Livability of/with Amsterdam

Branding the city from the in- and outside

Branding the city from the in- and outside

Traditionally, livability is the sum of the factors that add up to a community’s qualityof 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 tweetingabout 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?#dmi14

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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OperationalityThis project consists of four topics

Branding from the inside1. Geolocated data about mobility2. Geolocated data about user-activity3. Geolocated keywords

Branding from the outside4. Keyword specific data

- Language / thematic segmentation

All data extracted from DMI-TCAT

Branding the city from the in- and outside

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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/

Branding the city from the in- and outside

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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.

Branding the city from the in- and outside

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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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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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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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Branding the city from the in- and outside

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Branding the city from the in- and outside

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User language analysisMethod: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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Geolocated tweets: only 7 languages had coordinates, generating 30 points: https://mapsengine.google.com/map/edit?mid=zDNdWkEAeMxk.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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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

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

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

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

Semantic analysis#dmi14

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)

Spatio-temporal distributionhttp://cdb.io/1nOLa8a

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Branding the city from the in- and outside

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

#dmi14

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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