O slideshow foi denunciado.
Utilizamos seu perfil e dados de atividades no LinkedIn para personalizar e exibir anúncios mais relevantes. Altere suas preferências de anúncios quando desejar.
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’...
Exploratory
sub-questions:
● Can geolocated tweets give us insight about the locals' life on the modes of transportation, ...
Operationality
This project consists of four topics
Branding from the inside
1. Geolocated data about mobility
2. Geolocat...
Can geolocated tweets give us insight about the locals' life on the modes of transportation
twitter movement pattern of @f...
Can geolocated tweets give us insight about the locals' life on the modes of transportation
twitter movement pattern of @o...
Can geolocated tweets give us insight about the locals' life on the modes of transportation
collective twitter movement pa...
Can geolocated tweets give us insight about the locals' life on the modes of transportation
collective twitter movement pa...
Can geolocated tweets give us insight about the locals' life on the modes of transportation
Collective twitter movement pa...
Branding the city from the in- and outside
#dmi14
Branding the city from the in- and outside
#dmi14
User language analysis
Method:
database exploratory analysis
using:
(1) a 5,5% random sample query
using the keyword “Amst...
Geolocated tweets: only 7 languages had
coordinates, generating 30 points:
https://mapsengine.google.com/map/edit?mid=zD
N...
Language Analysis
Chinese-Speaking Twitteres
→ “阿姆斯特丹”(166 twitters )
time starts:2014-5-21
time ends: 2014-6-26
DMI-TCAT ...
Language Analysis
Chinese-Speaking Twitters
conclusion:
#dmi14
Branding the city from the in- and outside
2.content analys...
Language Analysis
Chinese-Speaking Twitters
conclusion:
#dmi14
Branding the city from the in- and outside
2.content analys...
Russian-speaking
‘twitterers’
● around 4,000 tweets for the period of study
● 292 geo-located tweets
● around 180 users
● ...
Semantic analysis
#dmi14
Branding the city from the in- and outside
Aim:
To capture the
dominant themes
in the discourse
o...
Spatio-temporal distribution
http://cdb.io/1nOLa8a
#dmi14
Branding the city from the in- and outside
Methodology
Geolocated
● Scraped the top 20 most active users with Python script (date, user, coordinates)
● Manually remo...
Branding the city from the in- and outside
Conclusions and limitations
1- Social media data can give us a proxy of “city c...
Próximos SlideShares
Carregando em…5
×

Digital methods: Livability of/ with Amsterdam

796 visualizações

Publicada em

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.

Publicada em: Dados e análise
  • Seja o primeiro a comentar

  • Seja a primeira pessoa a gostar disto

Digital methods: Livability of/ with Amsterdam

  1. 1. Livability of/with Amsterdam Branding the city from the in- and outside
  2. 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? #dmi14
  3. 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 #dmi14
  4. 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 #dmi14
  5. 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/ Branding the city from the in- and outside #dmi14
  6. 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. Branding the city from the in- and outside #dmi14
  7. 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 #dmi14
  8. 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 #dmi14
  9. 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 #dmi14
  10. 10. Branding the city from the in- and outside #dmi14
  11. 11. Branding the city from the in- and outside #dmi14
  12. 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 #dmi14
  13. 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 #dmi14
  14. 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) #dmi14 Branding the city from the in- and outside
  15. 15. Language Analysis Chinese-Speaking Twitters conclusion: #dmi14 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. 16. Language Analysis Chinese-Speaking Twitters conclusion: #dmi14 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. 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 #dmi14 Branding the city from the in- and outside
  18. 18. 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)
  19. 19. Spatio-temporal distribution http://cdb.io/1nOLa8a #dmi14 Branding the city from the in- and outside
  20. 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 #dmi14
  21. 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 #dmi14

×