This investigation is part of the Digital methods initiative of the University of Amsterdam (UVA), a two week long summer school held in June 2014.

1. Livability of/with Amsterdam
Branding the city from the in- and outside

2. Branding the city from the in- and outside
Traditionally, livability is the sum of the factors that add up to a community’s quality
of life—including the built and natural environments, mobility, social stability and equity,
educational opportunity, and cultural, entertainment and recreation possibilities.
However, our study focuses on livability as it is constructed by people tweeting
about amsterdam. The narrative emerges out of twitter data from two data sets:
- Geo-tagged data of Amsterdam
- Keywords about Amsterdam
A big exploratory question:
How can twitter data construct a narrative of the city of Amsterdam?
What is livability?
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3. Exploratory
sub-questions:
● Can geolocated tweets give us insight about the locals' life on the modes of transportation, the
street cleaning service and the safety of Amsterdam? Vignettes about the hashtags: #zwerfie,
#tram, #indetram, #tramlijn12(etc) and #bomb or #bom
● How do the most active users of geolocated tweets move through the city?
● How do tourists engage with Amsterdam city on Twitter? And which hashtags and what topics
do they associate the most with the city? The case of the Russian, Spanish and Chinese
tourists.
Branding the city from the in- and outside
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4. Operationality
This project consists of four topics
Branding from the inside
1. Geolocated data about mobility
2. Geolocated data about user-activity
3. Geolocated keywords
Branding from the outside
4. Keyword specific data
- Language / thematic segmentation
All data extracted from DMI-TCAT
Branding the city from the in- and outside
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5. Can geolocated tweets give us insight about the locals' life on the modes of transportation
twitter movement pattern of @fukcingband
bus nr 300
We selected the top 20 most active tweeters and
mapped them.
Interactive map: http://mngroen.nl/dmi/users/
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6. Can geolocated tweets give us insight about the locals' life on the modes of transportation
twitter movement pattern of @olfertjan
Citizens’ habits appearing on a map with their
twitter locations. Some people always tweet on
the same spot, others always tweet while
commuting.
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7. Can geolocated tweets give us insight about the locals' life on the modes of transportation
collective twitter movement pattern of tram users
Exploratory analysis using a sample from DMI-
TCAT geo-amsterdam database. All tweets and
query results using the keywords:
tram OR indetram OR in de tram OR zitindetram
OR tramlijn24 OR tram lijn 24 OR lijn24 OR lijn
24, etc (all the lines)
Branding the city from the in- and outside
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8. Can geolocated tweets give us insight about the locals' life on the modes of transportation
collective twitter movement pattern of metro users
Exploratory analysis using a sample from DMI-
TCAT geo-amsterdam database. All tweets and
query results using the keywords:
metro OR indemetro OR in de metro OR
zitindemetro OR zit in de metro
Interactive map: http://mngroen.nl/dmi/mobility/
Branding the city from the in- and outside
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9. Can geolocated tweets give us insight about the locals' life on the modes of transportation
Collective twitter movement pattern of train
users
Conclusions:
- Tram and train: A high frequency of tweets at the central
station for the tram and the train.
- Metro: The twitter use on the metro is spread throughout
the city without any peaks in high frequency of tweets at
any spots.
- Bus: Most tweeted. The highest frequency of tweets for
the bus are to be found around Dam square, het spui,
stadium and at Schiphol airport.
Overall it could be said that the waiting places in the city
are more frequently used for tweeting.
Branding the city from the in- and outside
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10. Branding the city from the in- and outside
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11. Branding the city from the in- and outside
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12. User language analysis
Method:
database exploratory analysis
using:
(1) a 5,5% random sample query
using the keyword “Amsterdam”
(1.000/~1.800.00 tweets).
(2) query using the city name in
Portuguese: “Amsterdão” (133
tweets).
Languages from the random
sample: Arabic, Catalan, Danish,
German, English, GB English,
Spanish, Finnish, French, Hebrew,
Hungarian, Italian, Japanese,
Dutch, Polish, Portuguese,
Russian, Slovak, Thai, Turkish.
Branding the city from the in- and outside
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13. Geolocated tweets: only 7 languages had
coordinates, generating 30 points:
https://mapsengine.google.com/map/edit?mid=zD
NdWkEAeMxk.ke_vBHF9E2Ts
- Language diversity cluster in Amsterdam;
- English: a spread pattern (global language);
- Portuguese: concentrated in Portugal and Brasil;
Source location:
- Users from the random sample are mostly based
in The Netherlands (Amsterdam, Utrecht), followed
by the USA (New York), France (Paris), Mexico and
Argentina;
- Portuguese users are based in Portugal and Brasil
(more a local than a global language);
Branding the city from the in- and outside
Geolocating user languages
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14. Language Analysis
Chinese-Speaking Twitteres
→ “阿姆斯特丹”（166 twitters ）
time starts:2014-5-21
time ends: 2014-6-26
DMI-TCAT query:
“阿姆斯特丹” : 88 from 46 users
+ manual twitter collection
“阿姆斯特丹”(38）
“Amsterdam”(in setting_cn 43)
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Branding the city from the in- and outside

15. Language Analysis
Chinese-Speaking Twitters
conclusion:
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Branding the city from the in- and outside
2.content analysis“What” and “how”
Chinese people think about “Amsterdam”
fewer Geotags in Twitters: (3/88)
>>can’t relate with the Geo
(1)Top words coming with “Amsterdam”
travel:
china town /hotel
transportation:
train /airport Schiphol
life:
gay/bike

16. Language Analysis
Chinese-Speaking Twitters
conclusion:
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Branding the city from the in- and outside
2.content analysis “what” and “how” Chinese
people think about “Amsterdam”
(2) words describing “Amsterdam”:
Though:
1.Confuse:
mainly on bikes and signs and single-way road)
2.complaint:
Price, food.
creative/ beautiful/incredible

17. Russian-speaking
‘twitterers’
● around 4,000 tweets for the period of study
● 292 geo-located tweets
● around 180 users
● 2-step methodology:
o (a) semantic analysis and
o (b) spatio-temporal distribution of the tweets
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Branding the city from the in- and outside

18. Semantic analysis
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Branding the city from the in- and outside
Aim:
To capture the
dominant themes
in the discourse
of the Russian-
speaking users in
relation to
‘Amsterdam’
-Word frequency
-
Manual
interpretation
(text of the
tweets and #’s)

19. Spatio-temporal distribution
http://cdb.io/1nOLa8a
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Branding the city from the in- and outside

20. Methodology
Geolocated
● Scraped the top 20 most active users with Python script (date, user, coordinates)
● Manually removed all non-human users
● Visualisation:
○ Applied data to google-map in two different maps:
■ Mobility map (based on mobility-related hashtags)
■ Movement of the top 20 most active users in the city area
Branding the city from the in- and outside
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21. Branding the city from the in- and outside
Conclusions and limitations
1- Social media data can give us a proxy of “city centers”: not only touristic city center but
also where localized populations are interacting with the space
2- Twitter generates ubiquitous usages and allows citizens to act as sensors for
transportation = Amsterdam is a smart city which can use those data shadows
3- The biases of Twitter as a platform (Boyd and Crawford, 2011). Social media are
performative artefacts.
4- The necessity of ground truthing to obtain more granular and qualitative data: Big data
cannot explain everything.
5- Methodological gap between keyword- and geolocation-data analysis
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