REVEALING SPATIAL AND TEMPORAL PATTERNS FROM FLICKRA CASE STUDY WITH TOURISTS IN AMSTERDAM

TOURISM IN AMSTERDAMRAPID GROWTH

Source: Nicky Otten (Flickr)

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

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 + / - + +

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?

FLICKR DATA COLLECTION

FLICKR DATA COLLECTIONOVERVIEW OF STEPS & TECHNIQUES

Flickr Database (API)

Request

Local database (PostgreSQL)Java application

XML-file

Metadata

Restriction: 1 request per second

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

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

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

FLICKR DATA EXPLORATIONPHOTOS IN QGIS

FLICKR DATA EXPLORATIONSELECTION OF PHOTOS IN GOOGLE EARTH

TOURIST CLASSIFICATIONBASED ON USER’S HOME LOCATION

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

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%

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%

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

TEMPORAL DISTRIBUTIONSDIFFERENT GRANULARITIES

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

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

Exact match

2 hours off

TIMESTAMP VALIDATIONTIME DIFFERENCE BETWEEN PHOTO TIMESTAMP AND REAL TIME

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

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

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

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

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

0

40

80

120

160

200

1 365

LocalsTourists

PHOTOGRAPHERS PER DAY OF THE YEAR (2005-2014)

Queens-day

TEMPORAL DISTRIBUTIONS

SPATIAL DISTRIBUTIONGRID-BASED CLUSTERING

SPATIAL DISTRIBUTIONGRID-BASED CLUSTERING

1 1

1 1 1 1

1

1 1

2

111

2 31

1

1 1 1

112

EXPLORING THE DATATOURIST COUNT PER HEXAGON IN GOOGLE EARTH

SPATIAL DISTRIBUTIONDENSITY-BASED CLUSTERING

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

SPATIAL DISTRIBUTIONDENSITY-BASED CLUSTERING

SPATIAL DISTRIBUTIONDENSITY-BASED CLUSTERING

SPATIAL DISTRIBUTIONDENSITY-BASED CLUSTERING

SPATIAL DISTRIBUTIONDENSITY-BASED CLUSTERING

TOURISTIC ROUTES

ONE DAY IN THE LIFE OF A TOURISTTOURISTIC ROUTES

LINEAR TRAJECTORIES OF MANY TOURISTSTOURISTIC ROUTES

LINEAR TRAJECTORIES BETWEEN CLUSTERSTOURISTIC ROUTES

TOURISTIC ROUTESRELATING TRAJECTORIES TO STREET NETWORK USING ROUTING ALGORITHM

As the crow flies Trajectory over network

STEP 1: CREATE A SIMPLIFIED PEDESTRIAN NETWORKTOURISTIC ROUTES

Original Aggregate road links Densify road links

TOURISTS TAKE THE MOST POPULAR ROUTESTOURISTIC ROUTES

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

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

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

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

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

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

THANK YOU FOR YOUR ATTENTION!ANY QUESTIONS OR REMARKS?