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Transcript of Revealing spatial and temporal patterns from Flickr photography: a case study with tourists in...
![Page 1: Revealing spatial and temporal patterns from Flickr photography: a case study with tourists in Amsterdam](https://reader030.fdocuments.in/reader030/viewer/2022013108/55c496a4bb61eb783b8b466b/html5/thumbnails/1.jpg)
REVEALING SPATIAL AND TEMPORAL PATTERNS FROM FLICKRA CASE STUDY WITH TOURISTS IN AMSTERDAM
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TOURISM IN AMSTERDAMRAPID GROWTH
Source: Nicky Otten (Flickr)
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MORE AND MORE CONCERNS ABOUT TOURISMA SELECTION OF RECENT NEWS ARTICLES
They are puking and peeing on the ZeedijkNOS, December 5 2014
Is Amsterdam becoming a second Venice?De Morgen, March 27 2015
The center of Amsterdam should not become too popularVolkskrant, October 25 2014
Amsterdam taken over by touristsRTL, April 3 2015
Amsterdam will welcome twice as many tourists in 2030Het Parool, December 9 2014
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INITIAL RESEARCH TOPICWAGENINGEN 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 + / - + +
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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?
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FLICKR DATA COLLECTION
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FLICKR DATA COLLECTIONOVERVIEW OF STEPS & TECHNIQUES
Flickr Database (API)
Request
Local database (PostgreSQL)Java application
XML-file
Metadata
Restriction: 1 request per second
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FLICKR DATA COLLECTIONSTEP 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
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FLICKR DATA COLLECTIONSTEP 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
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FLICKR DATA COLLECTIONSTEP 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
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FLICKR DATA EXPLORATIONPHOTOS IN QGIS
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FLICKR DATA EXPLORATIONSELECTION OF PHOTOS IN GOOGLE EARTH
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TOURIST CLASSIFICATIONBASED ON USER’S HOME LOCATION
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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
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NUMBER OF UNIQUE USERS
0
1.750
3.500
5.250
7.0006.914
6.257
2.821
17,6% 39,1% 43,2%Locals Tourists Unclassified
TOURIST CLASSIFICATION
Overall accuracy = 99%
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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%
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CLASSIFICATION RESULTS AMSTERDAMRELATIVE AMOUNT OF TOURISTS PER NATIONALITY (2013)
United States
United Kingdom
Germany
Italy
Spain
France
0% 5% 10% 15% 20%
Flickr nationalities 2013CBS hotel nationalities 2013
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TEMPORAL DISTRIBUTIONSDIFFERENT GRANULARITIES
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TEMPORAL DISTRIBUTIONSRELATIVE 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
10:0
011
:00
12:0
013
:00
14:0
015
:00
16:0
017
:00
18:0
019
:00
20:0
021
:00
22:0
023
:00
0:00
TouristsTourist photos
Many daytime photos
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TEMPORAL DISTRIBUTIONSRELATIVE 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
10:0
011
:00
12:0
013
:00
14:0
015
:00
16:0
017
:00
18:0
019
:00
20:0
021
:00
22:0
023
:00
0:00
TouristsLocals
Maximums shifted
Relatively more tourists photos
in the night
More local photos in
the evening
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Exact match
2 hours off
TIMESTAMP VALIDATIONTIME DIFFERENCE BETWEEN PHOTO TIMESTAMP AND REAL TIME
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TIMESTAMP VALIDATIONTIME 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
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0%
20%
40%
60%
80%-1
0:00
:00
-9:0
0:00
-8:0
0:00
-7:0
0:00
-6:0
0:00
-5:0
0:00
-4:0
0:00
-3:0
0:00
-2:0
0:00
-1:0
0:00
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
10:0
0:00
LocalsTourists
TIMESTAMP VALIDATIONTIME DIFFERENCE BETWEEN PHOTO TIMESTAMP AND REAL TIME
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PHOTOGRAPHERS PER DAY OF THE WEEK (2005-2014)
0%
5%
10%
15%
20%M
onda
y
Tues
day
Wed
nesd
ay
Thur
sday
Frid
ay
Satu
rday
Sund
ay
TouristsLocals
TEMPORAL DISTRIBUTIONS
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PHOTOGRAPHERS PER MONTH (2005-2014)
0%
2%
4%
6%
8%
10%
12%Ja
nuar
y
Febr
uary
Mar
ch
April
May
June July
Augu
st
Sept
embe
r
Oct
ober
Nov
embe
r
Dece
mbe
r
TouristsLocals
TEMPORAL DISTRIBUTIONS
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TOURISTS AND FOREIGN HOTEL GUESTS PER MONTH (2012+2013)
0%
2%
4%
6%
8%
10%
12%Ja
nuar
y
Febr
uary
Mar
ch
April
May
June July
Augu
st
Sept
embe
r
Oct
ober
Nov
embe
r
Dece
mbe
r
Tourists (Flickr 2012 + 2013)Hotel guests (CBS 2012 + 2013)
TEMPORAL DISTRIBUTIONS
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0
40
80
120
160
200
1 365
LocalsTourists
PHOTOGRAPHERS PER DAY OF THE YEAR (2005-2014)
Queens-day
TEMPORAL DISTRIBUTIONS
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SPATIAL DISTRIBUTIONGRID-BASED CLUSTERING
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SPATIAL DISTRIBUTIONGRID-BASED CLUSTERING
1 1
1 1 1 1
1
1 1
2
111
2 31
1
1 1 1
112
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EXPLORING THE DATATOURIST COUNT PER HEXAGON IN GOOGLE EARTH
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SPATIAL DISTRIBUTIONDENSITY-BASED CLUSTERING
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SPATIAL DISTRIBUTIONDENSITY-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
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SPATIAL DISTRIBUTIONDENSITY-BASED CLUSTERING
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SPATIAL DISTRIBUTIONDENSITY-BASED CLUSTERING
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SPATIAL DISTRIBUTIONDENSITY-BASED CLUSTERING
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SPATIAL DISTRIBUTIONDENSITY-BASED CLUSTERING
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TOURISTIC ROUTES
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ONE DAY IN THE LIFE OF A TOURISTTOURISTIC ROUTES
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LINEAR TRAJECTORIES OF MANY TOURISTSTOURISTIC ROUTES
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LINEAR TRAJECTORIES BETWEEN CLUSTERSTOURISTIC ROUTES
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TOURISTIC ROUTESRELATING TRAJECTORIES TO STREET NETWORK USING ROUTING ALGORITHM
As the crow flies Trajectory over network
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STEP 1: CREATE A SIMPLIFIED PEDESTRIAN NETWORKTOURISTIC ROUTES
Original Aggregate road links Densify road links
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TOURISTS TAKE THE MOST POPULAR ROUTESTOURISTIC ROUTES
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STEP 2: REDUCE TRAVEL COST PER ROAD SEGMENT BASED ON PHOTO DENSITYTOURISTIC 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
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1. Create pairs of time-ordered photo locations per user
Point A Point BPoint 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 ROUTESSTEP 3: CREATE PHOTO PAIRS FOR ROUTING
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TOURISTIC ROUTESSTEP 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
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TOURISTIC CLUSTERS AND ROUTESVALIDATION 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
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TOURISTIC ROUTESVALIDATION 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
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VALIDATION OF RESULTSTOURISTIC 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?
***
* **
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SUGGESTIONS FOR FUTURE WORKAND 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
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THANK YOU FOR YOUR ATTENTION!ANY QUESTIONS OR REMARKS?