Search Results (118)

In the era of big data, the rapid proliferation of user-generated content enriched with geolocations offers new perspectives and datasets for probing the spatiotemporal dynamics of tourist mobility. Mining large-scale geospatial traces has become central to tourism geography: it reveals preferences for attractions and routes to enable intelligent recommendation, enhance visitor experience, and advance smart tourism, while also informing spatial planning, crowd management, and sustainable destination development. Using Mount Huangshan—a UNESCO World Cultural and Natural Heritage site—as a case study, we integrate GPS trajectories and geo-tagged photographs from 2017–2023. We apply a Density-Field Hotspot Detector (DF-HD), a Space–Time Cube (STC), and spatial gridding to analyze behavior from temporal, spatial, and fully spatiotemporal perspectives. Results show a characteristic “double-peak, double-trough” seasonal pattern in the number of GPS tracks, cumulative track length, and geo-tagged photos. Tourist behavior exhibits pronounced elevation dependence, with clear vertical differentiation. DF-HD efficiently delineates hierarchical hotspot areas and visitor interest zones, providing actionable evidence for demand-responsive crowd diversion. By integrating sequential time slices with geography in a 3D framework, the STC exposes dynamic spatiotemporal associations and evolutionary regularities in visitor flows, supporting real-time crowd diagnosis and optimized spatial resource allocation. Comparative findings further confirm that Huangshan’s seasonal intensity is significantly lower than previously reported, while the high agreement between trajectory density and gridded photos clarifies the multi-tier clustering of route popularity. These insights furnish a scientific basis for designing secondary tour loops, alleviating pressure on core areas, and charting an effective pathway toward internal structural optimization and sustainable development of the Mount Huangshan Scenic Area. Full article

The Yangtze River Delta (YRD) region has experienced rapid development in its tourism industry, establishing itself as a leading force within China’s tourism sector. However, significant regional disparities continue to hinder its sustainable development. This study adopts a mixed-methods approach to analyze the spatiotemporal evolution of domestic tourist flows and tourism industry agglomeration patterns in the region. Using city-level data from 2016 to 2022, the analysis employs a comprehensive methodology including standard deviation, coefficient of variation, standard deviation ellipse, and locational entropy. The main findings are as follows: (1) In the pre-pandemic period (2016–2019), absolute disparities in tourist flows widened, whereas relative disparities narrowed. During the pandemic (2020–2022), absolute disparities decreased, while relative disparities initially increased before contracting. (2) Tourist flows displayed a southeast–northwest gradient, with high-value areas clustered along the southeastern coast. Standard deviation ellipse analysis reveals that tourist flows were primarily distributed along the eastern coastal corridor, parallel to the coastline. Prior to the pandemic, tourism growth showed a tendency toward spatial equilibrium; however, this trend was disrupted during the pandemic, resulting in a more decentralized spatial pattern. (3) Throughout the pandemic, tourism industry concentration increased significantly in most cities. Cities with renowned scenic attractions and diversified economic structures demonstrated stronger resilience, while those heavily reliant on tourism were more vulnerable to the pandemic’s effects. Full article

The rise of independent travel is reshaping tourism, moving away from mass tourism and rigid itineraries toward flexible, technology-driven, and sustainable experiences. This study examines how self-guided digital tours can reduce congestion at points of interest while maintaining visitor engagement. Using a stylized agent-based simulation implemented with the Mesa framework, we modeled guided and self-guided tourist groups to compare congestion patterns, travel flows, and completion rates. The results indicate that self-guided tours flatten congestion peaks and support decentralized, walking-based exploration while maintaining comparable engagement levels. The findings suggest that digital self-guided formats can complement urban visitor management and smart-city strategies by distributing tourist flows more evenly. Future research should calibrate the model with real-world data and case studies to validate and extend these results. This study contributes to the discourse on sustainable urban tourism by positioning self-guided tours as a tool for integrating visitor management into smart infrastructure and enhancing long-term cultural and environmental resilience. Full article

Tourist flow prediction plays a crucial role in enhancing the efficiency of scenic area management, optimizing resource allocation, and promoting the sustainable development of the tourism industry. To improve the accuracy and real-time performance of tourist flow prediction, we propose a BP model based on a hybrid genetic algorithm (GA) and ant colony optimization algorithm (ACO), called the GA-ACO-BP model. First, we comprehensively considered multiple key factors related to tourist flow, including historical tourist flow data (such as tourist flow from yesterday, the previous day, and the same period last year), holiday types, climate comfort, and search popularity index on online map platforms. Second, to address the tendency of the BP model to get easily stuck in local optima, we introduce the GA, which has excellent global search capabilities. Finally, to further improve local convergence speed, we further introduce the ACO algorithm. The experimental results based on tourist flow data from the Elephant Trunk Hill Scenic Area in Guilin indicate that the GA-AC*O-BP model achieves optimal values for key tourist flow prediction metrics such as MAPE, RMSE, MAE, and R², compared to commonly used prediction models. These values are 4.09%, 426.34, 258.80, and 0.98795, respectively. Compared to the initial BP neural network, the improved GA-ACO-BP model reduced error metrics such as MAPE, RMSE, and MAE by 1.12%, 244.04, and 122.91, respectively, and increased the R² metric by 1.85%. Full article

As the texture skeleton of the traditional village, the street space is the main area for tourists to visit in traditional villages; it is regarded as the spatial conversion place of human flow and the space frequently visited by tourists. Accumulating evidence shows that the visual quality of street spaces has an effect on pedestrians’ walking behaviors in urban areas, but this effect in traditional villages needs to be further explored. This paper takes Huangcun Village, Yixian County, Huangshan City, as the research area to explore the influence of the objective visual factors of street spaces on tourists’ subjective stay willingness. First, an evaluation system of the visual quality of street spaces was developed. With the assistance of computer vision and deep learning technologies, semantic segmentation of Huangcun Village street view images was performed to obtain a visual quality index and then calculate the descriptive index of Huangcun Village’s street space. Then, combining the data of tourists’ stay willingness with the visual quality of the street space, the overall evaluation results and space distribution of tourists’ stay willingness in Huangcun Village were predicted using the Trueskill algorithm and machine learning prediction model. Finally, the influence of the objective visual quality of the street space on tourist subjective stay willingness was analyzed by correlation analysis. This research could provide some useful information for street space design and tourism planning in traditional villages. Full article

As sustainable cultural heritage tourism increasingly demonstrates its unique value and appeal, effectively stimulating tourists’ emotional experiences and value co-creation behaviors has become a focal issue. This study investigates how multiple tourist motivations (self-enhancement, escapism, and social interaction) shape value co-creation through emotional mediators—namely aesthetic, nostalgic, and flow experiences. Data were collected from 470 valid responses from visitors to the UNESCO-listed Suzhou Classical Gardens in China and analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that these emotional experiences significantly drive value co-creation behavior: self-enhancement motivation enhances all three experiences, escapism mainly promotes nostalgic and flow experiences, and social interaction primarily affects aesthetic experience. These findings clarify the psychological mechanisms through which tourists’ motivations and emotional experiences influence value co-creation behavior in cultural heritage tourism. This research advances our understanding of the motivation–experience–behavior framework and emphasizes that enhancing emotional engagement is key to fostering sustainable cultural heritage tourism practices. The study provides practical implications for designing experiences and strategies that balance visitor satisfaction with the long-term vitality of cultural heritage sites and local communities, thereby contributing to broader sustainable development goals. Full article

Amid ongoing societal development, tourists’ travel behavior patterns have been undergoing substantial transformations, and understanding their evolution has emerged as a key area of scholarly interest. Taking Beijing as a case study, this research aims to uncover the spatiotemporal evolution patterns of tourist flows and their underlying driving mechanisms. Based on digital footprint relational data, a dual-perspective analytical framework—“tourist perception–tourist flow network”—is constructed. By integrating the center-of-gravity model, social network analysis, and regression models, the study systematically examines the dynamic spatial structure of tourist flows in Beijing from 2012 to 2024. The findings reveal that in the post-pandemic period, Beijing tourists place greater emphasis on the cultural connotation and experiential aspects of destinations. The gravitational center of tourist flows remains relatively stable, with core historical and cultural blocks retaining strong appeal, though a slight shift has occurred due to policy influences and emerging attractions. The evolution of the spatial network structure reveals that tourism flows have become more dispersed, while the influence of core scenic spots continues to intensify. Government policy orientation, tourism information retrieval, and the agglomeration of tourism resources significantly promote the structure of tourist flows, whereas the general level of tourism resources exerts no notable influence. These findings offer theoretical insights and practical guidance for the sustainable development and regional coordination of tourism in Beijing, and provide a valuable reference for the spatial restructuring of urban tourism in the post-COVID-19 era. Full article

This research aims to determine the utilization levels of local products and the challenges and opportunities of creating a recognizable food-centered cultural identity based on collaborative networks developed between agriculture and tourism. This has the potential to strengthen collaborative entrepreneurship. It uniquely contributes to the existing literature by exploring the connections between agri-food and tourism, while proposing strategies to maximize business opportunities centered on food culture. Descriptive and inferential statistics are conducted based on primary data collected by distributing a questionnaire to 59 public and private organizations in the Peloponnese region in Greece, which has significant agricultural production but limited tourist flows. The results indicate a lack of collective action and business recognition of the value of regional food culture among participants. The human resources employed in tourism lack the skills to highlight traditional food heritage. The presence of structural and operational barriers undermines efforts to facilitate communication, manage suppliers, and enhance the visibility of products designated with Geographical Indications. This paper offers preliminary results; however, extensive future studies are needed to validate the findings fully. The study highlights key implications: Improved communication between stakeholders could enhance the management of the local food network. Agri-food and tourism businesses can develop educational programs and food-focused tourism packages that promote social cohesion and preserve cultural heritage. Full article

Accurate and timely forecasting of cross-regional tourist flows is essential for sustainable destination management, yet existing models struggle with sparse data, complex spatiotemporal interactions, and limited interpretability. This paper presents SE-TFF, a multi-scale tourism-flow forecasting framework that couples a Squeeze-and-Excitation (SE) network with reinforcement-driven optimization to adaptively re-weight environmental, economic, and social features. A benchmark dataset of 17.8 million records from 64 countries and 743 cities (2016–2024) is compiled from the Open Travel Data repository in github (OPTD) for training and validation. SE-TFF introduces (i) a multi-channel SE module for fine-grained feature selection under heterogeneous conditions, (ii) a Top-K attention filter to preserve salient context in highly sparse matrices, and (iii) a Double-DQN layer that dynamically balances prediction objectives. Experimental results show SE-TFF attains 56.5% MAE and 65.6% RMSE reductions over the best baseline (ARIMAX) at 20% sparsity, with 0.92 × 10³ average MAE across multi-task outputs. SHAP analysis ranks climate anomalies, tourism revenue, and employment as dominant predictors. These gains demonstrate SE-TFF’s ability to deliver real-time, interpretable forecasts for data-limited destinations. Future work will incorporate real-time social media signals and larger multimodal datasets to enhance generalizability. Full article

This study investigates the impact of cultural openness on intertemporal consumption behavior in the Middle East and North Africa (MENA) region, using panel data from 14 countries spanning 2010 to 2022. Unlike prior research that primarily focused on lifestyle shifts or product preferences, this study explores how cultural globalization influences the trade-off between present consumption and future savings, as captured by the consumption-to-savings ratio (LCESR). Cultural openness is operationalized using the Cultural Globalization General Index (LCGGI), and its effect is analyzed alongside key control variables including Internet penetration, real GDP per capita, inflation, and tourism. To address endogeneity and unobserved heterogeneity, this study employs the system Generalized Method of Moments (GMM) estimator, supported by robustness check models. The findings reveal a significant positive relationship between cultural openness and LCESR in both the short and long run, indicating that increased exposure to global cultural flows enhances consumption tendencies in the region. Internet penetration and inflation negatively affect saving behavior, while GDP per capita shows a positive effect. Tourist arrivals exhibit limited influence. This study also highlights the importance of historical consumption behavior, as the lagged dependent variable strongly predicts the current LCESR. Robustness checks confirm the consistency of the results across all models. These insights suggest that cultural openness, digital infrastructure, and macroeconomic stability are pivotal in shaping consumption/saving patterns. The results carry important implications for financial education, digital consumption governance, and cultural policy strategies in the MENA region and similar emerging markets undergoing rapid cultural integration. Full article

As health and well-being become central concerns in the post-pandemic tourism landscape, health tourism is evolving to prioritize not only physical recovery but also psychological engagement and emotional value. This study explores how gamified design can enhance tourist participation and experience quality within health-related tourism contexts. By integrating theories from tourism psychology and game-based experience design, a structural equation model is proposed to examine the relationships among memorable tourism experiences, tourist motivation, game design elements, flow experience, and perceived value, and their joint influence on behavioral intention. Data collected from tourists who engaged in gamified experiences were analyzed using structural equation modeling (SEM) techniques. The results identify a dynamic “participation–immersion–value” mechanism, in which gameful design fosters flow and perceived value, thereby mediating gamification’s impact on behavioral intention. These findings offer valuable insights for health tourism developers and experience designers seeking to create emotionally engaging, motivating, and sustainable visitor experiences in the context of health and well-being. Full article

Suyukou Gully, located on the eastern slope of the Helan Mountains in northwest China, is a typical debris-flow-prone catchment characterized by a steep terrain, fractured bedrock, and abundant loose colluvial material. The area is subject to intense short-duration convective rainfall events, which often trigger destructive debris flows that threaten the Suyukou Scenic Area. To investigate the dynamics and risks associated with such events, this study employed the FLO-2D two-dimensional numerical model to simulate debris flow propagation, deposition, and hazard distribution under four rainfall return periods (10-, 20-, 50-, and 100-year scenarios). The modeling framework integrated high-resolution digital elevation data (original 5 m DEM resampled to 20 m grid), land-use classification, rainfall design intensities derived from regional storm atlases, and detailed field-based sediment characterization. Rheological and hydraulic parameters, including Manning’s roughness coefficient, yield stress, dynamic viscosity, and volume concentration, were calibrated using post-event geomorphic surveys and empirical formulations. The model was validated against field-observed deposition limits and flow depths, achieving a spatial accuracy within 350 m. Results show that the debris flow mobility and hazard intensity increased significantly with rainfall magnitude. Under the 100-year scenario, the peak discharge reached 1195.88 m³/s, with a maximum flow depth of 20.15 m and velocities exceeding 8.85 m·s⁻¹, while the runout distance surpassed 5.1 km. Hazard zoning based on the depth–velocity (H × V) product indicated that over 76% of the affected area falls within the high-hazard zone. A vulnerability assessment incorporated exposure factors such as tourism infrastructure and population density, and a matrix-based risk classification revealed that 2.4% of the area is classified as high-risk, while 74.3% lies within the moderate-risk category. This study also proposed mitigation strategies, including structural measures (e.g., check dams and channel straightening) and non-structural approaches (e.g., early warning systems and land-use regulation). Overall, the research demonstrates the effectiveness of physically based modeling combined with field observations and a GIS analysis in understanding debris flow hazards and supports informed risk management and disaster preparedness in mountainous tourist regions. Full article

This study investigates the impact of web-based augmented reality (Web AR) on destination visit intention through the lens of a stimulus–organism–response (SOR) framework, a technology acceptance model (TAM) and flow theory into an integrated theoretical framework. This study aims to address gaps in the literature by providing insights about the relevance of augmented reality to tourism marketing effectiveness. Structural equation modeling was used to test this conceptual framework using AMOS23 on quantitative data collected from questionnaires distributed locally and internationally and applied to 384 participants after going through a Web AR destination experience. The findings confirmed that Web AR stimuli (i.e., interactivity and vividness) positively impact tourists’ destination visit intention through the tourist organism in terms of perceived ease of use, perceived usefulness, perceived certainty, perceived enjoyment and perceived immersion. Therefore, the promotion of destinations through augmented reality technology contributes to the development of sustainable tourism. The findings of this study will shed light on an alternative idea for destination marketing to inspire destination management organizations (DMOs) wishing to develop a competitive edge and win within the tourism industry. The results thus contribute to the Web AR and the tourism marketing literature by providing theoretical guidance through a framework for the AR tourism experience, as well as a reference for DMOs. Full article

The COVID-19 pandemic has caused a profound interruption in the way people travel and has had a very negative impact on tourism and economics throughout the world, especially on the coastal fishing communities in South Korea. These previously problematic areas, having suffered a decrease in the local population as well as stood in the midst of the economic downturn, experienced a great cut in the number of tourists coming from far away, which additionally caused their collapse of resilience and sustainability. This research investigates the recovery trends of 45 seashore-fishing districts in South Korea and how the change in travel distance and the number of visitors before and after the pandemic have affected these trends. Through the utilization of big data from the Korea Tourism Data Lab (2019–2023) and Geographic Information System (GIS) analysis, we observe the changes in visitor flows, use the indices of resilience as an indicator to measure them, and investigate how proximity affects travel recovery. The survey results indicate that the regions neighboring metropolitan zones were not only the ones that suffered the most from travel distance during the pandemic but also experienced quick recovery after the pandemic. The new promotional campaigns, in tandem with an improved network of transportation, contributed to the swift recovery of these areas. The remote areas, on the other hand, persist in fighting the problems of regionalized tourism and have only limited accessibility. The proposition of “distance-dependent resilience” theory as well as the Blue Urbanism framework is offered in order to bring up the ideas of sustainable tourism and population stabilization. The study is expected to serve as a cornerstone for the practice of adaptive governance and strategic planning in the matter of the coastal areas after the pandemic. Full article

The impact of high temperatures on tourist flows in urban and rural areas is both complex and multi-dimensional, yet research remains limited regarding their spatial and temporal differences. This study aims to analyze the changes in tourist flows between urban and rural areas under high-temperature conditions and to identify the key factors driving these patterns, contributing to climate-resilient tourism planning. Using Shanghai, China, as a case study, we constructed an attraction-based tourist flow model with Baidu migration data, integrating a self-organizing feature map for urban–rural classification and Pearson correlation analysis to examine influencing factors. The results showed that high temperatures significantly reduced tourist flows in both urban and rural areas, with a more pronounced impact observed in rural areas. This reduction altered spatial patterns, shifting from a multicentric distribution to an urban-centered concentration. Furthermore, high temperatures affected the timing of tourist flows differently across regions. In urban areas, tourist flows tended to start earlier, and key driving factors, such as facility services and economic levels, remained stable and continued to exert a dominant influence. In contrast, rural tourist flows were delayed under high-temperature conditions, with tourists showing a preference for cooler attractions further from urban centers. These findings highlight the need for targeted climate adaptation strategies, including improving cooling infrastructure in urban areas and promoting eco-friendly, sustainable tourism initiatives in rural regions. This study offers empirical evidence to support policy efforts aimed at fostering coordinated urban–rural tourism development and advancing sustainable adaptation to climate change. Full article