Search Results (5,332)

Rurbanization and peri-urbanization are among the most dynamic territorial processes affecting metropolitan regions in Morocco, particularly within the Casablanca–Settat region. These transformations, driven by rapid urban growth, demographic pressure, and socio-economic change, generate complex transitional spaces between rural and urban environments. In this context, the present study proposes a hybrid methodology for detecting, classifying, and analyzing the rural–urban continuum by using remote sensing data and artificial intelligence techniques. The approach integrates Sentinel-2 satellite imagery, spectral indices, Global Human Settlement Layer datasets, and socio-demographic indicators derived from the Moroccan census. Two models, Self-Organizing Maps (SOM) and Graph Neural Networks (GNN), were applied to classify territories into four categories: urban, peri-urban, rurban, and rural. Model outputs were combined with expert-based decision rules to improve classification robustness and interpretability. The SOM model achieved up to $89.3\%$ agreement with expert classifications and a Cohen’s Kappa coefficient of $0.842$, demonstrating strong interpretability and consistency, while the GNN model reached $53\%$ agreement and effectively modeled spatial dependencies and neighborhood interactions. Diachronic analysis between 2014 and 2024 revealed a $54\%$ increase in peri-urban municipalities, a $24\%$ decrease in rurban territories, and a decline in rural municipalities, highlighting intensified urban sprawl and fragmentation of agricultural landscapes. Beyond its scientific contribution, this study provides a valuable decision-support framework for urban planners, environmental agencies, and policy makers involved in territorial governance and sustainable development. It can support land-use planning, monitoring of urban sprawl, protection of agricultural lands, and the implementation of adaptive territorial policies aimed at improving the resilience and sustainability of rurban environments. Full article

High-density urban parks are essential spaces for residents in core urban areas for restorative experiences and routine leisure. Research on the impact of cross-density contexts on the motivational structure of park recreation remains limited. Empirical identification under a high-density Built Environment remains limited, and cross-density comparison is largely absent. This study examines five high-density parks using 583 valid questionnaires and the Recreation Experience Preference (REP) scale with exploratory factor analysis (EFA) to identify the motivational structure and motivational expression strength of park recreation. Standardized density assessment and cross-density comparison in existing studies generate exploratory hypotheses. Results identify eight motivational dimensions, explaining 62.36% of the variance. Physical well-being, nature enjoyment, relaxation and family bonding, and social connection are consistently recognized across density contexts, while escape, introspection and self-realization, learning and exploration, and autonomy and independence are more likely to emerge as independent dimensions in high-density contexts. Physical well-being and social connection appear at higher proportions in low-density contexts. This study provides direct empirical evidence on the motivational structure of urban park recreation in high-density Built Environments, exploratory evidence for understanding the potential associations between urban spatial contexts and psychological needs, and a foundation for future research in human-centered urban landscape planning and management. Full article

Sustainable land-use regulation in historical and cultural cities requires balancing heritage conservation, development demand, cropland retention, and urban–rural spatial restructuring. However, the stage-wise reorganization of urban–rural construction land under these coupled pressures remains insufficiently understood. Taking Jingzhou District, China, as a case study, this study uses land-use data from 2000, 2005, 2010, 2015, and 2020 and integrates stage-wise random-forest analysis, consistency-based interaction-network mining, and multi-scenario simulation within the intPLUS framework. Population, GDP, and areal-water distance layers were matched to the corresponding stage-terminal snapshots where applicable, whereas 2020 POI data were used as contemporary spatial-context proxies. From 2000 to 2020, urban industrial land (UIL) expanded from 16.63 to 46.42 km², increasing by approximately 179.1%, whereas rural settlements (RS) increased more moderately from 56.59 to 60.27 km², increasing by approximately 6.5%. The stage-wise RF and interaction-network results show that UIL and RS followed different spatial association structures, with stronger UIL self-reinforcement and stronger RS self-continuity in the later stage. Historical validation showed overall accuracy values of approximately 91% and Kappa values around 0.80, but FoM values remained relatively low, ranging from 0.098 to 0.176. Class-specific mapping accuracy was higher for RS (81.90–82.37%) than for UIL (55.20–66.93%), indicating a weaker performance in locating UIL change. Therefore, the 2035 simulations should be interpreted as parameter-conditioned regulatory comparisons rather than deterministic pixel-level forecasts. The scenario results indicate that the conservation-oriented limited growth was associated with the restricted UIL expansion and better cropland retention under the prescribed demand and constraint settings, while the RS reduction occurred only under explicit village-consolidation and construction-land quota reallocation assumptions. By distinguishing UIL and RS, this study provides differentiated regulation-oriented evidence for sustainable land-use governance in historical and cultural cities. Full article

Due to climate change and rapid urbanization, cities worldwide face the dual challenge of improving flood resilience and providing accessible green space within limited land resources. Sponge City parks offer a landscape-based approach for integrating stormwater management with park services. However, how park morphology structures this combined performance remains insufficiently understood. This study examines 26 Sponge City parks in Shanghai and evaluates how node-, line-, and patch-type morphologies are linked to stormwater storage and service provision. Using geospatial analysis, DEM-derived catchment delineation, land-cover interpretation, and statistical analysis, this study compares estimated stormwater storage, storage efficiency, local park availability, and land-cover composition across different park morphologies. The results show that estimated performance of stormwater management and park service provision vary across morphological types, but these differences do not follow a simple node–line–patch hierarchy. Rather, the observed patterns are jointly shaped by park morphology, catchment setting, land-cover allocation, and surrounding urban context. These findings suggest that Sponge City parks should not only be evaluated by total stormwater storage. Their contribution depends on morphology, scale, catchment setting, land-cover allocation, and urban context. The study provides a morphology–performance perspective to support more differentiated planning of multifunctional green infrastructure. Full article

Increasing attention has been directed toward walkability evaluation because pedestrian environments are closely associated with mobility patterns, environmental quality, and everyday spatial experience. However, most existing walkability studies either emphasize objective spatial indicators or rely on subjective satisfaction surveys, while the relationship between expert evaluation and user satisfaction has received relatively limited attention, particularly regarding its nonlinear characteristics. In addition, walkability frameworks developed for urban public environments are often directly applied to university campuses without adequately considering the distinctive behavioral characteristics of campus pedestrian activities. To address these limitations, this study proposes an integrated AHP–Kano walkability evaluation framework for campus pedestrian systems. The framework combines the Analytic Hierarchy Process (AHP) with the Kano model to establish a perception-sensitive and behavior-oriented evaluation structure. AHP is employed to determine the relative importance of environmental indicators through expert judgment, while the Kano model is introduced to capture the asymmetric effects of different environmental attributes on user satisfaction. GIS analysis and field investigation were employed as supplementary spatial diagnostic tools to support the interpretation of pedestrian–environment characteristics. Using the Xiamen campus of Huaqiao University as a case study, this research constructs a multidimensional evaluation system covering accessibility, safety, comfort, landscape quality, and service functionality. Questionnaire surveys and expert evaluations were conducted to analyze the relationship between objective environmental importance and subjective perceptual response. The results indicate that safety- and accessibility-related attributes primarily function as must-be requirements that prevent dissatisfaction, whereas environmental cleanliness and selected experiential elements exhibit stronger satisfaction-enhancing effects. Several landscape-related indicators, commonly emphasized in urban walkability studies, demonstrate relatively weak perceptual sensitivity in campus contexts, reflecting the task-oriented and time-constrained nature of campus pedestrian behavior. The present study extends existing walkability research in several important respects. Rather than relying on conventional linear assumptions, the proposed framework incorporates nonlinear perceptual responses into walkability evaluation. The findings further demonstrate that pedestrian perception is highly context-dependent in campus environments, while the integrated framework further provides a behavior-sensitive basis for prioritizing spatial interventions. Full article

Urban ethnic enclaves are historically layered cultural landscapes whose public signage encodes community vitality, power relations, and cultural identity in ways that conventional land-use inventories cannot capture. Addressing the absence of scalable, longitudinal computational methods for monitoring such at-risk landscapes, this study develops a reproducible digital-mapping pipeline that operationalises linguistic-landscape analysis as a cultural-heritage monitoring tool for heritage-sensitive land-use planning. Taking Daerim-dong—Seoul’s primary Joseonjok (Korean Chinese) enclave—as a case, we process 38,640 Kakao Map Road View images across 17 annual cross-sections (2008–2024). The pipeline integrates four methodological components: a bounded Spatial Weighting Correction that adjusts for uneven historical coverage; zero-shot semantic sign-function classification using the Qwen2-7B-Instruct model; an exploratory Difference-in-Differences design probing the 2016–2017 THAAD geopolitical disruption; and a Boundary Permeability Ratio (BPR) for tracking enclave edge dynamics. The results document a three-phase trajectory—rapid bilingual expansion (2008–2016), stabilisation (2016–2019), and a COVID-period contraction (2019–2024)—and show that raw sign-count metrics can systematically overstate minority-language decline during economic crises once crisis-period signage is isolated. The BPR is presented as a candidate leading indicator of enclave contraction whose operational thresholds remain to be calibrated through multi-enclave validation. As a methodological proof-of-concept, the study illustrates how computational street-view analysis can support cultural-landscape governance, offering urban planners and heritage managers an actionable, transparent baseline for monitoring at-risk multicultural urban landscapes. Full article

Environmental datasets are often characterized by complex spatial structures and the presence of atypical observations that may influence the interpretation of landscape patterns. This study proposes a comparative framework for identifying spatial outliers in landscape structure using two complementary approaches: K-means clustering and multivariate visual exploration based on Chernoff faces. The analysis is conducted on two temporal snapshots (1956 and 2019) representing long-term changes in land use and land cover in the Zemplínska Šírava region, Eastern Slovakia. Outlier detection results from both approaches are systematically compared to assess their consistency and robustness. The two methods show substantial correspondence in the identification of anomalous landscape units. The number of land-cover classes increases from 19 in 1956 to 25 in 2019, reflecting increased landscape heterogeneity over time. Persistent spatial outliers across both methods and time periods include road networks and associated land and broad-leaved forest with continuous canopy, indicating the structural stability of these landscape elements despite long-term transformation. The results demonstrate that combining clustering-based approaches with multivariate visual analytics can improve the interpretation of complex spatial patterns in environmental data. However, the study is exploratory in nature, and the interpretation of Chernoff faces involves inherent visual subjectivity, which should be considered when evaluating the results. The proposed framework should therefore be regarded as a complementary exploratory tool rather than standalone analytical evidence. Future research may extend this framework by integrating identified spatial outliers into environmental assessment models focused on biodiversity patterns, ecological connectivity, and sustainable landscape planning. Full article

This article seeks to examine the extent to which the spatial planning of Poland (expressed by coverage of municipalities by Local Spatial Management Plans) reduces uncontrolled land-use changes. In the context of increasing urbanization pressure, spatial planning is theoretically expected to optimize land development and enhance land-use stability. The research tests the hypothesis that a higher share of municipal area covered by Local Spatial Management Plans should be associated with fewer uncontrolled transformations. Land-use changes were identified using CORINE Land Cover (CLC) data, while information on Local Spatial Management Plans coverage was obtained from Statistics Poland. Land-use stability was quantified using the hemeroby index. GIS tools and Multiscale Geographically Weighted Regression were used to assess the relationship between Local Spatial Management Plans coverage and changes in the hemeroby index, as well as to examine the spatial variability of this relationship across municipalities. The results indicate that the influence of Local Spatial Management Plans coverage on land-use stability is spatially differentiated. In some regions, higher planning coverage corresponds with greater landscape stabilization, while in others, planning instruments appear insufficient to counteract urbanization pressures. The findings provide an empirical verification of land management effectiveness in Poland and highlight regional disparities in the capacity of spatial planning to mitigate uncontrolled land-use change. Full article

As urbanization accelerates, urban residents face increasing life stress and mental health challenges, while urban green spaces that provide restorative experiences play an important role in promoting physical and mental well-being. However, most green-space accessibility studies have paid limited attention to whether residents can obtain specific health-supporting services, such as therapeutic landscape benefits. To address this gap, this study proposed a Healing Opportunity Assessment Model that incorporates park therapeutic quality into a potential accessibility model and calculates the Healing Opportunity Index (HOI) to measure residents’ opportunities to obtain therapeutic landscape services within a 15-min active transport threshold. Using Harbin as a case study, the results indicate that fitness facility quantity (0.180), waterscape attractiveness (0.150), and service-facility convenience (0.144) are the most important factors affecting park therapeutic quality. Under the 15-min active transport threshold, the distribution of healing opportunities remains highly uneven, suggesting that access to health-supporting therapeutic functions is still insufficiently balanced and that substantial improvement is needed in the current urban park system. This study connects park accessibility with residents’ opportunities to obtain therapeutic landscape benefits, providing quantitative support for identifying underserved communities and improving the equitable provision of health-supporting green-space services. Full article

Investigating the spatiotemporal dynamics of urban heat islands and their responses to urban forest (UF) landscape patterns is crucial for mitigating urban thermal stress. However, the nonlinear influence and conditional constraints of UF landscape composition and configuration on the warming effects across varying urbanization gradients remain inadequately understood. By integrating land use/cover data, MODIS-derived land surface temperature (LST), and meteorological datasets, this study employed the XGBoost-SHAP model to quantify the nonlinear and interaction effects of UF landscape patterns on developed and developing urban regions of the Pearl River Delta. The results indicate that (1) spatial clustering patterns of warming varied significantly between the two regions, with substantial seasonal heterogeneities (p < 0.05). Summer exhibited the most intense warming, characterized by more rapid temperature increase in developed areas than in developing regions. (2) Relative to UF landscape metrics, the proportion of impervious surfaces, precipitation, and temperature exerted greater influence on regional warming. Coverage area, fragmentation, and connectivity of UFs emerged as the primary landscape drivers modulating warming. In developed areas, spatial configuration metrics exerted greater influence on LST than compositional metrics. (3) The responses of LST to diverse UF landscape patterns are characterized by nonlinearity and pronounced threshold effects. These landscape thresholds vary by season, revealing critical tipping points for warming suppression; however, this regulatory effect is highly context-dependent. Specifically, under high percentages of impervious surface, the warming-suppression capacity of UFs intensifies with increasing percentage of UF area or core. Our findings highlight the necessity of strategic UF planning and forest fragmentation mitigation for developing effective climate resilience strategies. These results provide a foundation for adaptive planning tailored to specific urbanization stages and the implementation of targeted UF cooling strategies. Full article

Landscape Ecological Risk (LER) reflects the potential adverse effects of landscape change on ecological structure, function, and stability. In rapidly urbanizing megacities such as Dhaka, vegetation loss and built-up expansion have intensified environmental pressure over recent decades. This study examines the spatiotemporal dynamics of LER in Dhaka from 2004 to 2024 under the combined influence of vegetation change and urban expansion. Multi-temporal remote sensing data were used to generate land cover maps, derive Fractional Vegetation Cover (FVC), and quantify urbanization intensity using Nighttime Light (NTL) data. The Landscape Ecological Risk Index (LERI) was calculated using landscape pattern metrics, while bivariate spatial autocorrelation and geographically weighted regression (GWR) were applied to examine spatial associations and local spatial heterogeneity. The results show that vegetation degradation affected 34.39% of the study area during 2004–2024, while high-risk zones increased from 24.36% in 2004 to 42.95% in 2024. Land cover analysis further indicates a substantial expansion of built-up areas, accompanied by the contraction and fragmentation of vegetation, agricultural land, and lowland classes. Spatial analyses reveal that the relationships among vegetation cover, urbanization intensity, and ecological risk vary across the city and became increasingly spatially differentiated over time. These findings suggest that vegetation loss and urban expansion are spatially associated with increasing ecological risk in Dhaka. However, the results should be interpreted with caution because of uncertainties related to remotely sensed data, unsupervised land cover classification, resampling procedures, and limited ground validation. Despite these limitations, the study provides a spatially explicit framework for understanding ecological risk dynamics and offers useful evidence for green-space conservation, ecological restoration, and sustainable urban planning in rapidly urbanizing regions. Full article

Rooftop farms are urban green infrastructure integrating food production, ecological regulation, and public services, and their management increasingly relies on data-driven approaches. However, open built environments, microclimatic heterogeneity, and limited sensor deployment challenge continuous monitoring and short-term prediction of rooftop plant growth. This study proposes and validates a virtual sensor-driven digital twin system using a rooftop tomato case in Xiamen, China. The system adopts a five-layer architecture comprising data acquisition, transmission, modeling, processing, and application service layers. By coupling a Long Short-Term Memory (LSTM) weather prediction model with the Decision Support System for Agrotechnology Transfer (DSSAT) crop growth model, a predictive virtual sensor module was developed to forecast leaf area index (LAI), aboveground biomass, phenology, and yield for seven days. Results show that the system links environmental data acquisition, LSTM–DSSAT prediction, database storage, and three-dimensional visualization, transforming rooftop plant growth into an updatable, predictable, and visualized digital twin object. The coupled model showed high predictive accuracy, with R² values of 0.9814 for LAI and 0.9966 for aboveground biomass, while supporting phenology and yield prediction. The system supports irrigation optimization, landscape management, and activity planning in sensor-constrained rooftop farms. Full article

Exposure to urban green spaces has been associated with mental wellbeing, but the neural responses to specific visual properties of urban green spaces remain unclear. This study investigated regional electroencephalogram (EEG) responses to latent visual dimensions of virtual urban green space exposures. This study used a quantitative scene-based approach that extracted 41 visual metrics to capture the heterogeneous structural properties of 24 panoramic urban green images. EEG recordings were analyzed from 150 participants, each of whom viewed eight randomly selected images repeated three times. Dimension-wise factor analysis with varimax rotation was used to derive latent factor scores for four conceptual dimensions: naturalness, complexity, coherence, and visual scale. These factors were then used as predictors in crossed mixed-effects models of regional EEG relative power changes. The hypothesis-driven primary analysis showed a significant and positive association between parietal alpha–theta activity and a naturalness factor reflecting green–grey scene compositions. Exploratory frontal associations with a terrain-related visual scale factor reached nominal significance but did not survive false discovery rate correction. Overall, the findings support a quantitative, feature-based approach for linking urban green space structure with regional neurophysiological responses. This study provides a methodological step toward more evidence-informed assessment of smart and sustainable urban environments. Full article

Climate change and rapid urbanization increasingly threaten water security in large river basins, yet existing assessments often fail to capture the multi-scale interactions between hydroclimatic extremes and human activities. To address this gap, we developed an integrated framework combining risk assessment, multi-method driver diagnosis (Geodetector, Multi-Scale Geographically Weighted Regression (MGWR), and Structural Equation Modeling (SEM)), and Zoned Management. Using a landscape-derived Ecological Risk Index (ERI) as a proxy indicator of runoff and non-point source potential, based on established empirical linkages between landscape metrics and hydrological processes, we applied the framework to three major urban agglomerations in the Yangtze River Basin from 2000 to 2020. Our results reveal three distinct risk mechanisms: in the Chengdu–Chongqing area (CYUA), a 165.8% increase in impervious surfaces drives altered runoff; in the Middle Reaches (MRC), the q-value of the Standardized Precipitation Index (SPI) rose from 0.017 in 2000 to 0.146 in 2020, corresponding to a 759% relative increase. Although the absolute q-value of SPI remains moderate at around 0.15, its rapid rise suggests increasing hydrological sensitivity of the MRC’s river–lake system to precipitation extremes; in the Yangtze River Delta (YRD), socioeconomic activities exert overriding pressure. Based on these diagnostics, we propose tailored strategies for water environment management, adaptive planning, and disaster mitigation. This framework offers a scientific basis for differentiated water governance in large river basins facing coupled anthropogenic and hydroclimatic pressures. Full article

Against the backdrop of global climate change and increasing pressure to mitigate carbon emissions, counties serve as critical units for urban–rural spatial development and carbon governance. However, their carbon emission performance (CEP) and underlying spatial mechanisms remain insufficiently understood. This study focuses on 157 counties in Henan Province, selecting three time points: 2013, 2018, and 2023. The study measures the CEP and analyzes its spatiotemporal differentiation characteristics. First, considering that carbon emissions are undesirable outputs generated during the economic production process, this study employs the undesirable output slack-based measure (UN_SBM) model and the super-efficiency slack-based measure model with undesirable outputs (Un_Super_SBM) to evaluate county-level carbon emission performance. Second, landscape pattern indicators, including expansion, complexity, and compactness, are selected, and regression models are constructed to explore the influence of different factors on carbon emission performance. The results show the following: (1) The overall CEP of counties in Henan Province improved from 2013 to 2023, but there were significant spatial differences. (2) Both “Total landscape area” (TA) and “Area-weighted mean shape index” (AWMSI) had significant positive impacts on CEP, whereas the “Splitting index” (SPLIT) inhibited CEP. (3) The effects of vegetation cover and transportation conditions varied, reflecting the heterogeneity of development stages and spatial functional positioning across different counties. This study reveals the relationship between urban–rural spatial form and carbon emission performance at the county level, providing empirical evidence for optimizing construction land spatial structure, enhancing CEP, and promoting regional low-carbon development. Full article