Abstract
The rapid development of social media and location-based service has generated a myriad of spatial data tagged with geo-information. Constructing a network of tourism hotspots using these geotagged data would improve our understanding of tourism activities. Thus, using Flickr data, we built a spatially-embedded tourism hotspot network for Beijing and applied complex network analysis to study the network characteristics. The results indicate that the tourism hotspot network in Beijing is scale-free and small-world. In the hotspot network, the interconnected triplets have a tendency to be formed by the edges with greater weight values, and a high-weighted edge is often connected by two high-degree vertices. Moreover, the statistics of the network provides insights for additional travel bus routes in Beijing. Finally, this paper provides a guide for building spatially-embedded hotspot networks based on geotagged social media data, which helps to understand the laws of travel and provides decision support for the development of tourism resources.
Highlights
The mobile Internet and social media have developed rapidly in recent years
Complex network theory, which is widely used in geographical studies [4], [5], provides a new perspective to investigate human mobility patterns based on social media data
Applying network theory to large amounts of social media data containing geo-information represents a powerful method to examine the characteristics of tourism networks, which helps us better understand travel behaviors
Summary
The mobile Internet and social media have developed rapidly in recent years. When travelling, tourists typically upload photos, text, videos and other data to the Internet, recording their travel behaviors thereby. In addition to being rich in textand image-based information, social media data are rich in geo-information Both tourism hotspots and travel trajectories of individuals could be extracted from geotagged social media data [1]–[3]. Numerous trajectories extracted from social media data provide a basis to construct a spatially-embedded network of tourism hotspots. Applying network theory to large amounts of social media data containing geo-information represents a powerful method to examine the characteristics of tourism networks, which helps us better understand travel behaviors. We extracted tourist attractions from geotagged Flickr data in Beijing and utilized the travel trajectories of users to construct a spatially-embedded tourism hotspot network and evaluated its characteristics. The remainder of this paper is organized as follows: Section II illustrates related work; Section III elaborates on the method used to construct the tourism hotspot network; Section IV details the characteristics of the tourism hotspot network; and Section V describes conclusions and future work
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