Tourists Visit and Photo Sharing Behavior Analysis: A Case Study of Hong Kong Temples

Leung, Rosanna; Vu, Huy Quan; Rong, Jia; Miao, Yuan

doi:10.1007/978-3-319-28231-2_15

Cited by 12 publications

(7 citation statements)

References 12 publications

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“…The results complied with previous findings from other data sources, such as mobile phone data [10], or corresponded with existing survey results such as the American Community Survey data [39]. Although human behavior studies used tweets and other forms of social media data, they focused on a certain group of people, such as tourists using photo-sharing services [40][41][42], on the general public either at a regional scale [11], a country scale [43] or a global scale [5]. Limited work has been done to explore and model human mobility patterns at a city or town scale [44].…”

Section: Introductionsupporting

confidence: 86%

Discover Patterns and Mobility of Twitter Users—A Study of Four US College Cities

Shan

2017

IJGI

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Geo-tagged tweets provide useful implications for studies in human geography, urban science, location-based services, targeted advertising, and social network. This research aims to discover the patterns and mobility of Twitter users by analyzing the spatial and temporal dynamics in their tweets. Geo-tagged tweets are collected over a period of six months for four US Midwestern college cities: (1) West Lafayette, IN; (2) Bloomington, IN; (3) Ann Arbor, MI; (4) Columbus, OH. Various analytical and statistical methods are used to reveal the spatial and temporal patterns of tweets, and the tweeting behaviors of Twitter users. It is discovered that Twitter users are most active between 9:00 p.m. and 11:00 .pm. In smaller cities, tweets aggregate at campuses and apartment complexes, while tweets in residential areas of bigger cities make up the majority of tweets. We also found that most Twitter users have two to four places of frequent visits. The mean mobility range of frequent Twitter users is linearly correlated to the size of the city, specifically, about 40% of the city radius. The research therefore confirms the feasibility and promising future for using geo-tagged microblogging services such as Twitter to understand human behavior patterns and carry out other geo-social related studies.

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Section: Introductionsupporting

confidence: 86%

Discover Patterns and Mobility of Twitter Users—A Study of Four US College Cities

Shan

2017

IJGI

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“…Since the ICT and social media revolution, tourists leave more and more digital footprints on different UGC platforms (Litvin et al, 2008;Munar & Jacobsen, 2014). Next to the fast-growing field of studies analysing topics and trends in UGC (O'Connor, 2008;Wang, Kirillova, & Lehto, 2017), analysing the digital footprint geographically can also give an indication on how tourists perceive, experience and use the destination (Ganzaroli et al, 2017;García-Palomares, Gutiérrez, & Mínguez, 2015;Girardin, Calabrese, Dal Fiore, Ratti, & Blat, 2008;Kádár, 2014;Koerbitz, Önder, & Hubmann-Haidvogel, 2013) and which experiences they communicate to their peers (Leung, Vu, Rong, & Miao, 2016).…”

Section: Theoretical Framework: Tourist Behaviour In Historic Cities mentioning

confidence: 99%

“…Geographic clustering of digital footprints left behind by tourists on UGC is finding its way into tourism studies (Leung et al, 2016). In this study, we look whether there is a relationship between geographic locations of features, restaurants, and a quantitative attribute, ie number of reviews.…”

Section: Focal Point: Hot Spots and Cold Spotsmentioning

confidence: 99%

Distribution of tourists within urban heritage destinations: a hot spot/cold spot analysis of TripAdvisor data as support for destination management

Zee

Bertocchi

Vanneste

2018

Current Issues in Tourism

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The emergence of social media and Web 2.0 has a notable impact upon the tasks of destination managers as these platforms have developed into influential mechanisms affecting tourist behaviour. This paper shows how Destination Management Organizations (DMOs) can reap the benefits of the Web 2.0 revolution as it serves as an important source of user-generated information, bringing novel opportunities for data-driven destination management. To test the applicability of user-generated content for destination management, this paper analyses restaurant reviews from five Flemish art cities which were retrieved from the Web 2.0 platform TripAdvisor. Getis-Ord hot spot analysis revealed spatial clusters of frequently ('hot spots') and rarely ('cold spots') reviewed restaurants in four out of the five art cities. By comparing these spatial patterns, the digital footprints of tourists were uncovered and discussed with DMO directors. Found patterns appeared to reflect local policies aimed either at concentrating tourism, as in Bruges, the city with the most prominent hot spot, or spreading tourism over time and space as seen in Antwerp and Ghent where less prominent hot spots were present. The visualization proved to be a valuable input when discussing tourism management and fuelled the sharing of knowledge between the destinations.

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“…Thus, the exact local time of activities can be determined. This condition is not the case for previous studies using social media data such as geotagged photos (Vu et al 2015;Leung et al 2016), because the times that the photos were taken are based on the clock of photo-capturing devices. Tourists might not adjust to the local time when they travel to a destination in a different time zone.…”

Section: Discussionmentioning

confidence: 95%

“…Thus, shopping malls are considered primary tourist attractions, which receive more consideration from official surveys and statistical reports than other attractions (HKTB 2014). Secondary attractions, such as park or temples, are usually under surveyed because of time and budget constraints (Leung et al 2016). Utilizing mobile social media data is an efficient means to collect visitor data for second-or third-tier attractions.…”

Section: Temporal Analysismentioning

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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Tourists Visit and Photo Sharing Behavior Analysis: A Case Study of Hong Kong Temples

Cited by 12 publications

References 12 publications

Discover Patterns and Mobility of Twitter Users—A Study of Four US College Cities

Discover Patterns and Mobility of Twitter Users—A Study of Four US College Cities

Distribution of tourists within urban heritage destinations: a hot spot/cold spot analysis of TripAdvisor data as support for destination management

Tourist Activity Analysis by Leveraging Mobile Social Media Data

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