Search Results (147)

Tourism destinations increasingly serve both domestic and international visitors whose geographic behaviors may differ substantially, yet most analytical frameworks treat visitor distributions as spatially homogeneous. Few studies compare how domestic and international tourists cluster spatially within the same destination. Those differences matter enormously for destinations where visitor segments follow distinct geographic patterns. We analyzed 1547 bilingual TripAdvisor reviews from Chanthaburi Province, Thailand (2014–2023), combining Random Forest classification (83.26% accuracy for Thai, 96.45% for English) with Incremental Spatial Autocorrelation (ISA), Global Moran’s I, and Getis-Ord Gi* hotspot analysis. International visitors clustered more intensely overall (I = 0.253 vs. 0.213), but domestic visitors spread across all six tourism areas including agrotourism, while international visitors were concentrated in heritage, coastal recreation, and nature-temple zones with agrotourism absent. Both segments clustered strongly at cultural heritage sites and at beach destinations, contradicting the common assumption that coastal areas primarily serve international visitors, while agrotourism clustered exclusively among domestic visitors despite active policy promotion. These patterns reflect differential information access rather than attraction quality. The zone-level framework is transferable to secondary heritage destinations across Southeast Asia, where platform-based monitoring offers a practical alternative to large-scale visitor surveys. Full article

Modern web and tourist maps use pictorial symbols to help users quickly and easily identify Points of Interest (POIs). Pictorial symbols are sometimes misinterpreted due to poor design choices. As a result, it is important to evaluate pictorial symbols with map users. This paper uses an online questionnaire to examine how different graphic parameters—such as frame outline, frame background, frame shape, color hue, and pictogram category (semantic, visual, or arbitrary)—are perceived by map users. The evaluation of pictograms includes three aspects: understanding, to capture the map reader’s opinion; preference, to investigate the map maker’s choice; and appropriateness, to document the evaluation of an existing map. Seven popular Points of Interest (POIs) were selected for the evaluation of pictorial symbols: Hotel, Restaurant, Parking, Museum, Airport, Hospital, and Church. Based on the questionnaire results and the statistical analysis of 520 responses, several conclusions were drawn. Users prefer symbols with a frame outline and a frame background. They also prefer symbols with a white background, which increases contrast and improves legibility. In contrast, users do not have a strong preference for a specific frame shape. In general, users can recognize symbol groups based on frame shape, but the effect is stronger when the color hue appears in the frame background or outline. The statistical analysis demonstrates that perceived appropriateness constitutes an objective measure related to comprehension. Furthermore, appropriateness is independent of the pictogram classification as semantic, visual, or arbitrary. Instead, it is determined by the graphic ability of the pictogram to represent a specific POI. This conclusion reaffirms the importance of designing successful semantic and visual pictograms or adopting those already familiar to map users, as familiarity has also been identified as an important factor by this research. Overall, this paper, based on user evaluations, provides practical insights to improve pictorial symbols on a tourist map. Full article

Digital nomads blur the boundaries between tourism, work, and temporary residence, yet little is known about how local businesses interpret this ambiguous population. This study examines how Greek SMEs classify digital nomads and how these classifications shape perceived business benefits and harms, support for protective policy guardrails, and firm-level adaptation intentions. Using survey data from 747 SME owner-managers and managers in tourism-linked and adjacent sectors, the study tests an integrated framework with PLS-SEM and multi-group analysis. The findings show that SME responses are interpretive rather than automatic. Residency-Based Visitor Beliefs positively predicted support for protective policy guardrails (β = 0.334, p < 0.001), but did not directly predict adaptation intentions. Perceived Touristness positively predicted both guardrail support (β = 0.110, p < 0.001) and adaptation intentions (β = 0.181, p < 0.001). Perceived Business Benefits was the strongest predictor of adaptation intentions (β = 0.390, p < 0.001), while Perceived Business Harms also increased both guardrail support (β = 0.175, p < 0.001) and adaptation intentions (β = 0.310, p < 0.001). Mediation results showed that the effects of Residency-Based Visitor Beliefs on adaptation were fully transmitted through benefits and harms, whereas Perceived Touristness operated indirectly only through harms. Multi-group analysis further revealed significant heterogeneity across firm size, years in operation, and tourism dependence. The study contributes to digital nomad and tourism research by introducing a business-side classification perspective and by linking classification, evaluation, and response in a single model. Overall, the findings show that whether digital nomads are classified as tourists by businesses has measurable implications for regulatory preferences and market adaptation. Full article

With the advancement of urbanization, the functions of cities continue to expand and deepen, among which the tourism function plays an increasingly important role in urban and regional economic development. To resolve the challenges in data acquisition for urban function classification and assessment, this study introduces POI data and machine learning methods to construct an employment sector distribution model. This enables the estimation of tourism-related employment data in Pacific Island countries. The tourism function of these cities is quantitatively evaluated based on two dimensions: functional scale and functional intensity. The results show that: (1) The constructed employment sector distribution model demonstrates strong predictive performance. The error rate for the total employed population in each island country is below 10%. The Bootstrap robustness test confirms that predicted values for all countries fall within the 95% confidence interval. The number of tourism employees shows a significant positive correlation with inbound tourist numbers and the count of tourism-related POIs at the 0.01 level. Empirical validation shows tourism-related sector error rates of 4.44% for Ningbo and 9.02% for Wuxi, both of which are under 10%. (2) Tourism in thirteen countries, including Samoa and Tonga, constitutes a fundamental function of the national economy, whereas in Papua New Guinea, tourism is a non-fundamental function, reflecting a lower degree of economic reliance on the tourism sector. (3) A provisional typology of tourism functions is proposed, identifying Fiji and The Cook Islands as robustly specialized, while Papua New Guinea remains characterized by stable low-specialization. The remaining 11 countries occupy transitional positions where classification is sensitive to prediction uncertainty. Subject to this caveat, the PICs are provisionally categorized into three groups: medium-to-large specialized (Fiji, Cook Islands, Vanuatu, and Samoa), small specialized (Tuvalu, Palau, Solomon Islands, and Tonga), and low-specialization (Papua New Guinea, Kiribati, Federated States of Micronesia, Nauru, Niue, and Marshall Islands). The classification results can guide these island nations in enhancing their tourism functions, fostering sound regional development, and enabling more effective participation in global governance. Full article

The sustainability of experience-intensive wellness tourism services increasingly depends on managers’ ability to understand heterogeneous and implicit tourist preferences that are rarely captured through traditional survey-based approaches. In the context of Korean Templestay tourism, this study develops a data-driven decision-support framework that leverages large-scale unstructured review data to address managerial challenges such as choice overload, inefficient resource allocation, and cold-start conditions. Using 74,015 user-generated reviews collected between 2020 and 2024, the framework integrates Optical Character Recognition (OCR) to extract image-embedded text, achieving a validated character-level accuracy of 96.8%. In addition, a weak supervision strategy is applied to identify latent tourist preferences in a cost-efficient and scalable manner. Preference classification is conducted using Random Forest models combined with SMOTE, followed by clustering and user-based collaborative filtering to support personalized recommendations. The findings indicate that the Templestay market is better understood as an interconnected preference network rather than a set of mutually exclusive segments. Across user groups, “rest” emerges as a shared foundational value, while differentiated sub-preferences coexist within the network. The proposed framework successfully generates recommendations for all users in the dataset, demonstrating strong applicability for mitigating cold-start risks and supporting adaptive and sustainable program design. Full article

Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and improving pedagogical quality. However, conventional evaluation of campus space optimization faces two systemic dilemmas. First, top-down decision-making often neglects the authentic needs of diverse stakeholders and place-based knowledge, resulting in spatial interventions that lose regional distinctiveness. Second, routine public participation is constrained by geographical barriers, time costs, and sample-size limitations, which can amplify professional cognitive bias and impede comprehensive feedback formation. The compounded effect of these challenges contributes to a disconnect between spatial optimization outcomes and perceived needs, thereby constraining the distinctive development of rural educational spaces. To address these constraints, this study proposes a novel method that integrates regional spatial feature recognition with digital media-based public perception assessment. At the data collection and ethical governance level, the study strictly adheres to platform compliance and academic ethics. A total of 12,800 preliminary comments were scraped from major social media platforms (e.g., Douyin, Dianping, and Xiaohongshu) and processed through a three-stage screening workflow—keyword screening–rule-based filtering–manual verification—to yield 8616 valid records covering diverse public groups across China. All user-identifying information was fully anonymized to ensure lawful use and privacy protection. At the analytical modeling level, we develop a Transformer-based deep learning system that leverages multi-head attention mechanisms to capture implicit spatial-sentiment features and metaphorical expressions embedded in review texts. Evaluation on an independent test set indicates a classification accuracy of 89.2%, aligning with balanced and stable scoring performance. Robustness is further strengthened by introducing an equal-weight alternative strategy and conducting stability checks to indicate the consistency of model outputs across weighting assumptions. At the scenario interpretation level, we combine grounded-theory coding with semantic network analysis to establish a three-tier spatial analysis framework—macro (landscape pattern/hydro-topological patterns), meso (architectural interface), and micro (teaching scenes/pedagogical scenarios)—and incorporate an interpretive stakeholder typology (tourists, residents, parents, and professional groups) to systematically identify and quantify key features shaping public spatial perception. Findings show that, at the macro level, naturally integrated scenarios—such as “campus–farmland integration” and “mountain–water embeddedness”—exhibit high affective association, aligning with the “mountain-water-field-village” spatial sequence logic and suggesting broad public endorsement of ecological campus concepts, whereas vernacular settlement-pattern scenarios receive relatively low attention due to cognitive discontinuities. At the meso level, innovative corridor strategies (e.g., framed vistas and expanded corridor spaces) strengthen the building–nature interaction and suggest latent value in stimulating exploratory spatial experience. At the micro level, place-based practice-oriented teaching scenes (e.g., intangible cultural heritage handcraft and creative workshops) achieve higher scores, aligning with the compatibility of vernacular education’s “differential esthetics,” while urban convergence-oriented interdisciplinary curriculum scenes suggest an interpretive gap relative to public expectations. These results indicate an embedded relationship between public perception and regional spatial features, which is further shaped by a multi-actor governance process—characterized by “Government + Influencers + Field Study”—that mediates how rural educational spaces are produced, communicated, and interpreted in digital environments. The study’s innovative value lies in integrating sociological theories (e.g., embeddedness) with deep learning techniques to fill the regional and multi-actor perspective gap in rural campus POE and to promote a methodological shift from “experience-based induction” toward a “data-theory” dual-drive model. The findings provide inferential evidence for rural campus renewal and optimization; the methodological pipeline is transferable to small-scale rural primary schools with media exposure and salient regional ecological characteristics, and it offers a new pathway for incorporating digital media-driven public perception feedback into planning and design practice. The research methodology of this study consists of four sequential stages, which are implemented in a systematic and progressive manner: First, data collection was conducted: Python and the Octopus Collector were used to crawl online comment data related to Fuwen Township Central Primary School, strictly complying with the user agreements of the Douyin, Dianping, and Xiaohongshu platforms. Second, semantic preprocessing was performed: The evaluation content was segmented to generate word frequency statistics and semantic networks; qualitative analysis was conducted using Origin software, and quantitative translation was realized via Sankey diagrams. Third, spatial scene coding was carried out: Combined with a spatial characteristic identification system, a macro–meso–micro three-tier classification system for spatial scene characteristics was constructed to encode and quantitatively express the textual content. Finally, sentiment quantification and correlation analysis was implemented: A deep learning model based on the Transformer framework was employed to perform sentiment quantification scoring for each comment; Sankey diagrams were used to quantitatively correlate spatial scenes with sentiment tendencies, thereby exploring the public’s perceptual associations with the architectural spatial environment of rural campuses. Full article

Creativity has been frequently explored in artists’ work. However, this concept has also been studied in the economic context of business and management. The concept of creativity has also recently become a subject of tourism research, as tourism is considered an important industry. Tourism classifications that include experiences are becoming more widespread. Alternative and special-interest tourism encompasses a range of tourism types presenting unique experiences. Within the classifications of sport, adventure, and experiential tourism, commercial tandem paragliding can be examined through a creative tourism lens in terms of perceived learning, interaction, and unique involvement. Hence, this research was conducted in Babadağ, Fethiye, a renowned paragliding destination. A total of 360 visitors were included as the participants. PLS-SEM was used to estimate a structural equation model. The results clearly demonstrate the centrality of the creative tourist experience. Firstly, the direct effect of the creative tourist experience on behavioral intentions was found to be quite strong and significant. The results show that the creative tourist experience is strongly and positively associated with behavioral intentions (revisit, recommendation, and positive word of mouth). The effect of the creative tourist experience on memories indicates that creative experiences leave a strong impression on visitors’ memories. Similarly, the creative tourist experience had a significant and positive effect on satisfaction. Considering these three results together, it can be said that creative experiences strengthen cognitive/affective memories, increase overall evaluative satisfaction, and directly affect behavioral intentions. This finding is consistent with the experiential and creative tourism literature on the determinative role of experience quality in memory value and satisfaction. These findings reveal that creative tourist experiences strengthen both memories and satisfaction; that memories are positively related to satisfaction and behavioral intentions; and that satisfaction is positively related to behavioral intentions, thereby extending fundamental assumptions in the experience economy and creative tourism literature to the specific context of commercial tandem paragliding as a guided air-based adventure activity. The study’s unique contribution to the literature is that it not only examines creative tourism through cultural/workshop-based experiences, but also conceptualizes creative tourism through adventure activities involving high involvement and high arousal, and empirically demonstrates the importance of creative-experience quality in explaining behavioral intentions through “memorability” and “satisfaction”. Full article

The Balaton region is Hungary’s most important recreational area, known for Central Europe’s largest freshwater lake and its traditional vineyard and horticultural landscapes. Since 1990, vineyard and orchard abandonment and intensified shoreline urbanization have increasingly threatened both landscape character and ecological balance. This study analyses land-use changes in the Balaton hinterland and evaluates the effectiveness of regional land-use regulation between 1990 and 2018, with a focus on the 2000 Balaton Law (BKÜRT), which sought to preserve traditional land uses by permitting construction only where at least 80% of vineyard parcels remained cultivated. Spatial–temporal analysis was based on CORINE Land Cover (CLC) data from 1990 to 2018, supplemented by change layers from the Copernicus Land Monitoring Service. The CORINE Land Cover classification is a three-level hierarchical system (5 Level-1 groups, 15 Level-2 classes, and 44 Level-3 classes) developed by the EEA to provide standardized, satellite-based land cover information across Europe. Land cover was aggregated into major categories (using Level-1 and Level-2 classes) relevant to the Hungarian landscape. To address CLC limitations related to representing vineyards as relatively homogeneous units despite substantial differences in the density and scale of built structures, detailed case studies were conducted in three C1 vineyard zones—Alsóörs, Paloznak, and Szentantalfa—using historical aerial photographs, Google Earth imagery, and the Hungarian Ecosystem Map (NÖSZTÉP). Despite the restrictive regulatory framework, the CLC database showed that the share of vineyards in the vineyard regulation zone (C-1, C-2) decreased between 1990 and 2018 from 45.4% to 35.8% (the share of gardens and fruit plantations had changed from 9.7% to 15.5%). In the whole Balaton region, there was an approximately 18% decline in vineyard areas. Considering the M-2 horticultural zone, the garden coverage increased from 18.9% in 1990 (17.7% in 2000) to 30.5% (share of vineyards changed from 54.3% (54.6% in 2000) to 38.8%). At the regional level, gardens and fruit plantations had a smaller decrease (3.2%). Although overall trends were more favorable than at the national level, regulatory measures proved insufficient to prevent the conversion of vineyards and orchards in sensitive areas, particularly on slopes overlooking the lake, in proximity to tourist hubs, and in areas exposed to strong development pressure. By 2018, the C1 zone had expanded spatially but became less targeted, as the proportion of vineyards within it decreased. Boundary refinements failed to substantially improve regulatory precision or effectiveness. The case studies reveal a gradient of regulatory strictness reflecting differing landscape protection priorities and stages of vineyard transformation, with Alsóörs responding to long-standing, partly irreversible changes while attempting to slow further landscape alteration. To counter ongoing negative trends, more targeted and enforceable regulations are required, including a clearer separation of cultivated and recreational land uses, a maximum building size of 80 m² for recreational properties, and a reassessment of vineyard zone boundaries to better reflect active cultivation and protect sensitive landscapes. Full article

The intangible accessibility of real estate markets via platforms like Airbnb profoundly influences the urban development industry, propelled by the dynamics of short- to medium-term rentals for tourists. The suggested study aims to examine the association between the prices of listed properties and the influence of proximity to tourist attractions on location-driven pricing. The city of Rome acts as a case study from which to derive pertinent conclusions and proof on the phenomena intended for exploration. The methodological approach relies on a comprehensive classification of locations recognized as tourist attractions, drawn from public resources, travel guides, search engines, and online trends. The identified attractionswere subsequently classified to analyze how spatial proximity influences price formation. Data on short-term rental listings were obtained from the Inside Airbnb platform. The results enable the characterization of Rome as a polycentric urban system, composed of multiple tourism hubs whose spatial interactions are closely associated with prevailing hotel pricing patterns. This study emphasizes the influence of tourist demand on land values, a phenomenon intricately connected to urban gentrification and the capitalization of the real estate market. These findings enhance comprehension of tourism’s impact on the geographical and economic structure of cities. Full article

Citizen science approaches for monitoring, and even restoring, coral reefs have grown in popularity though tend to be restricted to those who have taken courses that expose them to the relevant methodologies. Now that cheap (~10 USD), waterproof pouches for smart phones are widely available, there is the potential for mass acquisition of coral reef images by non-scientists. Furthermore, with the emergence of better machine-learning-based image classification approaches, high-quality data can be extracted from low-resolution images (provided that key benthic organisms, namely corals, other invertebrates, & algae, can be distinguished). To determine whether informally captured images could yield comparable ecological data to point-intercept + photo-quadrat surveys conducted by highly proficient research divers, we trained an artificial intelligence (AI), CoralNet, with images taken before and during a bleaching event in 2015 in Chagos (Indian Ocean). The overall percent coral covers of the formal, “gold standard” method and the informal, “tourist diver” approach of 38.7 and 35.1%, respectively, were within ~10% of one another; coral bleaching percentages of 30.5 and 31.8%, respectively, were statistically comparable. Although the AI was prone to classifying bleached corals as healthy in ~one-third of cases, the fact that these data could be collected by someone with no knowledge of coral reef ecology might justify this approach in areas where divers or snorkelers have access to waterproof cameras and are keen to document coral reef condition. Full article

The southern Anhui region is home to numerous atypical traditional villages. These settlements serve not only as vital spaces for residents’ daily lives but also as core venues for the transmission and dissemination of traditional culture. Consequently, effectively identifying and classifying these deteriorating traditional villages to formulate corresponding conservation and revitalization strategies has become a critical issue in cultural heritage preservation and utilization. This study focuses on 40 traditional villages in Yixian County, Huangshan City, Anhui Province, as its research subjects. Based on Points of Interest (POI), media sources, village archives, and field surveys, a systematic analysis of these traditional villages was conducted. By integrating a literature review with expert consultation within a multidisciplinary framework, we constructed a three-tiered evaluation system comprising 15 indicators across three major domains: resident experience, tourist engagement, and village environmental quality. The clustering results indicate that 33 out of the 40 villages are atypical. Based on indicator aggregation, these atypical villages are further categorized into two types: B1 (endangered-type) and B2 (resident-dominant/tourist supplementary type). Using this classification system, we propose tailored development strategies for each village type. The findings offer both theoretical and practical guidance for broader traditional village conservation efforts. Full article

Understanding luxury tourists required a more comprehensive approach than traditional expenditure-based segmentation, which often overlooked travelers’ financial capacity. This study therefore aimed to develop and validate a new typology of luxury tourists by jointly analyzing income and expenditure patterns using the International Visitor Survey of South Korea. The study addressed the need to capture both tourists’ economic capability and consumption behavior to enhance the precision of market segmentation and support sustainable destination management. Using the Jenks natural breaks classification and logistic regression, four distinct tourist types were identified: economy, spurious, scrooge, and premier, each reflecting unique combinations of income and expenditure. The results revealed that age, nationality, occupation, and trip purpose significantly influenced tourists’ classification. Younger and middle-aged professionals from East Asia were more likely to belong to high-income and high-expenditure groups, whereas Western tourists tended to spend more relative to their income. This income–expenditure typology advanced theoretical understanding of luxury tourism segmentation and provided practical insights for destination marketing organizations. The findings offered new insights for understanding how the alignment between tourists’ financial capacity and spending behavior can redefine strategies for sustainable and inclusive tourism development. Full article

Tourist reviews provide essential insights into travellers experiences and public perceptions of destinations. In Kazakhstan, however, sentiment analysis, particularly using ensemble learning, remains underexplored for evaluating such reviews. This study proposes a hybrid stacking ensemble for sentiment analysis of English-language tourist reviews about Kazakhstan, integrating four complementary approaches: VADER, TextBlob, Stanza, and Local Context Focus Mechanism with Bidirectional Encoder Representations from Transformers (LCF-BERT). Each model contributes distinct analytical capabilities, including lexicon-based polarity detection, rule-based subjectivity evaluation, generalised star-rating estimation, and contextual aspect-oriented sentiment classification. The evaluation utilised a cleaned dataset of 11,454 TripAdvisor reviews collected between February 2022 and June 2025. The ensemble aggregates model outputs through majority and weighted voting strategies to enhance robustness. Experimental results (accuracy 0.891, precision 0.838, recall 0.891, and F1-score 0.852) demonstrate that the proposed method KazSATR outperforms individual models in overall classification accuracy and exhibits superior capacity for aspect-level sentiment detection. These findings underscore the potential of the hybrid ensemble as a practical and scalable tool for the tourism sector in Kazakhstan. By leveraging multiple analytical paradigms, the model enables tourism professionals and policymakers to better understand traveller preferences, identify service strengths and weaknesses, and inform strategic decision-making. The proposed approach contributes to advancing sentiment analysis applications in tourism research, particularly in underrepresented geographic contexts. Full article

As a perceptual representation of ecosystem structure and function, the soundscape has become an important indicator for evaluating ecological health and assessing the impacts of human disturbances. Understanding the spatiotemporal heterogeneity of soundscapes is essential for revealing ecological processes and human impacts in protected areas. This study investigates such heterogeneity in Zhangjiajie National Forest Park using deep learning approaches. To this end, we constructed a dataset comprising eight representative sound source categories by integrating field recordings with online audio (BBC Sound Effects Archive and Freesound), and trained a classification model to accurately identify biophony, geophony, and anthrophony, which enabled the subsequent analysis of spatiotemporal distribution patterns. Our results indicate that temporal variations in the soundscape are closely associated with circadian rhythms and tourist activities, while spatial patterns are strongly shaped by topography, vegetation, and human interference. Biophony is primarily concentrated in areas with minimal ecological disturbance, geophony is regulated by landforms and microclimatic conditions, and anthrophony tends to mask natural sound sources. Overall, the study highlights how deep learning-based soundscape classification can reveal the mechanisms by which natural and anthropogenic factors structure acoustic environments, offering methodological references and practical insights for ecological management and soundscape conservation. Full article

Ports with Mediterranean-like traffic profiles combine dense passenger, cargo, touristic, and local operations in confined waters where many small craft sail without AIS, increasing collision risk. Nature of such traffic in often unpredictable, due to often and sudden course corrections or changes. In such situations, it is possible that larger ships cannot manoeuvre to avoid collisions with small vessels. Hence, it is important to the port authority to develop a fast and adoptable mean to reduce collision risks. We present an end-to-end shore-based framework that detects and tracks vessels from fixed cameras (YOLOv9 + DeepSORT), estimates speed from monocular lateral video with an artificial neural network (ANN), and visualises collision risk in augmented reality (AR) for VTS/port operators. Validation in the Port of Split using laser rangefinder/GPS ground truth yields MAE 1.98 km/h and RMSE 2.18 km/h (0.605 m/s), with relative errors 2.83–21.97% across vessel classes. We discuss limitations (sample size, weather), failure modes, and deployment pathways. The application uses stationary port camera as an input. The core calculations are performed at user’s computer in the building. Mobile application uses wireless communication to show risk assessment at augmented reality smart phone. For training of ANN, we used The Split Port Ship Classification Dataset. Full article