Tourists visit and photo sharing behavior analysis: a case study of Hong Kong temples

Abstract: The 2008 Beijing Olympic Games had a great impact on the tourism industry in Beijing, especially on tourism flows and movements. This study used content analysis and social network analysis methods to examine 500 online trip diaries and analyze overseas tourist movement patterns in Beijing during the Olympics. The result revealed that overseas tourists were most interested in famous traditional attractions, and their movements were focused in the central city area of Beijing. The study identified the diversity of tourist attractions and the expansion of main visiting areas as the two main changes during the Olympics. Copyright © 2011 John Wiley & Sons, Ltd.

Abstract: 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 sampl...

Abstract: 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...

Abstract: The advancement of mobile technology provides an opportunity to obtain the real-time information of travelers, such as their spatial and temporal behaviors, during their visits to a destination. This study analyzes a large scale mobile phone dataset that captures the cellphone trace of international travelers who visited South Korea. We apply a trajectory data mining approach to understand the spatial structures of tourist activities within three different destinations. Through spatial clustering analysis and sequential pattern mining, the study reveals multiple “hot spots” (or popular areas) in travel destinations and spatial interactions across these places. As a result, this paper provides important tourism implications integrating spatial models with destination planning, which is the foundation of tourism design.

Abstract: We investigate how to organize a large collection of geotagged photos, working with a dataset of about 35 million images collected from Flickr. Our approach combines content analysis based on text tags and image data with structural analysis based on geospatial data. We use the spatial distribution of where people take photos to define a relational structure between the photos that are taken at popular places. We then study the interplay between this structure and the content, using classification methods for predicting such locations from visual, textual and temporal features of the photos. We find that visual and temporal features improve the ability to estimate the location of a photo, compared to using just textual features. We illustrate using these techniques to organize a large photo collection, while also revealing various interesting properties about popular cities and landmarks at a global scale.

Abstract: Models depicting the spatial movement patterns of tourists within a destination are proposed in this paper. They are developed using an inductive approach based on urban transportation modeling and tourist behavior, to identify explanatory factors that could influence movements. Factors identified included a set of destination characteristics and a set of tourist characteristics that influence decisionmaking and behavior. These factors influence movement patterns in two ways, resulting in four types of territorial and three linear path models. Understanding the movement of tourists within a destination has practical applications for destination management, product development, and attraction marketing.

Abstract: Consumers tend to seek heuristic information cues to simplify the amount of information involved in tourist decisions. Accordingly, star ratings in online reviews are a critical heuristic element of the perceived evaluation of online consumer information. The objective of this article is to assess the effect of review ratings on usefulness and enjoyment. The empirical application is carried out on a sample of 5,090 reviews of 45 restaurants in London and New York. The results show that people perceive extreme ratings (positive or negative) as more useful and enjoyable than moderate ratings, giving rise to a U-shaped line, with asymmetric effects: the size of the effect of online reviews depends on whether they are positive or negative.

Abstract: We describe a framework for automatically selecting a summary set of photos from a large collection of geo-referenced photographs. Such large collections are inherently difficult to browse, and become excessively so as they grow in size, making summaries an important tool in rendering these collections accessible. Our summary algorithm is based on spa-tial patterns in photo sets, as well as textual-topical patterns and user (photographer) identity cues. The algorithm can be expanded to support social, temporal, and other factors. The summary can thus be biased by the content of the query, the user making the query, and the context in which the query is made.A modified version of our summarization algorithm serves as a basis for a new map-based visualization of large collections of geo-referenced photos, called Tag Maps. Tag Maps visualize the data by placing highly representative textual tags on relevant map locations in the viewed region, effectively providing a sense of the important concepts embodied in the collection.An initial evaluation of our implementation on a set of geo-referenced photos shows that our algorithm and visualization perform well, producing summaries and views that are highly rated by users.

Abstract: Insight into tourist travel behaviors is crucial for managers engaged in strategic planning and decision making to create a sustainable tourism industry. However, they continue to face significant challenges in fully capturing and understanding the behavior of international tourists. The challenges are primarily due to the inefficient data collection approaches currently in use. In this paper, we present a new approach to this task by exploiting the socially generated and user-contributed geotagged photos now made publicly available on the Internet. Our case study focuses on Hong Kong inbound tourism using 29,443 photos collected from 2100 tourists. We demonstrate how a dataset constructed from such geotagged photos can help address such challenges as well as provide useful practical implications for destination development, transportation planning, and impact management. This study has the potential to benefit tourism researchers worldwide from better understanding travel behavior and developing sustainable tourism industries.