When and Where Tourists are Viewing Exhibitions: Toward Sophistication of GPS-Assisted Tourist Activity Surveys

Kawase, Junya; Kurata, Yohei; Yabe, Naoto

doi:10.1007/978-3-7091-1142-0_36

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…(2) In terms of research data, questionnaires, interviews, and online data have been the main data of tourism flow research for some time [30,31]. In recent years, GPS-based tracking data and base station signaling data have greatly improved the accuracy and quantity of data [32][33][34]. Today, with the big data era approaching, the acquisition of data on tourism flow has become easier.…”

Section: Introductionmentioning

confidence: 99%

Regionalization Analysis and Mapping for the Source and Sink of Tourist Flows

Zhang

Chen

et al. 2019

IJGI

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At present, population mobility for the purpose of tourism has become a popular phenomenon. As it becomes easier to capture big data on the tourist digital footprint, it is possible to analyze the respective regional features and driving forces for both tourism sources and destination regions at a macro level. Based on the data of tourist flows to Nanjing on five short-period national holidays in China, this study first calculated the travel rate of tourist source regions (315 cities) and the geographical concentration index of the visited attractions (51 scenic spots). Then, the spatial autocorrelation metrics index was used to analyze the global autocorrelation of the travel rates of tourist source regions and the geographical concentration index of the tourist destinations on five short-term national holidays. Finally, a heuristic unsupervised machine-learning method was used to analyze and map tourist sources and visited attractions by adopting the travel rate and the geographical concentration index accordingly as regionalized variables. The results indicate that both source and sink regions expressed distinctive regional differentiation patterns in the corresponding regional variables. This study method provides a practical tool for analyzing regionalization of big data in tourist flows, and it can also be applied to other origin-destination (OD) studies.

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Section: Introductionmentioning

confidence: 99%

Regionalization Analysis and Mapping for the Source and Sink of Tourist Flows

Zhang

Chen

et al. 2019

IJGI

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“…Finally, we note that mobile experimental test-beds like ours can be used for a variety of other research activities: market research (Kawase, Yohei, and Yabe 2012), other forms of operations research (Rajaram and Ahmadi 2003), and the testing of new creative concepts such as augmented reality and crowd games (Montola, Stenros, and Waern 2009). Mobile computing has the capacity to have a profound effect on tourism and location-based entertainment, and we are only beginning to investigate the opportunities.…”

Section: Mobile Computing Research and Tourism And Leisurementioning

confidence: 99%

Mitigating Theme Park Crowding with Incentives and Information on Mobile Devices

Brown

Kappes

Marks

2013

Journal of Travel Research

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Long wait times for rides and restaurants are major sources of visitor dissatisfaction with theme and amusement parks. Crowding is often worse than it needs to be because park visitors distribute themselves unevenly. In this paper, we discuss a novel method of routing visitors to less crowded areas by offering them well-timed incentives and information on mobile devices. Two large field studies (n = 910 and n = 1,370) were conducted to test how receptive visitors were to altering their paths in response to incentives and information. The results indicate that a system such as ours can mitigate suboptimal crowd distribution in real time in a typical theme park environment.

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