Mapping Cilento: Using geotagged social media data to characterize tourist flows in southern Italy

Chua, Alvin; Servillo, Loris Antonio; Marcheggiani, Ernesto; Moere, Andrew Vande

doi:10.1016/j.tourman.2016.06.013

Cited by 188 publications

(124 citation statements)

References 47 publications

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“…A similar study was carried out while exploring visitors activities in Hong Kong Parks [28] and Temples [29]. The Twitter Streaming API has been used by Reference [30] in which the Geo-tagged social media data is used to categorize tourists flow in Italy. This research used the Geo-tagged social media data from Twitter to characterize spatial, temporal and demographic features of tourists' flow in Cilento, Southern Italy.…”

Section: Literature Reviewmentioning

confidence: 99%

A Study of Tourist Sequential Activity Pattern through Location Based Social Network (LBSN)

Talpur

Zhang

2018

2018 International Conference on Orange Technologies (ICOT)

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Sequential Pattern Mining (SPM) is an important component in establishing patterns and mining trends of certain activities. In the past, this technique has been used in various fields such as consumer-watch, making future predictions and analyzing and interpreting large datasets for deeply embedded rules and associations. The qualitative details of Singapore tourists' foursquare check-ins, represented in a tabular form, is an example of a sequential database. Therefore, the Pattern-Growth method which uses Prefix-Span Algorithm is used in this study to obtain the Tourist Sequential Activity Patterns. Insights into tourist movement and activity patterns is deemed beneficial for the tourism sector in many ways, such as designing better travel packages for tourists, maximizing the tourist activity participation and meeting the tourist demands. This research proposes to adopt mobile social media data for effective capturing of tourist activity information in Singapore and utilizes advanced data mining techniques for extracting valuable insights into tourist behavior. The proposed methods and findings of the study have the potential to support tourism managers and policy makers in making better decisions in tourism destination management.

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Section: Literature Reviewmentioning

confidence: 99%

A Study of Tourist Sequential Activity Pattern through Location Based Social Network (LBSN)

Talpur

Zhang

2018

2018 International Conference on Orange Technologies (ICOT)

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“…In group 2, when the information is collected after the visit, the option D "Patterns of tourist mobility, based on geolocalized photographs" is mainly developed in References [128,149,157,230,240,243,266]. These studies are based on the database of tagged photographs available on social networks.…”

Section: Discussionmentioning

confidence: 99%

“…In option E about "movement of tourist based in geolocalized tweets" is mainly developed in References [134,230,244]. It is based on taking information from a collection of geolocated tweets from a specific area and reconstructing the movement of users.…”

Section: Discussionmentioning

confidence: 99%

Science Mapping of Tourist Mobility 1980–2019. Technological Advancements in the Collection of the Data for Tourist Traceability

et al. 2019

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The tracking of tourist movements is an essential aspect in the management of sustainable tourist destinations. The current information and communication technologies provide innovative ways of collecting data on tourist movements, but it is still necessary to evaluate tools and methods of study for this challenge. At this point, mobile technologies are the best candidate for this task. Given the relevance of the topic, this paper proposes a mapping science analysis of publications on "movement of tourists" and "traceability." It has been carried out in the two main sources WOS and SCOPUS. The term "traceability" is brought from industry and technology areas to be applied to the tourist movement/mobility tracking and management. The methodological scheme is based on a selection of search criteria with combinations of terms. The sources of specialized information in applied social sciences and technology were then selected. From there, the searches have been executed for their subsequent analysis in three stages-(I) relevance analysis filtering the results to obtain the most pertinent; (II) analysis of articles with similarity thematic, authors, journals or citations; (III) analysis of selected papers as input for the mapping analysis using Citespace. The automatic naming of clusters under the selected processing confirms that the analysis of movements is a valid scientific trend but research-oriented from the perspective of traceability is non-existent, so this approach is novel and complementary to existing ones and a potential contribution to knowledge about tourist movements. Finally, a set of methodological considerations and a classification of information capture tools are proposed. In this classification, mobile technology is the best option to enable tourist movement analysis.

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“…However, the proposed travel diaries are large scale and contain more contextual information about tourist activities than pure GPS information in previous studies (Versichele et al 2014;Vu et al 2015;Chua et al 2016, Vu et al 2017. Inferring tourist activities based on venue check-ins is more convenient than depending on other contents, such as photos, and textual comments.…”

Section: Discussionmentioning

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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Mapping Cilento: Using geotagged social media data to characterize tourist flows in southern Italy

Cited by 188 publications

References 47 publications

A Study of Tourist Sequential Activity Pattern through Location Based Social Network (LBSN)

A Study of Tourist Sequential Activity Pattern through Location Based Social Network (LBSN)

Science Mapping of Tourist Mobility 1980–2019. Technological Advancements in the Collection of the Data for Tourist Traceability

Tourist Activity Analysis by Leveraging Mobile Social Media Data

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