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

Abstract Visual content analysis of tourist photos is an effective way to excavate tourist behavior and explore tourists' cognition in the tourism destination. With the development of computer deep learning and big data mining technology, identifying the content of massive numbers of tourist photos by Artificial Intelligence (AI) approaches breaks through the limitations of manual approaches of identifying photos' visual information, e.g. small sample size, complex identification process and results deviation. In this study, 35,356 Flickr tourists' photos in Beijing were identified into 103 scenes by computer deep learning technology. Comparison through statistical analysis for behaviors and perceptions of tourists from different continents and countries was conducted. Tourists' cognitive maps with different perceptual themes were visualized according to photos' geographical information by ArcGIS. The field of how to apply AI technology into tourism destination research was explored and extended by this trial study.

[1]  Peng Wang,et al.  Learning from contextual information of geo-tagged web photos to rank personalized tourism attractions , 2013, Neurocomputing.

[2]  Hannah Badland,et al.  Public open space desktop auditing tool—Establishing appropriateness for use in Australian regional and urban settings , 2016 .

[3]  Lingxu Zhou.  Online rural destination images : tourism and rurality , 2014 .

[4]  Huy Quan Vu,et al.  Exploring the travel behaviors of inbound tourists to Hong Kong using geotagged photos. , 2015 .

[5]  Zheng Xiang,et al.  From digitization to the age of acceleration: On information technology and tourism , 2017 .

[6]  P. Albers,et al.  Travel photography: A methodological approach , 1988 .

[7]  Mor Naaman,et al.  How flickr helps us make sense of the world: context and content in community-contributed media collections , 2007, ACM Multimedia.

[8]  I. Önder,et al.  A comparison of pre-visit beliefs and projected visual images of destinations , 2017 .

[9]  Kevin Lynch,et al.  The Image of the City , 1960 .

[10]  Arzu Çöltekin,et al.  Towards (Re)Constructing Narratives from Georeferenced Photographs through Visual Analytics , 2014 .

[11]  W. Hunter.  China's Chairman Mao: a visual analysis of Hunan Province online destination image. , 2013 .

[12]  Daichi Oshimi,et al.  Host residents’ role in sporting events: The city image perspective , 2019, Sport Management Review.

[13]  Lisa Hyunjung Kim,et al.  A model of destination branding: Integrating the concepts of the branding and destination image , 2011 .

[14]  Henry Tsai,et al.  Travel photos : motivations, image dimensions, and affective qualities of places , 2014 .

[15]  Zhongliang Cai,et al.  Characterizing geographical preferences of international tourists and the local influential factors in China using geo-tagged photos on social media , 2016 .

[16]  J. Gutiérrez,et al.  Identification of tourist hot spots based on social networks: A comparative analysis of European metropolises using photo-sharing services and GIS , 2015 .

[17]  A. Mak.  Online destination image: Comparing national tourism organisation's and tourists' perspectives , 2017 .

[18]  S. Freire,et al.  Analysing spatiotemporal patterns of tourism in Europe at high-resolution with conventional and big data sources , 2018, Tourism Management.

[19]  Alexander Zipf,et al.  Road-based travel recommendation using geo-tagged images , 2015, Comput. Environ. Urban Syst..

[20]  Jennifer S. Evans-Cowley,et al.  StreetSeen Visual Survey Tool for Determining Factors that Make a Street Attractive for Bicycling , 2014 .

[21]  T. V. Leeuwen.  Semiotics and Iconography , 2004 .

[22]  Giustina Secundo,et al.  Creating value from Social Big Data: Implications for Smart Tourism Destinations , 2017, Inf. Process. Manag..

[23]  Michalis Kavaratzis,et al.  From city marketing to city branding: Towards a theoretical framework for developing city brands , 2004 .

[24]  Dae-Young Kim,et al.  The effect of searching and surfing on recognition of destination images on Facebook pages , 2014, Comput. Hum. Behav..

[25]  Ling Li,et al.  Big data in tourism research: A literature review , 2018, Tourism Management.

[26]  Carlo Ratti,et al.  Quantifying the shade provision of street trees in urban landscape: A case study in Boston, USA, using Google Street View , 2018 .

[27]  Ickjai Lee,et al.  Itinerary recommender system with semantic trajectory pattern mining from geo-tagged photos , 2018, Expert Syst. Appl..

[28]  David Gilbert,et al.  New York City and the Transatlantic Imagination , 2006 .

[29]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[30]  Hyunju Lee,et al.  Adaptive landmark recommendations for travel planning: Personalizing and clustering landmarks using geo-tagged social media , 2015, Pervasive Mob. Comput..

[31]  A. McGregor.  Dynamic texts and tourist gaze: Death, bones and buffalo , 2000 .

[32]  Li Chen,et al.  Image recognition based on deep learning , 2015, 2015 Chinese Automation Congress (CAC).

[33]  A. Morrison,et al.  Travel blogs on China as a destination image formation agent: A qualitative analysis using Leximancer , 2015 .

[34]  Vladimir S. Crnojevic,et al.  Detecting attractive locations and tourists' dynamics using geo-referenced images , 2011, 2011 10th International Conference on Telecommunication in Modern Satellite Cable and Broadcasting Services (TELSIKS).

[35]  Ickjai Lee,et al.  Sequential pattern mining of geo-tagged photos with an arbitrary regions-of-interest detection method , 2014, Expert Syst. Appl..

[36]  Zhenfeng Shao,et al.  ‘Big data’ for pedestrian volume: Exploring the use of Google Street View images for pedestrian counts , 2015 .

[37]  R. Fisher.  014: On the "Probable Error" of a Coefficient of Correlation Deduced from a Small Sample. , 1921 .

[38]  Chen Xu,et al.  Detecting tourism destinations using scalable geospatial analysis based on cloud computing platform , 2015, Comput. Environ. Urban Syst..

[39]  Xiang Li,et al.  Feeling a destination through the “right” photos: A machine learning model for DMOs’ photo selection , 2018 .

[40]  Jinhua Zhao,et al.  C-IMAGE: city cognitive mapping through geo-tagged photos , 2016 .

[41]  L. Moutinho,et al.  Do price promotions drive consumer spending on luxury hotel services? The moderating roles of room price and user-generated content , 2019, International Journal of Hospitality Management.

[42]  E. Marcheggiani,et al.  Mapping Cilento: Using geotagged social media data to characterize tourist flows in southern Italy , 2016 .

[43]  Charlotte M. Echtner,et al.  The Measurement of Destination Image: An Empirical Assessment , 1993 .

[44]  Wenjie Fan,et al.  Image Recognition Technology Based on Deep Learning , 2018, Wirel. Pers. Commun..

[45]  Javier Gutiérrez,et al.  Tourists' digital footprint in cities: comparing big data sources , 2017, Tourism Management.

[46]  J. Bao,et al.  Listen to the voices from home: An analysis of Chinese tourists’ sentiments regarding Australian destinations , 2019, Tourism Management.

[47]  Erik Cambria,et al.  Semi-supervised learning for big social data analysis , 2018, Neurocomputing.

[48]  B. Martín‐López,et al.  Using social media photos to explore the relation between cultural ecosystem services and landscape features across five European sites , 2017, Ecological Indicators.

[49]  S. Stepchenkova,et al.  Visual destination images of Peru: Comparative content analysis of DMO and user-generated photography☆ , 2013 .

[50]  Carlo Ratti,et al.  Mapping the spatial distribution of shade provision of street trees in Boston using Google Street View panoramas , 2018 .

[51]  Ashton M. Shortridge,et al.  Systematic review of the use of Google Street View in health research: Major themes, strengths, weaknesses and possibilities for future research☆ , 2018, Health & place.

[52]  Roger Hyam,et al.  Automated Image Sampling and Classification Can Be Used to Explore Perceived Naturalness of Urban Spaces , 2017, PloS one.

[53]  David A. Shamma,et al.  YFCC100M , 2015, Commun. ACM.

[54]  P. Bell.  Content Analysis of Visual Images , 2004 .

[55]  D. L. Gorlée.  Handbook of Semiotics , 1997, English and American Studies in German.

[56]  N. S. Valek,et al.  One place, two perspectives: Destination image for tourists and nationals in Abu Dhabi , 2018, Tourism Management Perspectives.

[57]  C. A. Gunn.  Vacationscape;: Designing tourist regions , 1972 .

[58]  Alastair M. Morrison,et al.  Destination image representation on the web : Content analysis of Macau travel related websites , 2007 .

[59]  W. Hunter,et al.  The social construction of tourism online destination image: A comparative semiotic analysis of the visual representation of Seoul , 2016 .

[60]  Krzysztof Janowicz,et al.  Extracting and understanding urban areas of interest using geotagged photos , 2015, Comput. Environ. Urban Syst..