This document discusses a research project aimed at analyzing spatial and temporal patterns of tourists in Amsterdam using geotagged Flickr photos. It outlines methodologies for data collection and analysis, focusing on detecting tourist distributions, clustering, and popular routes based on metadata from the photos. The results aim to provide insights for managing tourism in the rapidly growing city, alongside suggestions for future research opportunities.

1. REVEALING SPATIAL AND TEMPORAL PATTERNS FROM FLICKR
A CASE STUDY WITH TOURISTS IN AMSTERDAM

2. TOURISM IN AMSTERDAM
RAPID GROWTH
Source: Nicky Otten (Flickr)

3. MORE AND MORE CONCERNS ABOUT TOURISM
A SELECTION OF RECENT NEWS ARTICLES
They are puking and peeing on the Zeedijk
NOS, December 5 2014
Is Amsterdam becoming a second Venice?
De Morgen, March 27 2015
The center of Amsterdam should not become too popular
Volkskrant, October 25 2014
Amsterdam taken over by tourists
RTL, April 3 2015
Amsterdam will welcome twice as many tourists in 2030
Het Parool, December 9 2014

4. INITIAL RESEARCH TOPIC
WAGENINGEN UNIVERSITY AND AMS
Explore the possibilities to use (geo)tweets for detecting
spatial and temporal patterns of tourists in Amsterdam
But why Twitter? How about Flickr?
Twitter Flickr
Number of users + + + / -
Amount of data + + +
Connection of data to real location + / - + +
Use by tourists + / - + +
Interval between subsequent posts + / - + +

5. RESEARCH PROJECT
The objective of this exploratory research project is to develop,
implement and test methods that reveal spatial and temporal patterns
of tourists from a large dataset of geotagged Flickr photos
OBJECTIVE
RESEARCH QUESTIONS
RQ-01: What methods are available to detect spatial and temporal
patterns from geosocial data?
RQ-02: What methods need to be implemented to identify
temporal distributions, spatial clusters and popular routes of
tourists from the metadata of Flickr photos?
RQ-03: How well do the identified temporal distributions, spatial
clusters and popular routes resemble the spatial and temporal
behaviour of tourists?

6. FLICKR DATA COLLECTION

7. FLICKR DATA COLLECTION
OVERVIEW OF STEPS & TECHNIQUES
Flickr Database
(API)
Request
Local database
(PostgreSQL)
Java application
XML-file
Metadata
Restriction: 1 request per second

8. FLICKR DATA COLLECTION
STEP 1: HARVESTING PHOTO ID’S WITHIN BOUNDING BOXES (1550)
Search parameters:
• Xmin, Xmax, Ymin, Ymax
• Min date: January 1, 2005
• Max date: December 31, 2014
Search result:
• Photo ID
• User ID
• Photo title

9. FLICKR DATA COLLECTION
STEP 2: REQUESTING ADDITIONAL METADATA
Search parameters:
• Photo ID
Search result:
• Latitude, longitude
• Date and time
• User name
• User home location
• Tags
• Photo URL
• Location accuracy
2.849.261 photos
+/- 5 weeks of harvesting

10. FLICKR DATA COLLECTION
STEP 2: REQUESTING ADDITIONAL METADATA
Search parameters:
• Photo ID
484.346 photos
Search result:
• Latitude, longitude
• Date and time
• User name
• User home location
• Tags
• Photo URL
• Location accuracy

11. FLICKR DATA EXPLORATION
PHOTOS IN QGIS

12. FLICKR DATA EXPLORATION
SELECTION OF PHOTOS IN GOOGLE EARTH

13. TOURIST CLASSIFICATION
BASED ON USER’S HOME LOCATION

14. TOURIST CLASSIFICATION
1. Classification of user location by SQL
UPDATE users
SET countryname = 'Japan', istourist = 'True', classification = 'SQL'
WHERE geoname = '' AND userid IN
(SELECT userid FROM users WHERE (userlocation ~* 'y(japan|nippon|日本)y'))
(8628 users - 54%)
SQL AND ONLINE GEOCODING
Geonames API
(External database)
PostgreSQL
(Local database)
Java Application
2. Classification of user location by online geocoding
Tokyo Tokyo
Japan Japan
(450 users - 3%)
User location = Tokyo Tokyo = Japan

15. NUMBER OF UNIQUE USERS
0
1.750
3.500
5.250
7.000
6.914
6.257
2.821
17,6% 39,1% 43,2%
Locals Tourists Unclassified
TOURIST CLASSIFICATION
Overall accuracy = 99%

16. NUMBER OF UNIQUE PHOTOS
0
40.000
80.000
120.000
160.000
132.213
107.016
154.599
39,3% 27,2% 33,6%
Local Photos Tourist Photos Unclassified Photos
TOURIST CLASSIFICATION
Overall accuracy = 99%

17. CLASSIFICATION RESULTS AMSTERDAM
RELATIVE AMOUNT OF TOURISTS PER NATIONALITY (2013)
United States
United Kingdom
Germany
Italy
Spain
France
0% 5% 10% 15% 20%
Flickr nationalities 2013
CBS hotel nationalities 2013

18. TEMPORAL DISTRIBUTIONS
DIFFERENT GRANULARITIES

19. TEMPORAL DISTRIBUTIONS
RELATIVE NUMBER OF TOURISTS AND PHOTOS PER HOUR (2005-2014)
0%
2%
4%
6%
8%
10%
1:00
2:00
3:00
4:00
5:00
6:00
7:00
8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
22:00
23:00
0:00
Tourists
Tourist photos
Many daytime
photos

20. TEMPORAL DISTRIBUTIONS
RELATIVE NUMBER OF TOURISTS AND LOCALS PER HOUR (2005-2014)
0%
2%
4%
6%
8%
10%
1:00
2:00
3:00
4:00
5:00
6:00
7:00
8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
22:00
23:00
0:00
Tourists
Locals
Maximums shifted
Relatively more
tourists photos
in the night
More local
photos in
the evening

21. Exact match
2 hours off
TIMESTAMP VALIDATION
TIME DIFFERENCE BETWEEN PHOTO TIMESTAMP AND REAL TIME

22. TIMESTAMP VALIDATION
TIME DIFFERENCE BETWEEN PHOTO TIMESTAMP AND REAL TIME
Selecting
• all photos tagged with ‘clock’
• all photos near Central Station
!
1032 photos of locals
1134 photos of tourists
Result
• 70 suitable photos of tourists
• 50 suitable photos of locals

23. 0%
20%
40%
60%
80%
-10:00:00
-9:00:00
-8:00:00
-7:00:00
-6:00:00
-5:00:00
-4:00:00
-3:00:00
-2:00:00
-1:00:00
0:00:00
1:00:00
2:00:00
3:00:00
4:00:00
5:00:00
6:00:00
7:00:00
8:00:00
9:00:00
10:00:00
Locals
Tourists
TIMESTAMP VALIDATION
TIME DIFFERENCE BETWEEN PHOTO TIMESTAMP AND REAL TIME

24. PHOTOGRAPHERS PER DAY OF THE WEEK (2005-2014)
0%
5%
10%
15%
20% Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Sunday
Tourists
Locals
TEMPORAL DISTRIBUTIONS

25. PHOTOGRAPHERS PER MONTH (2005-2014)
0%
2%
4%
6%
8%
10%
12%
January
February
March
April
May
June
July
August
September
October
November
December
Tourists
Locals
TEMPORAL DISTRIBUTIONS

26. TOURISTS AND FOREIGN HOTEL GUESTS PER MONTH (2012+2013)
0%
2%
4%
6%
8%
10%
12%
January
February
March
April
May
June
July
August
September
October
November
December
Tourists (Flickr 2012 + 2013)
Hotel guests (CBS 2012 + 2013)
TEMPORAL DISTRIBUTIONS

27. 0
40
80
120
160
200
1 365
Locals
Tourists
PHOTOGRAPHERS PER DAY OF THE YEAR (2005-2014)
Queens-day
TEMPORAL DISTRIBUTIONS

28. SPATIAL DISTRIBUTION
GRID-BASED CLUSTERING

29. SPATIAL DISTRIBUTION
GRID-BASED CLUSTERING
1 1
1 1 1 1
1
1 1
2
111
2 31
1
1 1 1
112

30. EXPLORING THE DATA
TOURIST COUNT PER HEXAGON IN GOOGLE EARTH

31. SPATIAL DISTRIBUTION
DENSITY-BASED CLUSTERING

32. SPATIAL DISTRIBUTION
DENSITY-BASED CLUSTERING
DBSCAN: Density-Based Spatial Clustering for Applications with Noise
• Detects clusters with different shapes and sizes
• Not sensitive to noise very suitable for geosocial data
!
• Eps: radius search area
• MinPts: minimum number of points in neighborhood
Eps
Noise
MinPts=4

33. SPATIAL DISTRIBUTION
DENSITY-BASED CLUSTERING

34. SPATIAL DISTRIBUTION
DENSITY-BASED CLUSTERING

35. SPATIAL DISTRIBUTION
DENSITY-BASED CLUSTERING

36. SPATIAL DISTRIBUTION
DENSITY-BASED CLUSTERING

37. TOURISTIC ROUTES

38. ONE DAY IN THE LIFE OF A TOURIST
TOURISTIC ROUTES

40. LINEAR TRAJECTORIES OF MANY TOURISTS
TOURISTIC ROUTES

41. LINEAR TRAJECTORIES BETWEEN CLUSTERS
TOURISTIC ROUTES

42. TOURISTIC ROUTES
RELATING TRAJECTORIES TO STREET NETWORK USING ROUTING ALGORITHM
As the crow flies Trajectory over network

43. STEP 1: CREATE A SIMPLIFIED PEDESTRIAN NETWORK
TOURISTIC ROUTES
Original Aggregate road links Densify road links

44. TOURISTS TAKE THE MOST POPULAR ROUTES
TOURISTIC ROUTES

45. STEP 2: REDUCE TRAVEL COST PER ROAD SEGMENT BASED ON PHOTO DENSITY
TOURISTIC ROUTES
2,6
1,9
1,4
4,2
3,1
1,8
6,9
6,2
4,1
7,3
9,3
9,6

46. 1. Create pairs of time-ordered photo locations per user
Point A Point B
Point B Point C
… …
!
2. Calculate distance, time interval and speed per photo pair
3. Select all photo pairs within thresholds:
• Distance > 50 m and < 750 m
• Time interval > 0 sec and < 600 sec
• Speed > 1 km/h and < 5 km/h
4. Calculate closest network node for start and end of every pair
TOURISTIC ROUTES
STEP 3: CREATE PHOTO PAIRS FOR ROUTING

47. TOURISTIC ROUTES
STEP 4: CALCULATE ROUTES AND AGGREGATE INTO ROUTE DENSITY MAP
1. Calculate route for 6,477 photo pairs with pgRouting
2. Aggregate and count overlaying route segments
3. Visualize touristic route densities

48. TOURISTIC CLUSTERS AND ROUTES
VALIDATION OF RESULTS
Solution: Expert judgement by a questionnaire
Participants: 8 tourism experts from different departments of the
municipality of Amsterdam
Problem: No comparable quantitative data available

49. TOURISTIC ROUTES
VALIDATION OF RESULTS BY 8 TOURISM EXPERTS
Match: 75% Match: 38% Match: 75%
Match: 100% Match: 100% Match: 63%
Match: 100% Match: 67% Match: 67%
Match: 100% Match: 100% Match: 100%
WITH HIGH CONFIDENCE (5/5)3

50. VALIDATION OF RESULTS
TOURISTIC CLUSTERS AND ROUTES
Expert # Profession
Validity
results [1-5]
Usefulness
results [1-5]
1 Policy Advisor Traffic & Public Space 4 5
2 Data Analyst, Information en Statistics 4 4
3 Senior Advisor Traffic Management 4 4
4 Researcher, Information en Statistics 3 4
5 Senior Advisor Traffic Research 5 4
6 Urban Planner 5 5
7 Urban Planner 4 5
8 Urban Designer 4 5
4.1 4.5
How well do the study outcomes resemble the real world?
Are the study outcomes useful for you or for your organization?
*
**
* **

51. SUGGESTIONS FOR FUTURE WORK
AND POTENTIAL THESIS TOPICS
• Calibrate thresholds with quantitative data
• Extensive validation of results in cooperation with tourism experts
• Cooperate with municipality to define objectives, some suggestions:
Additional data sources: Instagram, Twitter, Sina Weibo
Divide spatial distributions in different temporal intervals
Compare spatial distribution of locals and tourists
Divide the spatial distributions in different nationalities
Use the presented patterns as input for an agent-based model
Discover typical tourism problems with other geosocial data types

52. THANK YOU FOR YOUR ATTENTION!
ANY QUESTIONS OR REMARKS?