Characterizing geographical preferences of international tourists and the local influential factors in China using geo-tagged photos on social media

Su, Shiliang; Wan, Cao; Hu, Yixuan; Cai, Zhongliang

doi:10.1016/j.apgeog.2016.06.001

Cited by 64 publications

(30 citation statements)

References 35 publications

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“…In addition, Xu et al [29] proposed a recommendation method for individualized travel locations based on the logs and contents of geo-tagged photos. (2) In the field of tourist behavior analysis, Su et al [30] collected tourist geographical data from Flickr for 333 prefecture-level cities in China and analyzed the geographical preferences of international tourists. Koylu et al [31] established a computer vision algorithm based on a convolutional neural network combined with kernel density estimation to identify objects of interest, while human activity patterns were also inferred from geo-tagged photos on Flickr.…”

Section: Introductionmentioning

confidence: 99%

Geo-Tagged Photo Metadata Processing Method for Beijing Inbound Tourism Flow

Wen

Zheng

et al. 2019

IJGI

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Technological advances have led to numerous developments in data sources. Geo-tagged photo metadata has provided a new source of mass research data for tourism studies. A series of data processing methods centering on the various types of information contained in geo-tagged photo metadata have thus been proposed; as a result, the development of tourism studies based on such data has advanced. However, an in-depth study of the data processing methods designed to conduct tourist flow prediction based on geo-tagged photo metadata has not yet been conducted. In order to acquire accurate substitutive data regarding inbound flows in cities, this paper introduces and designs several methods, including data screening, text data similarity calculation, geographical location clustering, and time series data modelling, in order to realize a data preprocessing model for inbound tourist flows in cities based on geo-tagged photo metadata. Wherein, the entropy filtering method was introduced to aid in determining whether the data were posted by inbound tourists; whether the inbound persons’ activities were related to tourism was judged through the calculation of tag text similarity; an efficient clustering method based on geographic grid partition was designed for cases in which the tag values were empty; finally, the time series of the inbound tourist flows of a certain region and period were obtained through data statistics and normalization. For the empirical research, Beijing City in China was selected as the research case, after which the feasibility and accuracy of the methods proposed in this paper were verified through data correlation analysis between Flickr data and real statistical yearbook data, as well as analysis of the prediction results based on a machine learning algorithm. The data preprocessing method introduced and designed in this paper provides a reference for the study of geo-tagged photo metadata in the field of tourism flow prediction. These methods can effectively filter out inbound tourist flow data from geotag photo metadata, thus providing a novel, reliable, and low-cost research data source for urban inbound tourism flow forecasting.

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Section: Introductionmentioning

confidence: 99%

Geo-Tagged Photo Metadata Processing Method for Beijing Inbound Tourism Flow

Wen

Zheng

et al. 2019

IJGI

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“…Every tourist generates their own social spaces on their social networks (Watkins, 2005). The large number of images present on social networks makes it possible to map tourist behaviour and experiences within a wide urban space (Su, Chen, Yixuan, & Zhongliang, 2016). As a result, we had able to identify areas where tourists move freely and others that they rarely visit (Cesario et al, 2016).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…Some studies have identified a source of potential bias given that users only cover a fraction of the entire sample (García‐Palomares et al, 2015). That said, the academic literature assumes that the images posted on these profiles coincide with the photographs that tourists take on their travels (Stepchenkova & Zhan, 2013; Donaire, Camprubí, & Galí, 2014; Su et al, 2016). Therefore we must be conscious of the fact that this is not a study of all the photographic images that have been generated.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Mapping tourist hot spots in African cities based on Instagram images

Agustí

2020

Journal of Tourism Research

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Knowing in which areas tourists concentrate is fundamental for managing its effects. However, to date, research in this subject has mainly been carried out only in European and North American cities. The current article, in contrast, focuses on six African cities. We have analysed the spatial concentration of the images captured by Instagram users to identify the most visited locations. The results obtained show how there is a strong spatial concentration of images in African cities, little territorial continuity between the spaces visited, and a predominance of private attractions. These are important differences with respect to what has been described in the literature in other contexts.

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“…Using destination cities as the basic research unit is essential to study the spatial distribution and travel trajectories. However, because it is difficult to obtain city-level data on the spatial and temporal behavior of tourists, especially in developing countries [4], our ability to study the behavior Sustainability 2019, 11, 5822; doi:10.3390/su11205822 www.mdpi.com/journal/sustainability Sustainability 2019, 11, 5822 2 of 17 of tourists at the city level is significantly limited. To address the statistical data shortage, some studies have used questionnaires to analyze the tourist flow and diffusion routes among different tourist destinations [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…However, most of these studies have emphasized small-scale tourist movement trajectories and patterns within cities or scenic areas [8,11,[22][23][24][25]. Moreover, studies involving medium-scale or large-scale (urban or urban-agglomeration scale) are concentrated on the static analysis of spatial distribution of geotagged photos instead of analyzing tourist movement trajectories between cities [4,13,26,27]. Therefore, an in-depth city-level study of the spatial behavior and movement patterns of inbound tourists is necessary.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Spatial Characteristics of Inbound Tourist Flows in China Using Geotagged Photos

Jing

Tang

et al. 2019

Sustainability

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As important modern tourist destinations, cities play a critical role in developing agglomerated tourism elements and promoting urban life quality. An in-depth exploration of tourist flow patterns between destination cities can reflect the dynamic trends of the inbound tourist market. This is significant for the development of tourism markets and innovation in tourism products. To this end, photos with geographical and corresponding metadata covering the entire country from 2011 to 2017 are used to explore the spatial characteristics of China’s inbound tourist flow, the spatial patterns of tourist movement, and the tourist destination cities group based on data mining techniques, including the Markov chain, a frequent-pattern-mining algorithm, and a community detection algorithm. Our findings show that: (1) the strongest flow of inbound tourists is between Beijing and Shanghai. These two cities, along with Xi’an and Guiling, form a “double-triangle” framework, (2) the travel between emerging destination cities in Central and Western China have gradually become frequently selected itineraries, and, (3) based on the flow intensity, inbound tourist destination cities can be divided into nine groups. This study provides a valuable reference for the development of China’s inbound tourism market.

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Characterizing geographical preferences of international tourists and the local influential factors in China using geo-tagged photos on social media

Cited by 64 publications

References 35 publications

Geo-Tagged Photo Metadata Processing Method for Beijing Inbound Tourism Flow

Geo-Tagged Photo Metadata Processing Method for Beijing Inbound Tourism Flow

Mapping tourist hot spots in African cities based on Instagram images

Exploring the Spatial Characteristics of Inbound Tourist Flows in China Using Geotagged Photos

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