Search Results (199)

Campus streetscapes are a key part of universities’ everyday public realm, yet the same scene may be perceived positively in one dimension while negatively in another. To diagnose such multi-dimensional perceptual differences and translate them into actionable design evidence, this study develops an interpretable vision analytics framework for adaptive campus design. Using 72,733 Baidu Street View images collected from 41 campuses in mainland China, the study integrates ResNet-50-based perception prediction, spatial element extraction, XGBoost–SHAP-based mechanism interpretation, Kruskal–Wallis H testing, and GIS-based scene mapping. Supported by supplementary in situ validation, six types of multi-dimensional perceptual differences were identified. Sky, buildings, vegetation, hardscape, and terrain were found to be the five most important spatial elements overall, among which sky, buildings, and vegetation repeatedly emerged as the dominant core elements distinguishing different perceptual types. These elements do not act independently or linearly, but jointly shape different types of multi-dimensional perceptual differences through nonlinear threshold effects and interactions. These perceptual difference types were further found to cluster in recognizable campus scenes, including main roads, plazas, lawns, forest belts, and lakeside spaces. Based on these findings, scene-specific piecemeal optimization strategies were derived to support the coordinated enhancement of perceived safety, liveliness, and beauty. Overall, the study shows that campus perception is shaped by holistic spatial configurations rather than the simple accumulation of isolated elements, and provides a quantitative basis for iterative, feedback-oriented adaptive campus design. Full article

Against the backdrop of rising psychological stress and declining physical fitness in cities, how streetscape characteristics and Myers–Briggs Type Indicator (MBTI) personality traits jointly influence cycling willingness across different contexts remains underexplored. Using Shenzhen, China, as a case study, we integrated objective bicycle-sharing travel records from 2021 and subjective pairwise ratings of 1000 street-view images from 960 participants. Cycling willingness was extrapolated through the TrueSkill algorithm and a ResNet50-based model, while street view elements were extracted via DeepLabV3+ and summarized into five indicators. Multivariate regression and multifactor ANOVA were used to test main and moderating effects across six cycling contexts. Results show that (1) Objective cycling indicators and subjective willingness exhibit a pattern of lower values in the center and higher values in the periphery. (2) The Spatial Green Index, Sky Openness Index, Path Freedom Index, and Facility Accessibility Index are the main influencing factors, while the Interface Enclosure Index has the weakest and most context-dependent effect. (3) Intuition/Feeling traits are more salient in leisure and exploration, Judging/Thinking in fitness and transport, and Extraversion/Feeling in social and companion contexts. These findings provide evidence for optimizing urban street cycling spaces in a multi-context and personality-informed manner. Full article

This study examines how high-speed rail (HSR) hubs shape public space vitality in central-city station areas, using Shapingba Station (Chongqing, China) as a representative case of station–city integration. We delineated pedestrian catchments using Baidu Map walking isochrones (300–1200 s) and integrated multi-source data, including Public Space Public Life (PSPL) field observations (eight monitoring points, 07:00–24:00), Baidu heat maps, point-of-interest (POI) records, streetscape semantic segmentation, and a perception questionnaire. Indicators were synthesized via entropy weighting, and multivariate associations between perceived vitality and environmental variables were examined using Mantel tests. Pedestrian flow exhibits a clear double-peak pattern (09:00–11:00 and 15:00–16:00), averaging 42,248 pedestrians per day (2347 per hour) and showing strong spatial heterogeneity across monitoring points. POIs show a pronounced core–periphery structure: totals increase from 803 (300 s) to 4365 (600 s) and 7539 (1200 s), while overall density declines from 7477 to 2492 POIs/km², highlighting a 600 s core where accessibility and functional agglomeration are most strongly coupled. Overall, this study contributes a replicable multi-source evaluation framework and quantitative evidence on accessibility–function coupling and micro-scale design effects in HSR station areas, enabling theory-informed comparisons across station typologies and urban contexts. Full article

Evaluating street interface morphology is essential for urban design, yet existing approaches often struggle to combine large-scale applicability with higher-level morphological interpretation. This study proposes a scalable framework for assessing street interface morphology using an automated multimodal large language model (MLLM) agent. Using street view imagery (SVI), the framework evaluates four core morphological dimensions—enclosure, continuity, transparency, and roughness–through two complementary analytical streams: objective geometric measurement and subjective morphological assessment. To support reliable evaluation, the framework incorporates a dual-benchmark strategy consisting of manually derived geometric measurements and expert-consensus ratings for calibration and validation. Applied in Shanghai, the framework demonstrated reliable performance across the evaluated dimensions. The optimized agent was further extended to continuous street-segment analysis, demonstrating its applicability to large-scale urban assessment. By integrating objective and subjective evaluation within a scalable and interpretable workflow, the proposed methodology provides a practical tool for street interface morphology analysis and urban design assessment. Full article

Historical and cultural districts are the outcome of cultural sedimentation brought about by urban development, and they embody distinctive urban historical and cultural connotations. Ignoring the protection of the historical and cultural value contained in streetscapes will not only decrease the life quality of residents but will also diminish distinctive local urban features. This study focused on the Fangcheng historical and cultural district in Shenyang. The scenic beauty estimation method was employed to evaluate urban style and features’ visual quality, while the semantic differential method was used to obtain the subjective perceptual features of samples. The study also systematically explored the dynamic relationship between urban style and features’ quality and subjective perception in historical and cultural districts. The results show that color richness, coherence, iconic status, and continuum all exert significant positive predictive effects on visual preferences regarding urban style and features. Color richness was the primary determinant of urban style and features’ visual quality. Continuum interfaces, a unified spatial texture, and coordinated dimensions contributed significantly to improving urban style and features’ visual quality in historic and cultural districts. The distinctiveness and cultural iconic status of historical and cultural districts enhanced the residents’ identity and place memory. Moreover, the coherence and continuum of style between the old and new elements promoted an integrated aesthetic experience. The evaluation results revealed that the overall visual quality of urban style and features of most streets was medium. However, streets with a higher visual quality cluster among historical streets and commercial streets. The residential streets demonstrated a significantly lower visual quality. Establishing a comprehensive evaluation system that integrates urban style and features, subjective perception, and the style of historical and cultural districts can contribute to covering the shortage in the traditional urban style and features’ research and also provide a basis for urban regeneration at the micro scale. Full article

The climate crisis exposes the inadequacy of modern urban paradigms grounded in the separation between nature and built form. In response, this paper reframes streetscapes as architectural and urban spaces where ecological performance and spatial composition are conceived as mutually constitutive. Rather than treating Nature-Based Solutions (NBS) as isolated techno-performative devices, the research interprets them as design components capable of shaping section, threshold, and relational depth within the street. Building on two European-funded research projects, the ClimaScapes research—which unfolds into the Climate-Adaptive Nature-Based Urban Regeneration (CANBUR) Framework—through the different phases of Research about Design, Research by Design and Research for Design, thus develops the design-driven Operational Methodology. The paper, repositioning streetscapes as strategic fields for urban and architectural design, presents (i) the tools developed within it and (ii) its application inside a neighborhood of Matera (Italy). The findings demonstrate that integrating NBS within coherent spatial configurations enables a shift from environmental optimization toward architectural composition, offering a transferable yet context-sensitive methodology for climate-adaptive regeneration in Euro-Mediterranean and comparable urban contexts. This approach suggests streetscapes evolve into resilient, climate-adaptive urban commons, reinforcing community ties, ecological sustainability, and the broader goal of future-proof cities. Full article

Although urban streetscape visual esthetic quality (VAQ) assessment has progressed markedly, its findings are rarely operationalized in urban planning policy-making. The resulting discontinuity in the assessment–policy linkage is a critical impediment to streetscape VAQ enhancement. We propose an eye-tracking-enabled, end-to-end decision support framework that links evidence acquisition, intervention prioritization, design strategy formulation, and outcome feedback. Eye tracking is integrated to establish a three-dimensional assessment system spanning spatial, psychological, and physiological dimensions. Within this integrated system, we construct a three-level eye-tracking-based visual characteristics (ET-VC) framework across streetscape elements, formal characteristics, and public esthetic perception (PAP). Together, the three-dimensional system provides a theoretical basis for acquiring the multi-modal data required for VAQ enhancement. Building on this integrated assessment, we embed scenario planning theory to construct a planning facing decision model with PAP as the core outcome. The model combines importance-performance analysis (IPA) with the coupling coordination degree model (CCDM) to guide resource allocation decisions and intervention prioritization, and further uses eye-tracking evidence to support the development of refined, actionable enhancement strategies. A case study in Wudadao validates the framework’s robustness and feasibility. The ET-VC results provide additional evidence for interpreting esthetic perception: (1) ET-VC indicators differ significantly across streetscape elements, and “being viewed more” does not necessarily correspond to higher esthetic ratings; (2) four groups of key formal characteristic indicators—color configuration, naturalness, historicity and planning/regulatory control, and visual scale—systematically reshape fixation onset and maintenance patterns; and (3) PAP appears to involve partially nonlinear relationships between material landscape features and additional top-down influences (e.g., historical narratives and individual experience), rather than being fully explained by linear associations alone. Overall, this study provides both a theoretical basis and an applied demonstration for evidence-based streetscape VAQ enhancement. Full article

While objective street metrics derived from imagery or GIS have become standard in urban analytics, they remain insufficient to capture subjective perceptions essential to inclusive urban design. This study introduces a novel Multimodal Street Evaluation Framework (MSEF) that fuses a vision transformer (VisualGLM-6B) with a large language model (GPT-4), enabling interpretable dual-output assessment of streetscapes. Leveraging over 15,000 annotated street-view images from Harbin, China, we fine-tune the framework using Low-Rank Adaptation(LoRA) and P-Tuning v2 for parameter-efficient adaptation. The model achieves an F1 score of 0.863 on objective features and 89.3% agreement with aggregated resident perceptions, validated across stratified socioeconomic geographies. Beyond classification accuracy, MSEF captures context-dependent contradictions: for instance, informal commerce boosts perceived vibrancy while simultaneously reducing pedestrian comfort. It also identifies nonlinear and semantically contingent patterns—such as the divergent perceptual effects of architectural transparency across residential and commercial zones—revealing the limits of universal spatial heuristics. By generating natural-language rationales grounded in attention mechanisms, the framework bridges sensory data with socio-affective inference, enabling transparent diagnostics aligned with Sustainable Development Goal 11(SDG 11). This work offers both methodological innovation in urban perception modeling and practical utility for planning systems seeking to reconcile infrastructural precision with lived experience. Full article

Evaluating pedestrian comfort in high-density cities requires methods integrating subjective experience with urban morphology. This study develops an integrated framework combining pairwise comparison scoring, semantic segmentation (DeepLabv3+), ensemble learning (Random Forest), and SHAP-based interpretability. EfficientNet-B7 is used to expand pairwise datasets and derive continuous comfort scores across Macau’s street network. Four experiential street types are identified: historical–cultural districts, urban lifestyle areas, natural corridors, and leisure zones. SHAP analysis illustrates stable associations between predicted comfort scores and multi-layered spatial configurations, including cultural legibility and sequencing in historic cores, moderate greenery with functional anchoring in residential areas, and scene coherence in tourism zones. Semantic features serve as effective morphological proxies within the modeling framework. Methodologically, the framework demonstrates how explainable machine learning can be applied to dense Asian cities under observational conditions. Design implications emphasize interface continuity, microclimate adaptation, and functional enrichment, suggesting that pedestrian comfort is closely related to coherent spatial–experiential structures rather than isolated environmental upgrades. Full article

Promoting older adults’ active travel (AT) is important for healthy ageing, yet the optimal spatial units and scales for built environment (BE) interventions remain unclear. Existing studies often ignore the Modifiable Areal Unit Problem and fail to distinguish macro-scale land-use patterns from micro-scale street design under potentially nonlinear behavior–environment relationships. This study aims to clarify how multi-scale BE influences older adults’ AT and to identify the most effective intervention scale. Using survey data from 2494 older adults in Wuhan, China, we construct six behaviorally meaningful sliding units (5, 10, and 15 min walking network buffers and distance-equivalent Euclidean buffers), derive macro- and micro-scale indicators from GIS, census data, and street view images, and build separate Extreme Gradient Boosting (XGBoost) models with Accumulated Local Effects plots for interpretation. A model comparison reveals pronounced scale effects: network-based buffers systematically outperform circular buffers, and the 15 min walking network buffer emerges as the optimal intervention unit. Across all scales, BE variables contribute more to model performance than socio-demographic factors, and macro-scale attributes (e.g., land-use mix, facility density, and transit access) consistently outweigh micro-scale street features. Nonlinear effects and thresholds are identified for key density, accessibility, and streetscape indicators. These findings underscore the necessity of multi-scale analysis and support planning “15 min life circles” for older adults that prioritize macro-scale land-use and facility optimization, complemented by targeted, context-specific street-level improvements to create safe, age-friendly walking environments. Full article

Two major global trends shaping 21st-century society are population aging and urbanization. Consequently, the living conditions of older adults have become an increasing focus of societal attention. Social–Emotional Responses play a crucial role in the mental health, emotional well-being, and social identity of older adults. Urban streets, as key sites for walking and social activity among older adults, can be seen as extensions of their homes—places where they regularly interact with neighbors and build new connections. Compared to built environments often termed “gray spaces,” exposure to green spaces has been shown to offer greater benefits to residents’ well-being. Among streetscape features, the Spatial Openness Level is closely associated with the psychological well-being of elderly individuals. Visual-spatial features correlate with an EEG-derived proxy for emotional state during exposure to street scenes. The Gray-Green space Exposure Ratio (GER) and Spatial Openness Level (SOL) serve as key indicators for evaluating streetscape quality. Designing age-friendly streets requires evidence-based tools that link visual features to emotional well-being. This study provides such a tool by combining EEG measurements with configurational analysis of street visual dimensions: SOL and GER. In this study, conducted in Wuhan City, objective physiological monitoring of brainwave activity was employed to examine the responses of older adults to variations in GER and SOL. The results indicate that SOL significantly influences the emotional states of older adults (correlation coefficient R² = 0.7262, p < 0.01). The results indicate that the effect of GER on the emotional states of older adults was moderated by gender. Specifically, GER exerted a significant effect on the emotional states of females (correlation coefficient R² = 0.6262, p < 0.01), whereas no significant effect was observed in males (p > 0.01). These results allow us to rank the nine tested scenes. For example, Scene L-3 (open space with abundant vegetation) scored highest on emotional well-being, while Scene H-1 (enclosed gray space) scored lowest. The difference is explained by the configurational logic: greenery delivers emotional benefits only when combined with sufficient openness. The findings will enable EEG data to extend beyond serving as a unique standalone outcome and integrate into a more comprehensive explanatory model. This model aims to elucidate how urban morphology influences the micro-foundations of social activity in later life. Furthermore, it seeks to equip urban designers and policymakers with an evidence-based tool for creating age-friendly environments, facilitating a shift from intuition-driven to evidence-based design. Future research should incorporate additional environmental factors to establish a more comprehensive assessment framework for age-friendly urban spaces. Full article

In high-density urban cores, pedestrian safety perception is shaped not only by street physical environments but also by pronounced spatial heterogeneity. However, existing studies often rely on global regression or small-sample surveys, making it difficult to simultaneously reveal city-scale regularities and localized mechanisms. Taking Futian District, Shenzhen, as a case study, this study develops an integrated analytical framework that combines street-view imagery, machine learning, and multiscale geographically weighted regression (MGWR) to measure pedestrian safety perception at the city scale and to unpack its spatial mechanisms. The results show that model explanatory power improves markedly after accounting for spatial non-stationarity, indicating strong context dependence in the formation of pedestrian safety perception. MGWR further reveals clear multiscale differentiation across streetscape visual elements: greenery-related elements (e.g., tree and plant) exhibit near-global and consistently positive effects, whereas traffic exposure and interface-related elements (e.g., car, road, and wall) operate more locally, with both the direction and magnitude of their effects varying substantially with neighborhood structure and traffic contexts. These findings suggest that the impacts of individual street elements on pedestrian safety perception are not universally transferable and should be interpreted within a spatial-scale and contextual framework. By integrating machine learning-based prediction with MGWR-based spatial interpretation, this study enables both efficient city-scale measurement and multiscale mechanism identification of pedestrian safety perception, providing empirical support for safety perception-oriented street planning and fine-grained urban design. Full article

Urbanisation and climate change are intensifying heat risks in cities worldwide through the combined effects of global warming and the urban heat island (UHI) phenomenon. Elevated urban temperatures threaten human health, strain infrastructure, increase energy demand and exacerbate socio-spatial inequalities. While architectural and urban design decisions are central to the formation and mitigation of UHI, moving from UHI mitigation to heat-resilient cities requires linking physical interventions with governance capacity, equity, and adaptive learning over time. This paper, therefore, develops a conceptual framework for resilient and sustainable urban environments that embeds built-environment strategies within a broader resilience-oriented governance context. The study combines a narrative review of UHI mechanisms, impacts and mitigation approaches with a systematic review of local-government strategies implemented between 2015 and 2025. Following preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines and a population, intervention, comparison, and outcome (PICO)-based search strategy, 100 studies were selected from Scopus and Web of Science and analysed thematically. The review identifies four main domains of local action: green infrastructure; cool and permeable materials; water-based and blue–green infrastructure; and policy, governance and technology. Within these domains, the paper highlights architectural and design-relevant interventions, including shade-oriented streetscapes, climate-responsive building envelopes, ventilation-sensitive urban form, and blue–green corridors, while also examining institutional, financial and social factors that shape implementation and effectiveness. The findings show that combinations of green infrastructure, cool materials and blue–green systems can reduce surface and near-surface air temperatures and improve thermal comfort, with co-benefits for public health, energy efficiency, biodiversity and liveability. However, implementation is frequently constrained by limited financial and technical capacity, fragmented institutions, context-specific trade-offs, and insufficient attention to equity. Building on these insights, the paper proposes a conceptual framework comprising ten components that connect context and drivers; assessment and diagnosis; intervention strategies; implementation mechanisms; enablers; barriers; equity operationalisation; outcomes and effectiveness; monitoring and evaluation; and feedback and iteration. The paper concludes that advancing from urban heat islands to resilient cities requires design innovation supported by enabling governance, equity-centred prioritisation, and iterative monitoring and learning. Full article

This study examines how visual features and green space morphology jointly shape restorative perception in dog-friendly urban green spaces using a data-driven analytical framework. A self-constructed dataset integrating street-view imagery, landscape element composition, and morphological metrics was developed to quantify visual entropy, visual richness, and spatial structure. Ten dimensions of visual perception were modeled using an XGBoost framework optimized with a genetic algorithm, achieving high predictive performance (R² = 0.827–0.989). Streetscape analysis revealed relatively stable visual entropy but pronounced heterogeneity in visual richness, reflecting variability in color, form, and spatial layering. Element-level decomposition showed the visual dominance of natural components, particularly trees, sky, and grass. Piecewise linear regression further identified threshold-dependent and dimension-specific effects of green space proportion, fragmentation, patch size, connectivity, aggregation, and shape complexity. Moderate fragmentation and aggregation enhanced perceived complexity and stimulation, whereas excessive shape complexity reduced most restorative responses. Full article

The safety of “City Walk” routes in high-density historic districts is a critical constraint for sustainable urban tourism. This study establishes an integrated safety assessment framework for Macao’s eight walking routes by coupling tourism attractiveness with spatial carrying capacity. Utilizing social media big data, multi-source spatial datasets and Spatial Lag Models, we conceptualize “attractions” and “streets” as a continuous system. The results reveal a spatial mismatch: while entertainment and green streetscapes drive attractiveness, excessive amenities in narrow alleys reduce perceived safety. A “crowded core–empty periphery” capacity pattern creates significant risks, with approximately 39% of nodes classified as medium-to-high risk due to high attractiveness overloading low carrying capacity. By diagnosing these “high-attractiveness, low-capacity” conflicts, this study demonstrates the effectiveness of multi-source data fusion in identifying resilience weaknesses, offering actionable insights for smart tourism management and the promotion of social sustainability in high-density destinations. Full article