Emotions in motion: tourist experiences in time and space

Zakrisson, Ingrid; Zillinger, Malin

doi:10.1080/13683500.2011.615391

Cited by 62 publications

(46 citation statements)

References 35 publications

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“…Research on intra-destination movements has gained more attention over the past decade, especially due to the rapid advances in GPS-based tracking technologies (Shoval & Isaacson, 2007). GPS-based intradestination studies have, for example, been used to analyse daily movements within a specific destination city (Edwards & Griffin, 2013;Lau & McKercher, 2006;McKercher et al, 2012;Shoval, 2008;Shoval et al, 2011), natural recreation area (Orellanna, Bregt, Ligtenberg, & Wachowicz, 2012;Smallwood, Beckley, & Moore, 2012;Zakrisson & Zillinger, 2012), or theme park (Birenboim, Anton-Clave, Russo,& Shoval, 2013).…”

Section: Geographical Dimensionmentioning

confidence: 99%

Measuring tourism destinations using mobile tracking data

2016

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Section: Geographical Dimensionmentioning

confidence: 99%

Measuring tourism destinations using mobile tracking data

2016

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“…Identification of these types is also important for the management (Beeco, Hallo, English & Giumetti, 2013). Recognising this, other scientists identified new categories analysing movements within destination and also created different categorisations based on different aspects (Arrowsmith & Chetri 2003;Beeco et al, 2012;Zakrisson & Zillinger, 2012). Not only the visited sights, spent time but the pattern of the selected routes can also be associated with certain tourist types and the relationship can be verified numerically (Gao & Hsueh, 2014).…”

Section: Figure 1 -Dimensions Of Databasementioning

confidence: 99%

“…Survey with interviewers gives more accurate results (Muhar, Arnberger & Brandenburg, 2005) but this, similar to counting performed by volunteers, requires significant labour force. Giving the visitors GPS devices (Arrowsmith & Chetri, 2003;Beeco et al, 2013;Oranella, Bregt, Ligtenberg & Wachowicz, 2012;Zakrisson & Zillinger, 2012) or TrackSticks (Rettie, 2012) can be applied rarely and the application of the method depends on the size of the studied area and the cultural background of the visitors. Collected data can be evaluated when the device is returned at the end of the well-defined counting period.…”

Section: Figure 1 -Dimensions Of Databasementioning

confidence: 99%

Determination of tourist flow patterns in a low mountain study area

Benkhard

2018

Tour Manage Stud

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Understanding the movement of visitors contributes to appropriate management plans for visitor destinations. Determination of this movement is composed of not only the identification of the starting and end points together with the sequence of stops but the characterisation of the route pattern as well. The type of this pattern depends on the human apart from outer physical parameters and the available time thus it has a relationship with the behaviour and type of the tourist. Understanding and making use of these are important measures for organisations managing natural areas. Observation of visitors in natural areas is not possible in every environment while reducing the tread of certain popular sites can be made only with the help of such knowledge. This explains that literature describing different patterns analyse interdestination relationships. In this paper the identification of the movement pattern of hikers in a mountainous study area is presented. Visitor flow analysis of a study area in Hungary (180 km 2 area of Central Börzsöny in Duna-Ipoly National Park) was performed on the basis of a questionnaire survey carried out with the help of volunteers. Based on the analysis of the obtained data the accurate description of the movement of both individual hikers and groups became possible. Mathematics based definition of hiking routes completed in mountainous areas is new to the available data in the literature. Patterns of one day routes were classified into 6 types and two types of hiking routes requiring several days were also identified. Different route types were observed at the different entrances of the destination. Such information helps the identification of visitor types at the starting points and the design of appropriate visitor management measures.Keywords: Visitor flow, tourist movement pattern, behaviour, spatial movement, itinerary model. ResumoCompreender o movimento dos visitantes contribui para desenvolver planos de gestão apropriados para os destinos. A determinação desse movimento é composta não apenas pela identificação dos pontos iniciais e finais, juntamente com a sequência de paragens, mas também pela caracterização do padrão de rota. O tipo desse padrão depende, do fator humano, além dos parâmetros físicos externos e do tempo disponível, tendo consequentemente uma relação com o comportamento e o tipo do turista. Compreender e fazer uso desses padrões são medidas importantes para as organizações que gerenciam áreas naturais. A observação de visitantes em áreas naturais não é possível em todos os ambientes, ao passo que a gestão de certos percursos pode ser feita apenas com a ajuda de tal conhecimento. Isso explica que a literatura que descreve diferentes padrões, analisa relacionamentos entre destinos. Neste trabalho é apresentada a identificação do padrão de movimento dos caminhantes numa área de estudo montanhosa. A análise do fluxo de visitantes de uma área de estudo na Hungria (área de 180 km2 do Börzsöny Central no Parque Nacional Duna-Ipoly) foi realizada com base num...

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“…However, GPS-based travel data are not always applicable in observing tourist behavior because GPS requires an open-sky environment to receive signals from satellites (Asakura and Iryo 2007). If researchers wish to fully understand tourist experiences and mobility, GPS data should be combined with methods, such as questionnaires (Zakrisson and Zillinger 2012). Recent studies still need to use written diaries and questionnaires to obtain information on participating tourist activities in addition to GPS data (McKercher et al 2012;Spangenberg 2014).…”

Section: Travel Diary For Tourist Activity Analysismentioning

confidence: 99%

“…Diaries are used to record tourists' daily movements, places visited, activities, time of day, duration of time spent at each place, and transport mode taken to navigate around the destination (Zillinger 2008;McKercher and Lau 2008). In addition to paper-based travel diaries, travel data can also be collected using mobile instruments with global positioning system (GPS) technology (Zakrisson and Zillinger 2012;McKercher et al 2012). The data are represented as sequential "dots" in spatial-temporal dimensions (longitude, latitude, and time stamp), which provide detailed and accurate information for evaluating the effects and performances of transportation policies (Asakura and Iryo 2007).…”

Section: Introductionmentioning

confidence: 99%

Tourist Activity Analysis by Leveraging Mobile Social Media Data

Law

et al. 2017

Journal of Travel Research

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Approaches to traditional travel diary construction rely on tourist participation and manual recording; hence, they are not only time-consuming but also limited in the scale and the number of samples. Online social network platforms have been used as alternative data sources for capturing the movements and travel patterns of tourists at a large scale. However, they fail to provide detailed contextual information on tourist activities for further analysis. In this paper, we present a new approach to travel diary construction based on the venue check-in data available in mobile social media with rich information on locations, time, and activities. Our case study focuses on the inbound tourism in Hong Kong using a data set comprising of 17,355 check-ins generated by 600 tourists. We demonstrate how the proposed travel diary can provide useful practical implications for applications in location management, transportation management, impact management, and tourist experience promotion among others.

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Emotions in motion: tourist experiences in time and space

Cited by 62 publications

References 35 publications

Measuring tourism destinations using mobile tracking data

Measuring tourism destinations using mobile tracking data

Determination of tourist flow patterns in a low mountain study area

Tourist Activity Analysis by Leveraging Mobile Social Media Data

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