Search Results (2,507)

Understanding how built structures and urban vegetation jointly relate to population patterns is increasingly important for sustainable urban planning. This study used airborne LiDAR data to analyze three-dimensional urban morphology across Zagreb, Croatia, at neighborhood and district scales. A 1 m nDSM and classified rasters were used to derive canopy, building, height, volumetric, and built–vegetation balance metrics. Spatial clustering was assessed using Moran’s I, LISA, and Getis–Ord Gi*, while relationships with population density were evaluated using correlation and spatial regression models. At the neighborhood level, canopy cover ranged from 4.5% to 84.7%, while UMI ranged from 0.002 to 10.368. UMI showed significant spatial clustering (Moran’s I = 0.457, p = 0.001), with 24 high–high and 64 low–low clusters. Composite balance metrics outperformed individual vegetation or building indicators; logUMI provided the strongest performance for log-transformed population-density models, with SLM pseudo-R² = 0.903. Agreement assessment showed high consistency for canopy cover (R² = 0.997), while building height agreement was weaker for global datasets. Results indicate that transformed built–vegetation balance metrics provide useful complementary indicators for describing urban morphology and population density patterns. Full article

The governance of land use carbon emissions is pivotal to achieving the goals of carbon peak and carbon neutrality. Rural revitalization significantly shapes the spatiotemporal patterns and evolutionary dynamics of land use carbon emissions, yet this relationship has received inadequate attention in existing literature. This study employs a combination of decoupling models, the Boston Matrix, spatial analysis, and interpretable machine learning models to conduct an empirical analysis of 124 regions in western China. The findings reveal diversified spatiotemporal evolution trends in rural revitalization land use carbon emissions. The decoupling relationship between rural revitalization and carbon emissions demonstrates a polarized nature, with over half of the assessed regions experiencing negative decoupling effects. The role of impact factors in decoupling relationships is characterized by a mixed nature, hierarchical intensity, nonlinear pathways, spatial heterogeneity and autocorrelation. The pathways of factor effects display nonlinear forms such as wave-like, inverted U-shaped, and U-shaped patterns, with the nature and intensity of effects dynamically shifting between “threshold mutations” and “inflection reversals” as factors evolve. The spatiotemporal evolution patterns, decoupling relationships, and SHAP values all exhibit significant spatial autocorrelation and form “spatial clusters” of various shapes. The decoupling of rural revitalization and carbon emissions in western China constitutes a complex systemic endeavor, necessitating comprehensive analysis from multiple dimensions—encompassing spatiotemporal evolution patterns, decoupling relationship, nonlinear mechanisms, and spatial effects—followed by the formulation of differentiated and precision-targeted governance strategies. Full article

Cultural heritage in major river basins serves as an important spatial carrier of historical civilization evolution, and the spatial differentiation characteristics and influencing factors of its activation level are closely related to heritage conservation, utilization, and sustainable development. This study focuses on the Henan section of the Yellow River Basin and selects 344 cultural heritage sites as the research objects. A comprehensive evaluation system for cultural heritage activation was constructed from three dimensions—culture, society, and economy. By integrating GIS-based spatial analysis with the GWR model, the study reveals the spatial differentiation characteristics of cultural heritage activation levels and their influencing factors. The results indicate that the activation level of cultural heritage exhibits a dual-core-dominated and multi-level spatial agglomeration pattern. Zhengzhou and Luoyang function as dual high-density core clusters with elevated heritage activation levels, while a continuous cultural heritage corridor has gradually formed along Sanmenxia, Luoyang, Zhengzhou, Jiaozuo, Hebi, and Puyang. Furthermore, heritage agglomeration, heritage spatial radiosity, per capita GDP, transportation accessibility, terrain relief, and NDVI on the activation level of cultural heritage demonstrate significant spatial heterogeneity. Based on the identification of spatial heterogeneity, this study proposes a core–corridor–node spatial pattern and a factor-adaptive targeted strategy for cultural heritage activation. These findings provide a scientific basis for differentiated conservation and precise activation of cultural heritage under the national strategy of ecological protection and high-quality development in the Yellow River Basin, while also offering valuable insights for the collaborative governance of cultural heritage in major river basins worldwide. Full article

This study analyzes the spatial distribution of restaurant services in Tanta, Egypt, using a multi-scalar framework that integrates spatial autocorrelation, kernel density estimation, diversity measures, and spatial econometric modeling. It is theoretically grounded in Central Place Theory (CPT) and Central Flow Theory (CFT) to examine how urban hierarchy and mobility dynamics jointly shape food service geography in a mid-sized Global South city. The findings reveal significant spatial inequalities, with nearly half of all restaurants concentrated in a limited number of central neighborhoods, while peripheral areas remain underserved. Spatial regression analysis indicates that these patterns are not adequately explained by population distribution, as total population and density variables showed non-significant effects in the OLS model. Instead, clustering is more strongly associated with accessibility and infrastructure. The transition from OLS to the Spatial Error Model (SEM) significantly improved the explanatory power (R² increased from 0.369 to 0.534), with a highly significant Lambda coefficient (λ = 0.69, p < 0.00001) confirming that unobserved spatial processes and mobility flows are the primary drivers of restaurant concentration. Correlation results further indicate that road density (Coefficient = 2.10, p < 0.01) and educational facilities have significant positive relationships with restaurant density, whereas most demographic indicators show weak effects. Furthermore, a significant negative interaction between population and road density (−2.63, p = 0.014) underscores that mobility corridors can override traditional residential thresholds, providing empirical support for CFT. Diversity analysis highlights clear intra-urban disparities, with high-diversity clusters located along major accessibility axes. Kernel density results point to a hybrid spatial structure, where traditional urban cores coexist with emerging secondary nodes. Overall, the study demonstrates that restaurant distribution in Tanta is better explained through a hybrid CPT–CFT framework, where accessibility and mobility flows outweigh population thresholds. These findings challenge traditional models and emphasize the need for dynamic, accessibility-oriented planning approaches to address spatial inequalities in urban services. Full article

Conventional clustering techniques for urban water consumption profiling treat each household as an independent entity, thereby disregarding the spatial, socioeconomic, and infrastructural contexts that jointly govern demand behavior. This structural limitation prevents the extraction of contextually coherent consumption profiles—a critical shortcoming for utility managers who must design spatially targeted conservation interventions. To overcome this, we propose Simple GLAC, a novel graph clustering framework that leverages graph neural networks with an adaptive attention mechanism to dynamically model these complex interdependencies. The model’s end-to-end training jointly optimizes a latent representation for cluster cohesion, separation, and spatial homogeneity, where each household’s multi-month consumption record serves as the node feature vector encoding temporal consumption patterns. Evaluated on a large-scale real-world dataset of 4590 residential households across four distinct graph topologies, Simple GLAC consistently achieves superior multi-metric performance over both traditional and graph-based benchmarks, yielding interpretable and operationally actionable consumption profiles aligned with the spatial, administrative, socioeconomic, and infrastructural dimensions of urban water governance in the studied context. This work provides a data-driven tool for utility managers to deploy targeted water conservation strategies, with findings grounded in a Colombian mid-sized city and generalization to broader urban settings identified as a priority direction for future work. Full article

Rural sustainable development is a core component of the global Sustainable Development Goals, and building digital villages and enhancing the resilience of rural economies are key pathways for underdeveloped regions to achieve rural sustainable development. The coordination and synergy between these two areas are central to rural revitalization. Taking 122 counties in Hunan Province as research units and using 2013–2023 spatial panel data, this study employs an improved coupling coordination model, spatial autocorrelation analysis and geographically weighted regression to explore their spatiotemporal evolution, clustering patterns and driving factors. The results show that both systems improved steadily: digital villages expanded from core areas, while economic resilience developed more balancedly. The coupling coordination evolved from near-disorder to a pattern characterized by regional equilibrium. The coupling coordination degree displayed significant positive spatial autocorrelation, forming an “High-High (H-H)” cluster in the Changsha-Zhuzhou-Xiangtan-Dongting Lake Plain and an “Low-Low (L-L)” cluster in western Hunan. Driving factors showed marked spatial heterogeneity. These findings provide empirical support for differentiated digital village policies in Hunan. Full article

Amid the ongoing acceleration of climate change over recent decades, extreme precipitation events have become more frequent and intense on a global scale, triggering severe natural hazards and considerable socioeconomic damage. Nevertheless, how extreme precipitation has evolved at the national level over long time spans, and what role atmosphere–ocean teleconnections play in driving regional differences, remains insufficiently explored. This study addresses that knowledge gap by conducting a comprehensive assessment of eight ETCCDI-based extreme precipitation indices (PRCPTOT, CWD, R20, R95p, R99p, RX1day, RX5day, and SDII) across six climatic sub-regions of China (Northeast, North, East, Central South, Northwest, and Southwest) over 1960–2020, drawing on daily records from 695 quality-controlled meteorological stations. Key atmospheric and oceanic circulation drivers were further diagnosed and their joint influence was quantified via multiple wavelet coherence (MWC). The analysis shows that five of the eight indices (CWD, R95p, R99p, RX1day, and RX5day) underwent statistically significant fluctuating changes (p < 0.05) throughout the 61-year record. Seven indices, all except CWD, demonstrated upward tendencies, with mutation points clustering after 2010, most notably between 2011 and 2016. Wavelet power spectra indicates elevated energy concentrations at multiple time scales, although only CWD exhibited a statistically significant periodicity of approximately 8–10 a (p < 0.05 against red noise). In terms of spatial patterns, index magnitudes generally increased along a northwest-to-southeast gradient. Stations registering significant upward shifts were concentrated in East and Central South China, whereas significant downward shifts appeared mainly in North China and the northern portion of East China. An altitude-dependent pattern was also detected: CWD rose with elevation, while the remaining indices declined sharply below 1288 m, fluctuated in the 1288–2090 m band, and dropped again above 2090 m. Wavelet coherence analysis uncovered significant resonance between extreme precipitation and four circulation indices—SCSMMI, WPSHI, PNA, and NAO. MWC further identified three driver combinations—ENSO-PNA, SCSMMI-WPSHI, and ENSO-NAO-EASMI—as the most influential, acting both individually and synergistically. These results furnish an empirical basis for forecasting, preventing, and managing precipitation-related disasters across China under future climate scenarios. Full article

Transmission error (TE) is a major excitation source in geared systems, but microgeometry deviations are usually evaluated through nominal amplitudes rather than their tooth-to-tooth spatial distribution. This study investigates how different tooth-level barreling deviation patterns influence TE modulation and spectral characteristics in a controlled single helical gear-pair model. The nominal barreling value was kept constant, while four deviation patterns were imposed on the 23-tooth pinion: harmonic, phase-shifted harmonic, clustered with an outlier, and random. The TE response was evaluated in the time domain and by Fast Fourier Transform (FFT)-based spectral analysis, with particular attention to the gear mesh frequency (GMF) and shaft-frequency-spaced sidebands. The results show that identical nominal barreling levels can produce different TE waveforms and spectral signatures. Harmonic distributions mainly preserve a regular response, whereas phase-shifted and clustered patterns increase waveform asymmetry and sideband activity. The clustered outlier case produced the most fault-like response. The findings indicate that tooth-level spatial distribution should be considered explicitly in simulation-based gear microgeometry and noise, vibration, and harshness (NVH) sensitivity studies. Full article

Coral reefs in the Gulf of Eilat maintain a high diversity of ~100 stony coral species. Despite intense competition for a limited substrate, this raises fundamental questions about spatial organization and mechanisms of coexistence. This study combines deep learning species classification with spatial point-pattern analysis to quantify the frequency of intragenus versus intergenus competitive contacts among four dominant coral genera, Acropora, Favia, Platygyra, and Stylophora, across 12 standardized transects at four reef sites. The ResNet-50 convolutional neural network achieved 92.3% test accuracy for genus-level identification in field imagery of 1100 test images, enabling automated detection of 487 coral–coral competitive pairs exhibiting direct physical contact. Intragenus pairs comprised only 18.3% (89/487) of contacts, significantly below the 50% expected under spatial randomness (z = −14.0, p < 0.0001) with pair correlation functions g(r) > 1 at sub-meter scales indicating conspecific clustering. Genus-specific pair frequencies correlated strongly with relative abundance and spatial coverage (r = 1), with ecological traits explaining dominance patterns: fast-growing, competitive Acropora generated high contact rates, while stress-tolerant Favia and Platygyra prevailed through longevity and defensive competition. These findings demonstrate that intergeneric competition dominates despite local congeneric aggregation, maintaining diversity through niche partitioning rather than intransitive networks, even as coral cover declines amid rising temperatures above 0.05 °C yr⁻¹ and historical eutrophication. The deep learning workflow provides a scalable baseline for monitoring anthropogenic impacts on coral competition dynamics. Full article

Assessing the ecological and environmental quality of old industrial cities is crucial for understanding the spatial heterogeneity of ecological quality and its associated factors during regional transformation. Taking Tangshan, a typical old industrial city in China, as a case study, this study employed Landsat 8/9 remote sensing imagery and multi-source auxiliary data from 2015 to 2024 to calculate annual Remote Sensing Ecological Index (RSEI) values using a unified multi-year standardization and principal component analysis framework. Global and local Moran’s I analyses were conducted to examine spatial clustering patterns, and the Optimal-Parameter Geographical Detector (OPGD) was used to quantify the spatial correspondence between RSEI and selected natural and anthropogenic explanatory factors. The results indicate the following. (1) The mean RSEI in Tangshan fluctuated between 0.34 and 0.54 from 2015 to 2024, exhibiting significant interannual variability. (2) Higher RSEI values were primarily distributed in the northern mountainous and southern coastal ecological zones, while lower values were concentrated in the central and eastern industrial-mining zones. (3) The global Moran’s I was significantly positive in all years (0.702–0.778, p = 0.001), indicating the persistence of spatial clustering; the proportion of non-significant local spatial units decreased from 72.00% in 2015 to 69.46% in 2024. (4) Land use/land cover (LULC) exhibited the most consistently high explanatory power. Elevation (ELE), nighttime light (NTL), and built-up intensity (BUILT) also formed a leading group of spatially associated factors, although their relative ranking varied between the optimal-parameter results and the robustness analysis. Slope (SLOPE), annual precipitation (Pre), and annual mean temperature (Tmean) generally showed relatively lower explanatory power. Interaction detection showed that pairwise factor combinations generally had higher q values than individual factors, with LULC × ELE showing consistently high explanatory power in representative years. This study provides a scientific reference for ecological and environmental monitoring and differentiated management in old industrial cities. Full article

Under the dual pressures of global climate change and rapid urbanization, the spatial contradiction between urban expansion and flash flood disasters in mountainous dam areas is increasingly evident. However, the mechanisms by which the multi-dimensional characteristics of urban expansion affect regional flash flood susceptibility (FFS) remain unclear, limiting scientific guidance for source-level disaster prevention. This study uses Zhaotong City, a flash flood-prone area in the lower Jinsha River basin of southwestern China, as a case study. Using land use and multi-source remote sensing data from 2000 and 2025, we identify urban expansion patterns and morphological characteristics, apply the XGBoost-SHAP model to evaluate flash flood susceptibility and determine dominant factors, and employ the generalized additive model (GAM) to quantify the nonlinear responses of expansion dimensions to FFS. Results show the following: (1) Urban expansion in Zhaotong City is primarily edge (51%) and leapfrog (46%), clustering along river valleys, dam areas, and transportation corridors. (2) The XGBoost model performs well (AUC = 0.877). Elevation, slope, normalized difference vegetation index (NDVI), and precipitation are the primary natural factors influencing FFS. About 15.66% of the city falls within the high/very high FFS zones, mainly in the Zhaolu Dam area, riverbanks of main and tributary streams, and the urban built-up area. (3) Urban expansion-related indicators explain 28.6% of the spatial variation in FFS, with leapfrog expansion as the primary driver (contribution rate 32.75%). Disorderly urban growth and morphological imbalance significantly increase flash flood susceptibility. This study provides a scientific basis for spatial planning, flash flood prevention and control, and climate-adaptive urban development in similar mountainous dam areas in Southwest China and Asia, supporting regional sustainable development goals. Full article

Rapid urbanization has intensified the need for vibrant, walkable, and socially sustainable urban environments, particularly within mixed-use and commercial districts. The way buildings and streets are spatially configured in these districts plays a critical role in shaping pedestrian movement, spatial accessibility, commercial vitality, and social interaction within these environments. This paper investigates the role of spatial configuration in shaping the resilience and sustainability of urban commercial districts through a comprehensive review of recent space syntax applications. The review synthesizes methodological approaches for examining spatial structures, urban morphology, spatial accessibility, and urban activity patterns, including segment-based spatial analysis, visibility graph analysis, agent-based modeling, and predictive spatial simulation. This study consolidates recent methodological developments in spatial analytics and identifies key analytical trends that clarify how spatial configuration contributes to urban vitality and sustainability in commercial districts. Particular attention is given to the methodological evolution of space syntax research and its increasing integration with complementary datasets and analytical frameworks for evaluating urban vitality. Across the reviewed studies, highly integrated and spatially accessible street networks were consistently associated with higher pedestrian flow, greater commercial density, stronger land-use clustering, and improved walkability, particularly within compact, mixed-use urban districts. Movement-based metrics such as integration and Normalized Angular Choice (NACH) repeatedly emerged as dominant predictors of pedestrian movement, land-use intensity, and commercial concentration. Despite significant methodological advances in spatial analysis, a persistent gap remains in linking configurational metrics with lived human experience and broader social sustainability outcomes. Overall, the findings demonstrate that spatial configuration is a fundamental driver of walkability, commercial vitality, and socio-spatial interaction, reinforcing the growing role of space syntax as a framework for evidence-based and sustainable urban design. Full article

Urban color plays a fundamental role in shaping the visual character and cultural identity of cities. Yet in many contexts, current practices remain fragmented, with color analysis often disconnected from planning implementation and governance. To address this issue, this study proposes a decision-support framework and a method for urban color evaluation and planning that integrates street view imagery, machine learning algorithms, and a parameter-based decision-support system. Using 430,000 street view images of Jiading District, Shanghai, we developed a computational model to systematically map building color characteristics in terms of hue, saturation, and brightness at both building and neighborhood scales. A multi-dimensional criteria framework encompassing the macro-environment, building characteristics, and micro-context is developed to guide automatic color scheme generation and evaluation for both existing and new buildings. The findings extract dominant color features and reveal spatial clustering patterns across Jiading District. The platform evaluates color schemes for new developments and generates color schemes for existing buildings, thereby linking urban color analysis with planning recommendations. This study presents a digital decision-support tool for urban color governance that integrates SVI, semantic segmentation, and rule-based reasoning. It shows how large-scale visual data can be organized and translated into structured references for planning practice, offering a more systematic and measurable support tool for urban color assessment. Full article

In multi-ethnic villages, public space serves as more than just a venue for social interaction; it is the vital ground where cultural integration and community identity take root. This study examines Cizhong Village in the Diqing Tibetan Autonomous Prefecture of Yunnan, employing a mixed-methods approach that combines questionnaire surveys (N = 120), semi-structured interviews (N = 32), and Social Network Analysis (SNA) to compare the village’s planned spatial network with residents’ actual movement patterns. Findings reveal a significant structural mismatch: while the planned network exhibits higher density (0.32) and clustering (0.70), the behavioral network demonstrates a stronger small-world index (2.14 vs. 1.94), indicating that villagers organically form compact activity clusters around key social hubs such as the church and supermarket. QAP correlation analysis further shows that Tibetan and Naxi behavioral networks are highly similar (r = 0.833, p < 0.001), whereas Han networks exhibit weaker correlations (r = 0.527–0.607, p < 0.05), revealing a spatial pattern of “broad integration with localized ethnic preferences”. Grounded theory coding of interview data (55 initial concepts, 14 categories, 4 core categories) validates these structural findings and identifies the core theme of “superposed space of multi-ethnic dynamic sharing”. Based on these results, three optimization strategies are proposed: improving connectivity between public spaces, revitalizing key social hubs, and respecting established ethnic spatial traditions. These insights provide an evidence-based framework for managing public spaces in multi-ethnic rural communities. Full article

Accurate prediction of invasive species diffusion is essential for effective management and ecological conservation. Existing spatio-temporal Cox process models face limitations due to the separability assumption, which fails to capture spatio-temporal coupling dynamics inherent in biological diffusion processes. This study proposes a Spatio-Temporal Interaction Kernel Cox (STIK-Cox) model that constructs a non-separable conditional intensity function integrating baseline intensity, spatial and temporal proximity kernels, seasonal fluctuation, and a spatio-temporal interaction term. The model employs maximum likelihood estimation with Limited-memory Broyden–Fletcher–Goldfarb–Shanno with Bounds (L-BFGS-B) optimisation and incorporates SHapley Additive exPlanations (SHAP) for interpretability analysis. Using the Vespa mandarinia (Hymenoptera, Vespidae) monitoring dataset from Washington State, the model achieves a comprehensive accuracy score of 0.957, a capture rate of 98.74% at a 0.5° threshold, and a mean prediction error of 0.0802°. K-function analysis confirms effective capture of spatial clustering patterns, while SHAP analysis reveals longitude as the primary predictive driver. The non-separable design outperforms conventional methods including inverse distance weighting and Poisson point processes. This framework demonstrates the potential of non-separable spatio-temporal point processes for invasive species early warning, providing a scientific basis for targeted monitoring and resource allocation in ecological management. Full article