Best PhD award talk

Best PhD award talk

Talk for the Best PhD award of Marco De Nadai in Fondazione Bruno Kessler

44a34405e4821fa9047cfa635e198f61?s=128

Marco De Nadai

February 28, 2020
Tweet

Transcript

  1. Marco De Nadai, supervised by Bruno Lepri and Nicu Sebe

    INTO THE CITY: a Multi-Disciplinary Investigation of Urban Life
  2. 2 Cities, very difficult to explain Not only agglomeration of

    residents, factories, shops • Millions of individuals • Continuously evolving A small change generates a cascading throughout COMPLEX SYSTEM
  3. 3 Cities have always been studied IDEAL CITY (XV century)

    SYSTEM (XIX century) LIVING ORGANISM (XX century)
  4. 4 Understand cities

  5. 5 Understand cities New data

  6. 6 Understand cities New methods New data

  7. 7 Describe people and places at scale Urban description 1

  8. 8 Describe people and places at scale Urban description 1

    Urban perception 2
  9. 9 CITY Multi-modal approach

  10. 10 CITY PEOPLE Multi-modal approach

  11. Urban description Q: Can we describe how people experience the

    city? 1
  12. The theory: Jane Jacobs One of the most influential books

    in city planning • Written in 1961 • Not empirically tested until 2015 • Tested in Seoul, from costly surveys collected in years • Operationalize the theory 12 Jacobs, Jane. The death and life of great American cities. Vintage, 1961 2 1 3 4 URBAN DESCRIPTION
  13. The theory: Jane Jacobs Diversity of built environment Urban vitality

    There are 4 diversity conditions 13 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY 2 1 3 4 URBAN DESCRIPTION
  14. Operationalize the theory • Land use mix 14 LAND USE

    SMALL BLOCKS AGED BUILDINGS DENSITY 2 1 3 4 URBAN DESCRIPTION
  15. Operationalize the theory • Land use mix 15 For district

    : % = − ( )∈+ %,) log(%,) ) log || %,) : % square footage of land use : {residential, commercial, recreation} LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY 2 1 3 4 URBAN DESCRIPTION
  16. Operationalize the theory • Small blocks |% | % 16

    LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY 2 1 3 4 URBAN DESCRIPTION
  17. Operationalize the theory • Aged buildings: 17 @AB = ∑DEF

    G HD(IDJ ̅ I)L M (NOF) N ∑DEF G HD = LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY 2 1 3 4 URBAN DESCRIPTION
  18. Operationalize the theory 18 LAND USE SMALL BLOCKS AGED BUILDINGS

    DENSITY 2 1 3 4 Employment density: |PQRSTUBV RBTRSBD| @WB@D Population density: |XTRYS@Z%T[D| @WB@D URBAN DESCRIPTION
  19. What is vitality? • Defined in various (fuzzy) ways in

    urban science and sociology • There is no standard • Important for companies (and retail) success • Influences the real estate market 19 asd 2 1 3 4 URBAN DESCRIPTION
  20. Vitality (empirical) • Call detail records, collected by mobile operators

    for billing reasons • We define vitality as the average number of people in a neighborhood 20 1 % || ( _∈` | ℎ % | : set of hours (60 days x 24h) : area of district 2 1 3 4 URBAN DESCRIPTION
  21. Describe urban areas and vitality 21 VITALITY For each neighborhood

    Mobile data 2 1 3 4 URBAN DESCRIPTION
  22. Describe urban areas and vitality 22 People + Companies GIS

    Predictive model VITALITY For each neighborhood Mobile data 2 1 3 4 URBAN DESCRIPTION • 6 Italian cities
  23. 23 Urban description 1 We can predict vitality ( −

    h: 0.76) 1 Physical characteristics describe and predict cities 2 We can test theories at scale with web and open data 3 De Nadai, Marco, et al. "The Death and Life of Great Italian Cities: A Mobile Phone Data Perspective." WWW, 2016.
  24. Urban perception Q: Can we link urban visual perception with

    the behavior of people at scale? 2
  25. Broken windows theory • City mismanagement • Dirty places •

    Poor infrastructure Lead to misbehavior => Crime Q: Are people avoiding places where they feel unsafe? 25 Wilson, James Q., and George L. Kelling. "Broken windows." Critical issues in policing: Contemporary readings (1982): 395- 407. 2 1 3 URBAN PERCEPTION 4
  26. 26 Urban perception from Place Pulse Salesses, P., Schechtner, K.,

    & Hidalgo, C. A. (2013). The collaborative image of the city: mapping the inequality of urban perception. PloS one 2 1 3 URBAN PERCEPTION 4
  27. 27 Security perception prediction * B. Zhou, A. Lapedriza, J.

    Xiao, A. Torralba, and A. Oliva. “Learning Deep Features for Scene Recognition using Places Database.” NIPS, 2014. • Learn human security perception • Transfer learning from Place205*, US, to Rome and Milan PERCEPTION SCORE [0-10] 2 1 3 URBAN PERCEPTION 4
  28. 28 2 1 3 URBAN PERCEPTION 4 Describe security perception

    Security perception Vitality Regression model
  29. 29 Urban perception 2 We can predict presence of people

    with perception 1 Women, Young people around 2 Visual elements for safety 3 De Nadai, Marco, et al. "Are Safer Looking Neighborhoods More Lively?: A Multimodal Investigation into Urban Life." ACM MM, 2016.
  30. 30 Describe the places at scale, automatically Urban description 1

    Urban perception 2
  31. 31

  32. Real estate prediction 32 PLACE Characteristics of the census cell

    NEIGHBORHOOD Description and perception PROPERTY Characteristics of the property 2 1 3 HOUSING PRICE 4
  33. Real estate prediction 33 2 1 3 HOUSING PRICE 4

    The neighborhood features are very important 1 Predictions improved by ~ 60% 2
  34. Crime prediction 34 PLACE Characteristics of the census cell NEIGHBORHOOD

    Description and perception MOBILITY Mobility of people from mobile phones 2 1 3 CRIME 4 • Bogota, Boston, Los Angeles, Chicago
  35. Crime prediction 35 CRIME We accurately describe crime only when

    we consider people + place + mobility 1 The built environment plays different roles, but it is important to describe crime 2 2 1 3 4
  36. 36

  37. Why does it matter? 37 • DATA MINING • Inexpensive

    way to understand urban mechanisms; • New stimulus to social research; • Deep understanding of city life through multi-modal data • Urban science at scale
  38. 38 Next steps DATA DRIVEN APPROACH: let the data speak

    STREET VIEW IMAGERY AERIAL IMAGERY + NEIGHBORHOOD OUTCOMES (CRIME, VITALITY…)
  39. 39 Next steps GENERATIVE MODELS FOR URBAN PLANNING CRIME VITALITY

    HOUSING PRICE PREDICT GENERATE? NEIGHBORHOOD OUTCOMES AERIAL + STREET VIEW IMAGERY
  40. STEFAN DRAGICEVIC LETOUZE’ EMMANUEL DANIELE QUERCIA MARTA C. GONZALEZ CESAR

    A. HIDALGO JACOPO STAIANO NICU SEBE BRUNO LEPRI ROBERTO LARCHER SANDY PENTLAND GLORIA ZEN XU YANYAN RADU L. VIERIU NIKHIL NAIK THANKS
  41. 41 Thank you FBK! Urban vitality 1 4 2 3

    Application usage Global urbanization Unsupervised Image to Image Translation …and if you are bored, learn more about: http://www.marcodena.it