Tourist movement patterns understanding from the perspective of travel party size using mobile tracking data: A case study of Xi'an, China

Zhao, Xiaoyu; Lu, Xiaoni; Liu, Yuanyuan; Lin, Jun; An, Jun

doi:10.1016/j.tourman.2018.06.026

Cited by 85 publications

(76 citation statements)

References 57 publications

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“…In recent years, research into tracking the movement of tourists in a destination has gained popularity as the requisite technology has become increasingly accessible, inexpensive, and easy to use (Shoval & Ahas, 2016). In conducting tourist tracking related research a range of data sources have been used by researchers such as Bluetooth, passive mobile positioning, geotagged photos, social media messages, and more (e.g., Versichele et al, 2014;Vu et al, 2015;Zhao et al, 2018). Still, GPS receivers are by far the most commonly used technology as two thirds of the studies in a recent review of tourist tracking research were based on GPS data (Shoval & Ahas, 2016).…”

Section: Tracking Touristsmentioning

confidence: 99%

Visitor bikeshare usage: tracking visitor spatiotemporal behavior using big data

Buning

Lulla

2020

Journal of Sustainable Tourism

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Bikeshare programs are a popular, convenient, and sustainable mode of transportation that provide a range of benefits to urban communities such as reduction in carbon emissions, decreased travel times, financial savings, and heightened physical activity. Although, tourists are especially inclined to use bikeshare to explore a destination as the programs are a convenient, cheap, flexible, and an active alternative to vehicles and mass transit little research or attention has focused on visitor usage. As such the current study investigated the spatial-temporal usage patterns of bikeshare by visitors to an urban community using GPS based big data (N ¼ 353,733). The results revealed differential usage patterns between visitors and local residents based on user provided ZIP Codes using a 50 mile geometric circular buffer around the urban destination. The visitors and residents significantly varied on numerous trip behaviors including route selection, time of rental, checkout/check-in locations, distance, speed, duration, and physical activity intensity. The user patterns uncovered suggest visitors primarily use bikeshare for leisure based urban exploration, compared to residents' primary use of bikeshare to be public transportation related. Implications for bikeshare, urban planning, and tourism management are provided aimed at delivering a more sustainable and richer visitor experience.

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Section: Tracking Touristsmentioning

confidence: 99%

Visitor bikeshare usage: tracking visitor spatiotemporal behavior using big data

Buning

Lulla

2020

Journal of Sustainable Tourism

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“…Finally, if the tourist travelled with types of companions not included in the survey, the probabilities the tourist had of visiting Timanfaya National Park were reduced by 21.71%. In fact, previous studies already indicated that the company during a trip is key to determine which attractions will be visited [72].…”

Section: Resultsmentioning

confidence: 99%

Preventing Overtourism by Identifying the Determinants of Tourists’ Choice of Attractions

Padrón‐Ávila

Martín

2019

Sustainability

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Popular tourism destinations based on specific attractions along with coastal and island destinations have been considered potential candidates to suffer from overtourism. In this context, in-depth knowledge of the determinants of tourists’ choices of attractions can be used to improve policies against crowding. This paper analyzes why tourists decide to visit certain attractions instead of others in the context of an island destination with sustainability concerns. To do so, discrete choice models are used to determine if a set of 96 variables can explain why 11 attractions are visited on the island of Lanzarote. The results show that 86 variables are significant to explain visits to at least one of the attractions. The analysis also identifies both similarities and differences on the effects these variables have on the probability of visiting each of the 11 attractions. These results are useful to cluster attractions depending on the profile of those tourists most likely to visit them and to cluster variables regarding their effect on visiting attractions. Furthermore, the results provide useful information for public and private managers involved in evenly reallocating tourist flows in time and space to avoid the negative impacts of overtourism.

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“…Likewise, the level of occupation of hotels could be increased (their average figures varied between 36.24% and 47.06%), or even that of tourist apartments, which have an average occupation rate of 34.12% [86]. Numerous authors have shown that tourists move about the area near their overnight stay site [43,45,88,89] and between various destinations [90,91]. In this sense, the existence has been demonstrated of Historical Ensembles, which have the competitive advantage of being located close to the main tourist points of Extremadura.…”

Section: Historical Ensembles As Potential Centers For Attracting Demmentioning

confidence: 99%

The Cultural Heritage and the Shaping of Tourist Itineraries in Rural Areas: The Case of Historical Ensembles of Extremadura, Spain

Martín

Gurría-Gascón

García-Berzosa

2020

IJGI

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Heritage is a very complex concept that is characterized by having multiple meanings. Despite this, there is no doubt as to its attraction for carrying out tourist activities. In the specific case of cultural heritage, there is widespread agreement in considering it as a tourist resource given the interest it raises amongst visitors of all origins. This is not, however, always the case and it can be observed that in certain areas, especially rural ones, certain limitations exist. They are caused by multiple factors such as the importance of the resource itself, although the distance to the main centers of tourist attraction is also considered a key point. Due to this, the study puts forward the need for joining forces and attractions in order to create tourist itineraries based on a peculiar kind of resource: Historical Ensembles. In order to achieve this aim, we used the network analysis tools included in a geographical information system and applied it to a rural context, which was that of Extremadura in Spain. To be precise, the closest facility and service area tools were used. The results of the various analyses reflect the real possibility of generating tourist itineraries as a first step in the design of tourist products. In this way, the administration can be provided with a management tool capable of laying the foundations of a coherent and efficient tourist policy.

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Tourist movement patterns understanding from the perspective of travel party size using mobile tracking data: A case study of Xi'an, China

Cited by 85 publications

References 57 publications

Visitor bikeshare usage: tracking visitor spatiotemporal behavior using big data

Visitor bikeshare usage: tracking visitor spatiotemporal behavior using big data

Preventing Overtourism by Identifying the Determinants of Tourists’ Choice of Attractions

The Cultural Heritage and the Shaping of Tourist Itineraries in Rural Areas: The Case of Historical Ensembles of Extremadura, Spain

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