Search Results (387)

Climate change and aging infrastructure are undermining the resilience of urban flood management systems, reducing their reliability and increasing the likelihood of systemic failure that may culminate in regime shifts. This study develops a conceptual and practitioner-oriented screening framework based on a quantitative mathematical model to examine facility-level resilience and identify threshold conditions that may trigger regime transitions under external disturbances and varying pre-disturbance facility conditions. The framework adopts the composite sigmoid function (CSF) to capture nonlinear performance trajectories of infrastructure systems. Building on this model, this study extends its application by developing a parameterization scheme directly linked to four resilience dimensions: robustness, redundancy, rapidity, and resourcefulness (4Rs), which can be derived from field investigations or expert surveys. The normalized 4R scores are mapped to the CSF parameters, thereby converting static resilience assessment results into degradation and recovery curves. To search for threshold conditions, a parametric analysis was conducted by systematically varying the 4R values across their defined ranges. Rather than indicating a single universal threshold value, the results revealed critical threshold regions formed by specific combinations of the 4R dimensions. Lower robustness reduced the initial performance buffer, and low redundancy accelerated and extended performance degradation, while insufficient rapidity and resourcefulness delayed or limited recovery, increasing the likelihood of transition into an alternative degraded regime. For example, even when R1 and R2 were set to relatively high normalized values of 0.90, and R3 was set to its maximum value of 1.00, full recovery could not be achieved when R4 decreased below approximately 0.20. An illustrative application was conducted using preliminary 4R assessment results for flood-control facilities in three districts of Seoul, Korea. The model-derived trajectories were qualitatively compared with reported historical vulnerability patterns. While this comparison was intended as a contextual assessment rather than an event-specific empirical validation, our framework supports comparative, scenario-based screening of potentially vulnerable facilities for preliminary maintenance and investment prioritization. Full article

Traditional space syntax research mainly focuses on the overall topology of street networks, with insufficient attention to street segments and spatial skeletons. To fill this gap, this study innovatively integrates segment and skeleton analysis into space syntax and proposes a multilevel segment–skeleton–network analytical framework. Taking Beijing’s Capital Core Area (CCA) as a case, we analyze its street network evolution, spanning more than 600 years from the Yuan Dadu to the present, based on multi-temporal historical maps and syntactic indicators. The results indicate that main streets and alleys present polarized development; traditional commercial centres gradually decline while maintaining high accessibility; and a distinctive ring-shaped public skeleton centred on the Forbidden City has taken shape. This integrated framework remedies the defects of a single network analysis. The outcomes offer practical implications for the protection and sustainable development of historic street systems. Full article

Historic urban districts face growing challenges in balancing visitor activity with the needs of residents, local businesses, accessibility, and mobility. This study investigates whether short-term visitor forecasts can support destination management and planning in these complex environments. Unlike most visitor-forecasting studies, which focus on large cities and major tourist destinations, this study examines forecasting in a small historic urban district where visitor activity is more variable and management resources are limited. Using hourly pedestrian counts, weather data, and temporal data from the historic urban district of Fredrikstad, Norway, this research forecasts pedestrian activity up to 24 h in advance. The sensors record all pedestrians and do not distinguish tourists from residents or other users. Consequently, pedestrian counts are treated as an operational proxy for overall visitor activity at this heritage destination. Forecasting performance was evaluated using statistical, machine-learning, deep-learning, and benchmark forecasting models. Random Forest achieved the strongest overall forecasting performance (MAE = 209.52; RMSE = 357.18), outperforming the seasonal naïve benchmark (MAE = 247.44; RMSE = 421.70). Random Forest and GRU both outperformed the seasonal naïve benchmark on MAE, demonstrating the value of incorporating weather, temporal, and sensor variables into short-term forecasting. The findings suggest that short-term visitor forecasts may support operational planning, mobility management, service coordination, and other day-to-day destination management decisions. By anticipating periods of increased visitor activity, destination managers, local authorities, and government officials will be better able to allocate resources and coordinate services. This study demonstrates the potential of predictive analytics and sensor technology to support visitor management and operational planning in historic urban districts. Full article

The traditional market towns of the Western Sichuan Plain, known as Changzhen, represent a distinctive form of vernacular settlement shaped by the interplay of geographical conditions, migration history, and socio-economic dynamics. In the context of rural revitalisation and integrated urban–rural development, the conservation and renewal of these towns require a deeper understanding of their underlying spatial logic. This study employs a mixed-methods approach combining typo-morphological analysis with spatial measurement and statistical testing to investigate the core morphological indicator that determines plot-type differentiation in linear market towns, using Luodai and Yuantong as two representative cases in the Chengdu Plain. A total of 1113 plots are digitised and measured across four morphological indicators, supplemented by XGBoost classification and SHAP analysis for cross-validation. The findings reveal that plot depth is the core morphological indicator distinguishing the linear type from the enclosed courtyard type, with an effect size substantially larger than that of frontage-to-depth ratio and distance from the street, indicating that plot depth matters more than plot location in determining plot type. The two towns present contrasting patterns of plot-type composition, with frontage enclosed courtyard plots significantly larger than their non-frontage counterparts and no meaningful correlation between plot area and courtyard ratio. This study provides a quantifiable morphological framework for understanding plot-level differentiation in linear market towns and offers practical parameters for conservation and renewal. The findings highlight a specific threshold, with depths below approximately 12 m associated with the linear type and depths above approximately 18 m associated with the enclosed courtyard type, providing a tangible reference for planning decisions in historic districts. Full article

From a Historic Urban Landscape (HUL) perspective, varying conservation intensities and statutory boundaries may create “zonal differentiation” within historic districts. However, conventional homogenized renewal strategies frequently overlook this heterogeneity, affecting physical townscapes and human perception. This study analyzes 1714 panoramic street-view images from the Core Protection Zone and Construction Control Zone of Jimei School Village. Eleven objective visual features and six model-predicted perception dimensions were examined, with locally experienced participants providing contextual validation. Integrating K-Means clustering, Random Forest modeling, and SHapley Additive exPlanations (SHAP) feature attribution, the study investigates nonlinear, model-based contribution patterns across the two zones. Results reveal significant but non-binary differences in objective features and predicted perceptions. Street-view typologies show zonal tendencies while also indicating within-zone diversity and cross-zone overlap. Feature attribution shows that color composition, greenery, building interfaces, and vehicle presence are more prominent in the Core Protection Zone, whereas greenery, spatial openness, and road-space organization play stronger roles in the Construction Control Zone. This study establishes an interpretable street-view-based framework for historic-district assessment, providing empirical support for differentiated and human-oriented zonal renewal. Full article

As urban renewal progresses, transforming old industrial plants into sports service complexes has become a prevalent strategy to revitalize urban stock space. To evaluate the actual performance of this spatial reproduction from the users’ perspective, this study analyzes 3702 consumer reviews from five typical Chinese sports service complexes using an interdisciplinary text-mining framework. We employ the LDA topic model to extract core dimensions of consumer concern and utilize the DistilBERT model for fine-grained, sentence-level sentiment computation. The results reveal that consumer attention spans five key topics: venue services, cultural business districts, sports training, industrial integration, and heritage utilization. Critically, the sentiment analysis uncovers a structural paradox: while topics associated with cultural and heritage utilization trigger highly positive emotions and strong place identity, core functional modules like sports training and venue services generate substantial negative feedback. This disparity highlights a profound friction between rigid historical industrial architectural structures and the flexible demands of modern servicescapes, reflecting a clear path dependency in spatial transformation. Based on these theoretical and empirical findings, we propose targeted optimization strategies, including the structural reconstruction of training services and the flexible upgrading of basic venue facilities, to transition these complexes from initial physical construction toward long-term, service-oriented operation. Full article

The purpose of this research is to propose a sustainable urban support regeneration system aimed at recovering the historic urban landscape of Rimac, Lima (2024). The Historic Center of Rimac is currently undervalued due to several issues affecting the district, including citizen insecurity, the limited percentage of green areas, excessive and unplanned urban growth, the abandonment of the district’s historic centers, and its disconnection from the surrounding urban environment. The methodology was developed through a review of bibliographic documents, such as scientific publications, together with the use of 2D and 3D software tools, which enabled the collection of environmental, cultural, physical–spatial, and urban information. The implementation of sustainable public spaces within the historic urban landscape of Rimac will transform the area into an attractive tourist and cultural destination. The viewpoints will provide panoramic views, the urban forests will improve air quality and increase green areas, and the site museum will preserve and disseminate the rich local history. Together, these interventions will promote urban regeneration, community wellbeing, and the sustainable development of the area, thereby offering an efficient proposal aimed at optimizing the district’s potential and implementing strategies that contribute to the achievement of the Sustainable Development Goals (SDGs). Full article

User-generated content (UGC) offers large-scale, naturalistic data for examining tourism destination image. Focusing on the Historic Centre of Macao (HCM), a World Cultural Heritage site, this study collected 3781 tourist reviews from Rednote, Ctrip, Dianping, and TripAdvisor and developed a multi-model text-mining framework integrating TF-IDF, BERTopic, and RoBERTa. The results show that HCM’s online tourism image comprises four dimensions: perceptions of history, culture, and heritage value; perceptions of spatial landmarks and urban landscapes; modes of travel behavior and embodied experience; and emotional evaluation and tourism experience quality. The TF-IDF results indicate that terms such as architecture, history, Portuguese, church, Ruins of St. Paul’s, and Senado Square constitute the core elements of tourists’ cognitive image. BERTopic further identified 18 valid topics and revealed three interrelated semantic clusters: heritage-space cognition, landmark and district experiences, and integrated tourism experiences. The RoBERTa-based sentiment analysis shows that tourists’ overall evaluations are dominated by positive emotions, while crowding, high visitor density, and gaps between expectations and actual experiences remain important sources of negative evaluations. This study demonstrates the applicability of the proposed framework in the Historic Centre of Macao and provides a methodological reference for tourism image research in other cultural heritage destinations. Full article

Analyzing the long-term evolution of meso-scale urban morphological types is constrained by the “small-sample, high-dimensional” nature of historical data, which weakens the robustness and interpretability of conventional data-driven methods and limits the use of morphological evidence in digital urban analysis and built environment governance. To address this, we propose a theory-guided modular principal component analysis (TG-MPCA) framework for sample-constrained morphological research. By embedding domain knowledge into dimensionality reduction, the framework extracts compact, low-dimensional morphological indices that are both morphologically interpretable and internally stable within the selected sample set. Applied to Harbin’s representative districts through unsupervised hierarchical clustering, evolutionary lineage analysis, and transition node identification, it reveals a “layered response pattern” among morphological modules, an asymmetrical mapping between morphological types and historical periods, a structural breakpoint around 1946 that parallels post-war Western urban restructuring, and anomalous deceleration and premature convergence in typological evolution during transitional periods. These observations offer a meso-scale morphological perspective for understanding both the spatial transformation of Chinese cities and the developmental challenges of Northeast China’s old industrial bases, while demonstrating the value of theory-guided quantitative analysis for transforming fragmented historical spatial information into interpretable morphological evidence in data-constrained contexts. Full article

As global urban development shifts towards stock upgrading and cultural tourism consumption, historic and cultural districts have become crucial spatial carriers for reshaping local identity and driving urban regeneration. Although the literature explores cultural value, current research remains limited to macro-scale assessments, leaving a gap in micro-scale, quantitative identification of spatial narrative capacity. To address this, the concept of ontological narrative is introduced, and a three-dimensional framework integrating physical space, functional formats, and historical events is constructed. Using event space as the unit of measurement, a mixed-methods approach combining spatial syntax, kernel density estimation, and the Analytic Hierarchy Process is applied to Chaozong Street in Changsha. The findings indicate that narrative intensity exhibits a spatial pattern of main-axis agglomeration and deep-alley attenuation. High-value nodes concentrate along primary streets with high accessibility. Conversely, narrative efficacy declines in branch alleys and functionally deficient zones. Furthermore, a four-quadrant diagnosis reveals widespread structural decoupling, such as high historical value paired with low vitality. This shows that historical assets require functional activation to become effective narratives. This research provides a precise analytical tool, grounded in node diagnosis, to counter homogenized urban renewal by fostering differentiated cultural expression. Full article

The long-term transformation of mono-industrial cities has become a critical issue in sustainable urban development, particularly where urban growth was initially shaped by state-led industrialization and strategic security concerns. This paper examines China’s Third Front Construction, a large-scale Cold War programme that relocated industrial and defence facilities to inland regions, through a paired comparison of Yuan’an and Xiaogan in Hubei Province. Focusing on Base 066 as a city-forming enterprise, the study combines archival research, local gazetteers, factory records, field investigation, historical satellite imagery, and urban morphological analysis to examine how policy shifts reshaped urban form, industrial layout, infrastructure, and public facilities. The findings show that Yuan’an developed as a dispersed, mountain-based industrial enclave structured by concealment, air defence requirements, and work unit organization, whereas Xiaogan evolved into a more compact and integrated urban industrial district after the relocation of Base 066. This transformation changed not only production space but also urban–rural relations, residential organization, and public service provision. The study demonstrates that Third Front cities should be understood as policy-produced urban systems whose later decline or integration reflects the changing relationship between security, industry, and urban sustainability. It further suggests that industrial heritage, adaptive reuse, and intercity memory networks can support the regeneration of former mono-industrial settlements. Full article

This study presents DEDICA (Database of Hydrogeological Instability Events in Calabria, southern Italy), developed by the District Basin Authority of the Southern Apennines (ABDAM) in collaboration with the CNR-IRPI. The database integrates digitized historical sources, chronicle-based records, and previously unpublished archival data that were systematically analyzed, validated, and georeferenced within a GIS environment. After two years of development, DEDICA includes 5329 landslides, 2097 flood events, and 1711 urban flooding occurrences spanning the period 1900–2025. The system supports continuous data updating, enabling both the integration of recent events and the refinement of historical records. The database provides a comprehensive tool for identifying areas prone to geo-hydrological hazards based on historical recurrence, supporting hazard assessment, land-use planning, and risk management strategies. The methodological framework, database structure, and data processing workflow are described in detail. Spatio-temporal analyses highlight the distribution of instability processes, identifying the most affected sectors and revealing seasonal patterns and long-term trends. DEDICA represents a pilot initiative within a broader program aimed at extending the inventory to all regions under ABDAM jurisdiction, ultimately contributing to the development of a unified geo-hydrological hazard database for southern Italy. Full article

As urban renewal shifts toward existing stock, historic-district regeneration requires evaluation methods that address material conditions, governance, and lived experience. Conventional physical metrics often miss subjective perceptions of access, management, and lived experience. Drawing on Lefebvre’s spatial triad, this study develops an operational, case-specific framework using user-generated content from Google Maps and Xiaohongshu. The analysis combines frozen BERT representations and K-means clustering for Gran Clariana, Barcelona (N = 876), with deterministic lexicon matching and corpus-specific sentiment procedures for two Shanghai historic districts (N = 522) and Het Westerpark, Amsterdam (N = 1310). In Shanghai, Spatial Practice was the most frequent dominant dimension, whereas Representational Space had the highest within-category positive sentiment share. For Gran Clariana, K = 3 was the most stable solution tested (mean ARI = 0.974), while Landscape Perceived Value was the leading profile discriminator (Welch F = 48.98, p < 0.001). In Westerpark, Spatial Practice was most lexically prominent, and Representational Space showed the largest, though weak, positive association with VADER sentiment (r = 0.138, p < 0.001). Taken together, these case-specific, exploratory mappings lack external validation but complement post-occupancy evaluation by interpreting platform-visible, inclusion-relevant perceptions rather than measuring demographic representation, affordability, accessibility compliance, or distributive justice. Full article

Ground deformation is a hazardous geological phenomenon. In this study, the small baseline subset (SBAS) with the coherence baseline interferometric technique was employed to derive historical ground deformation in Tianjin City, Northern China, between 2019 and 2024. Using InSAR-derived datasets for training and validation, three machine learning architectures, namely two-dimensional convolutional long short-term memory (ConvLSTM2D), hybrid convolutional neural network–long short-term memory (hybrid CNN-LSTM), and hybrid convolutional neural network–bidirectional long short-term memory (hybrid CNN-BiLSTM), were developed to further analyze ground deformation and make future predictions. It was found that from SBAS-InSAR, the deformation rates for the whole Dongli District, Tianjin, ranged from −40.98 to 27.18 mm/year, with a mean of −2.41 mm/year from 2019 to 2024. Model performance was evaluated using held-out validation samples derived from the InSAR deformation dataset. The ConvLSTM2D model achieved the best performance, with an R² value of 0.99 and root mean squared error (RMSE) of 1.37 mm, compared with the hybrid CNN-LSTM (R² = 0.99, RMSE = 2.16 mm) and hybrid CNN-BiLSTM (R² = 0.99, RMSE = 2.19 mm). This optimized ConvLSTM2D model was applied to estimate the predictions of the ground deformation rate with −43.71 mm/year in the high-deformation zone between 2025 and 2028. These findings predict a continuing trend of land instability, highlighting the necessity for urgent geohazard mitigation and urban planning strategies in the affected regions. Full article

Urban branding has become an influential mechanism through which cities construct identity, shape public perception, communicate cultural distinctiveness, and guide urban transformation and place-based development. In heritage contexts, its significance extends beyond promotion by supporting the continuity, recognition, and positioning of historic places. Yet existing research has focused on formal heritage districts and visual representation, offering limited explanation of how lived historic environments sustain identity and develop a foundation for heritage-based urban branding through locally embedded socio-spatial practices. This study examines how the historic area surrounding the Mosque of Sultan al-Ashraf Qaytbay within Cairo’s City of the Dead maintains a coherent heritage identity through the interaction of architectural assets, craft production, market exchange, adaptive reuse, cultural activities, place perception, and everyday community practices. It develops a domain-based analytical framework comprising five interrelated domains: heritage asset readiness, cultural activation, place perception, emergent branding outputs, and governance and institutional mediation. The framework is applied through an interpretive spatial-observational case study based on repeated site visits, structured observation, spatial mapping, and photographic documentation. Findings show that craft production, everyday exchange, adaptive reuse, and community-based activities sustain heritage identity, collective memory, and experiential continuity. Workshops and bazaars form an interconnected production–exchange system, while galleries and cultural spaces strengthen interpretation and public engagement. However, fragmented digital visibility, weak narrative coordination, and limited institutional mediation constrain the translation of these assets into coherent branding outcomes. The study therefore distinguishes heritage identity from branding formation and offers a qualitative diagnostic framework for identifying domain alignment and misalignment, supporting context-sensitive approaches to urban transformation, heritage management, and place-based development. Full article