Abstract
With the further advent of the era of big data, the scale of social media data containing geolocation information is exploding, providing a new source of big data information and perspective for an in-depth study of the changing spatio-temporal and geographical characteristics of the current tourist population. This paper extracts data on popular attractions in the Tibet Autonomous Region using the HDBSCAN algorithm combined with the TF-IDF algorithm based on information on images with geotags shared by users in the Flickr image sharing site from 2005-2018. Social network analysis was used to explore the changes in the spatial and temporal characteristics of inbound tourism flows in Tibet. The results show that: (1) in terms of temporal characteristics, the number of inbound tourists shows obvious off-peak seasons, with relatively high sensitivity to the influence of economic, policy and infrastructure construction factors; (2) in terms of spatial distribution characteristics, the inbound tourism flow in Tibet shows an “axis-scattered” distribution. The core area is centred on Lhasa and extends in three directions: west, north and east along important roads.
Highlights
Inbound tourism is an important part of China's tourism market and an important indicator of the country's tourism competitiveness[1]
The opening of the QinghaiTibet Railway on 1 July 2006 led to a rapid growth in inbound tourism in 2007, with a growth rate of 124.84%. 2008 saw a serious violent crime in Lhasa on 14 March causing a precipitous fall in inbound tourism that year, which took three years to recover and broke through to a new high of 9,856 annual arrivals in 2011. 2012 saw the financial crisis The outbreak of the financial crisis in 2012 saw tourism around the world take a huge hit, with the global economy and tourism market continuing to decline and inbound tourism flows to Tibet continuing to slump between 2012 and 2018, maintaining an annual decline of 10-30% except for a small rebound in 2017
According to the Tibetan Statistical Yearbook, the Tibetan tourism market developed steadily in 2018, which is at variance with the Flickr statistics, which may be related to the decline in the use of Flickr in recent years, Wood et al proved in 2013 that Flickr data can be used to effectively estimate the rate of visits to tourist attractions, verifying the reliability of Flickr data, but with the recent the explosion of social platforms such as Twitter and Facebook, whether Flickr data can still be used for tourism flow studies beyond 2018 needs to be further explored
Summary
Inbound tourism is an important part of China's tourism market and an important indicator of the country's tourism competitiveness[1]. The flow pattern of inbound tourists between tourism hotspots can reflect the dynamic trend of the international tourism source market, and is important for the development of tourism markets and tourism products[2]. The sample representativeness of survey questionnaires is poor, and it is difficult to accurately and comprehensively reflect the behavioural characteristics of inbound tourists[6]. With the increasing popularity and rapid industrial development of information technology of mobile application terminals such as modern mobile smart Internet and mobile phone intelligence, it has become possible and trendy to use social media data containing geographical location information to study tourism flows[7]. The Flickr website has over 49 million geotagged photos This social media data contains descriptive textual information such as captions, messages and tags, and spatial and
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