Search Results (91)

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

Background/Objectives: End-of-life (EOL) HIV cure research, which studies HIV persistence through pre- and post-mortem tissue collection, has focused primarily on people living with HIV (PLWH) with a prognosis of six months or less. However, the perspectives of long-term survivors (LTS) diagnosed before the advent of effective antiretroviral treatment (ART) remain underexplored. Understanding their motivations and concerns about EOL cure research is essential for creating inclusive and ethical research frameworks. Methods: Between 2023 and 2024, we conducted in-depth qualitative interviews with 16 PLWH aged 60 and older from diverse backgrounds across the United States, recruited through community-based organizations and HIV networks. We used inductive thematic analysis to explore LTS’ perspectives on EOL HIV research. Results: Participants included cisgender men (56.25%) and women (43.75%) with diverse racial identities. While participants supported EOL HIV cure research, their willingness to participate varied, influenced by awareness, logistics, and ethical concerns. Altruism-motivated participation, but misconceptions about procedures and concerns over bodily integrity represented potential barriers. Some viewed blood draws and leukaphereses as routine, while others expressed hesitancy with biopsies and post-mortem tissue retrieval. HIV stigma, historical mistrust, and cultural beliefs also played a role in willingness to participate. LTS emphasized the need for decentralized research sites, travel support, and financial safeguards. Conclusions: To include LTS in EOL HIV cure research, a community-driven approach is needed, focusing on clear communication, ethical considerations, logistical support, and linkages to EOL care. Addressing misconceptions and building trust, particularly within groups traditionally underrepresented in research, is essential to expanding participation. Full article

High-density cities have obvious characteristics of compact urban spatial form and intensive land use in terms of spatial environment, and have always been a topic of academic focus. As a typical coastal historical district, the Macau Peninsula pier district (mainly the Macau Inner Harbour) has a high building density and a low average street width, forming a vertical coastline development model that directly converses with the ocean. This area is adjacent to Macau’s World Heritage Site and directly related to the Marine trade functions. The distribution pattern of cultural heritage linked by the ocean has strengthened Macau’s unique positioning as a node city on the Maritime Silk Road. This text is based on the theory of urban development, integrates spatial syntax and POI analysis techniques, and combines the theories of waterfront regeneration, high-density urban form and post-industrial urbanism to integrate and deepen the theoretical framework, and conduct a systematic study on the urban spatial characteristics of the coastal area of the Macau Peninsula. This study found that (1) Catering and shopping facilities present a dual agglomeration mechanism of “tourism-driven + commercial core”, with Avenida de Almeida Ribeiro as the main axis and radiating to the Ruins of St. Paul’s and Praça de Ponte e Horta, respectively. Historical blocks and tourist hotspots clearly guide the spatial center of gravity. (2) Residential and life service facilities are highly coupled, reflecting the spatial logic of “work-residence integration-service coordination”. The distribution of life service facilities basically overlaps with the high-density residential area, forming an obvious “living circle + community unit” structure with clear spatial boundaries. (3) Commercial and transportation facilities form a “functional axis belt” organizational structure along the main road, with the Rua das Lorchas—Rua do Almirante Sérgio axis as the skeleton, constructing a “functional transmission chain”. (4) The spatial system of the Macau Peninsula pier district has transformed from a single center to a multi-node, network-linked structure. Its internal spatial differentiation is not only constrained by traditional land use functions but is also driven by complex factors such as tourism economy, residential migration, historical protection, and infrastructure accessibility. (5) Through the analysis of space syntax, it is found that the core integration of the Macau Peninsula pier district is concentrated near Pier 16 and the northern area. The two main roads have good accessibility for motor vehicle travel, and the northern area of the Macau Peninsula pier district has good accessibility for long and short-distance walking. Full article

Ting, Tai, Lou, and Ge are types of ancient buildings that represent traditional Chinese architecture and culture. They are primarily constructed using mortise and tenon joints, complemented by brick and stone foundations, showcasing traditional architectural craftsmanship. However, research aimed at conserving, inheriting, and rejuvenating these buildings is limited, despite their status as Provincial Cultural Relic Protection Units of China. Therefore, the aim of this study was to reveal the spatial distribution of Ting, Tai, Lou, and Ge buildings across China, as well as the factors driving differences in their spatial distribution. Tourist experiences and building popularity were also explored. The spatial analysis method (e.g., Standard deviation ellipse and Geographic detector), Word cloud generation, and sentiment analysis, which uses Natural Language Processing techniques to identify subjective emotions in text, were applied to investigated the research issues. The key findings of this study are as follows. The ratio of Ting, Tai, Lou, and Ge buildings in Southeast China to that in Northwest China divided by the “Heihe–Tengchong” Line, an important demographic boundary in China with the ratio of permanent residents in the two areas remaining stable at 94:6, was 94.6:5.4. Geographic detector analysis revealed that six of the seven natural and socioeconomic factors (topography, waterways, roads, railways, population, and carbon dioxide emissions) had a significant influence on the spatial heterogeneity of these cultural heritage buildings in China, with socioeconomic factors, particularly population, having a greater influence on building spatial distributions. All seven factors (including the normalized difference vegetation index, an indicator used to assess vegetation health and coverage) were significant in Southeast China, whereas all factors were non-significant in Northwest China, which may be explained by the small number of buildings in the latter region. The average rating scores and heat scores for Ting, Tai, Lou, and Ge buildings were 4.35 (out of 5) and 3 (out of 10), respectively, reflecting an imbalance between service quality and popularity. According to the percentages of positive and negative reviews, Lou buildings have much better tourism services than other buildings, indicating a need to improve services to attract more tourists to Ting, Tai, and Ge buildings. Four main types of words were used with high frequency in the tourism reviews collected form Ctrip, a popular online travel platform in China: (1) historical stories; (2) tourism; (3) culture; and (4) cities/provinces. Ting and Tai buildings showed similar word clouds, as did Lou and Ge buildings, with only the former including historical stories. Conversely, landmark was a high-frequency word only in the reviews of Lou and Ge buildings. Specific suggestions were proposed based on the above findings to promote tourism and revive ancient Chinese architecture. Full article

With the development of intelligent transportation systems (ITS), individual travel prediction has become a key technology for optimizing urban transportation. However, deep learning models are limited in decision-sensitive scenarios due to their lack of interpretability. To address the shortcomings of existing XAI methods in analyzing the dynamic features of historical travel sequences, this paper introduces an alternative interpretability method based on gradient information, overcoming the interpretability bottleneck of travel prediction models. This method calculates the gradient information of input features relative to the prediction result, breaking through the limitations of traditional interpreters that only analyze static features. It can trace the contribution weights of key time points in historical travel sequences while maintaining low computational cost. The experimental results show that features with higher gradients significantly affect predictions—masking the maximum-gradient feature reduces accuracy by approximately 30%. Descending-order masking strategies exhibit the strongest impact, highlighting nonlinear interactions among features. Contribution maps visualize how gradients capture regular patterns and anomalies. The method proposed in this paper provides a valuable tool for understanding the underlying principles of travel prediction models, bridging the gap in existing methods for temporal sequence analysis. Full article

This study addresses the optimization of the Vehicle Routing Problem (VRP) with prioritized customers by introducing and comparing two advanced solution approaches: a metaheuristic-based hyperheuristic framework and a Variational Autoencoder (VAE)-based hyperheuristic. The VRP with prioritized customers introduces additional complexity by requiring efficient routing while ensuring high-priority customers receive service within strict constraints. To tackle this challenge, the proposed metaheuristic-based hyperheuristic dynamically selects and adapts low-level heuristics using Simulated Annealing (SA) and Ant Colony Optimization (ACO), enhancing search efficiency and solution quality. In contrast, the VAE-based approach leverages deep learning to model historical routing patterns and autonomously generate new heuristics tailored to problem-specific characteristics. Through extensive computational experiments on benchmark VRP instances, our results reveal that both approaches significantly enhance routing efficiency, with the VAE-based method demonstrating superior generalization across varying problem structures. Specifically, the VAE-based approach reduces total travel costs by an average of $8\%$ and improves customer priority satisfaction by $95\%$ compared to traditional hyperheuristic methods. Moreover, a comparative analysis with recent state-of-the-art algorithms highlights the competitive performance of our approaches in balancing computational efficiency and solution quality. These findings underscore the potential of integrating metaheuristics with machine learning in complex routing problems and provide valuable insights for real-world logistics and transportation planning. Future research will explore the generalization of these methodologies to dynamic and large-scale routing scenarios. Full article

In the digital era, user-generated online reviews serve as a valuable resource for understanding visitor experiences in cultural institutions. This study analyses sentiments and thematic trends in TripAdvisor reviews of mountain museums, using Latent Dirichlet Allocation topic modelling and sentiment analysis. A dataset of 2157 reviews from ten museums was classified into local and non-local perspectives, revealing significant differences in visitor expectations. Findings indicate that local visitors prioritize historical authenticity and educational value, whereas non-local visitors emphasize aesthetic appeal, interactivity, and cultural immersion. Sentiment analysis highlights generally positive perceptions, with business travellers and groups of friends reporting the highest satisfaction levels. Comparative analysis across visitor types reveals distinct engagement patterns, with families valuing child-friendly exhibits, couples seeking cultural enrichment, and solo travellers focusing on intellectual depth. These insights inform strategic recommendations for museum management, including multilingual content, interactive elements, and guided tours dedicated to specific visitor profiles. Despite limitations related to lack of real-time feedback, this research demonstrates the potential of sentiment analysis in enhancing museum experiences. Future studies should integrate multimodal analysis and real-time tracking to further refine visitor experience evaluation. Full article

Urban mass tourism and rural depopulation increasingly threaten heritage sites worldwide, leading to socio-economic and environmental challenges. This study adopts a holistic approach to sustainable tourism planning by examining 84 cultural and natural heritage sites in and around Safranbolu and Amasra, two cities in Türkiye that are listed on the UNESCO World Heritage List and the Tentative List. Inspired by historical travelers’ itineraries, it proposes an eco-cultural tourism route to create a resilient heritage network. A participatory methodology integrates charettes within Erasmus+ workshops, crowdsourcing, various analysis methods while engaging stakeholders, and AI-powered clustering for route determination. The study follows a four-stage framework: (1) data collection via collaborative GIS, (2) eco-cultural route development, (3) stakeholder participation for inclusivity and viability, and (4) assessments and recommendations. Results highlight the strong potential of heritage assets for sustainable tourism while identifying key conservation risks. Interviews and site analysis underscore critical challenges, including the absence of integrated site management strategies, insufficient capacity-building initiatives, and ineffective participatory mechanisms. Moreover, integrating GIS-based crowdsourcing, machine learning clustering, and multi-criteria decision-making can be an effective planning support system. In conclusion, this study enhances the sustainability of heritage and tourism by strengthening participatory eco-cultural development and mitigating mass tourism and abandonment’s negative impacts on the heritage sites. Full article

This study explores the use of artificial intelligence and geospatial tools to analyze historical explorers’ narratives. Explorers’ accounts provide valuable insights into the cultural, environmental, and logistical dynamics of exploration journeys. However, traditional methods of analyzing these narratives are often subjective and difficult to reproduce on a large scale. The main objective is to overcome the limitations of traditional methods by using AI techniques to systematically extract and structure information from explorers’ narratives. This study employs Python scripts to extract factual data from narratives available on Project Gutenberg, followed by structuring the data in JSON format. Geographic data are enriched through geocoding using libraries such as Geopy and OpenCage. An interactive web interface based on Leaflet allows for the visualization and validation of explorers’ routes. The results show a concentration of visits in North and West Africa, with traditional modes of transport like caravans and traveling on foot being dominant. The main challenges faced were related to transportation, climatic conditions, and natural obstacles. Principal component analysis (PCA) and correspondence analysis reveal latent structures in the data, while clustering analysis segments the journeys based on similarity criteria. This research demonstrates the value of AI and geospatial tools for a more objective and detailed analysis of explorers’ narratives, opening new perspectives for historical and geographical studies. Full article

Cultural heritage is a precious treasure left to mankind by history. With the development of the times and the improvement of people’s education, more and more people are becoming aware of the importance of protecting cultural heritage. Chinese porcelain inlay is a type of architectural decoration born out of the specific historical, geographical, and cultural conditions of Fujian and Guangdong, and was included in the second batch of The National List of Intangible Cultural Heritage of China published in 2008 and the third batch of The National List of Intangible Cultural Heritage of China—Expanded Projects in 2011. It represents an important part of the complex traditional culture of Fujian and Guangdong, acting as the essence of national culture, a symbol of national wisdom, and the refinement of national spirit. Using targeted analysis and making changes based on negative reviews, organizations that protect cultural heritage can improve their actions and find new ways to spread cultural heritage. The craft of Chinese porcelain inlay is used as an example in this paper. It combines Python Octopus crawler technology, data analysis, and sentiment analysis methods to perform a cognitive social media visualization analysis of Chinese porcelain inlay, which is a form of national intangible cultural heritage in China. Then, by looking at network text data from social media, it seeks to find out how the Chinese porcelain inlay culture is passed down, what its main traits are, and how people feel about it. Finally, this study summarizes the public’s understanding of inlay porcelain and proposes strategies to promote its future development and dissemination. This study found that (1) as a form of national intangible cultural heritage in China and a unique traditional architectural decoration craft, Chinese porcelain inlay has widely recognized cultural and artistic value. (2) The emotional evaluation of Chinese porcelain inlay is mainly positive (73 and 60.76%), while negative evaluations account for 12.62 and 20.79% of responses, mainly reflected in regret regarding the gradual disappearance of old buildings, the lament that Chinese porcelain inlay is highly regional and difficult to popularize, the regret that the individual has not visited locations with Chinese porcelain inlay, a feeling of helplessness with regard to inconvenient transportation links to these places, and discontent with the prohibitively high prices of Chinese porcelain inlay products. These findings offer valuable guidance for the future dissemination and development of Chinese porcelain inlay as a form of intangible cultural heritage. (3) The LDA topic model is used to divide the perception of Chinese porcelain inlay into nine major themes: arts and crafts, leisure and entertainment, cultural travel, online appreciation, heritage protection, dissemination scope, prayer and blessing, inheritance and innovation, and collection and research. This also provides a reference for the future direction of the inheritance of Chinese porcelain inlay cultural heritage. Full article

In the early nineteenth century, the London Missionary Society (LMS) established missionary outposts and printing presses in Southeast Asia, publishing a number of scientific periodicals and books, which subsequently informed the preaching policy on the Chinese mainland. The motivation and discursive strategies of the LMS’s dissemination of scientific knowledge in Southeast Asia are worthy of in-depth investigation. This study employs the socio-cognitive discourse analysis approach to investigate the mental model and discursive strategies of LMS’s two representative scientific publications in Southeast Asia, namely the Chinese Monthly Magazine and Memoir of Things Seen and Heard by Travelling West around the World, thus providing insights into the rationale behind the scientific discourse of Protestant missionaries in nineteenth-century China. It is found that the scientific discourse of the LMS in Southeast Asia was to modify the comprehension of world formation held by local Chinese migrants, thereby influencing their perception of European scientific strength and Christian belief. This study is an interdisciplinary investigation of Protestant history in Southeast Asia, and the methodology of critical discourse analysis offers a new way of explaining history, which complements the conventional historical analysis. Full article

Suzhou classical gardens, as a unique form of urban green space in China, not only embody rich historical and cultural heritage but also showcase distinctive natural landscapes, exerting a profound impact on modern mental health. This study employs text mining and content analysis methods to qualitatively explore online comments about Suzhou Gardens, which were collected using the Octopus Collector program to mine public reviews from the travel review platform Ctrip. These online reviews were further combined with questionnaire survey data to quantitatively analyze public preferences among different gardens and their restorative experience characteristics. We utilized the ROST CM6 software for high-frequency word extraction, semantic network analysis, and sentiment analysis to reveal the emotional perceptions of the public towards these gardens. The sentiment analysis results indicate that a majority of online comments express positive emotions, frequently mentioning words such as “tranquil”, “quiet”, and “serene”, highlighting the significant psychological comfort these spaces provide. Additionally, through one-way ANOVA and Pearson correlation analysis, we found significant differences in emotional and cognitive dimensions among different gardens, which are closely related to specific spatial factors such as landscape element diversity, visual scale, and types of greenery. These findings suggest that the spatial characteristics of Suzhou Gardens play a crucial role in shaping visitors’ emotional responses. Based on these insights, we proposed a series of design recommendations aimed at enhancing the overall image and healing functions of Suzhou Gardens. The findings of this study not only enrich the theoretical framework of healing landscape design but also provide valuable insights for the practical application of these principles in modern urban green space design. The research underscores the importance of combining functionality with aesthetic elements to meet the psychological needs of contemporary society. Full article

The General Transit Feed Specification (GTFS) is an open standard format for recording transit information, utilized by thousands of transit agencies worldwide. In this study, a new tool named GTFS2STN for converting GTFS data into the spatiotemporal networks is introduced. To analyze the travel time variability, it is important to transform a transit network to a spatiotemporal network to enable a comprehensive analysis of transit system accessibility. GTFS2STN is a new tool that converts General Transit Feed Specification (GTFS) data into spatiotemporal networks, addressing the lack of open-source solutions for transit analysis. The tool includes a web application that generates isochrone maps and calculates travel time variability between locations. Validation against Google Maps APIs shows that journey time (i.e., the summation of the transit time, walking time, and waiting time) differences in the Mean Absolute Percentage Error are typically within 12%. A before–after analysis shows that for the transit journey time in 2024 in Nashville, Tennessee, 8 out of 10 pivotal bus stops showed a significantly decreased journey time compared with the case of 2019. A further set of before–after analyses shows that although journey time between transit sites significantly dropped on May 2020 during COVID-19 emergencies, the journey time almost totally recovered to the before-COVID-19 level by November 2020. By supporting any valid GTFS schedule, GTFS2STN enables the analysis of historical and planned transit systems, making it valuable for long-term accessibility assessment and travel time variability studies. Full article

Over the past several years, many iconic national parks in the US have experienced large increases in visitation. At the same time, lesser-known units have had declining numbers. Both issues challenge the sustainable management of parks. The purpose of this study is to use motivation theory to develop and test the differences between market segments at a western US national historic site. The ultimate goal is to contribute to sustainable visitation management by increasing visitation and enhancing the visitor experience. A market segmentation analysis can identify and describe promising target markets. The data were collected using an onsite exit survey, resulting in a sample of 570 visitors over the high season. Three clusters of visitors were identified: heritage immersers, history appreciators, and casual sightseers. The groups differed significantly with respect to visitation motives, travel, and visitation patterns. Segments are described and implications offered. The study confirms differences among cultural heritage visitors, with some being promising as target markets for the historic site. The study underscores the importance of tailoring site management and marketing strategies to meet the diverse needs of visitors and supports the use of motive-based segmentation as a means of better understanding and sustainably managing visitor experiences. Full article

Zoonotic spillover events pose a significant and growing threat to global health. By focusing on preventing these cross-species transmissions, we can significantly mitigate pandemic risks. This review aims to analyze the mechanisms of zoonotic spillover events, identify key risk factors, and propose evidence-based prevention strategies to reduce future pandemic threats. Through a comprehensive literature review and analysis of major databases including PubMed, Web of Science, and Scopus from 1960–2024, we examined documented spillover events, their outcomes, and intervention strategies. This article emphasizes that targeting the root cause—the spillover event itself—is key to averting future pandemics. By analyzing historical and contemporary outbreaks, we extract crucial insights into the dynamics of zoonotic transmission. Factors underlying these events include increased human–animal contact due to habitat encroachment, agricultural intensification, and wildlife trade. Climate change, global travel, and inadequate healthcare infrastructure exacerbate risks. The diversity of potential viral reservoirs and rapid viral evolution present major challenges for prediction and prevention. Solutions include enhancing surveillance of wildlife populations, improving biosecurity measures, investing in diagnostic capabilities, and promoting sustainable wildlife management. A “One Health” approach integrating human, animal, and environmental health is crucial. Predictive modelling, international cooperation, and public education are key strategies. Developing pre-exposure prophylactics and post-exposure treatments is essential for mitigating outbreaks. While obstacles remain, advances in genomics and ecological modelling offer hope. A proactive, comprehensive approach addressing the root causes of spillover events is vital for safeguarding global health against future pandemics. Full article