Tourists' digital footprint in cities: Comparing Big Data sources
TLDR
The main conclusion is that it is not sufficient to use one data source to analyse the presence of tourists in cities; several must be used in a complementary manner.About:
This article is published in Tourism Management.The article was published on 2018-06-01 and is currently open access. It has received 170 citations till now. The article focuses on the topics: Digital footprint & Tourism.read more
Citations
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Discovering the tourists' behaviors and perceptions in a tourism destination by analyzing photos' visual content with a computer deep learning model: The case of Beijing
Kun Zhang,Ye Chen,Chunlin Li +2 more
TL;DR: The field of how to apply AI technology into tourism destination research was explored and extended by this trial study, and 35,356 Flickr tourists' photos in Beijing were identified into 103 scenes by computer deep learning technology.
Journal ArticleDOI
Quantifying nature-based tourism in protected areas in developing countries by using social big data
TL;DR: Wang et al. as mentioned in this paper proposed and applied social big data to investigate nature-based tourism in an ASEAN Heritage Park, and they were able to effectively illustrate spatial patterns of visitation using 10 years of Flickr geo-tagged photographs.
Journal ArticleDOI
A systematic review of big data-based urban sustainability research: State-of-the-science and future directions
TL;DR: The aim of this study was to summarize the big data-based UESS research using a systematic review approach in combination with bibliometric and thematic analyses, which showed that the numbers of publications and citations of related articles have been increasing exponentially in recent years.
Journal ArticleDOI
Spatial structures of tourism destinations: A trajectory data mining approach leveraging mobile big data
TL;DR: A large scale mobile phone dataset that captures the cellphone trace of international travelers who visited South Korea is analyzed to understand the spatial structures of tourist activities within three different destinations and reveals multiple “hot spots” in travel destinations and spatial interactions across these places.
Journal ArticleDOI
Tourists’ digital footprint: The spatial patterns of tourist flows in Qingdao, China
Naixia Mou,Yunhao Zheng,Teemu Makkonen,Tengfei Yang,Jinwen(Jimmy) Tang,Jinwen(Jimmy) Tang,Yan Song +6 more
TL;DR: Wang et al. as discussed by the authors proposed a novel framework for analyzing tourist flows based on tourists' digital footprint data collected from online travel diaries, which is able to pinpoint the influence of distance decay and attractions popularity on the spatial patterns of tourist flows.
References
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