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This study aims to examine the evolution of spatial–temporal patterns in China’s hotel industry from 1978 to 2018.

A database comprising over 140,000 hotels with more than 30 rooms was created. The exploratory spatial–temporal data analysis (ESTDA) method, based on space–time cube model, was used to explore and visualize the spatial–temporal pattern of hotels.

The Chinese hotel industry can be divided into two development stages, namely, a large hotel-dominant stage before 2000 and a small–medium-sized hotel-dominant stage after 2000. China’s prefecture-level cities were clustered into four tiers. The higher the tier, the earlier the city will initiate hotel development. The Chinese hotel industry has four continuous hotspots (the Yangtze River Delta, Pearl River Delta, Bohai Rim and Sichuan and Chongqing) and some temporary hotspots.

This study lacks quantitative investigation, which could show the underlying mechanism of the evolution of the Chinese hotel industry.

This study is the first to investigate China’s hotel evolution over 40 years by applying big data and the ESTDA method. The systematic and evolutionary exploration will enable hotel researchers to understand the spatial–temporal nature of hotel distribution better. Introducing the ESTDA method into tourism and hotel research also provides an additional tool to researchers. Hotel investors and operators, city and tourism planners and market regulators can learn from the evolution of location patterns to make better where and when decisions.

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.

This study aims to investigate how the COVID-19 pandemic has impacted and changed Airbnb market in the Greater Melbourne area in terms of its temporal and spatial patterns and identify possible shifts in underlying trends in travel activities.

A panel data set of Airbnb listings in Melbourne is analysed to compare temporal patterns, spatial distribution and lengths of stay of Airbnb users before and after the COVID outbreak.

This study found that the COVID disruption did not fundamentally change the temporal cycle of the Airbnb market. Month-to-month fluctuations peaked at different levels from pre-pandemic times mainly because of lockdowns and other restrictive measures. The impact of COVID-19 disruptions on neighbourhood-level Airbnb revenues is associated with distance to CBD rather than number of COVID cases. Inner city suburbs suffered major loss during the pandemic, whereas outer suburbs gained popularity due to increased domestic travel and long stays. Long stays (28 days or more, as defined by Airbnb) were the fastest growing segment during the pandemic, which indicates the Airbnb market was adapting to increasing demand for purposes like remote working or lifestyle change. After easing of COVID-related restrictions, demand for short-term accommodation quickly recovered, but supply has not shown signs of strong recovery. Spatial distribution of post-pandemic supply recovery shows a similar spatial variation. Neighbourhoods in the inner city have not shown signs of significant recovery, whereas those in the middle and outer rings are either slowly recovering or approaching their pre-COVID levels.

The COVID-19 pandemic has significantly impacted short-term rental markets and in particular the Airbnb sector during the phase of its rapid development. This paper helps inform in- and post-pandemic housing policy, market opportunity and investment decision.

To the best of the authors’ knowledge, this is one of the first attempts to empirically examine both temporal and spatial patterns of the COVID-19 impact on Airbnb market in one of the most severely impacted major cities. It is one of the first attempts to identify shifts in underlying trends in travel based on Airbnb data.

Sequences of activities in people’s living environments have observable features that depend on subjective and meaningful aspects in their culture and lifestyle. Spatial and temporal sequences of activities are two ways of separating or aggregating activities. The theoretical framework of this study, based on cultural viewpoints, studies the activities, spatial and temporal distances and sequences of activities and their meanings in the dwelling environment. For the purposes of this study, a case study was done in the residential environment of the Qashqai tribe. For this study, a qualitative research method with data gathering techniques such as taking pictures from the environment and activities, drawing residential units’ maps, behaviour settings diagrams and semi-structured laddering interviews was used. Analytical findings were classified as either ‘spatial sequence’ or ‘temporal sequence’ of activities. The Means-End model, representing consequences and meanings of the sequences of activities, was presented in the form of ‘Feature-Consequence-Meaning’ diagrams. The results show that the sequences of activities in ‘Qashqai’ dwelling are influenced by such meanings as ‘social status’ and ‘family privacy’. Other consequences such as ‘desirable conditions of activities occurrence’ form conditions for lifestyle habitus in dwelling. In addition to providing a theoretical framework for the study of the human-environment relationship and the presentation of activity sequence properties, the results emphasize the meaningfulness of spatial and temporal sequences of activities in dwellings.

This paper aims to explore the spatio-temporal patterns of Chinese foreign direct investment (FDI) since the inception of the Belt and Road Initiative (BRI) in 2013 as an extended version of geographically weighted regression.

The panel data are used to examine spatial and temporal dynamics of the magnitude and the direction of China's outward FDI stock and its flow from 2011 to 2015 at a country level. Using the geographically and temporally weighted regression (GTWR), spatio-temporal distribution of FDI is explained through Logistic Performance Index, the size of gross domestic product (GDP), Shipping Linear Connectivity Index and Container Port Throughput.

A comparative analysis between participating and non-participating countries in the BRI shows that the size of GDP and Container Port Throughput of the participating countries have a positive effect on the increases of China's outward FDI Stock to Asia especially after 2013, while non-participating countries, such as North America, Western Europe and Western Africa, have no significant effect on it before and after the implementation of the BRI.

The findings, however, will not necessarily provide insight into the needs of China's outward FDI in certain countries to develop their economy. The findings provide the evidence to inform policy making to help identify the winners and losers of the investment, scale and direction of investment and the key drivers that shape the distributive investment patterns globally.

The study provides the empirical evidence to inform investment policy and strategic realignment by quantifying scale, direction and drivers that shape the spatio-temporal shifts of China's FDI.

The analysis also guides the Chinese government improve bilateral trade, build infrastructure and business partnerships with preferential countries participating in the BRI.

There is an urgent need to adopt a new perspective to unfold the spatial temporal complexity of FDI that incorporates space and time dependencies, and the drivers of the situated context to model their effects on FDI. The model is based on GTWR and an extended geographically weighted regression (GWR) allowing the simultaneous analysis of spatial and temporal decencies of exploratory variables.

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.

Responding to emergency incidents by emergency response organizations such as fire, ambulance and police during large disaster and emergency events is very important. The purpose of this paper is to provide some insights into response patterns during the 2013 ice storm in the city of Vaughan, Ontario, Canada, using temporal and spatial analyses.

The City of Vaughan Fire and Rescue Service data set containing all responses to fire and other emergency incidents from January 1, 2009 to December 31, 2016 was used. The 2013 Southern Ontario ice storm occurred from December 20, 2013 to January 1, 2014, and, for this study, December 20–31 is considered the “study period.” Temporal, spatial and spatiotemporal analyses of responses during the study period are carried out and are compared with the same period in other years (2009–2012 and 2014–2016).

The findings show that temporal patterns of response attributes changed significantly during the 2013 ice storm. Similarly, the spatial pattern of responses during the 2013 ice storm showed some major differences with other years. The spatiotemporal analyses also demonstrate significant variations in responses in the city during different hours of the day in the ice storm days.

This study is the first study to examine the spatiotemporal patterns of responses made by a fire department during the 2013 ice storm in Canada. It provides some insights into the differences between response volumes, temporal and spatial distributions during large emergency events (e.g. ice storm) and normal situations. The results will help in mitigating the number of responses in the future through public education and technological changes. Moreover, the results will provide fire departments with information that could help them prepare for such events by possible reallocation of resources.

While the economic benefits of the exhibition industry for the hotel sector have been addressed, the impact of exhibitions on individual hotels is unknown, especially when individual hotels’ star classification and locations are considered. This study aims to provide a better understanding of how room rates of different hotels change during different stages of the Canton Fair in China from a spatial-temporal perspective.

Room rates of 681 star-hotels within the city of Guangzhou before, during and after the Fair were extracted from websites. Through spatial interpolation and autocorrelation analysis and geographical detector (GeoDetector) technique, spatial and temporal patterns of hotel room rates and the interdependence between the convention center and the hotels with different star classification and locations were examined.

An inverse-U shape of room rate change was identified before, during and after the Fair, and the five-star hotels had the sharpest increase. Moreover, the distribution of hotel room rates followed the law of distance decay. The variation of hotel rates became larger when the distance to the convention center was larger. Spatial high-high clusters varied among hotels with different star classification.

This study contributed to the hotel literature by providing empirical evidence regarding how hotels with different star classification and locations were affected by events. This study also advanced the event literature by introducing GeoDetector. The findings of this study offered insights into the hotel location selection, pricing strategies and hotel collaboration with events.

虽然展览业对酒店业的经济效益已经得到解决, 但展览对单个酒店的影响尚不清楚, 尤其是在考虑单个酒店的星级和位置时。本研究旨在从时空角度更好地了解中国广交会不同阶段不同酒店的房价变化情况。

网站提取了广交会前、中、后广州市内681家星级酒店的房价。通过空间插值和自相关分析以及地理探测器（GeoDetector）技术, 研究了酒店房价的时空格局以及会议中心与不同星级和位置的酒店之间的相互依赖关系。

会前、会中、会后房价变化呈倒U型, 其中五星级酒店涨幅最大。此外, 酒店房价的分布遵循距离衰减规律。到会展中心的距离越远, 酒店价格的变化就越大。不同星级酒店的空间高-高集群存在差异。

该研究通过提供关于不同星级和位置的酒店如何受到事件影响的经验证据, 为酒店文献做出了贡献。这项研究还通过引入 GeoDetector 推进了事件文献。研究结果为酒店选址、定价策略和酒店与活动的合作提供了见解。

The purpose of this paper is to provide a guiding framework for studying the travel patterns of PT users. The combination of public transit (PT) users’ travel data and user profiling (UP) technology to draw a portrait of PT users can effectively understand users’ travel patterns, which is important to help optimize the scheduling of PT operations and planning of the network.

To achieve the purpose, the paper presents a three-level classification method to construct the labeling framework. A station area attribute mining method based on the term frequency-inverse document frequency weighting algorithm is proposed to determine the point of interest attributes of user travel stations, and the spatial correlation patterns of user travel stations are calculated by Moran’s Index. User travel feature labels are extracted from travel data containing Beijing PT data for one consecutive week.

In this paper, a universal PT user labeling system is obtained and some related methods are conducted including four categories of user-preferred travel area patterns mining and a station area attribute mining method. In the application of the Beijing case, a precise exploration of the spatiotemporal characteristics of PT users is conducted, resulting in the final Beijing PTUP system.

This paper combines UP technology with big data analysis techniques to study the travel patterns of PT users. A user profile label framework is constructed, and data visualization, statistical analysis and K-means clustering are applied to extract specific labels instructed by this system framework. Through these analytical processes, the user labeling system is improved, and its applicability is validated through the analysis of a Beijing PT case.