Search Results (4,908)

As a distinctive type of historic urban area shaped by complex topography, historic urban areas in mountainous cities have undergone multiple rounds of urban regeneration and social–spatial restructuring amid the continuous influx of capital and the development of the industrial economy. Under these multifaceted influences, the spatial form of historic urban areas in mountainous cities has undergone a series of transformations. To reveal the characteristics and underlying mechanisms of spatial evolution in historic urban areas in mountainous cities, this study has established a research framework tailored to such areas. Selecting the Yuzhong Peninsula Historic District in Chongqing—a typical example of historic urban areas in mountainous cities in China—as the study area, the research employs land transfer matrices, spatial design network analysis (sDNA), spatial overlay and statistical methods to analyse its spatial evolution characteristics across three dimensions: land-use evolution, road network evolution and building renewal. Seven key indicators were selected for historic urban areas in mountainous cities, and the GeoDetector was utilised to investigate the driving forces and interactions among these factors. The results indicate that in the Yuzhong Peninsula Historic District, residential and special-purpose land use has decreased whilst commercial land use has increased; the road network has been continuously improved, leading to increased choice and accessibility; and the proportion of small-scale buildings has decreased whilst that of large-scale buildings has increased. The primary factors influencing spatial evolution are elevation, social and location factors, with the combined interaction of these factors exerting a greater influence than any single factor alone, indicating that topography acts as a significant persistent spatial constraint guiding the spatial evolution of historic urban areas in mountainous cities. Finally, based on the above findings, recommendations are put forward for the sustainable conservation and utilisation of historic urban areas in mountainous cities, covering land-use management, traffic guidance and the typological control of urban architectural fabric. Full article

Historic urban regeneration requires not only material conservation but also public understanding of the cultural meanings embedded in buildings, street networks, and local narratives. This study examines how active digital information seeking is associated with visitors’ perceived cultural–cognitive compensation during the interpretation of historic built environments, and how this support relates to cultural heritage experience evaluation and perceived heritage memory quality. An on-site survey of 316 digitally engaged visitors was conducted at Taiping Old Street in Changsha, China. Factor analyses, regression models, bootstrap indirect-association analyses, and non-parametric group comparisons were applied. Active digital information seeking was positively associated with cultural–cognitive compensation, cultural heritage experience evaluation, and perceived heritage memory quality. Cultural–cognitive compensation formed significant indirect associations with memory quality (B = 0.177, 95% CI [0.111, 0.252]) and experience evaluation (B = 0.143, 95% CI [0.084, 0.211]). No robust differences among primary digital tools remained after correction for multiple testing. The findings suggest that digital interpretation can function as a human-centered information layer connecting preserved physical heritage with historical context and visitor understanding. For intelligent heritage regeneration, digital tools should be spatially anchored, complementary, source-traceable, and integrated with physical interpretation and community narratives. Full article

Against the backdrop of high-density Asian cities shifting from production- to consumption-oriented spaces, commercial pedestrian streets are key to urban public life and vitality. This study selects six major commercial pedestrian streets in Chongqing and employs natural language processing (NLP) and importance–performance analysis (IPA) methods to construct a four-dimensional evaluation framework (spatial, commercial, cultural, location/facility). It analyzes public perception and experience based on user-generated content (UGC). Findings show that: (1) Significant differences across dimensions form three development types: cultural identity, functional hub, and distinctive growth, reflecting structural bottlenecks in transitioning from single- to multi-functional spaces. (2) IPA identifies “business formats,” “cultural activities,” and “consumption experience” as priorities for improvement, while “commercial atmosphere” and “transportation conditions” are current strengths to maintain. (3) Sentiment analysis reveals that negative perceptions focus on basic functions and sense of place, whereas positive sentiments relate to cultural expression and spatial esthetics, highlighting the role of cultural soft power and visual design in street appeal. This study reveals public perception patterns via big data analysis, offering empirical support for the refined renewal, cultural preservation, and sustainable management of commercial pedestrian streets in high-density Asian cities. Full article

Grouping vehicles with similar paths is important for traffic analysis, but camera-image trajectories are distorted by perspective, and unsupervised clustering does not directly reveal how many traffic movements should be expected. This paper presents Homography-Guided Semantic Maneuver Graph Trajectory Clustering (HG-SMG-TC), a model-selection framework that uses a lightweight homography to estimate intersection structure while retaining isotropically normalized camera coordinates for clustering. Entry and exit endpoint groups are consolidated into physical approach-level groups, and their supported origin–destination relationships form a maneuver graph. Bootstrap resampling converts this structure into an interval for the expected number of observed movements, which guides clustering model selection. The method is evaluated on 67,029 vehicle trajectories from five urban intersection scenes in the Traffic Node Video Dataset, using separate target-estimation, model-selection, and independent-test recording blocks. Independent polygon-rule reference labels cover 89.6–98.4% of test trajectories. HG-SMG-TC reduces mean target-count error from 3.20 for untargeted selection and 2.53 for the point-target variant to 2.13, while achieving an adjusted Rand index of 0.734 and normalized mutual information of 0.839. The results show that the proposed semantic maneuver prior improves target alignment and provides a reproducible way to guide unsupervised trajectory clustering, while retaining explicit trade-offs across evaluation metrics. Full article

This study aims to identify the key destination-related factors associated with tourists’ revisit behavior in peri-urban rural areas within Chinese metropolitan regions in the context of short-video marketing, and to reveal the structural relationships and hierarchical characteristics among these factors. First, the LDA topic model was employed to conduct text mining on authentic tourist-generated comments posted on Douyin, through which the core factors related to revisit behavior were identified and conceptually standardized based on tourists’ expressions. Building on this process, 153 experts in relevant fields were invited to evaluate the direction and strength of the relationships among these factors. An integrated DEMATEL–ISM–MICMAC approach was then applied to analyze their causal attributes, hierarchical structure, and systemic roles. The results indicate that the identified factors do not operate independently but instead form a multilayered structure with clear hierarchical characteristics. Among them, rural visual imagery, escape-oriented experience, rural lifestyle experience, and rural industry integration occupy deeper structural levels and exert relatively strong structural influences on factors located at intermediate and surface levels. The findings further suggest that the sustained attractiveness of rural tourism destinations in metropolitan regions cannot rely solely on short-video exposure or isolated “internet-famous” attractions; rather, it requires coordinated alignment among digital communication content, rural industries, lifestyle experiences, and tourism supply. By integrating tourist-generated content, natural language processing, and expert-based structural assessment, this study extends research on short-video tourism marketing from a systems perspective and provides practical insights for peri-urban rural destinations in Chinese metropolitan regions seeking to optimize the structural configuration of tourism resources, products and services, and marketing communication. Full article

As cities accelerate climate adaptation and decarbonisation, municipally owned companies (MOCs) play an important role in delivering sustainable urban infrastructure, accessing transition finance, and supporting Positive Energy Districts (PEDs). However, the expansion of sustainability reporting requirements under the European Union’s Corporate Sustainability Reporting Directive (CSRD) raises questions about the preparedness of public infrastructure providers to meet evolving sustainability-information demands. This study examines ESG reporting readiness among Polish MOCs, focusing on current reporting activity, reporting intentions, regulatory awareness, indirect ESG information pressures, sustainable-finance and investment plans, and potential transition-risk exposure. The analysis is based on a Computer-Assisted Web Interviewing survey of 226 municipal enterprises conducted in August 2025. The results indicate substantial reporting gaps. Only 8% of surveyed MOCs had already prepared or planned to prepare a non-financial report. When companies planning EU Taxonomy reporting only were also included, 14% of the sample had some form of current or planned reporting, while the remaining 86% had neither current nor planned non-financial or EU Taxonomy reporting. Reporting readiness was lower among smaller companies, while indirect ESG information pressures arising from business relationships, stakeholder requests, and financing plans were also evident across the surveyed sample. The findings suggest that limited reporting preparedness, when combined with external sustainability-information demands, may contribute to potential transition-risk exposure relevant to municipal investment and financing processes. More broadly, ESG reporting readiness represents an organisational capability relevant to sustainable finance, municipal climate investment, and the development of PEDs and smart cities. Full article

Light exposure in urban street environments significantly shapes pedestrians’ subjective perception. While previous studies based on street view imagery (SVI) have predominantly regarded sky regions as the sole source of such exposure, glazed facades also contribute significantly through reflected light. Taking the Tianhe District of Guangzhou, China, as the study area, this research utilized luminance extraction and deep learning techniques to process SVIs and constructed an interaction regression model to analyze the channels through which light exposure affects pedestrian perception, as well as the specific role of glazed facades in this process. Key findings include: (1) elevated light exposure negatively affects overall perception, serving as a critical constraint on environmental quality; (2) under equivalent exposure conditions, glazed facades, although their overall main effect on perception remains negative, can partially buffer the perceptual burden induced by sky-dominated exposure; (3) based on these empirical channels, we propose classification and targeted renewal strategies for streets. This study proposes an integrated framework comprising “Sky/Glazed Facades—Light Exposure—Perception”, successfully incorporating reflected light from glazed facades into SVI-based luminous environment research. It reveals the differentiated channels of light exposure effects, providing guidance for both building forms and facade materials across urban blocks. Full article

Aesthetic perception of rural streetscapes reflects individuals’ cognitive responses to their surroundings and is central to understanding how landscape preferences are formed. Existing studies using Scenic Beauty Estimation (SBE) are constrained by incomplete indicator systems and overreliance on linear approaches. This study proposes an interpretable machine learning framework that integrates multi-source data to examine the nonlinear influences of streetscape features on SBE. Using Sanguan Village, a water-networked settlement in Jiangsu, we developed a 24-indicator system spanning color, spatial, natural, artificial, and cultural dimensions. Based on 523 panoramic images and aesthetic ratings from 1175 respondents, we compared OLS, DT, MLP, SVR, RF, and XGBoost models. The best-performing XGBoost, combined with SHAP analysis, revealed threshold effects and interaction patterns among variables. Green visibility, architectural aesthetics, building visibility, sky visibility, environmental coordination, and water are the top six feature variables most strongly associated with rural streetscape aesthetic perception, and each exhibits threshold effects. The saturation threshold for green visibility is 0.153, and architectural aesthetics can only make a positive contribution when its score exceeds 3.815. The appropriate range for building visibility is below 0.452, while the optimal value for sky visibility is approximately 0.194. We also explored the context-dependence of these threshold effects across urban and rural settings. This study proposes streetscape optimization strategies focusing on screening key factors, controlling their thresholds, and coordinating the allocation of streetscape features. The interpretable analytical framework for rural scenic beauty established in this research can facilitate evidence-based landscape optimization and provide scientific support for sustainable rural development and tourism in this case. Full article

Antenna selection is investigated as a means of reducing radio-frequency (RF) chain power in massive multiple-input multiple-output (MIMO) base stations under realistic channel state information (CSI) impairments. The study is motivated by the mismatch between conventional selection objectives and multi-cell operation with estimation error, pilot contamination, spatial correlation and inter-cell interference. APCS-Boost-R is introduced as the primary contribution. An interference-whitened D-optimal seed is combined with projected rank-one exchanges and a calibrated surrogate that incorporates a user-side interference-plus-noise report and a closed-form estimation-error correction. APCS-Boost-RG is retained as an optional graph neural network (GNN) refinement in which residual exchanges are ranked after the algebraic solution has been formed, while feasibility and non-degradation of the calibrated surrogate are verified deterministically. In a three-cell urban macro configuration derived from Third Generation Partnership Project (3GPP) TR 38.901 with 64 antennas, 16 active RF chains and eight users per cell, APCS-Boost-R achieves 19.364 bit/s/Hz over 200 paired realizations. Improvements of 2.58 percent over APCS-Boost, 6.76 percent over greedy search and 10.16 percent over a genetic algorithm are obtained. APCS-Boost-RG adds 0.019 bit/s/Hz but is treated as an optional refinement because it requires a second-stage neighborhood evaluation and offline model maintenance. In the archived common timing record, APCS-Boost-R requires 20.376 ms per three-cell realization, compared with 12.728 ms for APCS-Boost, 57.775 ms for norm-initialized greedy search and 41.302 ms for the genetic algorithm, while APCS-Boost-RG requires 24.0 ms versus 20.4 ms for APCS-Boost-R in the separate archived learned-stage record. Separate reconstructions on the documented reproducibility host require $55.3\pm 14.5$ ms for APCS-Boost-R and $592.2\pm 181.9$ ms for a complete APCS-Boost-RG rebuild. Additional paired examinations confirm robustness across stronger search budgets, report imperfections, regularized precoding, coordination, near-field sensitivity, hardware perturbations, and configurations ranging from 32 to 128 antennas and one to seven cells. Full article

Coupling coordination degree (CCD) between persistent late-night radiance and population density may vary nonlinearly with urban functional density, yet linear and single-city analyses cannot distinguish shared from city-specific patterns. A method-controlled dual-city XGBoost-SHAP framework was applied to 14,215 H3 cells in Shanghai and 31,067 in Beijing using six point-of-interest density factors. XGBoost outperformed OLS, with random hold-out R² values of 0.899 and 0.918 vs. 0.617 and 0.626. Public service (X2) ranked first in both cities, accounting for 39.77% and 58.73% of total mean absolute SHAP magnitude. The secondary hierarchy diverged as follows: commercial finance (X3) ranked second in Shanghai at 27.91% and formed the strongest interaction with X2, whereas transport infrastructure (X6) ranked second in Beijing at 18.63% and formed the strongest interaction with X2. Nonlinear analysis identified reproducible negative-to-positive crossings for X2, X3, and X6, peak-type responses for X4 and X5, and no stable second saturation threshold. Spatial OOF, grid, rank, LOWESS, and residual checks supported the leading-factor contrast while showing scale sensitivity and residual spatial dependence. The results identify a common leading attribution alongside city-specific secondary, nonlinear, and spatial patterns within the two observed megacities. Full article

This paper presents a pilot educational workflow that couples 3D remote sensing with heritage-driven pedagogy by engaging architecture master’s students in the documentation and digital archiving of Transylvanian cultural sites. Using terrestrial and mobile 3D scanning, students documented multiple typologies—wooden churches (Târgușor, Tioltiur), historical ensembles (Mociu, Coplean), industrial sites (1 Mai–Luduș, Vânătorilor–Luduș), and an urban street segment (Potaissa)—to generate dense point clouds that served as the basis for geometric reconstruction, semantic interpretation, and condition assessment. The study describes how the characteristics of different construction systems (timber, brick, stone, mixed structures) relate to point-cloud quality, survey coverage, and subsequent CAD/BIM drafting, with attention to the qualitative reading of minor deformations in wooden churches and of degradation patterns in masonry and industrial buildings. We also consider how artefacts in the data (noise, occlusions, registration errors) affect scene understanding and the interpretation of derived observations relevant to condition assessment and, prospectively, to monitoring. For the Tioltiur dual-sensor case, the TLS and SLAM datasets were compared through an internal CloudCompare registration check (final RMS 0.1121 on 50,000 points, fixed scale 1.0 and theoretical overlap 100%), surface-density displays (r = 0.005 for the Z+F dataset and for the GeoSLAM dataset), fitted-wall-plane readings (dip values around 89 deg. and 85 deg.) and a longitudinal section documenting roof/vault deformation. Beyond technical performance, the paper examines the self-reported formative impact on students’ digital skills and their understanding of cultural values, arguing that participation in 3D data acquisition, processing, and interpretation positions them as co-creators of a living digital archive. Pre- and post-workshop questionnaires (n = 13 each) are analysed descriptively—counts, percentages and medians with interquartile ranges—because the two instruments are unmatched and carry no shared identifier, so no paired test is applied; post-workshop self-ratings of technical competence, heritage understanding, archival awareness and collaboration were consistently high (medians 4–5), with uneven access to VR the main gap. By connecting point-cloud-based documentation workflows with heritage education, the project outlines a transferable, monitoring-ready baseline model in which 3D remote sensing supports both careful documentation and the transmission of regional identity and cultural meaning in architectural training. As an exploratory pilot with a small, self-reported sample, the study reports descriptive and qualitative findings rather than validated metric or statistical results. Full article

Spatial clustering does not show that specific built-form characteristics lead to the concentration of terrorist incidents, but it is common for incidents to be clustered in urban areas. This is an exploratory study that examines 7960 incidents of ISIS terrorism recorded in the GTD from 2012 to 19. Geocoded incident coordinates and Haversine great-circle distance were used to implement the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). An epsilon radius of 50 km and MinPts = 15 were chosen for the primary model, and 15 parameter combinations were analysed to investigate the robustness of the results. The main cluster found was 18 clusters with 7403 incidents (93.00%), and noise was the other cluster (557 incidents, 7.00%). The bulk of incidents (6118) were in Iraq, while the second largest cluster was in Syria with 675 incidents. The results show high geographical concentration, which is mainly due to the operational geography of ISIS, the intensity of the conflicts, the levels of exposure, and reporting. The analysis does not directly measure architectural morphology, sight lines, surveillance, permeability, crowding, and emergency egress. Based on this, the study suggests an initial multi-scalar urban design assessment framework that includes hotspot analysis as a first step, followed by site-specific assessment using the urban security, CPTED and crowd safety, and evacuation principles. The contribution is methodological and involves showing how the large-scale incident data can help inform, but not supplant, detailed architectural evaluation. Full article

This study examines the association between financial development and selected telecommunications infrastructure indicators in sub-Saharan Africa (SSA), focusing on mobile cellular subscriptions (MBS) and fixed telephone subscriptions (FTS). It also examines whether urbanization moderates these associations and explores the relationships of trade openness, final consumption expenditure, and economic growth with telecommunications infrastructure. The study is based on an unbalanced panel of 44 SSA countries with data for the period 1981–2021 and includes two-way fixed effects (TWFE) models as the baseline specification, Panel Fully Modified Ordinary Least Squares (FMOLS) to estimate long-run associations within the cointegrating framework, and TWFE models with Driscoll–Kraay standard errors as a robustness procedure. The results indicate that financial development is positively and significantly associated with both FTS and MBS across the main specifications, with a substantially stronger association for MBS. These results validate the hypotheses that financial development has a positive relationship with the fixed and mobile telecommunications infrastructure. The moderation analysis also shows that financial development is positively associated with MBS when the level of urbanization increases, while such a positive moderating relationship is not statistically significant for FTS. Trade openness is generally positively associated with telecommunications infrastructure, while the associations of final consumption expenditure and economic growth vary across the MBS and FTS specifications. Overall, the findings suggest that the association between financial development and telecommunications infrastructure is technology-specific and varies with the level of urbanization. Robustness tests with the balanced panel sample and the post-1990s subsample generally give similar results, but caution should be taken in comparing results across specifications because of the differences in samples. The findings provide evidence relevant to the telecommunications infrastructure dimension of the broader Sustainable Development Goal 9 agenda, but they should not be inferred as evidence of comprehensive SDG 9 progress or causal effects. Future studies should use more comprehensive digital infrastructure indicators, disaggregated data, and better identification strategies to mitigate potential reverse causality and other forms of endogeneity. Full article

The Old City of Sana’a, a UNESCO World Heritage Site, remains morphologically unclassified at the urban block scale, with understanding still grounded in descriptive narrative rather than quantitative data—leaving conservation decisions without a measurable, reproducible spatial evidence base. This study introduces a numerical taxonomy of 115 historic urban blocks built from four standardized morphometric indicators—area, compactness, elongation, and rectangularity—together with axially encoded orientation, reduced through Principal Component Analysis (74.7% variance, three components) and partitioned by K-means into five morphologically distinct block types ($n=36,29,19,16,15$). Kruskal–Wallis tests confirmed significant differentiation across all types for every rankable indicator (all $p<0.001$; Dunn’s post-hoc: 28 of 50 pairwise contrasts significant). Qibla deviation—a culturally specific measure of angular proximity to Mecca, deliberately withheld from clustering—emerged as the strongest discriminator between types (${\eta }_H^2=0.714$), showing that shape and orientation alone recover a coherent pattern in which one type’s mean orientation axis falls within $4.5°$ of the Qibla axis. However, this statistical discriminator reflects the distinctiveness of a single type rather than a universal cultural organizing principle across the fabric. Types are distributed across all three historic zones rather than clustering spatially, and shape regularity shows no association with distance from the Great Mosque. Building on these signatures, a percentile-based screening instrument offers a reproducible, block-level morphometric baseline to help prioritize this UNESCO-listed site’s conservation under active conflict-related threat. Full article

High-resolution mapping of urban surface CO₂ is essential for refined carbon monitoring, emission management, and low-carbon urban planning. Mobile monitoring provides dense street-level observations, but raw CO₂ measurements are often affected by transient traffic disturbances, vehicle idling, and localized plume events, which limits their direct use as stable spatial mapping targets. This study developed an integrated framework for predicting, mapping, and interpreting stable surface CO₂ patterns in Shenzhen by combining vehicle mobile observations, CSF processing, multiscale remote sensing predictors, machine learning. A CSF-based lower-envelope filter was used to suppress short-duration positive peaks and extract a more stable CO₂ accumulation signal from mobile observations. Multiscale predictors representing transportation, urban activity, surface environment, and built form were constructed to characterize both local and surrounding urban contexts. Compared with raw CO₂, the CSF-processed target substantially improved prediction performance. The best validation R² across the candidate models increased from 0.59 to 0.90 in April and from 0.62 to 0.93 in November. The predicted maps identified persistent high-CO₂ areas in central and southwestern Shenzhen. SHAP results showed that transport networks and urban activity reinforced surface CO₂ accumulation, whereas vegetation and open-surface contexts weakened accumulation at broader spatial ranges. These findings provide an interpretable framework for high-resolution urban CO₂ mapping and refined low-carbon governance. Full article