This document summarizes a research study that analyzed tourist visit behavior at temples in Hong Kong using geotagged photos shared on social media. The researchers extracted geotagged photos from Flickr of four popular temples - Tian Tan Buddha, Wong Tai Sin Temple, Man Mo Temple, and Tin Hau Temple. They used clustering algorithms to identify popular locations and analyzed the metadata of the photos to understand geographical differences in visits, numbers of photos over time, and distributions of visitors' trips. The study found geographical differences in which temples tourists from different countries visited most. It provides tourism managers insights into understanding tourist behaviors that can help with marketing strategies.

1. ENTER 2016 Research Track Slide Number 1
Tourist Visit and Photo Sharing
Behavior Analysis: A Case Study of
Hong Kong Temples
Rosanna Leung1
, Huy Quan Vu2
, Jia Rong2
and Yuan Miao2
1
Department of International Tourism and Hospitality, I-Shou University, Taiwan
rosannaleung@isu.edu.tw
2
Centre of Applied Informatics, College of Engineering and Science, Victoria University,
Australia
{huyquan.vu; jia.rong; yuan.miao}@vu.edu.au

2. ENTER 2016 Research Track Slide Number 2
Outline
• Background & Motivation
• Methodology
• Case Study
• Finding Analysis
• Conclusion

3. ENTER 2016 Research Track Slide Number 3
BACKGROUND & MOTIVATION

4. ENTER 2016 Research Track Slide Number 4
Hong Kong Inbound Tourism
Visitor Arrivals
HKTB Research and Statics, http://partnernet.hktb.com/en/research_statistics/index.html

5. ENTER 2016 Research Track Slide Number 5
Hong Kong Inbound Tourism
Expenditure
2014
Jan-Jun (HK$Mn)
2015
Jan-Jun (HK$Mn)
Overnight Visitors 105,271.22 96,473.52
Same-day In-town Visitors 37,238.25 41,576.41
Cruise-in/out Passsengers 21.62 36.60
Servicemen 30.71 16.96
Aircrew Members 748.98 936.59
Transit/Transfer Passengers 1,760.33 1,786.28
Total 145,071.09 140,826.36
HKTB Research and Statics, http://partnernet.hktb.com/en/research_statistics/index.html

6. ENTER 2016 Research Track Slide Number 6
Hong Kong Inbound Tourism
Activities
• Hong Kong Tourism Board (HKTB) reported that
inbound tourists spend more than
– 60% on shopping
– 18% on accommodations
– 12% on dining
• However, what other activities they involved in
trips stay unclear to industry practitioners.

7. ENTER 2016 Research Track Slide Number 7
Social Media & Travel
Experience Sharing
• Tourists post travel experience on social media to share
with friends & relatives.
• Shared photos present their personal opinions.
• The peers receive information that affect their impression
and decision-making.
• Many existing studies focus more on analysing textual
information
• How about the photos taken during the trips?

8. ENTER 2016 Research Track Slide Number 8
Geotagging
• Geotagging is
– a process of attaching geographical identification metadata into
multimedia documents, i.e. photos.
• GPS location is captured when a photo is taken and is
attached as a geotag in metadata.
• Geotags can be used to
– outline a tourist’s travel path, activities and interests
– get better understanding of tourists’ preferences and behavioural
differences

9. ENTER 2016 Research Track Slide Number 9
Research Aim
• This study aims to
analyze tourists’ visit behaviors using geotagged
photo images shared on social media sites
together with the associated Metadata.

10. ENTER 2016 Research Track Slide Number 10
METHODOLOGY

11. ENTER 2016 Research Track Slide Number 11
Data Source
• Huge amount of geotagged travel photos are available on
Flickr.com for public view.

12. ENTER 2016 Research Track Slide Number 12
Proposed Framework
1. Geotagged Photo Extraction using Flickr’s Application
Programming Interface (API)
2. Popularity Identification using P-DBSCAN Clustering

13. ENTER 2016 Research Track Slide Number 13
Flickr’s API

14. ENTER 2016 Research Track Slide Number 14
Geotagged Photo Extract
using Bounding Box

15. ENTER 2016 Research Track Slide Number 15
Imbalanced Data Set
• A large number of geotagged photos is returned from
data extraction, but not all of them are useful.
• Imbalance is common in raw collected dataset
– Many visitor but few photos taken
– Many photos taken by one visitor
• When identifying the popularity, the number of visitors as
an important parameter should have more weight than
the number of photos taken.

16. ENTER 2016 Research Track Slide Number 16
Geotagged Photo Clustering

17. ENTER 2016 Research Track Slide Number 17
Geotagged Photo Clustering

18. ENTER 2016 Research Track Slide Number 18
Geotagged Photo Clustering
• For each photo pi, it is a core photo, it is assigned to a
cluster c. Otherwise, it is marked as noise and discarded.
• Such process is repeated for the rest of the unprocessed
photo(s).
• After all the clusters of photos are obtained, their
geographical coordinates are examined to determine the
name and the spatial extent of the area.

19. ENTER 2016 Research Track Slide Number 19
CASE STUDY

20. ENTER 2016 Research Track Slide Number 20
Hong Kong Temples

21. ENTER 2016 Research Track Slide Number 21
Temple Clusters

22. ENTER 2016 Research Track Slide Number 22
FINDING ANALYSIS

23. ENTER 2016 Research Track Slide Number 23
Geographical Differences on Tourists’
Temple Visit Behaviors (Top 10 countries)
Country Tian Tan Wong Tai Sin Man Mo Tin Hau Total
USA 40 10 5 2 57
United Kingdom 34 4 3 2 43
China Mainland 12 7 7 4 30
Australia 18 1 0 1 20
Singapore 14 2 0 2 18
Germany 11 1 3 0 15
Taiwan 10 1 2 1 14
Spain 7 2 1 0 10
Japan 5 1 1 2 9
Russia 7 0 0 1 8

24. ENTER 2016 Research Track Slide Number 24
Regional Distribution of Flickr
User Profile per Year
Region 2010 2011 2012 2013 2014 Total
Europe 20 29 23 20 15 107 (33%)
North America 17 7 13 16 10 63 (20%)
Australia/New
Zealand
5 6 5 6 1 23 (7%)
South America 0 3 1 4 1 9 (3%)
Asia 18 21 16 19 14 88 (28%)
Hong Kong Residents 5 9 3 10 3 30 (9%)
Total 65 75 61 75 44 320 (100%)

25. ENTER 2016 Research Track Slide Number 25
Statistics of Tourists and Photos
Uploaded for the Popular Temples
Popularity Temple Photos
No. of
Visits
Average Photos
Upload Per Visitor
1 Tian Tan Buddha 4,965 541 9.18
2 Wong Tai Sin 1,372 126 10.88
3 Tin Hau 362 60 6.03
4 Man Mo 256 53 4.83
Total 6,955 780 8.92

26. ENTER 2016 Research Track Slide Number 26
Number of Photo Taken over
Years

27. ENTER 2016 Research Track Slide Number 27
Number of Photo Taken over
Months

28. ENTER 2016 Research Track Slide Number 28
Number of Photo Taken over
Days

29. ENTER 2016 Research Track Slide Number 29
Distributions of Tourists’ Visits
Tian
Tan
Wong Tai
Sin
Man Mo
Temple
Tin Hau
Temple
No of
Visitors
Visited 3 Temples 5 5 1 4 5
Visited 2 Temples 30 26 8 8 36
Visited 1 Temples 506 95 51 41 693
Total 541 126 60 53 734

30. ENTER 2016 Research Track Slide Number 30
Tourists Visiting Behaviour via
Photos Uploaded
Region Tian Tan Wong Tai Sin Man Mo Temple Tin Hau Temple
Tourist 1* Hong Kong 0 2012, 2013 (33) 0 2013 (5)
Tourist 2* Asia 2011 (2) 2010 (31) 0 0
Tourist 3* Asia 2011 (2) 0 2012 (12) 0
Tourist 4* Asia 0 2011 (10) 2015 (5) 0
Tourist 5 Asia 2010 (105) 0 0 2010 (6)
Tourist 6 Asia 2010 (11) 0 2010 (1) 0
Tourist 7 Asia 2013 (63) 0 0 2013 (10)
Tourist 8 Europe 2010 (13) 2010 (5) 0 0
Tourist 9 Europe 2011 (6) 2011 (8) 0 0
Tourist 10 Europe 0 2011 (1) 2011 (1) 0
Tourist 11 Europe 2013 (1) 2013 (2) 0 0
Tourist 12 Europe 2015 (2) 2015 (2) 0 0
Tourist 13 Europe 2015 (2) 2015 (3) 0 0
Tourist 14 N. America 2012 (2) 0 0 2012 (14)
Tourist 15 N. America 2013 (3) 2013 (41) 0 0
Tourist 16 N. America 2010 (29) 0 0 2010 (1)
Tourist 17 N. America 2014 (42) 2014 (25) 0 0
* repeat visits

31. ENTER 2016 Research Track Slide Number 31
CONCLUSION

32. ENTER 2016 Research Track Slide Number 32
Conclusion
• This study attempted to analyze tourists’ visit behaviors by taking use
of the geotagged photos uploaded by tourists to the social media sites.
• The data extraction method impacts attraction managers could make
uses of the widely available tourists’ photos from online media-sharing
sites.
– To collected relevant photos about their attractions so as to understand the tourists’
behavior and travel pattern.
– To identify the tourists’ behavioral difference among different regions.
– The more comprehensive data they have, their marketing strategies could be more
precise.

33. ENTER 2016 Research Track Slide Number 33
THANK YOU & QUESTIONS?