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Online learning is well-known by its flexibility of learning anytime and anywhere. However, how behavioral patterns tied to learning anytime and anywhere influence learning outcomes are still unknown.

This study proposed concepts of time and location entropy to depict students’ spatial-temporal patterns. A total of 5,221 students with 1,797,677 logs, including 485 on-the-job students and 4,736 full-time students, were analyzed to depict their spatial-temporal learning patterns, including the relationships between identified patterns and students’ learning performance.

Analysis results indicate on-the-job students took more advantage of anytime, anywhere than full-time students. Students with a higher tendency for learning anytime and a lower level of learning anywhere were more likely to have better outcomes. Gender did not show consistent findings on students’ spatial-temporal patterns, but partial findings could be supported by evidence in neural science or by cultural and geographical differences.

A more accurate approach for categorizing position and location might be considered. Some findings need more studies for further validation. Finally, future research can consider connections between other well-known performance predictors (such as financial situation, motivation, personality and major) and the type of learning patterns.

The findings gained from this study can help improve the understandings of students’ learning behavioral patterns and design as well as implement better online education programs.

This study proposed concepts of time and location entropy to identify successful spatial-temporal patterns of on-the-job and full-time students.

The research analyses the spatial-temporal behaviour of international visitors using mobile positioning data (MPD) in three second-tier cities in Hungary: Szeged, Debrecen and Pécs. The purpose of this study is to identify the origins of visitors, length of stay, seasonal concentrations and mobility between cities.

A large volume of data generated by foreign mobile phone users was processed and analysed for six consecutive months in 2018. The movements of around 3.5 million foreigners visiting Hungary were captured.

The data showed significant differences in tourist turnover, mobility patterns and flows indicating different levels of tourism activity and potential in the three cities. This included day trips, cross-border tourism, seasonal fluctuations and activity-driven demand (e.g. special events and cultural tourism).

MPD cannot fully explain or predict drivers of visitor behaviour, such as purpose of visit, the precise activities that they undertake there and the experiences that they gain. It can, however, provide information on spatial and temporal flows, patterns and concentrations of visitors.

The investigated cities should follow distinct tourism development policies to attract more tourists and strengthen the links with their neighbouring destinations at the same time as differentiating themselves through marketing as competitive alternative destinations.

Augmenting tourism has socio-economic implications for residents and communities. The development of cultural tourism and local event-based tourism must incorporate community needs.

The paper analyses the extent to which MPD can provide insights into visitors’ spatio-temporal mobility and flows in relatively under-visited second-tier cities and it highlights opportunities and gaps for big data research in an urban context.

Incorporating dynamic spatial effects exhibits considerable potential in improving the accuracy of forecasting tourism demands. This study aims to propose an innovative deep learning model for capturing dynamic spatial effects.

A novel deep learning model founded on the transformer architecture, called the spatiotemporal transformer network, is presented. This model has three components: the temporal transformer, spatial transformer and spatiotemporal fusion modules. The dynamic temporal dependencies of each attraction are extracted efficiently by the temporal transformer module. The dynamic spatial correlations between attractions are extracted efficiently by the spatial transformer module. The extracted dynamic temporal and spatial features are fused in a learnable manner in the spatiotemporal fusion module. Convolutional operations are implemented to generate the final forecasts.

The results indicate that the proposed model performs better in forecasting accuracy than some popular benchmark models, demonstrating its significant forecasting performance. Incorporating dynamic spatiotemporal features is an effective strategy for improving forecasting. It can provide an important reference to related studies.

The proposed model leverages high-frequency data to achieve accurate predictions at the micro level by incorporating dynamic spatial effects. Destination managers should fully consider the dynamic spatial effects of attractions when planning and marketing to promote tourism resources.

This study incorporates dynamic spatial effects into tourism demand forecasting models by using a transformer neural network. It advances the development of methodologies in related fields.

纳入动态空间效应在提高旅游需求预测的准确性方面具有相当大的潜力。本研究提出了一种捕捉动态空间效应的创新型深度学习模型。

本研究提出了一种基于变压器架构的新型深度学习模型, 称为时空变压器网络。该模型由三个部分组成：时空转换器、空间转换器和时空融合模块。时空转换器模块可有效提取每个景点的动态时间依赖关系。空间转换器模块可有效提取景点之间的动态空间相关性。提取的动态时间和空间特征在时空融合模块中以可学习的方式进行融合。通过卷积运算生成最终预测结果。

结果表明, 与一些流行的基准模型相比, 所提出的模型在预测准确性方面表现更好, 证明了其显著的预测性能。纳入动态时空特征是改进预测的有效策略。它可为相关研究提供重要参考。

所提出的模型利用高频数据, 通过纳入动态空间效应, 在微观层面上实现了准确预测。旅游目的地管理者在规划和营销推广旅游资源时, 应充分考虑景点的动态空间效应。

本研究通过使用变压器神经网络, 将动态空间效应纳入旅游需求预测模型。它推动了相关领域方法论的发展。

La incorporación de efectos espaciales dinámicos ofrece un considerable potencial para mejorar la precisión de la previsión de la demanda turística. Este estudio propone un modelo innovador de aprendizaje profundo para capturar los efectos espaciales dinámicos.

Se presenta un novedoso modelo de aprendizaje profundo basado en la arquitectura transformadora, denominado red de transformador espaciotemporal. Este modelo tiene tres componentes: el transformador temporal, el transformador espacial y los módulos de fusión espaciotemporal. El módulo transformador temporal extrae de manera eficiente las dependencias temporales dinámicas de cada atracción. El módulo transformador espacial extrae eficientemente las correlaciones espaciales dinámicas entre las atracciones. Las características dinámicas temporales y espaciales extraídas se fusionan de manera que se puede aprender en el módulo de fusión espaciotemporal. Se aplican operaciones convolucionales para generar las previsiones finales.

Los resultados indican que el modelo propuesto obtiene mejores resultados en la precisión de las previsiones que algunos modelos de referencia conocidos, lo que demuestra su importante capacidad de previsión. La incorporación de características espaciotemporales dinámicas supone una estrategia eficaz para mejorar las previsiones. Esto puede proporcionar una referencia importante para estudios afines.

El modelo propuesto aprovecha los datos de alta frecuencia para lograr predicciones precisas a nivel micro incorporando efectos espaciales dinámicos. Los gestores de destinos deberían tener plenamente en cuenta los efectos espaciales dinámicos de las atracciones en la planificación y marketing para la promoción de los recursos turísticos.

Este estudio incorpora efectos espaciales dinámicos a los modelos de previsión de la demanda turística mediante el empleo de una red neuronal transformadora. Supone un avance en el desarrollo de metodologías en campos afines.

The purpose of this paper is to describe the core of an advanced destination management system, which uses a series of data matching techniques and business analytics.

This study first proposes the conceptual framework for an advanced destination management system and then illustrates the core components of the proposed system using real-world data from Northern Indiana. In this study, search interests, devices used and other forms of website use derived from online clickstream data were merged with visitor demographic and tripographic information obtained from an online survey to develop an analytic model used to describe the core market structure.

Key demographic factors (e.g. gender, age and income), search interests, referred websites, the number of total sessions, temporal aspects and spatial aspects of visitor travel provide essential information defining the structure and dynamics of the visitor marketing in Northern Indiana.

The process and data used in this study provide a “proof of concept” for developing highly personalized marketing systems, which can substantially improve the competitiveness of a destination management organization.

Guest experience and satisfaction have been central constructs in the hospitality management literature for decades. In recent years, the use of big data as an increasing trending practice in hospitality research has been characterised as a modern approach that offers valuable insights into understanding and enhancing guest experience and satisfaction. Recognising such potential, both researchers and practitioners need to better understand big data’s application and contribution in the hospitality landscape. The purpose of this paper is to critically review and synthesise the literature to shed light on trends and extant patterns in the application of big data in hospitality, particularly in research focusing on hotel guest experience and satisfaction.

This research is based on a Preferred Reporting Items for Systematic Reviews and Meta-analysis literature review of academic journal articles in Google Scholar published up to the end of 2020.

By data types, user-generated content, especially online reviews and ratings, was at the centre of attention for hospitality-related big data research. By variables, the hospitality-related big data fell into two crucial factor categories: physical environment and guest-to-staff interactions.

This paper shows that big data research can create new insights into attributes that have been extensively researched in the hospitality field. It facilitates a thorough understanding of big data studies and provides valuable insights into future prospects for both researchers and practitioners.

The purpose of this paper is to detect the determinants of cruise tourists’ expenditure level during their visits to an emergent Mediterranean port city. The article also aims to discuss its findings and contrast them with previous similar studies in other territorial contexts.

The study is based on surveys conducted on 1,010 cruise tourists that visited the city of Tarragona (Catalonia) during 2017. An ordered logit model is implemented to measure the impact of different variables related to the tourists’ characteristics and their activities developed at the destination.

Results underline diverse significant influences of multiple factors on the expenditures, such as the travel party, the age of the visitors, the length of stay and the tourists’ activities in the city. Although no incidence has been detected of variables related to the satisfaction with the visit of the cruise passengers in general, a positive association has been identified for those cruise passengers travelling on super-sized ships.

This study tests the effect of different variables that the literature pinpointed as determinants of the cruise tourists’ expenditures as well as other variables that have been underexplored in existing studies. The findings of this article are of special value for public and private organisations to optimally manage and market cruise tourism and boost the local economic impact.

本文的主要目的是研究游轮游客在地中海某新兴港口城市旅游期间支出水平的决定因素。同时, 本文也对研究结果进行了探讨, 并将其与以往在其他区域背景下的类似研究进行对比。

本研究对1010名游轮游客进行了调查, 这些游客在2017年访问了塔拉戈纳（加泰罗尼亚）。随后, 通过运用有序逻辑模型, 对涉及旅游目的地的游客特征及其旅游活动的不同变量的影响进行了衡量。

研究结果表明, 旅行团体规模、游客年龄、停留时间和游客的城市活动等多种因素对旅游支出有着不同程度的显著影响。尽管本研究未检测到与游轮游客的总体访问满意度相关的变量, 但在大型游船上的游轮游客中, 检测出了积极的相关关系。

本研究检验了现有文献提出的不同变量, 以及文献中尚未充分探讨的其他变量对游轮游客支出的影响。本研究的成果对公共和私人组织进行游轮旅游的优化管理和市场营销, 以及提高地方的经济影响力的实践具有特殊的价值。

邮轮旅游，巡洋舰，塔拉戈纳，游客的支出

研究论文

El objetivo principal de este artículo es detectar los determinantes del nivel de gasto económico de los turistas de cruceros durante sus visitas en una ciudad portuaria emergente y mediterránea. El artículo también pretende discutir sus hallazgos y contrastarlos con estudios similares realizados previamente enotros contextos territoriales.

El estudio se basa en encuestas realizadas a 1.010 turistas de cruceros que visitaron la ciudad de Tarragona (Catalunya) durante 2017. Se ha implementado un modelo logit ordenado, para medir el impacto de diferentes variables relacionadas con las características de los turistas y sus actividades desarrolladas en el destino.

Los resultados subrayan diversas influencias significativas de múltiples factores sobre los gastos económicos de los turistas de cruceros, como el tamaño del grupo de viaje, la edad de los visitantes, la duración de la estancia y las actividades desarrolladas en la ciudad. Aunque en general no se ha detectado ninguna incidencia de variables relacionadas con la satisfacción con la visita a la ciudad, se ha identificado una asociación positiva para los pasajeros de cruceros que viajan en barcos de gran tamaño.

Este estudio prueba el efecto de diferentes variables que la literatura existente ha identificado como determinantes del gasto económico de los turistas de cruceros, así como otras variables no exploradas previamente. Los hallazgos de este trabajo son de especial valor para las organizaciones públicas y privadas para administrar y comercializar de manera óptima el turismo de cruceros e impulsar el impacto económico local.

Turismo de cruceros, cruceros, Tarragona, gasto turístico

This paper discusses research which has been conducted irregularly over a period of some thirty years on the topic of tourist pressure and how this phenomenon can be portrayed. This is a relatively undeveloped topic which has not received much attention in the tourist literature, and yet it is felt that the concept mad its application hold considerable potential benefit for tourism planning and management. The key issue is to develop a methodology which allows the phenomenon to be adequately described and portrayed in such a manner that the resulting information can be integrated into the appropriate management of tourist numbers.