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Are Safer Looking Neighborhoods More Lively? A multimodal Investigation into Urban Life

Are Safer Looking Neighborhoods More Lively? A multimodal Investigation into Urban Life

Presentation for the Responsive Environments: City eMotion course of Harvard University 2017.
Study from Google street view images in order to understand safety perception in urban enviroment with Deep Learning techniques. Original paper: https://arxiv.org/abs/1608.00462

Marco De Nadai

March 30, 2017
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  1. Marco De Nadai
    Are Safer Looking
    Neighborhoods More Lively?
    A multimodal Investigation
    into Urban Life

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  2. Who am I?
    2
    • Ph.D. student in Computer science
    • Reality: I’m in Computational Social Science
    • University of Trento (Italy) - FBK

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  3. Outline
    3
    • Motivation to study cities
    • How we do it
    • Security perception
    • Methods
    • Results
    • Next

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  4. Cities, very difficult to explain
    4
    COMPLEX
    Not only agglomeration of
    residents, factories, shops
    • Millions of individuals
    • Continuously evolving
    A small change generates a
    cascading throughout

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  5. The role of data mining and machine learning
    5
    DATA MINING
    • Inexpensive way to understand mechanisms;
    • New stimulus to social research;
    MACHINE LEARNING
    • New tools to expand the notion of what is predictable;
    Shmueli, Galit. "To explain or to predict?." Statistical science 25, no. 3 (2010): 289-310.

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  6. Predict deprivation and per-capita
    income solely relying on mobility
    diversity and social diversity
    6
    Pappalardo, L., Vanhoof, M., Gabrielli, L., Smoreda, Z.,
    Pedreschi, D., & Giannotti, F. (2016). An analytical framework to
    nowcast well-being using mobile phone data. International
    Journal of Data Science and Analytics

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  7. Predict poverty from satellite
    imagery
    (75% variation of economic outcomes)
    7
    Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S.
    (2016). Combining satellite imagery and machine learning to
    predict poverty. Science

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  8. Predict crime rates from POIs,
    mobility, demographics
    8
    Wang, H., Kifer, D., Graif, C., & Li, Z. (2016). Crime rate inference
    with big data. In Proceedings of the 22nd ACM SIGKDD.

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  9. Understand underlying mechanisms of a city
    9
    Arbesman, Samuel. Overcomplicated: Technology at the Limits of Comprehension. Penguin, 2016.

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  10. Understand underlying mechanisms of a city
    10
    Arbesman, Samuel. Overcomplicated: Technology at the Limits of Comprehension. Penguin, 2016.
    Describe

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  11. Understand underlying mechanisms of a city
    11
    Arbesman, Samuel. Overcomplicated: Technology at the Limits of Comprehension. Penguin, 2016.
    Describe Predict

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  12. Understand underlying mechanisms of a city
    12
    Arbesman, Samuel. Overcomplicated: Technology at the Limits of Comprehension. Penguin, 2016.
    Describe Predict Generate

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  13. Understand underlying mechanisms of a city
    13
    Arbesman, Samuel. Overcomplicated: Technology at the Limits of Comprehension. Penguin, 2016.
    Describe Predict Generate

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  14. Multi-modal understanding
    14

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  15. A multitude of dimensions and aspects!
    15
    DESCRIBE
    &
    PREDICT
    PREDICT
    &
    GENERATE
    Crime
    3
    Urban vitality
    1
    Security perception
    2
    Structural design
    4

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  16. Security perception
    SUBJECTIVE CHARACTERISTICS
    Q: how can new sources of data and deep learning models help to
    link urban visual perception and the behavior of people?

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  17. Broken windows theory
    • City mismanagement
    • Dirty places
    • Poor infrastructure
    Lead to misbehavior => Crime
    17
    Wilson, James Q., and George L. Kelling. "Broken windows."
    Critical issues in policing: Contemporary readings (1982): 395-
    407.
    2
    1 3
    SECURITY PERCEPTION 4

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  18. Jane Jacobs and Newman
    Two of the most influential books in
    city planning
    • Lit streets
    • Street-facing windows
    • Physical demarcation private-public
    18
    Klemek, C. (2011) ‘Dead or Alive at Fifty? Reading Jane Jacobs
    on her Golden Anniversary’ Dissent, Vol. 58, No. 2, 75–79.
    2
    1 3
    URBAN VITALITY 4

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  19. Are people avoiding places
    where they feel unsafe?
    QUESTION

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  20. Answer by surveys!
    • High costs
    • Not scalable
    • # people
    • # cities
    • How should we design the survey?
    20

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  21. Visual perception and liveliness
    AUTOMATICALLY LINKED

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  22. The multi-modal approach
    22
    SAFETY PERCEPTION LIVELINESS
    1 2
    MULTI- MODAL APPROACH

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  23. Safety perception: MIT Place Pulse
    23
    1 2
    MULTI- MODAL APPROACH

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  24. Place Pulse 2.0
    24
    Salesses, P., Schechtner, K., & Hidalgo, C. A. (2013). The collaborative image of the city: mapping the
    inequality of urban perception. PloS one
    1 2
    MULTI- MODAL APPROACH

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  25. Safety perception: MIT Place Pulse
    25

    1
    10
    1 2
    MULTI- MODAL APPROACH
    Place Pulse
    • New York
    • Boston
    • Linz
    • Salzburg
    Place Pulse 2
    • Rome
    • Milan
    PROBLEM:
    • Just thousands of examples
    • Sparse in space and # votes

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  26. Safety perception: fix sparse images
    • From 6.7 to 100 images per km^2
    • 360 degrees of images
    • Learning safety perception,
    predict in Rome and Milan
    • Aggregation per district
    26
    1 2
    MULTI- MODAL APPROACH

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  27. Deep Learning Networks (DNNs)
    • How do you extract features?
    • Handcraft features
    • Implicitly (Kernels)
    • Self-taught learning (learn representation)
    • Black boxes
    • Hard to train
    27
    1 2
    MULTI- MODAL APPROACH

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  28. DNNs: Representation
    28
    1 2
    MULTI- MODAL APPROACH
    * From Paolo Frasconi slides – Deep Learning course – University of Trento

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  29. DNNs: Representation
    29
    1 2
    MULTI- MODAL APPROACH

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  30. DNNs: Representation
    30
    1 2
    MULTI- MODAL APPROACH

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  31. DNNs: pre-trained Imagenet/Places
    31
    1 2
    MULTI- MODAL APPROACH
    • Trained on 1.2 millions of images
    Hard to train?

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  32. Deep Neural Networks
    32
    1 2
    MULTI- MODAL APPROACH
    x1
    x2
    x3
    Layer 1 Layer 2
    Layer 4
    Layer 3
    x4
    Score

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  33. Safety perception: fix sparse votes
    • Learning safety perception,
    predict in Rome and Milan
    • Standard architecture AlexNet CNN
    33
    * B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva. “Learning Deep Features for Scene Recognition using
    Places Database.” NIPS, 2014.
    1 2
    MULTI- MODAL APPROACH

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  34. Safety perception: fix sparse votes
    • Learning safety perception,
    predict in Rome and Milan
    • Standard architecture AlexNet CNN
    34
    * B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva. “Learning Deep Features for Scene Recognition using
    Places Database.” NIPS, 2014.
    Model type
    NY - NY
    NY - Boston
    Boston - Boston
    Boston - NY
    1 2
    MULTI- MODAL APPROACH

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  35. Safety perception: fix sparse votes
    • Learning safety perception,
    predict in Rome and Milan
    • Standard architecture AlexNet CNN
    35
    * B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva. “Learning Deep Features for Scene Recognition using
    Places Database.” NIPS, 2014.
    ** Ordonez, Vicente, and Tamara L. Berg. "Learning high-level judgments of urban perception.” ECCV, 2014.
    Model type State of art [**] Our model
    NY - NY 0.687 0.718
    NY - Boston 0.701 0.734
    Boston - Boston 0.718 0.744
    Boston - NY 0.636 0.693
    1 2
    MULTI- MODAL APPROACH

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  36. Safety perception: aggregation
    36
    1 2
    MULTI- MODAL APPROACH
    3.5 3.7 3.9 4.2 4.4 4.6 4.8 5.0
    Safety score
    DUOMO
    SAN SIRO
    QUARTO
    OGGIARO
    CITTA'
    STUDI
    BICOCCA
    TRASTEVERE
    TIBURTINO
    OSTIENSE
    PRIMAVALLE
    LESS SAFE SAFER
    ROME MILAN

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  37. 1 2
    Liveliness: mobile phone data
    • Anonymized mobile phone calls
    activity as a proxy for urban
    liveliness
    • Broken down by gender, age
    • 3 months time span (in 2015)
    • Rome and Milan
    38
    MULTI- MODAL APPROACH

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  38. Liveliness: metrics
    • People around in district
    |',)*+
    |
    |'
    |
    • Fraction females
    |',)*+
    |
    |',)*+
    |
    • People below 30 (and above 50)
    |(< 30)',)*+
    |
    |',)*+
    |
    39
    0.2
    0.0
    0.2
    0.4
    0.6
    0.8
    1.0
    Land Use Mix
    0.8 1.2 1.8 2.7 4.1 6.1 9.3 14.0 21.1 31.9
    Activity density ⇥ 103
    ROME
    MILAN
    0.8 1.2 1.8 2.7 4.1 6.1 9.3 14.0 21.1 31.9
    Activity density 103
    MILAN
    MULTI- MODAL APPROACH 1 2

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  39. 1
    Link: regression
    40
    4.2 4.4 4.6 4.8 5.0
    afety score
    DUOMO
    SAN SIRO
    QUARTO
    OGGIARO
    CITTA'
    STUDI
    BICOCCA
    MULTI- MODAL APPROACH 2
    0.2
    0.0
    0.2
    0.4
    0.6
    0.8
    1.0
    1.2
    Land Use Mix
    0.8 1.2 1.8 2.7 4.1 6.1 9.3 14.0 21.1 3
    Activity density ⇥ 103
    MILAN
    SAFETY PERCEPTION LIVELINESS

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  40. = :
    :
    + )
    )
    + ⋯ + >
    >
    +
    The Linear Regression
    41
    Liveliness metric
    1
    MULTI- MODAL APPROACH 2

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  41. The Linear Regression
    42
    Liveliness metric
    Perceived safety
    = :
    :
    + )
    )
    + ⋯ + >
    >
    +
    1
    MULTI- MODAL APPROACH 2

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  42. The Linear Regression
    43
    Liveliness metric
    Perceived safety
    Population density
    = :
    :
    + )
    )
    + ⋯ + >
    >
    +
    1
    MULTI- MODAL APPROACH 2

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  43. Spatial auto-correlation
    44
    Getis, Arthur, and Daniel A. Griffith. "Comparative spatial filtering in
    regression analysis." Geographical analysis 34.2 (2002): 130-140.
    “everything is related to everything else,
    but near things are more related than
    distant things.”
    Tobler's first law of geography
    • We control for spatial auto-correlation
    1 2
    MULTI- MODAL APPROACH

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  44. The multi-modal approach
    45
    (SPATIAL) OLS
    REGRESSION
    SAFETY PERCEPTION LIVELINESS
    1
    MULTI- MODAL APPROACH 2

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  45. Safety perception and liveliness
    46
    1
    Presence
    of people
    Women, Young people
    around
    Visual elements
    for safety
    2 3

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  46. Presence of people
    1st

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  47. 2
    1
    Safety perception <-> presence of people
    48
    3
    TESTS
    Urban metric Beta coefficient P-value
    Population density
    Employees density
    Deprivation
    Distance from the center
    Safety appearance
    adj − R) 0.91

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  48. 2
    1
    Safety perception <-> presence of people
    49
    3
    TESTS
    Urban metric Beta coefficient P-value
    Population density 0.155 **
    Employees density 0.328 **
    Deprivation -0.022
    Distance from the center -0.257 **
    Safety appearance 0.105 **
    adj − R) 0.91
    ** p-value < 0.001; * p-value < 0.01;

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  49. Women and young people around
    2nd

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  50. 1 2
    51
    3
    TESTS
    ** p-value < 0.001; * p-value < 0.01;
    Safety perception <-> women around
    Urban metric Beta coefficient P-value
    Percentage of women (census)
    Deprivation
    Distance from the center
    Safety appearance
    adj − R) 0.65

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  51. 1 2
    52
    3
    TESTS
    ** p-value < 0.001; * p-value < 0.01;
    Safety perception <-> women around
    Urban metric Beta coefficient P-value
    Percentage of women (census) 0.001
    Deprivation -0.005
    Distance from the center -0.003
    Safety appearance 0.020 **
    adj − R) 0.65

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  52. 1 2
    53
    3
    TESTS
    ** p-value < 0.001; * p-value < 0.01;
    Safety perception <-> young people around
    Urban metric Beta coefficient P-value
    Percentage of people < 30 years (census)
    Deprivation
    Distance from the center
    Safety appearance
    adj − R) 0.66

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  53. 1 2
    54
    3
    TESTS
    ** p-value < 0.001; * p-value < 0.01;
    Safety perception <-> young people around
    Urban metric Beta coefficient P-value
    Percentage of people < 30 years (census) -0.001
    Deprivation 0.032 **
    Distance from the center -0.150 **
    Safety appearance -0.048 **
    adj − R) 0.66

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  54. Visual elements for safety
    3rd

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  55. Deep Neural Networks
    56
    x1
    x2
    x3
    Layer 1 Layer 2
    Layer 4
    Layer 3
    x4
    Score
    1 2 3
    TESTS

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  56. 1 2 3
    Visual elements for safety perception
    57
    TESTS
    HIGH SAFETY PERCEPTION
    RANDOMLY OBSCURE
    PART OF THE IMAGE
    AND PREDICT

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  57. 1 2 3
    Visual elements for safety perception
    58
    TESTS
    HIGH SAFETY PERCEPTION
    RANDOMLY OBSCURE
    PART OF THE IMAGE
    AND PREDICT

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  58. 1 2 3
    Visual elements for safety perception
    59
    TESTS
    LOW SAFETY PERCEPTION
    RANDOMLY OBSCURE
    PART OF THE IMAGE
    AND PREDICT

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  59. Street facing windows and greenery
    contribute positively to the appearance
    of safety
    1 2 3
    TESTS

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  60. Why does it matter?

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  61. “What this [paper] does is put the facts on the table,
    and that’s a big step”

    “It will bring up a lot of other research, in which, I
    don’t have any doubt, this will be put up as a
    seminal step”
    Luis Valenzuela, Urban Planner
    Harvard University
    Source: http://news.mit.edu/2016/quantifying-urban-revitalization-1024

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  62. Useful!
    • Evaluate the districts vitality through visual appearance
    • Plan regulatory interventions
    • Mobile phone data as valid proxy to census
    63
    Barlacchi, Gianni, et al. "A multi-source dataset of urban life in the city of Milan and the Province of
    Trentino." Scientific data 2 (2015).

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  63. Practical applications
    • Real estate evaluation (land and buildings)
    • Know in advance the best places for retails (age/gender)
    • Analyze economic shocks in a region and respond to them
    64

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  64. In the (next) future
    • Temporal dimension (aesthetic change, people’s behavior)
    • How urban perception influences our mobility?
    65
    A B

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  65. 66
    1
    Presence
    of people
    Women, Young people
    around
    Visual elements
    for safety
    2 3
    Safety perception and liveliness
    a multi-modal approach

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  66. Thanks
    @denadai2

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