Search Results (1,306)

China’s rapid economic growth is partly due to the productivity gains from agglomeration, whereby firms and workers in larger and denser cities benefit from proximity through knowledge spillovers, thicker labor markets, and shared infrastructure. This study examines the changing nature and location of agglomeration economies in China using resident-based measures of urban scale from the 2000, 2010, and 2020 population censuses. Chinese “cities” are administrative jurisdictions that contain both dense urban districts and lower-density counties, so the agglomeration elasticities are estimated separately for districts and counties for a balanced panel of 298 prefectural jurisdictions. Agglomeration economies occur only in urban districts, while coefficients on urban scale for counties and county-level cities are close to zero or significantly negative. Moreover, district-level elasticities decline over time, from 0.24 in 2000 to 0.15 in 2020, assuming no feedback from productivity to urban scale. Allowing for such feedback, the temporal decline is even greater, from 0.24 in 2000 to 0.08 in 2020. However, urban growth is shifting increasingly toward counties rather than districts, foregoing the potential agglomeration effects. Changes in location of construction workers also shows this dispersed urban growth. Hence, recent urban growth is increasingly in locations without agglomeration benefits. Full article

The rapid growth of hiking tourism presents a critical challenge for balancing visitor safety with the sustainable management of ecologically fragile mountain environments. Traditional models developed in urban settings struggle to capture the highly non-linear, heterogeneous, and zero-inflated characteristics of wilderness trekking behavior. In order to quantify the nonlinear and threshold-based effects of environmental variables on hikers’ spatial decisions in unstructured wilderness and to identify distinct behavioral regimes for segmented management, this study introduces an explainable machine learning framework to reconstruct hikers’ spatial decision-making in a complex mountainous system in Inner Mongolia, China. Random Forest (RF), XGBoost, and LightGBM were compared in predicting trail density and the Euclidean distance to the nearest trail. Results show that transforming behavioral traces into continuous proximity surfaces dramatically improves model performance, with XGBoost achieving the highest predictive accuracy for Trail_Dist. By integrating the SHapley Additive exPlanations framework, this study moves beyond black-box prediction to reveal the nonlinear mechanisms driving hiker behavior. Key findings include: (1) Nighttime light range exhibits a U-shaped threshold effect as the primary anthropogenic attractor. (2) Elevation shows an exponential inhibitory trend above 1238 m. (3) Strong spatial coupling exists between elevation and slope, alongside a landscape compensation effect where high Normalized Difference Vegetation Index (NDVI) areas attract off-trail movements. This research provides a robust methodological pathway for predicting behavior in unstructured outdoor environments. It offers a scientific foundation for smart scenic area management, including optimized route planning, precise ecological protection zoning, and targeted emergency rescue preparedness. Full article

Under the macro background of the rural revitalization strategy and urban-rural integrated development, rural settlements are undergoing a profound transformation from physical morphology to functional connotation. However, existing studies mainly focus on the expansion of single land elements, lacking long-term quantitative monitoring of the coupling relationship between rural development and policy texts. Taking Fuzhou City as a case study, this research selects long-term Global Human Settlement Layer (GHSL) and Night-Time Light (NTL) data from 1990 to 2025, combined with policy text quantification methods. Based on rural development units, the Coupling Coordination Degree Model (CCDM), Macro-Micro Matching Index (MMI), and gravity center migration analysis are employed to systematically reveal the spatiotemporal evolution characteristics and driving mechanisms of rural settlement morphology under policy institutional changes. The research results indicate that: (1) Fuzhou’s rural settlements exhibit relatively stable gravity centers of construction land, while the gravity center of economic vitality has significantly shifted toward the southeastern coastal area under policy guidance; (2) The coupling coordination degree of rural human–land relationships has generally increased, but with significant spatial heterogeneity, forming a pattern of high-quality coordination in coastal areas and low-efficiency lag in mountainous regions; (3) The shift in policy orientation from scale expansion to functional enhancement has driven economic factors to concentrate in key policy areas ahead of physical spatial expansion. The analytical framework combining remote sensing monitoring and policy quantification constructed in this study reveals the precedence of factor flow and the lag of physical space driven by policies, providing a scientific basis for the differentiated governance of rural areas in coastal mountainous cities. Full article

Pulse-width-modulated (PWM) automotive headlights enhance nighttime event-based camera detection, yet systematic parameter optimization for vulnerable road user detection remains unexplored. This study evaluates PWM frequency, duty cycle, light distribution, ego-vehicle speed, and ambient lighting under European New Car Assessment Programme-inspired crossing scenarios for cyclist and pedestrian detection. Results establish performance ranging from substantial improvements to severe degradation relative to continuous illumination. Cyclist detection achieves robust performance with high-frequency modulation across light distributions, while low-frequency operation with low beam produces severe degradation through background noise accumulation. Pedestrian detection requires high beam with street lighting enabled; low beam universally fails regardless of modulation parameters. Limited parameter combinations achieve simultaneous improvements for both targets. Detection performs optimally on retroreflective surfaces, while low-reflectivity clothing limits capability, requiring target-specific optimization. Full article

Rapid urbanization in China has reshaped the coupling coordination between pollution and carbon emissions. However, existing studies largely rely on linear approaches and lack multidimensional and nonlinear assessments of urban growth patterns. Using panel data for 289 prefecture-level cities from 2010 to 2023, including built-up land, nighttime lights, CO₂ emissions, and PM_2.5 concentrations, this study develops three indicators: Urban Expansion Intensity (UEI), Urban Sprawl Index (USI), and Urban Compactness (UC). By integrating a coupling coordination model, K-means clustering, Geographically and Temporally Weighted Regression (GTWR), and interpretable XGBoost-SHAP analysis, four urban growth patterns are identified: High-Speed Low-Efficiency Expansion (HLE), Low-Speed Low-Efficiency Expansion (LLE), High-Speed High-Efficiency Compact (HHC), and Low-Speed High-Efficiency Compact (LHC). Results indicate that: (1) USI and UC exhibit significant nonlinear threshold effects on CCD; moderate expansion and higher compactness enhance synergy, whereas excessive dispersion or over-compactness weakens coordination. (2) UEI plays a relatively indirect and spatially heterogeneous role. (3) HHC and LHC cities achieve the highest CCD levels, while HLE cities perform the lowest. (4) Urban expansion shows an overall contraction trend, yet substantial regional disparities persist. These findings highlight nonlinear and spatially heterogeneous mechanisms linking urban growth patterns and pollution–carbon coupling coordination, providing implications for differentiated spatial governance. Full article

To address the limitations of existing perception methods for nighttime encounter situation recognition of unmanned surface vessels (USVs), this study proposes an image-based method for navigation-light recognition and encounter situation recognition. In accordance with the International Regulations for Preventing Collisions at Sea (COLREGs), a parameterized 3D geometric model of vessel navigation lights and encounter scenario models is established. Based on the camera imaging principle, a dataset of navigation-light images under various encounter situations is generated through simulation experiments. By analyzing the variation patterns of navigation-light images in different encounter situations, a feature vector composed of area-domain and azimuth-domain features is constructed, and an encounter situation recognition method is developed accordingly. To mitigate the effects of water reflections and interfering light sources in real images, a navigation-light image-processing method is designed for the stable extraction of feature parameters. Simulation results show that the classification accuracy ranges from 96.6% to 98.3% at different distance conditions. In field experiments conducted with a small USV under a three-light configuration, the proposed method achieves a navigation-light recognition accuracy of 96.2% and an encounter situation recognition accuracy of 94.94%. The proposed method provides an interpretable and lightweight complementary visual solution for nighttime encounter situation recognition, complementing existing nighttime perception technologies. Full article

Rapid urbanization in the Yellow River Basin intensifies the conflict between urban expansion and the thermal environment, threatening ecological security and sustainable development. Utilizing multi-source data (2000–2023) including nighttime light (NTL) and land surface temperature (LST), this study applies spatial analysis and Geographically Weighted Regression (GWR) to explore the spatial associations between urban development and LST and its drivers across core cities. The results indicate significant spatiotemporal differentiation: mid-downstream cities exhibited contiguous urban expansion, whereas upstream growth remained constrained by local topography, with heat islands consistently concentrating in built-up areas. The warming rate decreased gradually from downstream (0.29–0.40 °C/year) to upstream (0.20–0.30 °C/year). The LST-NTL correlation strengthened notably in mid-downstream regions but remained moderate upstream. GWR analysis revealed that urban development intensity, represented by NTL, is the primary driver of LST increase downstream, while natural factors predominantly mitigate warming upstream. This long-term, multi-city comparison provides a scientific basis for precise urban heat island management and sustainable planning in the basin. Full article

Poor visibility contributes to nighttime accidents at highway intersections, especially in developing countries where vehicles mix with vulnerable road users (VRUs) such as pedestrians and cyclists. Unlike downtown intersections with traffic signals and ambient lighting, rural intersections have no signals and minimal ambient light, forcing drivers to rely on roadway lighting for hazard recognition. Improving illumination arrangements can significantly reduce the likelihood of crashes. However, there are significant differences in the effects of illumination on drivers’ visual search ability at different vehicle speeds. Therefore, the collaborative matching of illumination and speed limits can effectively improve traffic efficiency and reduce the probability of nighttime accidents. In this paper, we establish a collaborative optimization model of illumination and speed limits at rural highway intersections that considers drivers’ visual recognition of VRUs. We then design an experiment with illuminance, vehicle speed, and VRU type/location as control variables to collect recognition distances, and finally analyze their effects to calculate speed limits under different illuminances. Results indicate that pedestrians and cyclists appearing from the left side are recognized 24.73% and 15.79% earlier than those from the right, suggesting that VRUs from the right side are more vulnerable. Additionally, the safety benefit of improving illumination on increasing speed limits gradually diminishes as illuminance rises. Therefore, determining the most suitable illumination and speed limit configuration requires a comprehensive evaluation of the cost–benefit relationship between lighting investments and the gains resulting from higher speed limits. Full article

Cultivated land fragmentation has become a critical constraint on regional agricultural sustainable development. Revealing its spatial patterns and driving mechanisms is of great significance for optimizing the utilization and management of cultivated land resources and enhancing regional agricultural productivity. This study focuses on the main grain-producing areas in the middle and lower reaches of the Yangtze River Basin. It constructs a Cultivated Land Fragmentation Index (CLFI) using an integrated method that combines landscape index analysis with an entropy-weighted approach, based on 2023 land-use data. The optimal analytical grain size and extent were determined before employing geographic detectors to identify dominant factors influencing cultivated land fragmentation. The key findings include the following: (1) The appropriate spatial resolution for fragmentation analysis was identified as 330 m, with an optimal analysis extent of 8910 m. (2) CLFI values ranged from 0.001 to 0.973, exhibiting significant spatial heterogeneity. The central plains and northeastern regions demonstrated low fragmentation levels and better contiguous cultivated land distribution, while the western and peripheral areas showed higher fragmentation. A provincial-scale comparison revealed that Jiangxi Province had the highest fragmentation level (0.255), whereas Jiangsu Province had the lowest (0.146). The topographic gradient analysis indicated a decreasing trend from the Guizhou Plateau (0.503) to the North China Plain (0.125), with plateaus and basins showing significantly higher fragmentation than hilly and plain regions. (3) Dominant controlling factors varied among provinces: In provinces with greater topographic relief (Anhui, Hubei, Hunan, Jiangxi), natural factors like elevation, slope gradient, and NDVI primarily controlled fragmentation patterns; in contrast, socioeconomic factors such as nighttime light intensity dominated in Jiangsu Province, characterized by flat terrain and high urbanization. Multi-factor interactions generally enhanced explanatory power regarding spatial patterns, confirming that cultivated land fragmentation is a result of comprehensive multi-factor interactions. This study reveals the spatial distribution characteristics of cultivated land fragmentation at the pixel scale in the study region, providing theoretical foundations and decision-making references for the efficient utilization of cultivated land resources and rural land system reforms. Full article

Rapid urbanization and agricultural expansion in arid regions have profoundly altered carbon cycles and landscape stability. Focusing on the Hexi Corridor, China, this study integrates multi-source geospatial data (1990–2020) to analyze the spatiotemporal evolution and driving factors of land-use carbon emissions (LUCE) and landscape ecological risks (LER). By integrating carbon accounting, LER assessment, bivariate spatial autocorrelation, and the Optimal Parameter Geographic Detector (OPGD), we quantify the intricate relationship between carbon dynamics and landscape integrity. Results indicate a transformative pattern of anthropogenic expansion and natural contraction, with a 2315.49 km² net loss of unused land. Net carbon emissions surged 4.6-fold, while forest and grassland sinks exhibited a significant “lock-in effect” due to fragile ecological foundations. Simultaneously, LER followed an “inverted U-shaped” trajectory; the refined 5 × 5 km grid scale revealed a significant drop in high-risk areas from 44.65% to 10.96% following ecological restoration. Spatial analysis reveals a significant “spatial mismatch” between LUCE and LER, with oases manifesting “high carbon–low risk” clustering. Driver detection confirms a driving asymmetry. LUCE is dominated by anthropogenic factors (nighttime light, q > 0.90), whereas LER is profoundly constrained by natural backgrounds. Future governance must shift toward a collaborative system centered on source-based emission control and precise regional management to synergize low-carbon transition with landscape security. Full article

As modern urban systems advance, video surveillance has become indispensable for ensuring high-quality urban development. Nighttime images acquired in urban monitoring scenarios are often degraded by haze and non-uniform illumination, resulting in reduced visibility, color distortion, and blurred structural boundaries. To address these issues, this paper proposes a nighttime image dehazing framework that combines mixed-norm variational atmospheric-light estimation with adaptive boundary-constrained transmission refinement. Specifically, an $L_2-L_{\mathrm{p}}$ mixed-norm regularization model is introduced to improve atmospheric-light estimation under complex nighttime illumination and suppress halo diffusion and color distortion around strong light sources. In addition, an adaptive boundary-constrained transmission refinement strategy with weighted soft-threshold shrinkage is developed to reduce residual artifacts while preserving structural edges. Experimental results on synthetic and real nighttime haze datasets demonstrate that the proposed method consistently outperforms representative state-of-the-art methods in both visual quality and quantitative metrics, showing superior robustness and restoration performance for nighttime urban monitoring applications. Full article

With urbanization transforming from scale expansion to high-quality development and the increasing prominence of the ecological environment constraints of drainage basins, systematically identifying the mechanism of action of non-point source pollution from a high-quality development perspective is significant for coordinating urban development and environmental protection. Based on remote sensing data on atmospheric pollution and multi-source spatial big data such as nighttime light (NTL), LandScan population, point of interest (POI), and land use data from 2013 to 2025, this study applies methods including deposition flux analysis, deep learning fusion, bivariate spatial autocorrelation, and geographically weighted regression (GWR) to empirically analyze the spatiotemporal evolution characteristics, spatial correlation, and local impacts of high-quality urban development on non-point source pollution in the Chenghai drainage basin. We find that, firstly, non-point source pollution and high-quality urban development in the Chenghai drainage basin both present significant stage-specific and spatial heterogeneity. In other words, the two are not mutually independent spatial elements in space; instead, they are closely and significantly correlated, with their correlation types showing obvious spatial agglomeration characteristics. Secondly, the impact of high-quality urban development on non-point source pollution evolves in stages. It gradually shifts from a whole-region, homogeneous, strongly positive driving force to spatial differentiation. Specifically, from 2013 to 2017, the whole-region regression coefficients are generally greater than 0.5, meaning that urban development represents a strong, whole-region driving force promoting pollution. However, after 2017, this impact evolves into a stable spatial differentiation pattern. It mainly shows that the northern urban core area, where coefficients are greater than 0.5, maintains a continuous strong positive driving force. Meanwhile, the peripheral area, where coefficients are generally lower than 0, creates a negative inhibition effect. Based on the above rules, further analysis shows that the impact of high-quality urban development on non-point source pollution is absolutely not a simple linear relationship. Instead, it is a result of the coupling effect of multiple factors, including development stage, spatial location, and governance level. Therefore, to positively affect the ecological environment through high-quality development, model transformation and precise governance are essential. The findings of this study deepen our understanding of the transformation of urban development models and the response mechanism of non-point source pollution. They also provide a scientific basis and decision support for promoting the coordinated governance of high-quality urban development and non-point source pollution by region and stage in plateau lake drainage basins, as well as for improving the sustainable development of drainage basins. Full article

Precise, spatially explicit sub-provincial GDP estimates are essential for regional planning, especially in mountainous areas where official economic data remain spatially coarse and unevenly distributed. This study develops a multisource county-level GDP spatialization framework for Sichuan Province, China, integrating corrected NPP/VIIRS nighttime-light (NTL) data with Points of Interest (POIs), land-use structure indicators (proportion of farmland (PFL); proportion of construction land (PCL)), elevation, precipitation, accessibility and population density within a unified indicator system. Two regression approaches—Ordinary Least Squares (OLS) as a global benchmark and Geographically Weighted Regression (GWR) as the spatially adaptive primary model—are calibrated on county-level cross-sectional data for 2020 (n = 183) and evaluated using R², adjusted R², AICc and residual spatial diagnostics. The multisource GWR model achieves R² = 0.882 (adjusted R² = 0.872, AICc = 5712.26), substantially outperforming both the global OLS benchmark (R² = 0.801) and NTL-only GWR baseline (R² = 0.662), confirming that spatial nonstationarity is an intrinsic feature of the GDP–proxy relationship and that integrating complementary geospatial proxies is the primary pathway to improved estimation accuracy in topographically heterogeneous regions. The GWR-based GDP surface exhibits a pronounced basin–plateau contrast: high-value clusters concentrate along the Chengdu Plain and adjacent city corridors, while extensive low-value zones prevail across the western highlands (global Moran’s I = 0.33, Z = 14.26, p < 0.001). Spatially varying GWR coefficients reveal that elevation and precipitation constrain GDP most strongly in high-altitude counties, construction land exerts a consistently positive but spatially graded effect, and the influences of accessibility and population density are context-dependent and locally differentiated. These findings support differentiated territorial development policies: plateau counties require accessibility-first strategies; hill counties benefit from targeted small-city industrialization; and basin cores need managed growth to balance agglomeration advantages against congestion pressures. The framework relies exclusively on globally or nationally available data and is portable to other mountainous regions, though cross-regional validation and extension to multi-year panels using geographically weighted panel regression remain important directions for future work. Full article

Net primary productivity (NPP) is a critical indicator of carbon sequestration and biomass accumulation in terrestrial ecosystems, directly reflecting ecosystem carbon sink capacity. Existing NPP studies have primarily emphasized climate-driven interannual variability. Spatially explicit analyses that jointly quantify multi-factor driving mechanisms, thresholds, and land-use transition risks remain limited. Here, we develop an integrated multi-method analytical workflow (DRIBS) that integrates Distributional Response, Informative Boundary constraints, and Spatial Interpretability Optimization, and apply it to the Jiziwan region in the Yellow River Basin, one of China’s major ecological restoration hotspot regions. From 2000 to 2020, the annual increasing rate of NPP was 5.80 gC·m⁻²·yr⁻¹, and 78% of the area showed a significant increasing trend. Among them, grasslands and croplands in the eastern and western parts exhibited strong fluctuations and low long-term stability. Evapotranspiration (ET) and fractional vegetation cover (FVC) were the dominant drivers of NPP spatial heterogeneity, and precipitation around ~220 mm marked a critical water-stress threshold. Population density and nighttime lights showed a non-linear “ecological adaptation window”, implying both disturbance and management potential. Land-use transitions exhibited divergent risk signatures: grassland/cropland-to-forest transitions produced stable enhancement (priority restoration zones), whereas cropland/unused-to-urban transitions were associated with degradation risk (urgent management). Overall, DRIBS provides an interpretable “change-mechanism-threshold-risk” assessment to support carbon-sink regulation and restoration prioritization in arid and semi-arid regions. Full article

This study develops a framework to capture spatiotemporal population dynamics and assess future earthquake exposure risk, using Hubei Province as a case study. Future population changes at the county level were projected under different shared socioeconomic pathways (SSPs). These projections were then integrated with NPP-VIIRS nighttime light data and the normalized difference vegetation index (NDVI) to simulate the spatiotemporal dynamics of the population from 2020 to 2070 at a 500 m grid resolution. Combined with seismic hazard zoning, the evolution of population exposure risk under different pathways was assessed. The results indicate the following: 1. Different SSPs profoundly influence future population exposure patterns. Under the SSP3 (regional rivalry) pathway, population growth is the fastest with the strongest agglomeration effect and significantly elevated exposure levels. 2. The refined spatiotemporal population model can more realistically reveal the heterogeneity and evolutionary trajectory of population distribution, providing a high-precision data foundation for exposure analysis and effectively enhancing the scientific rigor of risk assessment. 3. Population exposure risk under various pathways exhibits distinct spatiotemporal dynamics, and monitoring its evolution under different scenarios helps identify high-risk counties that require priority attention. This study is expected to provide precise scientific evidence for implementing differentiated disaster prevention and mitigation strategies and territorial spatial resilience planning in Hubei Province, while it demonstrates the forward-looking value of combining long-term scenario simulations with refined exposure assessments. Full article