Modelling the spatial-temporal movement of tourists

Tourism is one of the most rapidly developing industries in the world. The study of spatio-temporal movement models of tourists are undertaken in variety of disciplines such as tourism, geography, mathematics, economics and artificial intelligence. Knowledge from these different fields has been difficult to integrate because tourist movement research has been conducted at different spatial and temporal scales. This thesis establishes a methodology for modelling the spatial-temporal movement of tourists and defines the spatial-temporal movement of tourists at both the macro and micro level. At the macro level, the sequence of tourist movements is modelled and the trend for tourist movements is predicted based on Markov Chain theory (MC). Log-linear models are then adopted to test the significance of the movement patterns of tourists. Tourism market segmentation based on the significant movement patterns of tourists is implemented using the EM (Expectation-Maximisation) algorithm. At the micro level, this thesis investigates the wayfinding decision-making processes of tourists. Four wayfinding models are developed and the relationships between the roles of landmarks and wayfinding decision-making are also discussed for each type of the wayfinding processes. The transition of a tourist movement between the macro and micro levels was examined based on the spatio-temporal zooming theory. A case study of Phillip Island, Victoria, Australia is undertaken to implement and evaluate the tourist movement models established in this thesis. Two surveys were conducted on Phillip Island to collect the macro and micro level movement data of tourists. As results show particular groups of tourists travelling with the same movement patterns have unique characteristics such as age and travel behaviours such as mode of transport. Effective tour packages can be designed based on significant movement patterns and the corresponding target markets. Tourists with various age groups, residency, gender and different levels of familiarity with physical environment have different wayfinding behaviours. The results of this study have been applied to tourism management on Phillip Island and the novel methods developed in this thesis have proved to be useful in improving park facilities and services provided to tourists, in designing tour packages for tourism market promotion and in understanding tourist wayfinding behaviours

Overseas Tourist Movement Patterns in Beijing: The Impact of the Olympic Games

Tourist movement patterns are essential for tourism planners to make decisions on tourism product development, destination planning and marketing. The 2008 Beijing Olympic Games have a great impact on the tourism industry in Beijing, especially on tourism flows and movements. In order to understanding changes of tourist movement patterns during the Games, the paper used content analysis method to analyze 500 trip diaries gathered from 6 different websites from January 2001 to April 2009. The paper also utilized social network analysis software Ucinet 6 and NetDraw to map itineraries and analyze overseas tourist movement patterns in Beijing. As a result, the paper summarized the changes of main tourism attractions and main tourist movement patterns in Beijing during the Olympic periods

Development of tour packages through spatio-temporal modelling of tourist movements

An ideal tour package should consist of itinerary elements such as the sequence of attractions with the scheduled arrival times and visiting durations at each attraction according to tourists’ preferences and characteristics. This research presents the methodology of designing tour packages by incorporating tourists’ spatio-temporal movements and tourist characteristics within scheduling processes. Therefore, it can provide tourists with personalised itineraries and support other stakeholders in managing the resources and facilities within attractions

Como consomem os turistas um destino de enoturismo no Centro de Portugal? Uma análise espaciotemporal

Space-time tourist behaviour is influenced by numerous factors related both to tourists and the destination. Yet, however complex it may be, understanding and to some extent managing the way tourists move in space and time is crucial to ensuring the quality of their experience, as well as the effective and sustainable management of destinations and attractions. In the rural wine tourism context, studies on space-time behaviour are rare. The present study uses empirical data collected from tourists staying in hotels of the Bairrada Wine Route territory (N = 116), combining a GPS tracking study with a questionnaire survey. Using a time-geographical analytical approach, the GPS tracking data were mapped for a more detailed analysis of the tourists’ movements in the Bairrada terroir. The findings highlight specificities of tourist consumption in the context of rural wine regions and provide valuable insights for destination planning, service design and marketing of the Bairrada Wine Route.O comportamento turístico espaciotemporal é influenciado por diversos fatores relacionados tanto com os turistas como com o destino. No entanto, por complexo que seja, compreender e, em certa medida, gerir a forma como os turistas se movem no espaço e no tempo é crucial para assegurar a qualidade da sua experiência, bem como a gestão eficaz e sustentável de destinos e atrações. No contexto do enoturismo, são raros os estudos sobre o comportamento espaciotemporal. O presente estudo utiliza dados empíricos recolhidos junto de turistas alojados em hotéis do território da Rota do Vinho da Bairrada (N= 116), combinando um estudo de rastreamento por GPS com um inquérito por questionário. Utilizando uma perspetiva temporal de análise, os dados de rastreamento por GPS foram mapeados para o estudo mais aprofundado dos movimentos dos turistas no terroir da Bairrada. Os resultados destacam as especificidades do consumo turístico no contexto das regiões vitivinícolas e fornecem informações relevantes para o planeamento do destino, conceção do serviço e marketing da Rota do Vinho da Bairrada.info:eu-repo/semantics/publishedVersio

Solução ubíqua baseada em NFC para a análise de dados turísticos em cidades inteligentes.

El registro y el análisis detallado de las trayectorias del visitante y los movimientos individuales en tiempo real de las decenas de miles de visitantes es una de las áreas más importantes de la investigación en turismo. Para observar los movimientos turísticos, está disponible una variedad de técnicas. Nuevas técnicas de seguimiento se están explorando y gracias al avance de la tecnología es posible disponer en cualquier momento y desde cualquier lugar (computación ubicua) de la de información que se ha utilizado para registrar el movimiento de turistas, con alta resolución. En estos entornos (ambientes etiquetados) donde el usuario interactúa con su medio ambiente, una tecnología emergente conocida como Near Field Communication [NFC] ofrece una manera natural para la interacción entre los usuarios y su entorno. Este artículo elabora una propuesta ubicua, basada en NFC, que permite obtener datos turísticos en tiempo real que son analizados con el método de cadenas de Markov por medio de pruebas experimentales y estadísticas, gracias a que se demuestra que el movimiento de un turista está influenciado por el estado o sitio turístico donde se encuentre antes de pasar a otro, corroborando la hipótesis que indica que es posible capturar información dejada por los turistas por medio de herramientas tecnológicas, y que gracias al procesamiento de esa información se puede obtener una traza que muestre la actividad realizada, la misma que, por medio de su visualización permitirá la toma de decisiones que favorezcan la actividad turística como parte de la economía regional y nacional. Detailed recording and analysis of visitor paths and individual mouse movements in real time of tens-of-thousands of visitors is one of the most important areas of tourism research, and to observe tourist movements a variety of techniques are available. New tracking techniques are explored and due to the advance of technology we can have information at any time and from anywhere (pervasive computing). This has been used to record movement information of tourists with high resolution. In these environments (tags environments) where the user interacts with the environment, an emerging technology called NFC (Near Field Communication) is providing a natural means of interaction between the users and their environment. This paper shows the implementation of an NFC-based pervasive solution that allows tourist tracking data to be obtained in real time; it is simplified and analyzed with the Markov chains method by experimental and statistical testing. It is also demonstrated that the movement of a tourist is influenced by the state or tourist site where he or she is to move to another, corroborating the hypothesis "that if you can capture the information left by tourists through technological tools, thanks to the processing of such information you can obtain a trace that is a sample of the activity which, through its display, allows decisions that promote tourism as part of the regional and national economy".O registro e a análise pormenorizados dos percursos do visitante bem como os movimentos individuais em tempo real das dezenas de milhares de visitantes pertencem a uma das mais importantes áreas de pesquisa em turismo. Para observar os movimentos turísticos, encontra-se disponível uma variedade de técnicas. Novas técnicas de monitorização estão sendo exploradas e graças aos avanços da tecnologia é possível ter em qualquer momento e desde qualquer lugar (computação ubíqua) a informação que foi usada para registrar o movimento de turistas, com alta resolução. Nesses ambientes (ambientes etiquetados) onde o usuário interage com o seu ambiente, uma tecnologia emergente conhecida como Near Field Communication [NFC] fornece uma maneira natural para a interação entre os usuários e seu ambiente. Este artigo desenvolve uma proposta ubíqua, baseada em NFC, que permite obter dados turísticos em tempo real que são analisados com o método de cadeias de Markov através de testes experimentais e estatísticas, graças a que se demonstra que o movimento de um turista é influenciado pelo estado ou local turístico onde se encontra antes de passar para outro, confirmando a hipótese que é possível capturar informação deixada pelos turistas através de ferramentas tecnológicas, e que graças ao processamento dessa informação pode obter-se um traço que mostre a atividade realizada, o mesmo que, através da sua visualização permitirá tomar decisões que promovam o turismo como parte da economia regional e nacional.

A big-data analytics method for capturing visitor activities and flows: the case of an island country

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Understanding how people move from one location to another is important both for smart city planners and destination managers. Big-data generated on social media sites have created opportunities for developing evidence-based insights that can be useful for decision-makers. While previous studies have introduced observational data analysis methods for social media data, there remains a need for method development—specifically for capturing people’s movement flows and behavioural details. This paper reports a study outlining a new analytical method, to explore people’s activities, behavioural, and movement details for people monitoring and planning purposes. Our method utilises online geotagged content uploaded by users from various locations. The effectiveness of the proposed method, which combines content capturing, processing and predicting algorithms, is demonstrated through a case study of the Fiji Islands. The results show good performance compared to other relevant methods and show applicability to national decisions and policies

Building a conceptual framework for determining individual differences of accessibility to tourist attractions

This paper introduces a conceptual framework for determining individual differences of accessibility to tourist attractions. The framework includes three components: factors affecting accessibility to tourist attractions; the individual characteristics of tourists, such as age, gender; and other explanatory variables that assist in explaining why accessibility to tourist attractions varies. This research indicates that measures of accessibility should include not only commonly used factors such as socio-demographic variables and distance, but also the facilities available at various attractions, management and operational aspects relating to the attraction (what we term "functions"), and the infrastructure used to move between specific attractions (what we term "connectivity of networks"). A case study of the Ningaloo Coast region was conducted to identify individual difference in evaluation of accessibility to tourist attractions. We show that the evaluation of accessibility varies among tourist groups due to their spatial abilities, individual values and needs, and preparatory set

Learning Periodic Human Behaviour Models from Sparse Data for Crowdsourcing Aid Delivery in Developing Countries

In many developing countries, half the population lives in rural locations, where access to essentials such as school materials, mosquito nets, and medical supplies is restricted. We propose an alternative method of distribution (to standard road delivery) in which the existing mobility habits of a local population are leveraged to deliver aid, which raises two technical challenges in the areas optimisation and learning. For optimisation, a standard Markov decision process applied to this problem is intractable, so we provide an exact formulation that takes advantage of the periodicities in human location behaviour. To learn such behaviour models from sparse data (i.e., cell tower observations), we develop a Bayesian model of human mobility. Using real cell tower data of the mobility behaviour of 50,000 individuals in Ivory Coast, we find that our model outperforms the state of the art approaches in mobility prediction by at least 25% (in held-out data likelihood). Furthermore, when incorporating mobility prediction with our MDP approach, we find a 81.3% reduction in total delivery time versus routine planning that minimises just the number of participants in the solution path.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013

景区旅游者空间行为研究综述

研究景区内旅游者空间行为对于提升景区管理、旅游产品开发、景区规划和景区营销水平都具有非常重要的意义。近年来,越来越多的学者意识到该研究的重要价值,同时随着GPS等现代信息技术的广泛应用,使得景区内的微观旅游者空间行为研究成为旅游者空间行为研究的热点。文章从行为数据获取、描述、解释和预测模拟等4个方面梳理了近年来国内外学者对景区内旅游者空间行为研究的现状。结合精细化行为分析这一趋势,指出:(1)GPS与问卷结合是景区内旅游者空间行为研究有效的数据源;(2)旅游者空间运动是描述和认识旅游者空间行为的重要方式,而景区旅游者空间运动往往可以被抽象为一个离散的过程;(3)离散选择模型用以揭示景区内旅游环境与空间行为作用机理,其中影响因素和选择项确定是模型构建的核心;(4)基于多主体的行为仿真能够为景区管理者提供有效的决策支持,在政策评估和预判等方面具有重要价值。国家自然科学基金项目“基于行为分析的景区人流模拟与空间优化—GPS与问卷结合的研究”和“基于轨迹数据的景区游客时空运动模式挖掘及内在机理研究”(41671141,71601164);;教育部人文社会科学基金项目“大数据驱动的景区游客时空运动模式挖掘与分析研究”(16YJC630177);;福建省自然科学基金项目“游客景点选择行为导向的景区服务设施布局方法”(2015J01226);;厦门大学中央高校基金项目“地图空间-认知空间-行为空间的作用机理及景区优化方法”(20720170046)共同资助~

Estimating spatial location of attraction through time with animal movement data

As statistical methodology catches up with GPS technology, researchers are gaining new insight on movement behavior and wildlife decision-making that were previously unobtainable. New areas of research target underlying drivers of animal movement including understanding how animal movement is driven by points of attraction on the landscape. Estimating attraction points using movement data can be challenging when an animal changes behavior, and the change is not obvious. I have developed a parametric statistical modeling framework that mechanistically models animal movement data to identify if animal movement is driven by points of attraction on the landscape. The framework is flexible enough to identify an arbitrary number of attraction points and change points. We optimally estimate the number of attraction points in an animal movement path by framing the question in a model selection context and using reversible-jump Markov chain Monte Carlo methods. I validated the framework using extensive simulation experiments which all suggested appropriate model fit and selection. I applied the modeling framework to a case study of four mule deer in the Mojave National Preserve. Thirty-seven percent of attraction points were associated with known water sources, a disproportionately large amount, given the composition of water in the Mojave National Preserve. These preliminary results support the importance of water sources on mule deer movement behavior