Search Results (496)

Existing studies on urban public space vitality predominantly focus on single temporal scales or macro-urban levels, lacking a systematic understanding of day–night and weekday–weekend differentiation patterns at the meso-scale. This study examines 149 public spaces in the Yuexiu District, Guangzhou, employing Baidu heatmap data and the geographically weighted random forest (GWRF) model to analyze built environment impacts across four temporal scenarios. The SHAP interaction analysis is incorporated to quantitatively evaluate factor interdependencies and their temporal variations. Findings reveal significant spatiotemporal heterogeneity. Building density shows greater night-time importance while residential density exhibits enhanced daytime importance, particularly on weekend. Weekday–weekend comparison demonstrates contrasting spatial reorganization patterns, with weekday showing divergence and weekend showing convergence in factor importance distributions. The factor interaction analysis highlights stable synergistic relationships between density and diversity, alongside temporal transitions in density–residential density interactions from competitive to synergistic during night-time. Low-vitality public spaces are concentrated in peripheral areas with high building density but insufficient commercial facilities and functional mix. These findings deepen our understanding of the spatiotemporal mechanisms underlying public space vitality generation and the interaction effects among built environment factors, thereby providing an empirical foundation for the formulation of temporally adaptive planning strategies. Full article

Accurate mapping of land use and land cover (LULC) is crucial for various applications such as urban planning, environmental management, and sustainable development, particularly in rapidly growing urban areas. African cities such as Yaoundé, Cameroon, are particularly affected by this rapid and often uncontrolled urban growth with complex spatio-temporal dynamics. Effective modeling of LULC indicators in such areas requires robust algorithms for high-resolution images segmentation and classification, as well as reliable data with great spatio-temporal distributions. Among the most suitable data sources for these types of studies, Sentinel-2 image time series, thanks to their high spatial (10 m) and temporal (5 days) resolution, are a valuable source of data for this task. However, for an effective LULC modeling purpose in such dynamic areas, many challenges remain, including spectral confusion between certain classes, seasonal variability, and spatial heterogeneity. This study proposes a hybrid deep learning architecture combining U-Net and Convolutional Long Short-Term Memory (ConvLSTM) layers, allowing the spatial structures and temporal dynamics of the Sentinel-2 series to be exploited jointly. Applied to the Yaoundé region (Cameroon) over the period 2018–2025, the hybrid model significantly outperforms the U-Net and ConvLSTM models alone. It achieves a macro-average F1 score of 0.893, an accuracy of 0.912, and an average IoU of 0.811 on the test set. These segmentation performances reached up to 0.948, 0.953, and 0.910 for precision, F1-score, and IoU, respectively, on the built-up areas class. Moreover, despite its better performance, in terms of complexity, the figures confirm that the hybrid does not significantly penalize evaluation speed. These results demonstrate the relevance of jointly integrating space and time for robust LULC classification from multi-temporal satellite images. Full article

The urban physical environment is composed of multiple elements that collectively influence the spatial pattern of crime. Existing research has predominantly focused on the relationship between individual types of facilities and crime, yet there remains a gap in comprehensively examining the integrated effects of the urban physical environment. This study, taking 87 police precincts in the central city of Changchun as units of analysis, innovatively constructs an integrated “Factor Analysis–Negative Binomial Regression” framework. First, factor analysis is applied to reduce the dimensionality of 14 categories of Points of Interest (POI) data, extracting three comprehensive factors that characterize the macro-level functional structure of the city: the “Business and Economic Activities Factor,” the “Residential, Educational, and Transportation Factor,” and the “Leisure and Entertainment Factor.” This approach effectively addresses the issue of multicollinearity among variables and uncovers the underlying macro-level functional factors. Subsequently, a negative binomial regression model is employed to analyze the impact of each factor on crime counts. The results indicate that: (1) The spatial distribution of urban crime is markedly heterogeneous and is systematically driven by the urban functional structure; (2) Both the “Business and Economic Activities Factor” and the “Leisure and Entertainment Factor” exhibit significant positive effects on crime, with each unit increase in their scores associated with an approximately 20% increase in the relative risk of crime; (3) The influence of the “Residential, Educational, and Transportation Factor” is not significant. Collectively, the findings demonstrate that shifting the perspective from “micro-level facilities” to “macro-level functional dimensions” can provide deeper insights into the fundamental formative mechanisms underlying the spatial pattern of crime. Full article

Historic waterfront streets are not only an important component of urban public spaces but also highlight the distinctive features and historical contexts of the city. High-quality streetscape visual perception plays a crucial role in advancing the cultural, social, environmental, and economic sustainability of the urban street space. This study was initiated to construct a multi-dimension and multi-scale comprehensive evaluation framework to assess the visual quality of waterfront streets, taking “Water City” Liaocheng as a typical case. Technical methods of semantic segmentation, sDNA (Spatial Design Network Analysis), GIS (Geographic Information System), and statistical analysis were utilized. Following the extraction and classification of street space elements, a multi-dimensional evaluation index system of natural coordination, artificial comfort, and historical culture for the visual assessment was established. Space syntax was performed on waterfront streets by sDNA to quantify macro-level scale spatial structure and meso-level scale pedestrian accessibility. The results of micro-scale visual perception, meso-scale behavioral walkability, and macro-scale spatial structure, were integrated to construct a multi-scale diagnostic framework for eight classifications. This framework provides a scientific basis to put forwards the refined and sustainable optimization strategies for historic waterfront streets. 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

Unstructured POI name texts are widely used in fine-grained urban analysis, yet missing labels and semantic ambiguity often limit their value for spatial inference. This study proposes a large language model-based semantic–spatial inference framework (LLM-SSIF), a lightweight semantic–spatial pipeline that translates POI texts into interpretable, fine-grained spatial evidence through an end-to-end workflow that couples scalable label expansion with scale-controlled spatial diagnostics at a 500 m resolution. A key advantage of LLM-SSIF is its deployability: LoRA-based parameter-efficient fine-tuning of an open LLM enables lightweight adaptation under limited compute while scaling fine-label coverage. Trained on a nationwide cuisine-labeled dataset (~220,000 records), the model achieves strong multi-class short-text recognition (macro-F1 = 0.843) and, in the Guangzhou–Shenzhen demonstration, expands usable fine-category labels by ~14–15× to support grid-level inference under long-tail sparsity. The spatial module then isolates cuisine-specific over/under-representation beyond overall restaurant intensity, revealing contrasting cultural configurations between Guangzhou and Shenzhen. Overall, LLM-SSIF provides a reproducible and transferable way to translate unstructured POI texts into spatial–statistical evidence for comparative urban analysis. Full article

Reducing energy intensity is critical for combating climate change, yet current progress remains insufficient to meet international targets. Green-oriented policy narratives hold significant potential for mitigating energy intensity, but existing research lacks regional-level quantitative analysis. This study examines how green-oriented policy narratives influence urban energy intensity. We analyze textual data from Chinese provincial Party newspapers using large language models and LDA topic modeling to measure narrative-related variables, then combine these measures with panel data from 288 Chinese cities spanning 2010–2022. The findings reveal that green-oriented policy narrative exposure significantly reduces urban energy intensity through promoting green credit development and stimulating green innovation, with the negative effect strengthening as the prominence of the public and narrativity of narratives increase. Heterogeneity analysis further shows that narrative effectiveness is amplified in cities with higher internet penetration and marketization levels. This study broadens research on energy intensity determinants beyond traditional policy instruments, extends green-oriented narrative effects from the micro to macro level, and offers insights for leveraging narratives and contextual conditions to promote energy conservation. Full article

As a key component of urban green spaces, which provide sustainability-relevant ecosystem services such as carbon sequestration, soils support plant growth and represents an important carbon pool in urban ecosystems. However, surface soil organic carbon (SSOC) in urban green spaces can be highly heterogeneous due to the combined influences of natural conditions and human activities. To quantify national-scale patterns and major correlates of SSOC in China’s urban green spaces, we compiled published surface (0–20 cm) SSOC observations from 154 field studies and synthesized SSOC density and stocks across 224 Chinese cities, providing a nationally comparable assessment at the city scale. Measurements were harmonized to a consistent depth, and a random forest gap-filling approach was used to extend estimates for data-poor cities. The mean SSOC density and total SSOC stock of urban green spaces were 3.22 kg C m⁻² and 57.87 × 10⁹ kg C, respectively, and SSOC density showed no obvious latitudinal gradient across the 224 cities. Variable importance from the random forest analysis indicated that soil physicochemical properties (e.g., bulk density, total nitrogen, and texture) were the strongest predictors of SSOC density, whereas climatic and topographic variables showed comparatively lower importance. This pattern may suggest that anthropogenic modification and management dampen macro climatic signals such as temperature and precipitation at the national scale. Full article

Urban tree-management operations generate substantial amounts of woody biomass that often remain underutilized despite their potential value as a local renewable fuel. This study investigates the possibility of using woodchips and sawdust delivered from municipal tree-cutting activities as boiler fuel, with a specific focus on how fuel moisture, particle size, and natural-fiber packaging influence combustion performance and emission characteristics. In collaboration with a municipal greenery-cutting company, representative batches of biomass were collected, characterized through proximate and ultimate analyses, and combusted in a small-scale boiler. Unlike conventional densification routes (pelletization/briquetting), the proposed approach uses combustible natural-fiber packaging to create modular ‘macro-pellets’ from minimally processed urban residues. The study quantifies how this low-energy packaging concept affects emissions and boiler efficiency relative to loose chips/sawdust at two moisture levels. The results demonstrate that packaging the fuel in jute bags markedly improved performance for both woodchips and sawdust by stabilizing the fuel bed, enhancing air distribution, and reducing emissions of incomplete combustion products. Boiler efficiency increased from approximately 60% for raw unpackaged fuels to 71–75% for the dried and jute-packaged variants. The findings highlight that simple preprocessing steps—drying and packaging in natural-fiber bags—can substantially enhance the energy recovery potential of urban green waste, offering a practical pathway for integrating municipal biomass residues into a sustainable fuel. Full article

In the context of the “Dual Carbon” goals, accurately predicting the spatiotemporal evolution of urban Net Primary Productivity (NPP) is crucial for resilient urban planning. While recent studies have coupled land use models with ecosystem models to project NPP dynamics, they often face challenges in acquiring high-resolution future vegetation parameters and typically overlook the stability of NPP under changing climates. To address these gaps, this study focuses on Nanjing and develops a long-term, multi-scenario analysis framework based on the Dynamic Land Cover–Climate Model (DLCC–NPP). This framework innovatively integrates the PLUS model with a Random Forest (RF) algorithm. By establishing a direct statistical mapping between macro-climate/micro-land cover and NPP, the RF model functions as a statistical downscaling tool. This approach bypasses the uncertainty accumulation associated with simulating future vegetation indices, enabling precise spatiotemporal NPP prediction at a 30 m resolution. Using this approach, we systematically analyzed the NPP dynamics from 2004 to 2044 under three SSP scenarios. The results revealed that Nanjing’s NPP exhibited a fluctuating upward trend, with urban forests contributing the highest productivity (mean NPP ~266.15 gC/m²). Crucially, the volatility analysis highlighted divergent response characteristics: forests demonstrated the highest stability and “buffering effect,” whereas grasslands and croplands showed high volatility and sensitivity to climate fluctuations. Spatially, a distinct “stable high-NPP core, decreasing periphery” pattern was identified, driven by the interaction of urban expansion and ecological conservation policies. In conclusion, the DLCC–NPP framework effectively overcomes the data scarcity bottleneck in future simulations and characterizes the spatiotemporal heterogeneity of vegetation carbon fixation in urban ecosystems, providing scientific support for optimizing green space patterns and enhancing urban ecological resilience in high-density cities. Full article

Urban forests and green spaces are increasingly promoted as Nature-Based Solutions (NbS) to mitigate climate risks, enhance human well-being, and support resilient and sustainable cities. Focusing on the theme of optimizing urban green space ecosystem services to foster resilient and sustainable cities, this study systematically analyzes 861 relevant publications indexed in the Web of Science Core Collection from 2005 to 2025. Using bibliometric analysis and scientific knowledge mapping methods, the research examines publication characteristics, spatial distribution patterns, collaboration networks, knowledge bases, research hotspots, and thematic evolution trajectories. The results reveal a rapid upward trend in this field over the past two decades, with the gradual formation of a multidisciplinary knowledge system centered on environmental science and urban research. China, the United States, and several European countries have emerged as key nodes in global knowledge production and collaboration networks. Keyword co-occurrence and cluster analyses indicate that research themes are mainly concentrated in four clusters: (1) ecological foundations and green process orientation, (2) nature-based solutions and blue–green infrastructure configuration, (3) social needs and environmental justice, and (4) macro-level policies and the sustainable development agenda. Overall, the field has evolved from a focus on ecological processes and individual service functions toward a comprehensive transition emphasizing climate resilience, human well-being, and multi-actor governance. Based on these findings, this study constructs a knowledge ecosystem framework encompassing knowledge base, knowledge structure, research hotspots, frontier trends, and future pathways. It further identifies prospective research directions, including climate change adaptation, integrated planning of blue–green infrastructure, refined monitoring driven by remote sensing and spatial big data, and the embedding of urban green space ecosystem services into the Sustainable Development Goals and multi-level governance systems. These insights provide data support and decision-making references for deepening theoretical understanding of Urban Green Space Ecosystem Services (UGSES), improving urban green infrastructure planning, and enhancing urban resilience governance capacity. Full article

Traditional settlements exhibit remarkable climatic adaptability, representing a form of “Morphological Intelligence” developed over centuries. However, this inherent, physics-based wisdom remains underutilized in contemporary urban planning and design. This systematic review aims to decode such intelligence by analyzing the relationship between the morphological characteristics of traditional settlements and their thermal performance. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, literature retrieval and evaluation were conducted via the databases of Web of Science, Scopus, and China National Knowledge Infrastructure (CNKI) for articles published during 2004~2024. A total of 82 related articles with available full texts were selected from 1227 records for in-depth analysis, including peer-reviewed journal articles and reputable conference publications. This study first presents an overview of bibliometric and methodological landscapes, revealing that research is increasingly concentrated in Asia’s tropical and subtropical climates, predominantly employing case studies and computational simulations. Secondly, we synthesize a few key climate-adaptive morphological features across macro- (e.g., settlement layout), meso- (e.g., street canyon geometry), and microscales (e.g., courtyards). The findings illustrate a reliance on methods and metrics developed for modern urban contexts, which could not fully capture the specific morphological characteristics of traditional settlements. Most importantly, this study summarizes four core principles of “Morphological Intelligence” in traditional settlements, i.e., strategic solar control, facilitated natural ventilation, use of thermal mass, and integration of natural elements and creation of thermal buffer zones. By identifying the limitations of existing investigations, this study highlights a few directions for future studies, including conducting more systematic multi-scalar integrated analysis, focusing on the development of dedicated quantitative metrics and analytical frameworks, delving into more mechanism-oriented investigation, assessing morphological resilience under urbanization, and translating principles into contemporary design guidelines. This study provides a foundational framework for translating the “Morphological Intelligence” of traditional settlements into actionable, evidence-based strategies for resilient and energy-efficient urban planning and design amidst climate change. Full article

Beamforming is highly significant for the physical layer of wireless communication systems, for multi-antenna systems such as multiple input multiple output (MIMO) and massive MIMO, since it improves spectral efficiency and reduces interference. Traditional linear beamforming methods such as zero-forcing beamforming (ZFBF) and minimum mean square error (MMSE) beamforming provide closed-form solutions. Yet, their performance drops when they face non-ideal conditions such as imperfect channel state information (CSI), dynamic propagation environment, or high-dimensional system configurations, primarily due to static assumptions and computational limitations. These limitations have led to the rise of deep learning-based beamforming, where data-driven models derive beamforming solutions directly from CSI. By leveraging the representational capabilities of cutting-edge deep learning architectures, along with the increasing availability of data and computational resources, deep learning presents an adaptive and potentially scalable alternative to traditional methodologies. In this work, we unify and systematically compare our two unsupervised learning architectures for uplink receive beamforming: a simple neural network beamforming (NNBF) model, composed of convolutional and fully connected layers, and a transformer-based NNBF model that integrates grouped convolutions for feature extraction and transformer blocks to capture long-range channel dependencies. They are evaluated in a common multi-user single input multiple output (MU-SIMO) system model to maximize sum-rate across single-antenna user equipments (UEs) under 3GPP-compliant channel models, namely TDL-A and UMa. Furthermore, we present a FLOPs-based asymptotic computational complexity analysis for the NNBF architectures alongside baseline methods, namely ZFBF and MMSE beamforming, explicitly characterizing inference-time scaling behavior. Experiments for the simple NNBF are performed under simplified assumptions such as stationary UEs and perfect CSI across varying antenna configurations in the TDL-A channel. On the other hand, transformer-based NNBF is evaluated in more realistic conditions, including urban macro environments with imperfect CSI, diverse UE mobilities, coding rates, and modulation schemes. Results show that the transformer-based NNBF achieves superior performance under realistic conditions at the cost of increased computational complexity, while the simple NNBF presents comparable or better performance than baseline methods with significantly lower complexity under simplified assumptions. Full article

Enhancing blue infrastructure is a critical pathway to strengthening urban water resilience and improving living environments. However, divergent perceptions and demands among multiple stakeholders may lead to misalignment between governance priorities and implementation pathways, thereby limiting governance effectiveness. Recognizing and addressing these differences has become essential for enhancing the performance of blue infrastructure governance and public satisfaction. Taking Shaanxi Province as a case study, this research systematically identifies core issues and disparities in public demands regarding water governance of blue infrastructure by analyzing governmental documents and public demands. The study aims to support a shift in governance strategy from a “provision-driven” to a “demand-driven” approach. A “topic identification–demand extraction–problem diagnosis” framework is adopted: first, the LDA model is used to analyze government platform texts and derive a macro-level thematic framework; subsequently, the BERTopic model is applied to mine public comments and identify micro-level demands; finally, the Jaccard similarity algorithm is employed to compare the two sets of topics, revealing the gap between policy provisions and public demands. The findings indicate the following: first, government agendas are highly concentrated on macro-level strategies (the topic “Integrated Water Ecosystem Management and Strategic Planning” accounts for 72.91% of weighting), whereas public appeals focus on specific, micro-level daily concerns such as infrastructure quality, drinking water safety, and drainage blockages; second, the Jaccard semantic correlation between the two is generally low (ranging from 6.05% to 14.62%), confirming a significant “topic-term overlap”; third, spatial analysis further reveals a geographical mismatch, particularly in core urban areas, which exhibit a “system-lag” type of misalignment characterized by high public demand but insufficient governmental attention. The research aims to clarify governance discrepancies, providing a basis for optimizing policy priorities and enabling targeted governance, while also offering insights for establishing a sustainable water resource management system. Full article

The increasing demand for active and light mobility (including bicycles, e-bikes and e-scooters) has become a key driver of sustainable urban transport, calling for a renewed approach to urban planning. A central challenge is redesigning infrastructure around users’ needs, inspired by the “15-min city” concept developed by Carlos Moreno. However, the existing literature on user preferences in this domain remains fragmented, both methodologically and thematically, and often lacks integration of user behaviour analysis. This paper presents a structured review of recent international studies on factors influencing route and infrastructure choices in active and light mobility. The findings are organized into an analytical framework based on five macro-criteria: external and infrastructural factors, transport mode, user typology, experimental methodology and infrastructure attributes. The synthesis tables aim to summarize the findings to guide planners, researchers and decision-makers towards more inclusive, adaptable and effective mobility systems, through the development of user-oriented planning tools, attractiveness indexes and strategies for cycling and micromobility networks. Moreover, the review contributes to an ongoing national research initiative and lays the groundwork for developing decision-making tools, attractiveness indexes and route recommendation systems. Full article