). In contrast, the Geographically Weighted Regression (GWR) model greatly improves the model fit (R² = 0.359, RMSE = 148.288) and eliminates the spatial autocorrelation of residuals, proving that the slope-erosion relationship has spatial non-stationarity. Sensitivity analysis shows that the C factor has the highest sensitivity (0.980), followed by the LS factor (0.626). Based on these findings, this study proposes that cover and management measures such as vegetation restoration should be prioritized in high-risk headwater sub-basins. It should be noted that the values estimated in this study are potential soil loss amounts, rather than actually measured erosion values. Full article

This study provides evidence on the impact of a heavy rail-based suburban metro station in Fremont, California, on house prices using geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR). The dataset comprises sale price, sale date, and property and locational characteristics for single-family houses sold during the January 2000–April 2018 period within two miles of the Warm Springs Station on the San Francisco Bay Area Rapid Transit system. The results demonstrate that, relative to the referent period (2000–2001): (a) the station-led house price impacts are spatially heterogeneous—the house price increased in one pocket only, comprising less than one-tenth of the dataset, not across the entire 0–2 mile station area, (b) the price increase in that pocket began right after the project announcement period, and (c) the price impacts indicated by the MGWR model markedly differ from baseline ordinary least square regression (OLS) estimates. Overall, the study’s findings highlight the need to move beyond average estimation techniques, such as OLS, to those that account for spatially heterogeneous price impacts, such as GWR and MGWR. Full article

Focusing on China’s information technology policy system, this study investigates the spatio-temporal evolution of policy–reference patterns and their implications for policy diffusion. Using 33,702 policy documents and 3150 citation links from the PKULaw database, we construct a policy citation network and combine social network analysis with ordinary least squares (OLS) and geographically and temporally weighted regression (GTWR). The results show that observed policy–reference relationships remain strongly characterized by top-down administrative coordination, while the policy–reference network has expanded to involve a broader range of regions and increasingly diverse interregional connections. These changes are accompanied by increasing citation intensity and textual differentiation, while pronounced regional disparities persist. The regression results reveal that citation-based diffusion indicators are associated with regional development capacity. Economic and industrial foundations are positively associated with policy–reference outcomes, whereas the associations of urbanization and R&D investment vary across different diffusion dimensions. GTWR further reveals that these associations vary across space and time. By integrating policy citation networks with spatio-temporal analysis, this study advances understanding of policy-system evolution under centralized governance and informs differentiated digital policy coordination. Full article

The Yellow River Basin (YRB), a core ecological–economic corridor in China, plays a critical role in meeting climate, land, and water-related Sustainable Development Goals (SDGs). To capture the dynamic evolution of Eco-environmental Quality (EEQ), this study developed a process-oriented space-time cube framework based on the Remote Sensing Ecological Index (RSEI) using MODIS data from 2000 to 2021 on the Google Earth Engine (GEE) platform. By integrating Emerging Hotspot Analysis (EHSA) with the RSEI cube, we identified and tracked 17 EEQ hotspot–coldspot evolution types and revealed their spatial clustering, temporal persistence, and cumulative transition effects. The driving mechanisms were further investigated through a comprehensive analytical framework combining Geodetector (GD), Ordinary Least Squares (OLS), and Multiscale Geographically Weighted Regression (MGWR). Results show that: (1) the RSEI-based space–time cube effectively detects co-evolving hotspot–coldspot trajectories and uncovers fine-grained spatiotemporal instability across the basin; (2) dynamic hotspot types, including diminishing, oscillating, and sporadic patterns, occupy a larger proportion of the basin than persistent hotspots, indicating widespread ecological fluctuation; (3) hotspot transition analysis reveals that only 4.94% of the 17.04% emerging hotspots became stable from 2000 to 2010, to 2011 to 2021, while the majority remained transitional or reverted to coldspots, reflecting fragile early-stage ecological recovery; and (4) Fractional vegetation cover (FVC) exhibited the highest explanatory power for EEQ hotspot–coldspot transition dynamics, followed by topography and temperature, whereas anthropogenic factors exerted their influence primarily through interactions with natural factors. These findings provide a robust scientific basis for precision ecological restoration, risk identification in fragile ecological zones, and adaptive sustainable management of large river basins under complex environmental pressures. Full article

Water is a valuable resource that needs to be managed carefully. Therefore, sustainable water consumption is a critical issue, mainly in urban areas, where tourism activities are of great importance. In mainland Portugal, this has become a crucial matter, particularly because of climate limitations and the recent growth of the tourism sector. To manage water resources effectively, we must analyze the primary factors driving consumption. Therefore, an econometric model to identify the main factors is a relevant tool for policy design. This paper tries to fill this gap. In the first stage, an ordinary least squares (OLS) model was implemented. In a second stage, the autocorrelation is evaluated, and a weighted geographical regression (GWR) model is used to improve the previous results. The models were implemented in the 278 municipalities of continental Portugal, considering the average situation of the last decade. The results were promising since the OLS model presented an R² of 0.42 identifying purchasing power, urban green spaces, tourism and education as the main explanatory factors for water consumption. The geographical weight regression presented an R² of 0.646 proving the importance of considering spatial context for the identification of these factors’ influence on water consumption. Full article

Accurately identifying the functional characteristics of urban rail transit stations and classifying them accordingly helps uncover passenger flow patterns and optimize resource allocation, thereby enhancing the coordination efficiency of multimodal urban transportation systems. Existing studies on the delineation of station influence areas often exhibit overlapping zones, leading to insufficient characterization of regional heterogeneity. Additionally, classification methods predominantly rely on static single indicators and lack integration of multi-scale features. To address these limitations, this paper proposes a non-overlapping zoning algorithm for precisely defining station influence areas. By incorporating multidimensional indicators—including dynamic passenger flows, resident attributes, connection characteristics, and spatial distribution—a fine-grained station classification model is developed using an enhanced Partitioning Around Medoids (PAM) algorithm. Building on the classification outcomes, a dual-scenario framework (weekday vs. weekend) is established, and Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) models are applied to analyze the spatiotemporal patterns of passenger flows. A case study of Beijing rail transit stations demonstrates that the enhanced PAM algorithm significantly improves clustering performance. Four distinct station types are identified on weekdays: Peripheral Basic-Service Type, Core Commuting-Aggregation Type, Exurban Residential-Transit-Dependent Type, and Multifunctional-Complex Type. On weekends, stations are classified into three categories: Peripheral Living-Service Type, Core Leisure-Vitality Type, and Central Mixed-Use Type. Furthermore, the driving factors of passenger flows exhibit notable spatiotemporal heterogeneity: on weekdays, commuting demand dominates, with jobs–housing ratio, educational attainment ratio, and road network density serving as core positive factors; on weekends, leisure demand becomes prominent, showing strong synergistic effects among jobs–housing ratio, Points of Interest (POI) density, and road network connectivity. The research findings provide theoretical support for the functional classification and refined management of rail transit stations. Full article

Quantifying mountain forest carbon stocks and elucidating their spatially heterogeneous driving mechanisms are both critical for terrestrial carbon management under the global carbon neutrality agenda. Conventional single-source remote sensing approaches can neither fully exploit multi-source data synergies nor adequately resolve spatial heterogeneity in complex terrains. This study develops an integrated framework combining multi-source remote sensing classification, InVEST-based carbon estimation, and multiscale geographically weighted regression (MGWR) and applies it to Zixi County, a subtropical mountainous bamboo-abundant region in southeastern China. Sentinel-2 imagery, PlanetScope data, and DEM derivatives were fused with an optimized Random Forest classifier, achieving an overall accuracy of 0.8565 (Kappa = 0.7065). Carbon stocks were then estimated via the InVEST model. MGWR analysis (adjusted R² = 0.930, AICc = 594.032) substantially outperformed the global OLS model (adjusted R² = 0.795, AICc = 1717.450), confirming strong spatial non-stationarity across all drivers. Canopy density exhibited the strongest positive local effect (coefficient range: 0.343–0.768); slope position showed predominantly negative regulation with localized positive reversals (−0.778 to 0.270); elevation displayed a broad-scale positive gradient (0.133–0.140); and total vegetation cover exhibited bidirectional effects (−0.134 to 0.208) with pronounced east–west divergence. This framework not only provides a robust methodological reference for carbon stock assessment in complex mountain landscapes but also supports targeted forest management and carbon sequestration strategies through spatially explicit driver identification. Full article

Ground-level ozone (O₃) has become a major air pollutant in China following PM_2.5, particularly in the southeastern coastal region, where the frequent interaction of typhoons and the subtropical high complicates pollution control. In this paper, spatial autocorrelation and a multiscale geographically weighted regression (MGWR) model were employed to estimate the spatiotemporal heterogeneity and driving mechanisms of O₃ in the Southeast Coastal urban agglomerations from 2015 to 2024. Temporally, the annual average O₃ concentration exhibited a fluctuating trend of an initial increase, followed by a decrease and a subsequent rebound. A bimodal monthly pattern was observed, with peaks in May–June and August–September and minima in winter. Diurnally, the concentration showed a consistent pattern of being higher in the daytime and lower at night, peaking in the afternoon, driven by solar radiation and temperature. Spatially, O₃ exhibited a distinct north–south gradient, with the highest in Jiangsu Province, followed by Shanghai, Zhejiang and Guangdong, and the lowest in Fujian. Significant spatial autocorrelation was detected, with hot spots in the Yangtze River Delta and cold spots in Fujian and adjacent areas. Seasonally, the most severe pollution with the greatest spatial heterogeneity, occurred in summer, contrasting with the uniformly low concentrations in winter. Compared with OLS and GWR, the MGWR demonstrated superior explanatory power. O₃ was jointly influenced by precursors, natural factors, and socioeconomic factors, with the influence intensity ranked as follows: NO₂ > average elevation > population density > annual precipitation> wind speed > built-up area > proportion of the secondary industry in GDP. Notably, the effects of NO₂, annual precipitation, and the proportion of the secondary industry exhibited strong spatial heterogeneity, operating at finer spatial scales. These findings provide scientific support for sustainable air quality management and region-specific O₃ control in southeastern coastal China. Full article

Day–night mobility differentiation provides important insights into the spatial organization of migrant activities, yet its spatial variation and underlying drivers remain insufficiently understood in Asian metropolitan areas. Using kernel density estimation (KDE), spatial autoregressive models, and multiscale geographically weighted regression (MGWR), the study examines how the built environment, socioeconomic context, economic attractiveness, and accessibility factors shape variations in migrant mobility across space among Chinese migrants in Seoul, South Korea. The results reveal pronounced spatial clustering, with higher levels of mobility differentiation concentrated in central and southeastern Seoul, whereas lower levels are observed in migrant-concentrated districts such as Guro-gu and Geumcheon-gu. Migrant stock is identified as the most influential and spatially consistent determinant, exhibiting a significant negative association across most areas. Land-use mix also negatively affects mobility differentiation, while office facilities, industrial facilities, and subway accessibility exert positive effects. Model comparison demonstrates that MGWR substantially outperforms ordinary least squares (OLS) and geographically weighted regression (GWR), achieving the highest explanatory power (R² = 0.758; adjusted R² = 0.705) and the lowest corrected Akaike information criterion (AICc) (763.656). Furthermore, MGWR uncovers considerable spatial heterogeneity in the effects of employment facilities, apartment concentration, and service-oriented facilities. These findings suggest that migrant day–night mobility differentiation is shaped by both citywide contextual factors and localized neighborhood characteristics, highlighting the importance of accounting for spatially varying relationships when examining migrant mobility patterns in metropolitan areas. Full article

Traditional Chinese villages are important carriers of rural heritage, collective memory, vernacular landscapes, and living cultural traditions. However, rapid urbanization, agricultural modernization, climate change, and tourism development have increasingly threatened their spatial integrity and cultural continuity, highlighting the need for evidence-based conservation and adaptive management. This study examines the spatial distribution patterns and associated factors of 8155 national-level traditional villages in China. An integrated spatial analytical framework was developed by combining kernel density estimation, spatial autocorrelation analysis, Geodetector, and multiscale geographically weighted regression (MGWR). The results show that: (1) traditional villages are unevenly distributed across China and form a distinct “three-core and multi-node” spatial pattern, with major high-density clusters concentrated in several cross-provincial regions and secondary clusters distributed in other heritage-rich areas; (2) the spatial differentiation of traditional village density is statistically associated with natural, cultural, and socioeconomic factors, among which temperature and precipitation show the strongest explanatory power, while cultural endowment, ecological quality, and socioeconomic variables show more context-dependent associations; and (3) compared with OLS and conventional GWR, MGWR improves model performance by capturing spatially heterogeneous and scale-dependent relationships through variable-specific bandwidths. These findings provide national-scale empirical evidence for differentiated conservation planning and support the integration of traditional village protection with rural revitalization, cultural heritage conservation, and sustainable regional development. Full article

Northeast China is one of China’s major commodity grain bases and plays a strategic role in national food security. Against the background of rural population outflow and agricultural modernization, new agricultural business entities (NABEs), including family farms, farmers’ cooperatives, and agribusinesses, have become important actors in reshaping agricultural production organization. Based on registration data for 2014, 2018, and 2023, this study uses kernel density estimation (KDE), standard deviational ellipse (SDE) analysis, spatial autocorrelation analysis, ordinary least squares (OLS) regression, and multiscale geographically weighted regression (MGWR) to examine the spatiotemporal patterns and driving factors of NABEs in Northeast China. The results show that: (1) NABEs expanded rapidly from 2014 to 2023 and became increasingly concentrated in agriculturally advantageous plain areas. (2) Family farms showed the fastest expansion, farmers’ cooperatives had the widest spatial coverage, and agribusinesses were mainly concentrated around transport corridors and market nodes. (3) In terms of industrial structure, crop-production entities remained dominant, followed by animal husbandry entities, while forestry, fishery, and agricultural support service entities accounted for relatively small shares; however, their numbers continued to increase. (4) The OLS results showed that the reclamation rate and road network density had relatively stable associations with the spatial distribution of multiple entity types, whereas economic development, science and technology investment, and fiscal support showed differentiated relationships across entity types and regions. (5) The MGWR results further reveal spatial heterogeneity in the effects of driving factors. These findings provide empirical evidence for type-specific cultivation and differentiated policy support for NABEs in major grain-producing areas. Full article

The urban heat island (UHI) effect has become a global environmental challenge, and quantifying the spatial heterogeneity of its driving mechanisms while developing differentiated regulation strategies remains a critical research gap. This study takes Berlin, Germany as a case study, integrating spatial autocorrelation analysis with a coupled geographically weighted regression (GWR) model to systematically investigate the spatial heterogeneity of the driving mechanisms of Local Climate Zones (LCZs) on surface urban heat island intensity (SUHII), and proposes refined regulation strategies. First, the WUDAPT method was employed to generate a LCZ map, and global and local Moran’s I were used to identify SUHII spatial clustering characteristics, dividing the study area into High–High (HH), Low–Low (LL), and Not Significant (NS) clustering zones. Second, Ordinary Least Squares (OLS) and GWR coupled models were constructed to analyze the global and local relationships between LCZ composition and SUHII. The results indicate: (1) Berlin’s SUHII exhibits significant spatial clustering characteristics (Moran’s I = 0.984), with clear differentiation between the HH zone (25.8%, mean 2.67 °C) and the LL zone (26.4%, mean −0.16 °C); (2) the GWR model (R² = 0.921, AICc = 1279.538) significantly outperforms the OLS model (R² = 0.822, AICc = 2871.608), confirming strong spatial heterogeneity in the LCZ-SUHII relationship, with more pronounced advantages of GWR in urban–rural fringe areas; (3) LCZ 5 (low-density mid-rise buildings) and LCZ 2 (high-density mid-rise buildings) are key warming factors across the entire study area, but their warming effects are stronger in suburban areas than in central urban areas; LCZ A (dense trees) and LCZ G (water bodies) are key cooling factors across the entire area, but their cooling effects are stronger in central urban areas than in the suburbs. Based on these findings, this study establishes a differentiated strategy framework of “Zoning—Identifying Heterogeneity—Regulating”, proposing that HH zones should implement “carbon sink enhancement and source reduction”, NS zones should balance “ecological expansion with growth management”, and LL zones should adopt “strict protection and development restriction”. This framework provides a quantifiable scientific basis and practical guidance for refined urban thermal environment management. Full article

This study investigates the association between urban greenness and sales in commercial alleys. We focus on 1090 commercial alleys in Seoul, South Korea, defined as neighborhood-scale open commercial streets or districts composed of small retail, service, cafe, and restaurant businesses, and combine spatially explicit measures of greenness with data on weekend sales to assess how variation in vegetation is associated with local economic performance. Greenness is measured by the normalized difference vegetation index (NDVI) derived from remote sensing imagery. We employ a set of global and spatially explicit models, including Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), Multiscale Geographically Weighted Regression (MGWR), and a Python Geographical Random Forest (PyGRF, v0.0.12), to capture both overall and location-specific relationships. The results show that higher levels of greenness are significantly associated with higher weekend sales, with spatial heterogeneity observed across different areas of the city. The green investment efficiency index (GIEI) results further identify clusters of high investment efficiency in areas characterized by strong greenness–sales associations and relatively limited existing greenness. High GIEI values were concentrated in areas near natural amenities and dense residential neighborhoods, indicating potential priority locations for targeted greening interventions. By linking objective measures of greenness to observed sales at the scale of everyday commercial environments, this study contributes to a better understanding of how urban greenness is associated with consumer behavior and local economic activity. The findings provide practical implications for identifying areas where greening strategies may be considered as part of broader efforts to support more resilient and sustainable neighborhood commercial areas, while recognizing that the observed relationships are associative rather than causal. Full article

This study presents a reproducible high-resolution framework for assessing urban chikungunya environmental suitability and outbreak-related spatial heterogeneity during the 2025 outbreak in the western Guangdong–Hong Kong–Macao Greater Bay Area. Using Sentinel-2–derived environmental indicators together with a random forest–based residual correction of Landsat surface temperature, we developed a 10 m weighted additive Mosquito Habitat Suitability Index (MHSI). Index weights were empirically derived by comparing reported case locations at the street and town level with randomly sampled background points. The optimized weighting scheme indicated that humidity- and water-related conditions contributed more strongly to habitat suitability than vegetation and temperature. Reported case locations generally corresponded to higher MHSI values than background locations, suggesting that the index captures broad spatial patterns of environmental suitability. Comparison with a coarser, model-derived global chikungunya risk map was used as an external comparative consistency assessment rather than predictive validation, showing moderate agreement at the macro-spatial scale (Pearson r = 0.3421) after correction for spatial autocorrelation. Residual-difference analysis, combined with multiple points-of-interest (POI) categories, ordinary least squares (OLS), and geographically weighted regression (GWR), further suggested that human activity, transport connectivity, and healthcare accessibility may account for part of the remaining spatial mismatch not explained by environmental suitability alone. Sensitivity analyses indicated that the broad LST downscaling pattern and the exploratory GWR interpretation were reasonably stable under alternative sampling, smoothing, grid-size, and bandwidth settings. Taken together, this framework provides preliminary spatial evidence for high-resolution environmental suitability assessment and exploratory interpretation of outbreak-related spatial heterogeneity, while underscoring the need for finer-scale epidemiological data and more explicit representation of human-driven processes. Full article

Urban park use is a key indicator of sustainable urban development, reflecting the accessibility and social value of urban green infrastructure. However, existing studies often struggle to distinguish stable spatial differences from short-term temporal dynamics. Using monthly data for 125 urban parks in Las Vegas from 2022 to 2024, this study examines how park visitation is shaped by spatial, temporal, and contextual factors. It addresses three objectives: identifying cross-park determinants of visitation, examining within-park monthly dynamics, and assessing spatial variation in key relationships. Park visitation is measured using observed visit counts, with dwell time and travel distance used as alternative behavioral outcomes for robustness tests. To address these research questions, this study asks: (1) what structural and contextual factors explain cross-park differences in park visitation; (2) how park visitation responds to changing contextual conditions within parks over time at the monthly scale; and (3) whether the relationships between park visitation and its key determinants vary across space. To answer these questions, the analysis combines annual cross-sectional ordinary least squares (OLS) regression, monthly panel models, Random Forest analysis, robustness tests, and geographically weighted regression. This study employs a triangulated analytical framework combining cross-sectional ordinary least squares (OLS) regression monthly fixed-effects (FE) panel models, and Random Forest (RF) analysis. These factors function as stable support for sustainable park use. Crime exposure shows no stable global linear effect, but its association with visitation appears conditional on temporal and spatial context. Overall, the findings suggest that park visitation is shaped by the interaction of physical design, safety conditions, and urban context. By explicitly separating cross-sectional spatial and economic inequalities from within-park temporal dynamics, this study offers policy-relevant evidence for urban planners and park managers seeking to promote more inclusive, efficient, and sustainable urban park systems through integrated design, economic activation, and safety-oriented interventions. Full article