Urban*: Crowdsourcing for the Good of London

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For the last few years, we have been studying existing social media sites and created new ones in the context of London.

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  • we use the gravity model to study flows of passengers on London’s rail system. extensive network. 588 stations. click for oyster card. oyster cards record the point and time of entry… and link all journeys to a user id, so we can analyse individual travel patterns. stats – 1 month = 77 million journeys by 5 million users between 588 stations.
  • the world is currently undergoing a massive influx of people into cities. which means that (next slide)
  • the world is currently undergoing a massive influx of people into cities. which means that (next slide)
  • the world is currently undergoing a massive influx of people into cities. which means that (next slide)
  • in particular, transport analysts and planners need to be able to understand and predict passenger flow so far there has been little work on modeling flows on public transport systems in an urban environment, or at the intra- city level. one reason for this may be lack of data, but with adoption of AFC in cities all over the world, this data is now readily available.
  • Urban*: Crowdsourcing for the Good of London

    1. 1. Urban*: Crowdsourcing for the good of London@danielequerciaYahoo! Labs, Barcelona
    2. 2. daniele quercia
    3. 3. offline & online
    4. 4. offline & online
    5. 5. Facebook+
    6. 6. census deprivation + data
    7. 7. So what?
    8. 8. Situation(Its already 75% in the USA)
    9. 9. SituationBy 2025 another 1.2 billion living in urban areas
    10. 10. SituationCities in developing countries: 5M new inhabitants each month
    11. 11. ProblemInequality! Timely allocation ofscarce resources
    12. 12. census deprivation + londoners on twitter
    13. 13. 1 census deprivation + sentiment
    14. 14. [CSCW’12] Tracking Gross Community Happiness from Tweets
    15. 15. 3 match sentiment with (census) deprivation2 classify sentiment of profiles1 collect profiles & geo-reference them
    16. 16. 250K profiles in London (31.5M tweets)3 seeds: newspaper accounts1 collect profiles & geo-reference them1,323 in London neighborhoods  573 in 51 neighborhoods
    17. 17. social medialanguage personality
    18. 18. r=.350 word count r=.365 MaxEntpredicting socioeconomic well-being with twitter
    19. 19. 2 census deprivation + topics
    20. 20. social mediaenvironmentsportshealth Royal weddingSpanish/Portuguesecelebrity gossipsTalk of the City [ICWSM’12]
    21. 21. read profiles & define topicscreate virtual bins (latent topics)assign words to a bin (@ random)for each bin:select pair of wordsif co-occur more than chance:keep them in the binelse:put them into another bin (@ random)FacebookTwitter
    22. 22. read profiles & define topicscreate virtual bins (latent topics)assign words to a bin (@ random)for each bin:select pair of wordsif co-occur more than chance:keep them in the binelse:put them into another bin (@ random)FacebookTwittersocialeconometrics
    23. 23. read profiles & define topicscreate virtual bins (latent topics)assign words to a bin (@ random)for each bin:select pair of wordsif co-occur more than chance:keep them in the binelse:put them into another bin (@ random)FacebookTwittersocialeconometrics
    24. 24. Latent Dirichlet Allocation (LDA)
    25. 25. Latent Dirichlet Allocation (LDA)
    26. 26. Analyze geo-referenced tweets(not only residents but also visitors)
    27. 27. Linear RegressionR2=.49 (49% of IMD variability explained)
    28. 28. 3 census deprivation + tube trips
    29. 29. 4 census deprivation + “mental maps”Psychological Maps 2.0 [WWW’13]
    30. 30. draw a map
    31. 31. WEIRD trap!Few hundreds of WEIRDosWhite,Educated,Industrialized,Rich, and Democratic undergraduates
    32. 32. WWW
    33. 33. launched few months ago > 2K players
    34. 34. Regions
    35. 35. Regions
    36. 36. Boroughs
    37. 37. Boroughs
    38. 38. Londoners vs. UK vs. World
    39. 39. Vibility vs. Exposure
    40. 40. Visibility & Social Deprivation
    41. 41. 5 Beyond visibility…UrbanGems.orgTo quantify “fuzzy” concepts
    42. 42. (a) (b) (c) (d)(e) (f) (g) (h)Figure 5: Visual Wordsfor Beauty (top row) and Ugly (bottom row).and guardrails. The red dots on top-ranked pictureshose on bottom-ranked ones mean two different thingsormer reflect positive(e.g., happy) visual words, whilePicture Quality Bias. Photos might not necessarilywhat they aresupposed to show (representativeurban sin each neighborhood), and somepictures might beof
    43. 43. Research?This work is at intersection of two emerging fields:a) computational aestheticb) computational geo-cultural modeling
    44. 44. unleashing the potential of mobile datavs0
    45. 45. @danielequercia

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