Search Results (755)

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

Non-grain production of cultivated land has increasingly affected regional food security and the stability of agricultural ecosystems. In traditional rice-producing regions, changes associated with non-rice cultivation, fallow land, rice-fishery integrated farming, and intensive agricultural management are reshaping the spatial structure of paddy landscapes. To identify the long-term spatiotemporal evolution of paddy systems, this study investigated Northern Jiangxi, China, using Landsat surface reflectance imagery from 2000, 2005, 2010, 2015, and 2020 on the Google Earth Engine (GEE) platform. The Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) were used to construct a phenology-based Flooding Frequency (FF) indicator. Based on the annual frequency with which pixels satisfied the condition LSWI > EVI, cultivated land was classified into three categories: non-flooded cropland, standard rice paddy, and high-frequency flooded cropland. In this study, non-flooded cropland was used as an indicator of potential non-rice cultivation rather than as direct evidence of confirmed non-grain production. Landscape metrics, transition matrices, gravity center migration, standard deviation ellipses, and geographically weighted regression (GWR) were then used to examine paddy landscape dynamics, fragmentation patterns, and county-level spatial associations with socioeconomic factors. The results suggest that the paddy system in Northern Jiangxi experienced marked stage-based fluctuations between 2000 and 2020. Standard rice paddy recovered during 2005–2010, whereas non-flooded cropland expanded considerably during 2010–2015, accompanied by intensified paddy landscape fragmentation. Non-flooded cropland was mainly distributed around urban fringes, transport corridors, and some hilly margins. Standard rice paddy was concentrated in traditional grain-producing areas, including the Poyang Lake Plain and the Gan-Fu Plain. High-frequency flooded cropland was primarily located in low-lying lake areas, where its dynamics were likely associated with rice-fishery integrated farming, continuous irrigation, and hydrological fluctuations. Landscape metrics showed that the largest patch index and mean patch size of standard rice paddy declined after 2010, indicating reduced spatial continuity of core paddy fields. The GWR analysis provided auxiliary evidence that total population, per capita gross domestic product (GDP), and urbanization rate were spatially associated with changes in non-flooded cropland at the county level; however, the results should be interpreted as exploratory associations rather than causal mechanisms. Overall, paddy landscape change in Northern Jiangxi was expressed not only through changes in cultivated land area, but also through the reorganization of paddy function, spatial continuity, and land-use intensity. Future cropland protection should therefore move beyond area-based control toward integrated management of quantity, quality, function, and spatial configuration. Future research should further verify these findings using dynamic cropland boundaries, higher-resolution imagery, and more detailed socioeconomic data. Full article

Net primary productivity (NPP) is a core diagnostic variable of terrestrial carbon cycling, yet consistent annual NPP simulation remains challenging in alpine heterogeneous regions where topography, hydrothermal gradients, vegetation structure, and nutrient constraints interact. Remote-sensing products such as MODIS provide valuable observational constraints, whereas light-use-efficiency models such as CASA retain process transparency and scenario transfer capability. This study develops a CASA-based, MODIS-constrained framework for annual NPP simulation over the Gannan Plateau. The framework preserves a locally parameterized CASA baseline and adds a geographically weighted regression (GWR) residual-alignment layer trained on CASA–MODIS residuals during 2005–2013. The fitted correction relationship was then applied to the 2014–2020 temporal transfer period and evaluated in a 2030s SSP scenario transfer experiment. MODIS was treated as the correction target rather than ground truth, and GLASS was adopted as an independent product-level benchmark. During 2014–2020, the GWR-corrected product showed improved pooled pixel-level agreement with the MODIS-constrained target relative to parameter-localized CASA, with R² increasing from 0.438 to 0.708 and RMSE decreasing from 91.4 to 68.7 g C m⁻² yr⁻¹. Residual Moran’s I also decreased, indicating weaker residual spatial organization after correction. Product-level comparison with GLASS showed a moderate, directionally consistent improvement relative to uncorrected CASA, although this comparison was not interpreted as ground-truth validation. The 2030s scenario transfer experiment indicated that the correction layer changed the spatial expression of NPP divergence among SSP pathways. Overall, the proposed framework provides a process-model-preserving and observation-constrained approach for improving agreement between annual NPP estimates and the MODIS-constrained target in alpine heterogeneous regions, while its applicability remains subject to product uncertainty, spatial dependence, and future nonstationarity. Full article

Climate and human activity shape cropland dynamics, while their cascading interactions remain an intricate black box blocking mechanistic understanding of land system shifts. Taking Jilin Province as a case, we developed an integrated framework combining OPGD (optimal parameter geographic detector), SEM (structural equation modeling) and GWR (geographically weighted regression) to quantify multi-scale cascading mechanisms of 13 environmental factors and cropland. Cropland conversion showed distinct spatial disparities: western plains saw mainly grassland reclamation, central urban plains experienced cropland occupation by construction, and eastern mountain areas had forest-to-cropland expansion. Over 2010–2019, cropland decreased by 1.39% (1257.7 ha) for construction, compensated by conversions from 4.99% grassland (4518.8 ha) and 3.01% forestland (2720.2 ha). Climate dominated cropland variations through indirect human-mediated pathways, with path coefficients of 0.664 for Climate → Human and 0.571 for Human → Cropland; climate exerted direct driving effects on grass–cropland transition in western ecologically fragile plains. Greenhouse gases, evapotranspiration, humidity, temperature and leaf area index controlled overall cropland variations. Central and western croplands were dominated by topography, whereas precipitation, population and GDP determined eastern cropland dynamics. This framework differentiated direct and indirect driving pathways of cropland evolution, guiding policy formulation for regional grain security. Full article

Improving water use efficiency (WUE) is a crucial approach to addressing agricultural water scarcity and promoting the sustainability of agricultural ecosystems. Although extensive research has been conducted on the factors associated with cropland WUE, the relationship between cropland fragmentation and WUE remains poorly understood. This study focused on northern China and employed an integrated “size–shape–configuration” framework to measure cropland fragmentation from 2005 to 2020. A geographically weighted regression (GWR) model was then applied to analyze the spatially varying relationships between cropland fragmentation and WUE. The results showed that over half of northern China experienced intensified cropland fragmentation. Noticeable spatial heterogeneity was observed, with relatively low fragmentation in the North China Plain and the Northeast China Plain and higher levels in the topographically complex northwestern region. Throughout the study period, the size-based fragmentation index (FI_size) consistently exhibited higher mean values than the shape-based (FI_shape) and configuration-based (FI_config) indices. The mean cropland WUE across the 1027 analysis units increased from 0.962 to 1.071, although 220 units—mainly distributed in the three northeastern provinces—experienced a decline. Among the analysis units with statistically significant local coefficients, FI_config was predominantly negatively associated with WUE, whereas FI_size and FI_shape were predominantly positively associated with WUE, with the positive associations mainly concentrated in the North China Plain. Moreover, FI_config exhibited larger absolute local coefficient magnitudes and greater spatial variability than the other two fragmentation dimensions. Environmental factors also showed distinct spatial associations with WUE, with annual precipitation and NDVI being predominantly positively associated with WUE, whereas soil erodibility and slope were mainly negatively associated. These findings highlight the need for region-specific management of cropland landscape patterns. This study provides preliminary quantitative evidence of the relationships between cropland fragmentation and WUE, offering valuable insights for improving WUE and promoting sustainable agricultural management. Full article

Clarifying how ecosystem services (ESs) change over time and space, and how their trade-offs and synergies evolve, is essential for regional ecological protection and high-quality development. Using Shandong Province as a case study, this research quantified carbon storage (CS), water yield (WY), soil conservation (SC), and habitat quality (HQ) with the InVEST model. GeoDetector, geographically weighted regression (GWR), XGBoost-SHAP, Spearman’s rank correlation, bivariate spatial autocorrelation, and spatial overlay analysis were then combined to examine ES patterns, driving mechanisms, and interaction relationships. The main findings are as follows. (1) During 2000–2020, the most evident land-use changes occurred in cropland, grassland, built-up land, and water bodies. (2) The dominant drivers varied markedly among services: CS and HQ were mainly shaped by land-use type and human activity, WY was chiefly controlled by precipitation, and SC was most sensitive to topographic conditions. Factor interactions were generally stronger than single-factor effects, with two-factor enhancement being the prevailing interaction type. (3) ES trade-off/synergy relationships were relatively stable through time. A strong synergy persisted between CS and HQ, whereas CS and SC exhibited a moderate synergistic relationship. By contrast, WY showed evident trade-offs with both HQ and CS, with the WY–HQ trade-off being particularly pronounced. (4) Spatial overlay results showed that the overall ES synergy level remained low. Low-synergy areas accounted for 69.23–70.94% of the study area across the study period. Although strong-trade-off areas expanded overall, high-synergy areas remained limited, indicating considerable room to improve the coordinated provision of ESs in Shandong Province. Full article

The coastal wetlands of Yancheng, Jiangsu Province, serve as a crucial overwintering and stopover site for rare waterbirds such as red-crowned cranes. The changes in their landscape pattern are directly related to the protection of regional wetland ecological functions and biodiversity. Using land use data from 2010 to 2022 to extract landscape pattern indicators for each period, this study adopts the Covariance–Analytic Hierarchy Process (Cov-AHP) to construct a comprehensive Landscape Fragmentation Index (LFI) for coastal wetlands, considering aspects including patch density, boundary complexity, spatial connectivity, and landscape diversity. Combined with multi-source indicators of Ecology–Economy–Society (EES), the Generalized Additive Model (GAM) and Geographically Weighted Regression (GWR) are adopted to systematically analyze the nonlinear response relationships and spatially heterogeneous driving mechanisms of landscape fragmentation. GWR is employed to reveal the spatial heterogeneity of the influence of each driving factor on fragmentation by mapping local regression coefficients. The results show that: (1) During the study period, the overall landscape fragmentation of the coastal wetlands in Yancheng, Jiangsu Province, exhibited a slow increase trend, with a spatial gradient pattern of “higher in the north and lower in the south, and higher in coastal areas than in inland areas,” reflecting the combined effects of varying levels of economic development and human activity intensity across different administrative regions and along the coast-inland gradient. (2) Based on the deviance explained by the GAMs, social factors generally had higher explanatory power for LFI than ecological and economic factors. Specifically, human population density and the proportion of construction land showed a significant positive correlation with LFI, while NDVI and the proportion of farmland exhibited obvious nonlinear effects under different fragmentation levels. (3) The GWR results indicated that the regression coefficients of the main driving factors were highly spatially non-stationary, and regions with high coastal development intensity had the most significant promoting effect on landscape fragmentation. The local coefficient maps further reveal that GDP and NDVI exhibit the strongest spatial heterogeneity, with their effects shifting from positive to negative across different sub-regions. The study demonstrates that the integrated framework of Cov-AHP combined with GAM and GWR can effectively characterize the spatiotemporal dynamics and multi-dimensional driving mechanisms of coastal wetland landscape fragmentation, providing a reference for the protection of coastal wetlands in Yancheng, Jiangsu Province, and the conservation of waterbird habitats represented by red-crowned cranes. Full article

Highway corridors are increasingly being discussed not only as zones of ecological disturbance but also as components of regional green infrastructure with potential carbon sequestration functions, yet their long-term evolutionary characteristics and multi-scale associated factors remain insufficiently understood. Using multi-source time-series data from 2000 to 2023, we developed an analytical framework integrating the CASA model, Random Forest, and geographically weighted regression (GWR). To ensure methodological rigor, we implemented a Spatial K-fold Cross-Validation strategy and incorporated Partial Dependence Analysis (PDA) to identify non-linear thresholds. The results indicate that: (1) Vegetation carbon sequestration within Shandong’s highway corridors increased significantly, with total sequestration rising from 5.54 × 10⁶ t in 2000 to 1.55 × 10⁷ t in 2023, representing an average annual growth rate of approximately 5.0%. This growth transitioned from a relatively stable phase to a more rapid growth phase. (2) A clear distance-related ecological pattern was observed. Statistical tests (Kruskal–Wallis H test) confirmed that vegetation carbon sequestration exhibited a significant non-monotonic gradient ($p<0.05$), with a stable peak zone observed 50–100 m from the roadbed. This peak zone is associated with a spatial “trade-off” pattern between the attenuation of traffic-related stressors and roadside ecological management. (3) The observed spatial pattern was associated with a nonlinear coupling of natural background conditions and human disturbance. Precipitation and temperature were the dominant associated factors, while PDA further identified a critical precipitation threshold (~750 mm) and localized tipping points for human interference, with a distinct road-disturbance-sensitive zone evident within 200–500 m. The results suggest that high-standard ecological design and active restoration measures are associated with lower ecological disturbance and higher vegetation carbon sequestration performance in some highway corridors. However, these relationships should be interpreted cautiously, as they may also be influenced by differences in climate background, topography, land-use context, and road construction history. These findings provide empirical evidence to inform differentiated ecological restoration and low-carbon management of traffic corridors. 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

Understanding how landscape patterns affect habitat quality in fragile karst regions is critical for biodiversity conservation, yet the driving mechanisms remain poorly understood, particularly regarding karst geomorphic heterogeneity. Taking Guizhou Province, a typical karst area of southwestern China, this study integrated land-use and natural geographic data (DEM, karst landforms, soil types, slope, soil thickness, vegetation cover, bedrock exposure, and rocky desertification) from 2000 to 2020. We quantified landscape pattern indices and habitat quality using Fragstats and InVEST, then explored spatial relationships via bivariate spatial autocorrelation and geographically weighted regression (GWR). Results show that land-use intensity increased and landscape structure stabilized, while fragmentation slightly decreased, but connectivity weakened. Habitat quality declined 4.4% over two decades. Globally, habitat quality was positively correlated with aggregation, cohesion, contagion, and largest patch index, and negatively correlated with shape complexity, patch density, diversity, and splitting indices. Locally, six karst zones exhibited distinct clustering patterns, revealing nonlinear interactions between natural vulnerability (e.g., bedrock exposure, thin soil) and human activities. After 2010, the dominant driver shifted from natural conditions to human–land interactions, with human activities contributing approximately 60% of habitat quality degradation. These findings provide a quantitative, spatially explicit basis for ecological zoning and differentiated policy making in Guizhou and similar fragile regions. Full article

Promoting the coordinated development of urban resilience (Ur) and urban land use efficiency (Ulue) is key to solving regional sustainable development challenges. Based on panel data of relevant indicators from the Yangtze River Economic Belt (YREB) for the period 2011–2024, this study employs the coupling coordination degree model, kernel density estimation, and exploratory spatial analysis tools to analyze the spatiotemporal differentiation characteristics and spatial correlation of the coupling coordination degree between Ur and Ulue (Uucc). And the spatial patterns of Uucc driving factors are investigated using the Geographically Weighted Regression (GWR) method. The results indicate: (1) In terms of the temporal dimension, the Uucc in the YREB shows an upward trend, with its level continuously improving. The number of cities with higher Uucc levels gradually increases, exhibiting multi-center and multi-layered characteristics. (2) In the dimension of space, Uucc exhibits strong spatial correlation. LISA high-value clusters are concentrated in the downstream region of the YREB, while low-value clusters are found in the upstream and midstream regions. The spatial clustering presents a pattern of “large dispersion, small agglomeration”. (3) The driving factors of Uucc display significant spatial heterogeneity. Urbanization, government regulation capacity and technological innovation are the dominant drivers in the downstream region. Urbanization has a significantly positive effect on Uucc, while its impact gradually weakens from the downstream to the midstream and upstream regions. Urbanization and openness level are the dominant drivers in the midstream region. This study provides references for governments to formulate differentiated policy for different regions. Full article

Farmland abandonment is a widespread land-use transition that may reshape surface soil organic carbon (SOC), yet its effects in the hilly red-soil region of South China remain insufficiently understood. Here, 30 m-resolution CLCD land-cover data, 90 m-resolution SOC data, and environmental variables were integrated with trajectory tracking, paired-sample comparison, temporal gradient analysis, geostatistics, and geographically weighted regression (GWR) to identify abandoned farmland and assess the SOC responses. The results showed that farmland abandonment rates fluctuated between 0.7% and 6.6% during 2000–2020, with abandonment hotspots progressively shifting toward the 2015–2020 period. Approximately 87% of abandoned farmland occurred in low-slope areas (0–15°). At the regional scale, farmland abandonment did not produce a consistent enhancement of surface SOC, with a negligible mean difference between abandoned and control farmland (−0.020 g/kg). This weak regional mean response masked contrasting local changes, with 43.76% of samples showing SOC gains and 46.59% showing SOC losses, indicating that spatial heterogeneity rather than the net regional effect is central to interpreting abandonment-induced SOC responses. Abandonment duration exhibited pronounced temporal gradient effects, with the strongest SOC accumulation occurring during the 5–10-year stage, followed by gradual stabilization. The GWR results indicated that abandonment duration was negatively associated with SOC change rates, whereas the annual mean NDVI showed a positive association, reflecting the combined effects of vegetation recovery, topographic conditions, and human activity intensity. These findings support spatially differentiated carbon accounting and abandoned-farmland management in subtropical hilly agroecosystems. Full article

Urban heat stress and deteriorating air quality are environmental risks in semi-arid cities, positioning urban forests as vital nature-based solutions for climate adaptation. Despite growing recognition of urban greening imperatives, South Africa’s (SA) Northern Cape Province remains characterized by sparse vegetation Land Use/Land Cover (LULC) and built environment expansion. The study’s research problem focuses on how vegetation LULC dynamics influence urban forests’ potential in mitigating heat stress and atmospheric pollution in arid urban systems. The study adopts a multi-scale analytical approach, conducting the LULC and NDVI analysis through a multi-temporal Landsat satellite imagery analysis quantifying LULC change from 2004 to 2024. Grounded in the Integrated Spatial Justice-Ecosystem Services (ISJES) Framework, the analysis reveals significant decline in dense vegetation LULC from 9021.77 km² (2.4%) to 1262.10 km² (0.3%), while barren land expanded from 73,417.01 km² (19.7%) to 222,866.82 km² (59.8%) intensifying urban thermal exposure. Built-up areas expanded from 91.06 km² to 357.072 km², further constraining ecological buffers across the province’s urban nodes and undermining urban climate resilience. The Global Moran’s I statistic for the NDVI change surface (I = 0.7843, Z = 443.87, p < 0.0001) confirms spatial clustering of degradation hotspots of NDVI decline affecting 66.5% of the study area. Furthermore, Geographically Weighted Regression (GWR) results confirm that vegetation loss is being driven by the combined and spatially differentiated effects of mining proximity, urban expansion, livestock pressure, declining rainfall, and rising temperatures. In terms of thermal regulation findings, the Getis-Ord Gi hot spot analysis identifies significant NDVI decline covering 23.5% of the study area at the 99% confidence level, expanding to 33.5% and 39.5% at the 95% and 90% confidence levels, respectively; hence, there is a need for urban forest corridors, climate-sensitive spatial planning frameworks, and targeted greening interventions in heat-vulnerable arid geographies. This study provides the first comprehensive, multi-decadal quantification of vegetation loss across SA’s largest province. 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