Mapping destination images and behavioral patterns from user-generated photos: a computer vision approach

ABSTRACT Destination image studies were traditionally based on questionnaire surveys, but the recent rise of user-generated content and social media big data analytics provide new opportunities for advancing tourism research. This study adopts one of the latest artificial intelligence computer vision technologies to identify the differences in the perceived destination image and behavioral patterns between residents and tourists from user-generated photos. Data were mined from Flickr, which yields 58,392 relevant geotagged photos taken in Hong Kong. The findings reveal that the perceptual differences between the two groups lay on seven types of perceptions. The differences in spatial distribution and behavioral trajectory were visualized through a series of maps. This study provides new insights into the destination image which has implications for the tourism promotion and spatial development of the destination.

[1]  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 .

[2]  Jiaying Zhang,et al.  Understanding Community Attitudes Towards Tourism and Host—Guest Interaction in the Urban—Rural Border Region , 2006 .

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

[4]  Ye Chen,et al.  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 , 2019 .

[5]  The presence of tourists and residents in shared travel photos , 2019, Tourism Management.

[6]  J. Choe,et al.  Effects of tourists’ local food consumption value on attitude, food destination image, and behavioral intention , 2018 .

[7]  Raquel Camprubí,et al.  Tourist clusters from Flickr travel photography , 2014 .

[8]  Xiaolu Zhou,et al.  Analyzing and visualizing the spatial interactions between tourists and locals: A Flickr study in ten US cities , 2018 .

[9]  Siti Aqilah Jahari,et al.  Destination image as a mediator between perceived risks and revisit intention: A case of post-disaster Japan , 2014 .

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

[11]  Weimin Zheng,et al.  Using a heuristic approach to design personalized tour routes for heterogeneous tourist groups , 2019, Tourism Management.

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

[13]  C. Monterrubio.  Hosts and guests’ social representations of nudism: A mutual gaze approach , 2019, Annals of Tourism Research.

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

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

[16]  Martina G. Gallarza,et al.  Destination image: towards a conceptual framework. , 2002 .

[17]  Z. Schwartz,et al.  What can big data and text analytics tell us about hotel guest experience and satisfaction , 2015 .

[18]  D. Gursoy,et al.  Residents’ support for tourism: An Identity Perspective , 2012 .

[19]  Frank Go,et al.  Deconstruction Destination Image in the Information Age , 2003, J. Inf. Technol. Tour..

[20]  Jo-Hui Lin,et al.  Evaluating ecotourism sustainability from the integrated perspective of resource, community and tourism , 2006 .

[21]  T. Lee,et al.  Cultural inheritance of Hakka cuisine: A perspective from tourists’ experiences , 2016 .

[22]  Ying Dai,et al.  Different cultures, different photos: A comparison of Shanghai's pictorial destination image between East and West , 2019, Tourism Management Perspectives.

[23]  J. Crompton.  Motivations for pleasure vacation , 1979 .

[24]  Patrick E. Osborne,et al.  Combining GPS & survey data improves understanding of visitor behaviour , 2017 .

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

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

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

[28]  Irem Arsal,et al.  Residents as Travel Destination Information Providers: An Online Community Perspective , 2010 .

[29]  Dongzhi Chen,et al.  How are Tourists Different? - Reading Geo-tagged Photos through a Deep Learning Model , 2020, Journal of Quality Assurance in Hospitality & Tourism.

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

[31]  Jonas Larsen,et al.  Practices and Flows of Digital Photography: An Ethnographic Framework , 2008 .

[32]  Claudia Notzke,et al.  Indigenous tourism development in the arctic , 1999 .

[33]  Dimitrios Buhalis,et al.  Smart Tourism Destinations Enhancing Tourism Experience Through Personalisation of Services , 2015, ENTER.

[34]  Weimin Zheng,et al.  Using a four-step heuristic algorithm to design personalized day tour route within a tourist attraction , 2017 .

[35]  David L. Corsun,et al.  A taxonomy of hosts visiting friends and relatives , 2007 .

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

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

[38]  R. Freeman.  Strategic Management: A Stakeholder Approach , 2010 .

[39]  E. Ruiz,et al.  Sustainable Tourism and Residents’ Perception towards the Brand: The Case of Malaga (Spain) , 2019, Sustainability.

[40]  Kyoji Kawagoe,et al.  Tweet-mapping Method for Tourist Spots Based on Now-Tweets and Spot-photos , 2015, KES.

[41]  A. Mak,et al.  Exploring the discrepancies in perceived destination images from residents’ and tourists’ perspectives: a revised importance–performance analysis approach , 2017 .

[42]  Shah Jahan Miah,et al.  A Big Data Analytics Method for Tourist Behaviour Analysis , 2017, Inf. Manag..

[43]  Yaniv Belhassen,et al.  Testing an integrated destination image model across residents and tourists , 2017 .

[44]  Piera Buonincontri,et al.  The experience co-creation in smart tourism destinations: a multiple case analysis of European destinations , 2016, J. Inf. Technol. Tour..

[45]  Chung-Shing Chan,et al.  Nature-based tourism development in Hong Kong: Importance–Performance perceptions of local residents and tourists , 2016 .

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

[47]  Paulo Duarte,et al.  The contribution of cultural events to the formation of the cognitive and affective images of a tourist destination , 2017, Journal of Destination Marketing & Management.

[48]  Bob McKercher,et al.  Understanding Tourist Movement Patterns in a Destination: A GIS Approach , 2006 .

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

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

[51]  Mike Thelwall,et al.  Flickr’s potential as an academic image resource: An exploratory study , 2010, J. Libr. Inf. Sci..

[52]  B. Garrod,et al.  Photographs in tourism research: Prejudice, power, performance and participant-generated images , 2019, Tourism Management.

[53]  Rob Law,et al.  Tourism and online photography , 2011 .

[54]  Raffaele Filieri,et al.  Resident-tourist value co-creation: The role of residents' perceived tourism impacts and life satisfaction , 2017 .

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

[56]  R. Sharpley,et al.  Host perceptions of tourism: A review of the research , 2014 .

[57]  Fusun Istanbullu Dincer,et al.  Tourists’ Approach to Local Food , 2015 .

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

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

[60]  Dimitrios Buhalis,et al.  DMO online platforms: Image and intention to visit , 2018 .

[61]  Zheng Xiang,et al.  A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism , 2017 .

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

[63]  Dirk Neumann,et al.  Social media analytics and value creation in urban smart tourism ecosystems , 2017, Inf. Manag..