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How to test an urban theory - Data Science Lecture

44a34405e4821fa9047cfa635e198f61?s=47 Marco De Nadai
November 12, 2020

How to test an urban theory - Data Science Lecture

Slides for the lecture "Testing an urban theory" @ University of Trento

44a34405e4821fa9047cfa635e198f61?s=128

Marco De Nadai

November 12, 2020
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  1. Testing an urban theory Marco De Nadai (http://www.marcodena.it)

  2. 2 Cities have always been studied IDEAL CITY (XV century)

  3. 3 Cities have always been studied IDEAL CITY (XV century)

    SYSTEM (XIX century)
  4. 4 Cities have always been studied IDEAL CITY (XV century)

    SYSTEM (XIX century) LIVING ORGANISM (XX century)
  5. 5 Understand cities

  6. 6 Understand cities New data

  7. 7 Understand cities New methods New data

  8. How can we test an urban theory? 8 1 Take

    the theory “Operationalize” the theory Does it work? Is it still valid? Why does it matter? 2 3 4
  9. 9 Follow and ask please! How to test an urban

    theory 1 There are no stupid questions 2
  10. Take the theory STEP 1

  11. The theory: Jane Jacobs One of the most influential books

    in city planning • planning models that dominated mid-century planning • American housing policy (HOPE VI) • Melbourne, Toronto etc. 11 1 2 3 4 THEORY TEST Klemek, C. (2011) ‘Dead or Alive at Fifty? Reading Jane Jacobs on her Golden Anniversary’ Dissent, Vol. 58, No. 2, 75–79.
  12. The theory: not tested! • Not empirically tested until 2015

    • Tested in Seoul, from costly surveys collected in years • Theory from 1961! 12 1 2 3 4 THEORY TEST Sung, Hyungun, Sugie Lee, and SangHyun Cheon. "Operationalizing Jane Jacobs’s Urban Design Theory Empirical Verification from the Great City of Seoul, Korea." Journal of Planning Education and Research (2015.
  13. The theory: Jane Jacobs One of the most influential books

    in city planning • Death: caused by the elimination of pedestrian activity • Life: created by a vital urban fabric at all times of the day 13 1 2 3 4 THEORY TEST
  14. Jacobs’ diversity conditions Diversity => Urban vitality There are 4

    diversity conditions To be ensured in each city’s district (10,000+ inhabitants) 14 1 2 3 4 THEORY TEST SMALL BLOCKS LAND USE AGED BUILDINGS DENSITY
  15. Land Use Mix 2+ primary uses (contemporarily) JACOBS’ VIEW: People

    come for different purposes, continuously EFFECT: “sidewalk ballet” and “eyes on the street” 15 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY 1 2 3 4 THEORY TEST
  16. Small blocks City blocks should be small/short LAND USE SMALL

    BLOCKS 16 1 2 3 4 THEORY TEST AGED BUILDINGS DENSITY JACOBS’ VIEW: improves walkability EFFECT: Increase face-to-face interactions
  17. Aged buildings Buildings mixed (age and types) 17 AGED BUILDINGS

    1 2 3 4 THEORY TEST LAND USE DENSITY SMALL BLOCKS JACOBS’ VIEW: To ensure economic diversity EFFECT: high-/low-income residents new/small enterprises
  18. Density Concentration of people and enterprises JACOBS’ VIEW: People have

    a reason to live in a district EFFECT: Attract people 18 SMALL BLOCKS DENSITY 1 2 3 4 THEORY TEST LAND USE AGED BUILDINGS
  19. Necessary, diversity conditions All four factors are necessary 19 LAND

    USE SMALL BLOCKS AGED BUILDINGS DENSITY 1 2 3 4 THEORY TEST
  20. Border Vacuums • Patches of land dedicated to one single

    use • They could be either bad and good: • Parks are good for pedestrian activity • But they are exposed to criminality and deprivation if not well managed (e.g. night) 20 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY VACUUMS 1 2 3 4 THEORY TEST
  21. “Operationalize” the theory STEP 2

  22. The data: 22 1 3 4 THEORY TEST 2

  23. The data: 23 1 3 4 THEORY TEST 2 Get

    the data at: https://developer.foursquare.com/
  24. The data: 24 1 3 4 THEORY TEST 2

  25. The data: 25 1 3 4 THEORY TEST 2

  26. The data: 26 1 3 4 THEORY TEST 2 Get

    the data at: https://overpass-turbo.eu/
  27. The data: 27 1 3 4 THEORY TEST 2

  28. The data: 28 1 3 4 THEORY TEST 2 •

    Urban Atlas: https://land.copernicus.eu/local/urban- atlas/urban-atlas-2012
  29. The data: 29 1 3 4 THEORY TEST 2 •

    ISTAT (Census): https://www.istat.it/it/archivio/104317
  30. 30 2 1 3 4 URBAN DESCRIPTION GIS data “Operationalize”

    the theory
  31. 31 2 1 3 4 URBAN DESCRIPTION GIS data “Operationalize”

    the theory
  32. 32 2 1 3 4 URBAN DESCRIPTION GIS data “Operationalize”

    the theory
  33. 33 2 1 3 4 URBAN DESCRIPTION GIS data “Operationalize”

    the theory
  34. 34 2 1 3 4 URBAN DESCRIPTION GIS data Does

    it work? Is it still valid? 3 “Operationalize” the theory
  35. “Operationalize” Land Use Mix For district : ! = −

    ' "∈$ !," log(!," ) log || !,#: % square footage of land use : {residential, commercial, recreation} 35 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY VACUUMS 1 3 4 THEORY TEST 2 Ref: R. Cervero. Land-use mixing and suburban mobility. University of California Transportation Center, 1989 EFFECT: The higher, the better. 1 0
  36. 36 1 3 4 THEORY TEST 2

  37. 37 1 3 4 THEORY TEST 2

  38. “Operationalize” Small blocks Block size is a proxy for an

    high number of peoples’ interactions For district : 1 | | * 0∈023456(7) () EFFECT: The lower, the better 38 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY VACUUMS 1 3 4 THEORY TEST 2
  39. 39 1 3 4 THEORY TEST 2

  40. Aged buildings Aged buildings are supposed to be a proxy

    for new, small enterprises. For district : 1 |7 | * 8∈9! 7 : set of companies EFFECT: The higher, the worse 40 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY VACUUMS 1 3 4 THEORY TEST 2
  41. Aged buildings For district the weighted standard deviation of buildings

    age. EFFECT: The higher, the better 41 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY VACUUMS 1 3 4 THEORY TEST 2 !"# = ∑!"# $ %!('!( ̅ ')% + ('(#) ' ∑!"# $ %!
  42. 42 1 3 4 THEORY TEST 2

  43. 43 1 3 4 THEORY TEST 2

  44. “Operationalize” Density For district : Employment density: |:;<23=>? <>3<2>!| @A>@!

    Population density: |B3<C2@D73E!| @A>@! EFFECT: The higher, the better 44 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY VACUUMS 1 3 4 THEORY TEST 2
  45. 45 1 3 4 THEORY TEST 2

  46. “Operationalize” Vacuums Distance to huge parks for district : 1

    7 * 8∈F! ( , , ) 7 : the set of the blocks : the set of parks EFFECT: The higher, the better 46 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY VACUUMS 1 3 4 THEORY TEST 2
  47. 47 1 3 4 THEORY TEST 2

  48. Diversity Vitality

  49. Call Detail Records Data collected by mobile operators for billing

    reasons • Unique userID • Gender and age • Geographical location (Antenna) • Datetime 49 1 2 3 4 THEORY TEST
  50. Call Detail Records 50 1 2 3 4 THEORY TEST

  51. “Operationalize” Vitality • Mobile phone Internet activity as a proxy

    for urban vitality • We calculate the activity density in each district 1 || % "∈$ | % | : set of hours (180 days x 24h) • Six Italian cities with 100,000+ inhabitants (e.g. Rome, Milan…) • 6 months time span (in 2014) 51 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 1 2 3 4 THEORY TEST MILAN
  52. 52 2 1 3 4 URBAN DESCRIPTION Mobile data GIS

    data “Operationalize” the theory
  53. 53 2 1 3 4 URBAN DESCRIPTION Mobile data GIS

    data “Operationalize” the theory
  54. 54 2 1 3 4 URBAN DESCRIPTION Mobile data GIS

    data “Operationalize” the theory
  55. 55 2 1 3 4 URBAN DESCRIPTION Mobile data GIS

    data “Operationalize” the theory
  56. 56 2 1 3 4 URBAN DESCRIPTION Mobile data GIS

    data Does it work? Is it still valid? 3 “Operationalize” the theory
  57. Demo 1 https://github.com/denadai2/test-the- theory- lesson/blob/master/step1_compute_fe atures.ipynb 57

  58. Is the theory still valid? STEP 3

  59. −3 −2 −1 0 1 2 3 −9 −8 −7

    −6 −5 −4 −3 −2 . Is the theory still valid? 59 Intersections density (log + Z-score) Activity density (log) 1 2 3 4 THEORY TEST
  60. −3 −2 −1 0 1 2 3 −9 −8 −7

    −6 −5 −4 −3 −2 R2 : 0.63 Is the theory still valid? 60 Intersections density (log + Z-score) Activity density (log) 1 2 3 4 THEORY TEST
  61. Demo 2 https://github.com/denadai2/test-the- theory- lesson/blob/master/step2_exploration. ipynb 61

  62. The log Linear Regression 62 = ! ! + "

    " + ⋯ + "# "# + 1 2 3 4 THEORY TEST
  63. The log Linear Regression 63 Activity density 1 2 3

    4 THEORY TEST = ! ! + " " + ⋯ + "# "# +
  64. The log Linear Regression 64 Activity density Land Use Mix

    Employment density 1 2 3 4 THEORY TEST = ! ! + " " + ⋯ + "# "# +
  65. Demo 3 http://setosa.io/ev/ordinary-least- squares-regression/ and https://github.com/denadai2/test-the- theory- lesson/blob/master/step3_Regression.i pynb 65

  66. Jacobs’ theory holds and is still valid 66 1 2

    3 4 THEORY TEST Urban metric Beta coefficient Employment density 0.434*** Intersections density 0.191*** Housing types 0.1854*** Closeness highways -0.102*** 3rd places x closeness highways 0.07** Closeness parks x closeness highways -0.07*** − 0.77 *** p-value < 0.001; ** p-value < 0.01; 4-fold Cross-validation: 75% training – 25% testing, 1000 interactions
  67. Jacobs’ theory holds and is still valid 67 URBAN VITALITY

    1 2 3 4 THEORY TEST URBAN METRICS Predict $: 0.77
  68. …But something is different 68 1 2 3 4 THEORY

    TEST LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY VACUUMS
  69. Why does it matter? STEP 4

  70. Web data and mobile phone records offer insights on how

    most urban dwellers experience entire cities 1 2 3 4 THEORY TEST
  71. Why does it matter? • Evaluate the districts vitality •

    Know in advance the best places for retails • Quantifying regulatory interventions • We created the recipe for city that works 71 1 2 3 4 THEORY TEST
  72. Let’s test an urban theory 72 1 The Jacobs’ theory

    We created the metrics We tested the theory Framework for urban vitality 2 3 4
  73. Test the un-tested And now?

  74. Broken windows theory • City mismanagement • Dirty places •

    Poor infrastructure Lead to misbehavior => Crime Q: Are people avoiding places where they feel unsafe? 74 Wilson, James Q., and George L. Kelling. "Broken windows." Critical issues in policing: Contemporary readings (1982): 395- 407.
  75. 75 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
  76. 76 … 1 5 Place Pulse • New York •

    Boston • Linz • Salzburg Place Pulse 2 • Rome • Milan URBAN PERCEPTION Safety perception: MIT Place Pulse
  77. 77 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. • Learning human security perception PERCEPTION SCORE [0-10]
  78. Demo 4 https://github.com/denadai2/test-the- theory- lesson/blob/master/step_extra_vision.i pynb 78

  79. 79 Urban metric Standardized Beta coefficient Population density 0.155** Employees

    density 0.328** Deprivation -0.022 Distance from the center -0.257** Security perception 0.105** adj − R$ 0.91 ** p-value < 0.001; * p-value < 0.01; Security perception -> presence of people
  80. Take home Try to test your favorite theory! 80

  81. Thanks @denadai2