Search Results (468)

The scientific establishment of ecological security pattern and identification of ecological restoration priority areas are key for territorial space ecological restoration and people’s well-being enhancement. Although numerous studies have addressed this topic, most focused on regional and urban scales. As the most basic administrative units in China, townships serve as a crucial link between macro-ecological protection strategies and micro-ecological restoration practices and are essential for effectively implementing ecological restoration and supporting rural revitalization practices, but research at this scale is currently lacking. Therefore, taking a typical township in Shanghai as an example, this study incorporated the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, Morphological Spatial Pattern Analysis (MSPA), landscape connectivity analysis, and circuit theory to construct an ecological security pattern and identify ecological restoration priority areas at the township scale, as well as to discuss corresponding ecological restoration strategies. The results showed that: (1) The study area contained 19 significant ecological sources (area of approximately 4.85 km²), exhibiting a spatial pattern characterized by “north–south concentration, central dispersion”. High-resistance areas were mainly distributed in areas with dense human activity and high development intensity, reflecting the significant impact of human activities on ecological processes. There were 32 main ecological corridors with a total length of 58.06 km, showing significant spatial imbalance, with some northern ecological sources at the risk of forming ecological isolated islands. (2) The ecological restoration priority areas mainly consisted of 41 ecological pinch points (area of approximately 27.24 ha) and 30 ecological barrier points (area of approximately 25.67 ha), which were crucial for enhancing ecological network connectivity and maintaining ecological security. (3) Based on the current land use status and spatial distribution characteristics of key ecological restoration areas, a hierarchical and categorized ecological restoration strategy was formulated. This study can strengthen research on identifying ecological restoration priority areas at the township scale. The methodological system established can provide a theoretical framework for ecological restoration research in similar areas. Moreover, this study pinpointed key areas and the spatial layout for ecological restoration, which helped to enhance the level of refined ecological governance at the township level and can also provide precise spatial decision-making basis for ecological restoration of the township territorial space. Full article

Modern geo-information platforms commonly adopt multi-window map interfaces that integrate heterogeneous data, such as dynamic maps and live camera feeds. These interfaces impose high cognitive load and slow spatial event detection. Operators must rapidly locate the source of visual alarms, a task often leading to delays under high visual workload. To address this challenge, this study investigated whether spatialized auditory cues can improve alarm localization in such complex monitoring interfaces. A controlled experiment with 24 participants used a within-subjects design to test factors of auditory spatial cueing (none, binaural, monaural), display dynamics (dynamic, static), and interface complexity (4, 8, 12 panes). Behavioral and eye-tracking data measured detection accuracy, efficiency, and gaze patterns. Results showed that dynamic displays and high interface complexity impaired performance, indicating increased cognitive load. In contrast, monaural lateralized auditory alarms substantially improved detection efficiency and mitigated visual overload. Interaction analyses revealed that binaural cues reduced the performance costs of dynamic displays, whereas monaural cues compensated for high-density layouts. These findings demonstrate that spatialized auditory alarms effectively support spatiotemporal situational awareness and improve operator performance in high-load geo-surveillance systems. The study offers empirical and practical implications for designing cognitively ergonomic, multimodal interfaces that move beyond purely visual alarm designs. Full article

Fine-grained food image segmentation is essential for accurate dietary assessment and nutritional analysis, yet remains highly challenging due to ambiguous boundaries, inter-class similarity, and dense layouts of meals containing many different ingredients in real-world settings. Existing methods based solely on CNNs, Transformers, or Mamba architectures often fail to simultaneously preserve fine-grained local details and capture contextual dependencies over long distances. To address these limitations, we propose HDF (Hybrid Decoder for Food Image Segmentation), a novel decoding framework built upon the MambaVision backbone. Our approach first employs a convolution-based feature pyramid network (FPN) to extract multi-stage features from the encoder. These features are then thoroughly fused across scales using a Cross-Layer Mamba module that models inter-level dependencies with linear complexity. Subsequently, an Attention Refinement module integrates global semantic context through spatial–channel reweighting. Finally, a Food Co-occurrence Module explicitly enhances food-specific semantics by learning dynamic co-occurrence patterns among categories, improving segmentation of visually similar or frequently co-occurring ingredients. Evaluated on two widely used, high-quality benchmarks, FoodSeg103 and UEC-FoodPIX Complete, which are standard datasets for fine-grained food segmentation, HDF achieves a 52.25% mean Intersection-over-Union (mIoU) on FoodSeg103 and a 76.16% mIoU on UEC-FoodPIX Complete, outperforming current state-of-the-art methods by a clear margin. These results demonstrate that HDF’s hybrid design and explicit co-occurrence awareness effectively address key challenges in food image segmentation, providing a robust foundation for practical applications in dietary logging, nutritional estimation, and food safety inspection. Full article

With the rapid urbanization in China, building energy consumption has become a critical challenge for sustainable urban development. Conventional simulation methods are computationally intensive and inefficient for large-scale urban layout optimization, highlighting the need for fast and reliable predictive approaches. Existing machine learning models often overlook spatial relationships among buildings and rely heavily on manual feature engineering, which limits their applicability at the urban block scale. To address these limitations, the study proposes a building energy consumption prediction model for urban blocks based on Generative Adversarial Networks (GANs), which preserves spatial information while significantly advancing computational speed. The optimal GAN model is further integrated with a Multi-Island Genetic Algorithm (MIGA) to form a GAN-MIGA optimization framework, which is applied to the layout optimization of a target urban block in Lanzhou. Key findings include: (1) the GAN model achieves an average prediction error of 6.8% compared with conventional energy simulations; (2) the GAN-MIGA framework reduces energy consumption by 48.78% relative to the worst-performing solution and by 22.53% compared with the original block layout; (3) the spatial distribution patterns of energy consumption predicted by the GAN are consistent with those obtained from traditional simulation methods; (4) the regression model derived from GAN-MIGA optimization results achieves an R² value exceeding 0.84; and (5) building layout design strategies are formulated based on key morphological indicators in the regression model. Overall, this study demonstrates the effectiveness of the GAN-based method for urban scale building energy prediction and layout optimization. The proposed GAN-MIGA framework provides practical tools and theoretical support for energy-efficient design, policy formulation, and smart city development, contributing to more sustainable urban energy planning. Full article

Large sports stadiums require robust real-time monitoring due to high crowd density, complex spatial configurations, and limited network infrastructure. This research evaluates a hybrid edge–cloud architecture implemented in a national stadium in Thailand. The proposed framework integrates diverse surveillance subsystems, including automatic number plate recognition, face recognition, and panoramic cameras, with edge-based processing to enable real-time situational awareness during high-attendance events. A simulation based on the stadium’s physical layout and operational characteristics is used to analyze coverage patterns, processing locations, and network performance under realistic event scenarios. The results show that geometry-informed sensor deployment ensures continuous visual coverage and minimizes blind zones without increasing camera density. Furthermore, relocating selected video processing tasks from the cloud to the edge reduces uplink bandwidth requirements by approximately 50–75%, depending on the processing configuration, and stabilizes data transmission during peak network loads. These findings suggest that processing location should be considered a primary architectural design factor in smart stadium systems. The combination of edge-based processing with centralized cloud coordination offers a practical model for scalable, safety-oriented monitoring solutions in high-density public venues. Full article

Street layout has a significant effect on accessibility and intelligibility, which ultimately affects navigation and movement efficiency. While previous research has examined planned and unplanned street patterns, most studies focus on single-scale analyses or isolated typologies, limiting understanding of how hybrid networks function across multiple spatial levels. Addressing this gap, this study investigates the effects of hybrid planned and organically evolved street layouts on spatial accessibility in Mandalay, Myanmar. The research employs space syntax analysis to assess the citywide, township-level, and micro-scale networks through measures of angular integration, choice, axial connectivity, and intelligibility. Using the Four-Point Star Model to identify Mandalay’s distinct spatial features, a global accessibility assessment compares it to 50 other cities. The results show that grid-based layouts with central townships exhibit the highest integration and connectivity, while organic and fragmented networks, particularly in Amarapura, reduce spatial coherence and accessibility. Micro-scale analysis indicates that hybrid layouts with cul-de-sacs and distorted grids can improve accessibility when they connect effectively with secondary roads. By analysing street networks across multiple spatial scales, this research presents significant implications for efficient accessibility and transport planning in mixed-pattern cities. Full article

A well-structured urban park system (UPS) is crucial for optimizing urban spatial layout and improving the quality of the human living environment. In response to the tendency of current planning to prioritize quantitative indicators while overlooking the relational structure arising from the collective spatial configuration of parks, this study introduces Social Network Analysis (SNA) to evaluate the spatial structure of Shanghai’s park system by constructing a service-coverage overlap network. The findings reveal the following: (1) Parks with high degree centrality are concentrated in high-density urban core areas due to service overlap, whereas large suburban parks with high betweenness centrality function as critical bridging hubs, reflecting a polycentric structure. (2) There is a discernible discrepancy between these emergent network tiers and the statutory park hierarchy, highlighting a tension between bottom-up spatial patterns and top-down planning frameworks. (3) Stability simulations indicate a dual character of the system, where the network topology is vulnerable to attacks yet functionally resilient to failures due to spatial redundancy, suggesting that a decline in service quality may precede the loss of basic accessibility. This study demonstrates the value of SNA in diagnosing park system structure, identifying key nodes, and assessing system resilience. The insights advocate for planning approaches that transcend rigid hierarchical frameworks, integrate the actual functional roles of parks, and protect structural hubs, thereby enhancing systemic resilience and promoting equitable service provision. Full article

Visual Impression in Architectural Space (VIAS) plays a central role in user response to environments, yet designer-controlled spatial variables often produce uncertain perceptual outcomes across cultural contexts. This study develops a multi-modal framework integrating VIAS theory, spatial documentation, and sentiment-aware NLP to evaluate temporary event spaces. Using a monthly market in Matsue, Japan as a case study, we introduce (1) systematic documentation of controlled spatial variables (layout, visibility, advertising strategy, (2) culturally balanced datasets comprising native Japanese and international participants across onsite, video, and virtual interviews, and (3) an adaptive sentiment-weighted keyword extraction algorithm suppressing interviewer bias and verbosity imbalance. Results demonstrate systematic modality effects: onsite participants exhibit festive atmosphere bias (+18% positive sentiment vs. video), while remote modalities elicit balanced critique of signage clarity and missing amenities. Cross-linguistic analysis reveals native participants emphasize holistic atmosphere, whereas international participants identify discrete focal points. The adaptive algorithm reduces verbosity-driven score inflation by 45%, enabling fair cross-participant comparison. By integrating spatial variable documentation with sentiment-weighted linguistic patterns, this framework provides a replicable methodology for validating architectural intent through computational analysis, offering evidence-based guidance for inclusive event space design. Full article

With accelerating urbanisation, extreme rainfall events have become increasingly frequent, leading to rising urban flooding risks that threaten city operation and infrastructure safety. The rapid expansion of impervious surfaces reduces infiltration capacity and accelerates runoff responses, making cities more vulnerable to short-duration, high-intensity storms. Although the SWMM is widely used for urban stormwater simulation, its application is often constrained by the lack of detailed drainage network data, such as pipe diameters, slopes, and node connectivity. To address this limitation, this study focuses on the main built-up area within the Second Ring Expressway of Chengdu, Sichuan Province, in southwestern China. As a regional core city, Chengdu frequently experiences intense short-duration rainfall during the rainy season, and the coexistence of rapid urbanisation with ageing drainage infrastructure further elevates flood risk. Accordingly, a technical framework of “open road data substitution–automated modelling–SWMM-based assessment” is proposed. Leveraging the spatial correspondence between road layouts and drainage pathways, open road data are used to construct a virtual drainage system. Combined with DEM and land-use data, Python-based automation enables sub-catchment delineation, parameter extraction, and network topology generation, achieving efficient large-scale modelling. Design storms of multiple return periods are generated based on Chengdu’s revised rainfall intensity formula, while socioeconomic indicators such as population density and infrastructure exposure are normalised and weighted using the entropy method to develop a comprehensive flood-risk assessment. Results indicate that the virtual drainage network effectively compensates for missing pipe data at the macro scale, and high-risk zones are mainly concentrated in densely populated and highly urbanised older districts. Overall, the proposed method successfully captures urban flood-risk patterns under data-scarce conditions and provides a practical approach for large-city flood-risk management. Full article

This study investigates the spatial effects of employment density on the economic, technological, and carbon efficiency of China’s manufacturing sector, using panel data from 30 provinces from 2008 to 2022. A multidimensional performance framework and spatial econometric models are employed to identify both direct impacts and spatial spillovers. The results show that employment density significantly enhances local economic performance while imposing negative spillover effects on neighboring regions. Technological performance exhibits uneven spatial returns, indicating a “technology siphoning” effect in more agglomerated provinces. Carbon efficiency presents a divergent pattern of “local improvement but neighboring deterioration,” highlighting cross-regional ecological externalities. In addition, human capital, capital investment, and regional policy intensity are found to regulate the strength and direction of spatial spillovers across the three performance dimensions. Based on these findings, this study recommends optimizing the spatial layout of manufacturing and population, strengthening interregional innovation collaboration, promoting green transformation, and improving the quality of human capital. These policy implications provide empirical support for advancing sustainable manufacturing development and enhancing regional governance capacity. Full article

The persistent expansion in the intensity and scope of human disturbance has become a key driver of global biodiversity loss, affecting wildlife behavior and population stability across multiple dimensions. As a characteristic symbiotic assemblage in the subalpine forest ecosystems of Sichuan, the giant panda (Ailuropoda melanoleuca), red panda (Ailurus fulgens), and forest musk deer (Moschus berezovskii) exhibit significant research value in their responses to human disturbance. However, existing studies lack systematic analysis of multiple disturbances within the same protected area. This study was conducted in the Sichuan Liziping National Nature Reserve, where infrared camera traps were deployed using a kilometer-grid layout. By integrating spatiotemporal pattern analysis and Generalized Additive Models (GAM), we investigated the characteristics of human disturbance and the response strategies of the three species within their habitats. The results show that: (1) A total of seven types of human disturbance were identified in the reserve, with the top three by frequency being cattle disturbance, goat disturbance, and walking disturbance; (2) Temporally, summer and winter were high-occurrence seasons for disturbance, with peaks around 12:00–14:00, while the giant panda exhibited a bimodal diurnal activity pattern (10:00–12:00, 14:00–16:00), the red panda peaked mainly at 8:00–10:00, and the forest musk deer preferred crepuscular and nocturnal activity—all three species displayed activity rhythms that temporally avoided peak disturbance periods; (3) Spatially, giant pandas were sparsely distributed, red pandas showed aggregated distribution, and forest musk deer exhibited a multi-core distribution, with the core distribution areas of each species spatially segregated from high-disturbance zones; (4) GAM analysis revealed that the red panda responded most significantly to disturbance, the giant panda showed marginal significance, and the forest musk deer showed no significant response. This study systematically elucidates the spatiotemporal differences in responses to multiple human disturbances among three sympatric species within the same landscape, providing a scientific basis for the management of human activities, habitat optimization, and synergistic biodiversity conservation in protected areas. It holds practical significance for promoting harmonious coexistence between human and wildlife. Full article

Traditional villages, protected as cultural heritage in our country, are rich in historical information, cultural landscapes, and traditional domestic architecture. This article explores the spatial distribution of traditional villages and proposes a new paradigm for the sustainable development of traditional dwellings. It addresses the challenges these villages face, such as natural, social, and inherent issues, arising from rapid socioeconomic development and urbanization. This study analyzes the spatial distribution and architectural features of traditional villages and dwellings in Northern Shaanxi based on 179 national and provincial villages. Using ArcGIS 10.1, the geographic concentration index, kernel density analysis, and the analytic hierarchy process, this study applied both macro and micro level perspectives. The research shows that: (1) The traditional villages in northern Shaanxi exhibit a spatial distribution pattern of “overall aggregation, local dispersion, and uneven distribution.” This pattern is influenced by interactions between natural and human factors. (2) Traditional dwellings in these villages are primarily cave dwellings and courtyard buildings, each reflecting unique architectural features in terms of floor plan layout, facade form, structure, materials, and decoration. (3) Traditional village dwellings in northern Shaanxi face practical challenges related to protection, development, and governance. The top three challenges, based on weighted indicators, are issues related to inheritance, an imperfect protection mechanism, and inherent shortcomings of the buildings. Based on these findings, this study proposes three practical suggestions for the sustainable development of traditional village dwellings in Northern Shaanxi. These suggestions aim to enhance the comprehensive and multi-dimensional sustainable development of traditional village dwellings. Full article

A well-designed layout for urban parks plays a crucial role in constructing livable cities and enhancing residents’ well-being. The provision of age-friendly park access is fundamental to building an elderly-friendly city. However, previous studies have lacked comprehensive analyses that integrate the distribution of the elderly population, park accessibility, park quality, environmental characteristics, and social equity within a unified framework. Specifically, the supply–demand imbalance mechanism underlying the spatial variations in accessibility has not been adequately addressed. This study employs an improved two-step floating catchment area (2SFCA) method, combined with Lorenz curves and urban park-adapted Gini coefficients, to examine the supply–demand relationship and allocation differences between the elderly population and parks at the neighborhood and community levels. The analysis highlights issues related to equity and accessibility and explores their spatial disparity and influencing factors. The key findings are as follows: (1) The classic 2SFCA model exhibits significant biases in evaluating park supply–demand relationships, accessibility, and equity at a fine-grained scale, indicating the necessity of high-precision modeling. (2) Park accessibility in the Old City of Nanjing follows a dual-ring pattern of high accessibility, contrasted with clustered areas of low accessibility, while accessibility equity shows a central–peripheral gradient. Overall equity is relatively low, with good walking accessibility within only about one-third of communities. (3) Park supply levels, neighborhood construction year, and plot ratios are the primary factors influencing park accessibility for elderly residents. The comprehensive aging index is positively correlated with the equity in park layout, whereas housing prices and neighborhood size do not exhibit a simple linear relationship with park accessibility or equity for elderly residents. These findings provide a comprehensive and realistic perspective for understanding elderly park accessibility and equity, offering decision-making references for enhancing urban livability, managing an aging society, and formulating spatial equity policies in the future. Full article

As a core sector of the Belt and Road Initiative (BRI) and dual-circulation pattern, Xinjiang’s cultural tourism industry—its ninth-largest industrial cluster—plays a key role in enhancing industrial competitiveness and regional coordinated development. To fill the research gap of insufficient analysis on China’s western frontier regions in existing tourism cluster studies, this research focuses on 14 prefecture-level cities in Xinjiang (2009–2023) and innovatively adopts a spatiotemporal synergy and dual-dimensional correlation framework, addressing the limitations of previous single-dimensional research. Tourism Location Quotient (TLQ) quantified specialized agglomeration, Local Moran’s I identified spatial correlation patterns, gravity models analyzed horizontal inter-cluster interactions, and Gray Relational Model (GRM) measured vertical driving relationships between cluster development and related dimensions. This approach facilitates an in-depth analysis of the spatiotemporal evolution trajectory of Xinjiang’s tourism clusters and their horizontal-vertical linkage mechanisms. Findings show: (1) Xinjiang’s tourism clusters present a spatial pattern of “Northern Xinjiang as the core, Eastern Xinjiang with differentiated development, and Southern Xinjiang as lagging.” With narrowing regional gaps, their evolution transitions from a “fixed gradient” to “co-evolution.” (2) Agglomeration effects are significant: Urumqi propels Northern Xinjiang to form a “high-high agglomeration zone,” while Southern Xinjiang remains a “low-low agglomeration zone” led by Kashgar. (3) Horizontal linkages evolve from a Urumqi-centered single-core structure to a multi-axis cluster network, and vertical linkages are mainly driven by destination attractiveness and economic support capacity. This study clarifies the spatiotemporal evolution logic and associated driving mechanisms of tourism clusters in arid, multi-ethnic frontier regions, providing a scientific basis for optimizing regional tourism layouts and promoting high-quality development. Full article

Enhancing agricultural green total factor productivity (AGTFP) under ecological and environmental constraints is essential for advancing green agricultural development in the Beijing–Tianjin–Hebei (BTH) region. Using panel data from 13 prefecture-level cities from 2001 to 2022, this study applies a super-efficiency EBM model incorporating undesirable outputs together with the Malmquist–Luenberger index to measure AGTFP. Global and local Moran’s I indices as well as the spatial Durbin model are then employed to examine the temporal evolution, spatial disparities, and spatial interaction effects of AGTFP during 2001–2022. The findings indicate that: (1) From 2001 to 2022, the AGTFP in the BTH region grew at an average annual rate of 7.7%. This trend reflects a growth pattern primarily driven by green technological progress in agriculture, while substantial disparities in AGTFP persist across different subregions. (2) the global Moran’s I values show frequent shifts between positive and negative spatial autocorrelation, suggesting that a stable and effective regional coordination mechanism for green agricultural development has yet to be formed; (3) the determinants of AGTFP exhibit pronounced spatiotemporal heterogeneity, and the fundamental drivers of the region’s green agricultural transition increasingly rely on endogenous growth generated by technological innovation and rural human capital; (4) policy recommendations include strengthening benefit-sharing and policy coordination mechanisms, promoting cross-regional cooperation in agricultural science and technology, and implementing differentiated industrial layouts to support green agricultural development in the BTH region. These results provide valuable insights for promoting coordinated and sustainable green agricultural development across regions. Full article