Ph.D. Qualifying 2016

The presentation for my Ph.D. qualifying of 2016. This is the undergoing projects and plans for my Ph.D.

Transcript

1. Marco De Nadai Into the city A Multi-Disciplinary Investigation of Urban Life
2. By 2010 urban population began to exceed the rural one UN population division, 2010 Urban Rural Population:
3. By 2010 urban population began to exceed the rural one UN population division, 2010 90% of global gross added value comes from urban areas (2006) Urban Rural Population:
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
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.
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
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
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.
9. Understand underlying mechanisms of a city 9 Arbesman, Samuel. Overcomplicated: Technology at the Limits of Comprehension. Penguin, 2016. Describe
10. Understand underlying mechanisms of a city 10 Arbesman, Samuel. Overcomplicated: Technology at the Limits of Comprehension. Penguin, 2016. Describe Predict
11. Understand underlying mechanisms of a city 11 Arbesman, Samuel. Overcomplicated: Technology at the Limits of Comprehension. Penguin, 2016. Describe Predict Generate
12. Understand underlying mechanisms of a city 12 Arbesman, Samuel. Overcomplicated: Technology at the Limits of Comprehension. Penguin, 2016. Describe Predict Generate
13. Multi-modal understanding 13
14. Multi-modal understanding 14
15. A multitude of dimensions and aspects! 15 Urban vitality 1 DONE ON-GOING / FUTURE
16. A multitude of dimensions and aspects! 16 Urban vitality 1 Security perception 2 DONE ON-GOING / FUTURE
17. A multitude of dimensions and aspects! 17 Crime 3 Urban vitality 1 Security perception 2 DONE ON-GOING / FUTURE
18. A multitude of dimensions and aspects! 18 Crime 3 Urban vitality 1 Security perception 2 Structural design 4 DONE ON-GOING / FUTURE
19. A multitude of dimensions and aspects! 19 DESCRIBE & PREDICT Crime 3 Urban vitality 1 Security perception 2 Structural design 4 DONE ON-GOING / FUTURE
20. A multitude of dimensions and aspects! 20 DESCRIBE & PREDICT PREDICT & GENERATE Crime 3 Urban vitality 1 Security perception 2 Structural design 4 DONE ON-GOING / FUTURE
21. Urban Vitality OBJECTIVE CHARACTERISTICS Q: Can we describe and predict vitality from urban physical characteristics?
22. 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 22 Jacobs, Jane. The death and life of great American cities. Vintage, 1961 2 1 3 URBAN VITALITY 4
23. The theory: Jane Jacobs Diversity => Urban vitality There are 4 diversity conditions Operationalize the theory 23 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY 2 1 3 URBAN VITALITY 4
24. The theory: Jane Jacobs 2+ primary uses (contemporarily) 24 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY 2 1 3 URBAN VITALITY 4
25. The theory: Jane Jacobs 2+ primary uses (contemporarily) 25 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY For district : % = − ( %,+ log (%,+ ) log || +∈5 %,+: % square footage of land use : {residential, commercial, recreation} 1 0 2 1 3 URBAN VITALITY 4
26. The theory: Jane Jacobs City blocks should be small/short 26 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY BLOCKS 2 1 3 URBAN VITALITY 4
27. The theory: Jane Jacobs City blocks should be small/short 27 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY BLOCKS For district : |% | % 2 1 3 URBAN VITALITY 4
28. The theory: Jane Jacobs Buildings mixed (age and types) 28 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY 2 1 3 URBAN VITALITY 4
29. The theory: Jane Jacobs Buildings mixed (age and types) 29 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY Standard deviation of building ages 2 1 3 URBAN VITALITY 4
30. The theory: Jane Jacobs Concentration of people and enterprises 30 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY 2 1 3 URBAN VITALITY 4
31. The theory: Jane Jacobs Concentration of people and enterprises 31 LAND USE SMALL BLOCKS AGED BUILDINGS DENSITY Population density: |% | % 2 1 3 URBAN VITALITY 4
32. Vitality Anonymized Mobile phone Internet activity as a proxy for urban vitality 32 1 % || ( | % | G∈H : set of hours (180 days x 24h) : area of district 2 1 3 URBAN VITALITY 4
33. The Death and Life of Great Italian Cities 33 DATA • Web and Open data (physical characteristics) • Mobile phone data (proxy for vitality) MODEL • Fit with Ordinary Least Squares regression (OLS) • Predict with Cross-validated OLS De Nadai, Marco, et al. "The Death and Life of Great Italian Cities: A Mobile Phone Data Perspective." WWW, 2016.
34. The log Linear Regression 34 Vitality (Ground truth) = K K + N N + ⋯ + P P + 2 1 3 URBAN VITALITY 4
35. The log Linear Regression 35 Vitality (Ground truth) Land Use Mix = K K + N N + ⋯ + P P + 2 1 3 URBAN VITALITY 4
36. The log Linear Regression 36 Vitality (Ground truth) Land Use Mix Employment density = K K + N N + ⋯ + P P + 2 1 3 URBAN VITALITY 4
37. 37 Urban metric Beta coefficient Employmentdensity 0.434*** Intersections density 0.191*** Housing types 0.185*** Closeness highways -0.102*** 3rd places x closenesshighways 0.07** Closeness parks x closeness highways -0.07*** adj − RN 0.77 *** p-value < 0.001; ** p-value < 0.01; Describe urban vitality 2 1 3 URBAN VITALITY 4
38. Result 38 De Nadai, Marco, et al. "The Death and Life of Great Italian Cities: A Mobile Phone Data Perspective." WWW, 2016. Physical characteristics describe and predict urban vitality 2 1 3 URBAN VITALITY 4
39. 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?
40. Broken windows theory • City mismanagement • Dirty places • Poor infrastructure Lead to misbehavior => Crime 40 Wilson, James Q., and George L. Kelling. "Broken windows." Critical issues in policing: Contemporary readings (1982): 395- 407. 2 1 3 SECURITY PERCEPTION 4
41. Are Safer Looking Neighborhoods More Lively? 41 DATA • Web data (Google Street View imagery) • Mobile phone data (proxy for vitality) MODEL • Convolutional Neural Network (CNN) • Spatial Ordinary Least Squares De Nadai, Marco, et al. "Are Safer Looking Neighborhoods More Lively?: A Multimodal Investigation into Urban Life." ACM MM, 2016. 1 SECURITY PERCEPTION 4 2 3
42. Place Pulse 2.0 42 1 SECURITY PERCEPTION 4 Salesses, P., Schechtner, K., & Hidalgo, C. A. (2013). The collaborative image of the city: mapping the inequality of urban perception. PloS one 2 3
43. Security perception prediction • Learning safety perception, predict in Rome and Milan • Standard architecture AlexNet CNN • Trained on Places205* • Data Augmentation 43 * B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva. “Learning Deep Features for Scene Recognition using Places Database.” NIPS, 2014. 1 SECURITY PERCEPTION 4 2 3
44. Describe safety perception 44 REGRESSION SAFETY PERCEPTION LIVELINESS 1 SECURITY PERCEPTION 4 2 3
45. Security perception <-> presence of people 45 Urban metric Beta coefficient Population density Employees density Deprivation Distance from the center Safety appearance adj − RN 0.91 ** p-value < 0.001; * p-value < 0.01; 1 SECURITY PERCEPTION 4 2 3
46. 46 Urban metric Beta coefficient Population density 0.155** Employees density 0.328** Deprivation -0.022 Distance from the center -0.257** Safety appearance 0.105** adj − RN 0.91 ** p-value < 0.001; * p-value < 0.01; Security perception <-> presence of people 1 SECURITY PERCEPTION 4 2 3
47. 47 Urban metric Beta coefficient % of women (from census) 0.001 Deprivation -0.005 Distance from the center -0.003 Safety appearance 0.020** adj − RN 0.65 ** p-value < 0.001; * p-value < 0.01; Security perception <-> women around 1 SECURITY PERCEPTION 4 2 3
48. Visual elements for security perception 48 HIGH SAFETY PERCEPTION RANDOMLY OBSCURE PART OF THE IMAGE AND PREDICT 1 SECURITY PERCEPTION 4 2 3
49. Visual elements for security perception 49 HIGH SAFETY PERCEPTION RANDOMLY OBSCURE PART OF THE IMAGE AND PREDICT 1 SECURITY PERCEPTION 4 2 3
50. Result 50 De Nadai, Marco, et al. "Are Safer Looking Neighborhoods More Lively?: A Multimodal Investigation into Urban Life." ACM MM, 2016. Security perception can predict Presence of people 3 4 1 SECURITY PERCEPTION 2
51. Crime OBJECTIVE CHARACTERISTICS Q: can we describe how physical characteristics influence crime? Q: can crime be predicted from the urban physical characteristics?
52. Crime theory 52 CRIME Felson, M. (1994). Crime and everyday life: Insight and implications for society. Thousand Oaks, CA: Pine. 1 CRIME 4 2 3
53. MOTIVATED OFFENDER Crime theory 53 CRIME Felson, M. (1994). Crime and everyday life: Insight and implications for society. Thousand Oaks, CA: Pine. 1 CRIME 4 2 3
54. SUITABLE VICTIM MOTIVATED OFFENDER Crime theory 54 CRIME Felson, M. (1994). Crime and everyday life: Insight and implications for society. Thousand Oaks, CA: Pine. 1 CRIME 4 2 3
55. ABSENCE OF CAPABLE GUARDIAN SUITABLE VICTIM MOTIVATED OFFENDER Crime theory 55 CRIME Felson, M. (1994). Crime and everyday life: Insight and implications for society. Thousand Oaks, CA: Pine. 1 CRIME 4 2 3
56. Crime: does place matters/predicts? 56 DATA • Web and Open data (physical characteristics) • Mobile phone data (proxy for mobility) MODEL • Fit with Spatial Negative Binomial Model (NB) • Predict with Cross-validated Random Forest 1 CRIME 4 2 3
57. Crime data: describe and predict 57 CITY STRUCTURE OSM, Foursquare Data 1 CRIME 4 2 3
58. Crime data: describe and predict 58 CITY STRUCTURE DEPRIVATION Census Data OSM, Foursquare Data 1 CRIME 4 2 3
59. Crime data: describe and predict 59 PEOPLE’S MOBILITY CITY STRUCTURE DEPRIVATION O/D matrices (Mobile phone data) Census Data OSM, Foursquare Data 1 CRIME 4 2 3
60. log ((% ) = ( ) + W + ( Y Y P Y[K + ( + P\+ ] +[K The Negative Binomial Regression 60 1 CRIME 4 2 3
61. log ((% ) = ( ) + W + ( Y Y P Y[K + ( + P\+ ] +[K The Negative Binomial Regression 61 1 CRIME 4 2 3 Crime (Ground truth)
62. log ((% ) = ( ) + W + ( Y Y P Y[K + ( + P\+ ] +[K The Negative Binomial Regression 62 1 CRIME 4 2 3 Offset (population) Crime (Ground truth)
63. log ((% ) = ( ) + W + ( Y Y P Y[K + ( + P\+ ] +[K The Negative Binomial Regression 63 1 CRIME 4 2 3 Offset (population) Crime (Ground truth) Features (e.g. land use mix, deprivation)
64. log ((% ) = ( ) + W + ( Y Y P Y[K + ( + P\+ ] +[K The Negative Binomial Regression 64 1 CRIME 4 2 3 “everything is related to everything else, but near things are more related than distant things.” Tobler's first law of geography
65. log ((% ) = ( ) + W + ( Y Y P Y[K + ( + P\+ ] +[K The Spatial Negative Binomial Regression 65 (significant) Spatial Eigenvectors 1 CRIME 4 2 3 Getis, Arthur, and Daniel A. Griffith. "Comparative spatial filtering in regression analysis." Geographical analysis 34.2 (2002): 130-140. • Eigenvector Spatial Filtering
66. 66 Metric Social disorganization Daily routine City structure Full RMSE 231.93 312.70 145.04 127.76 McFadderPseudo-R^2 0.077 0.085 0.113 0.143 Predict (nowcast) crime * 5-fold Cross-validation 1 CRIME 4 2 3 PRELIMINARY RESULTS
67. Preliminary result 67 Physical characteristics better describe and predict crime 4 1 CRIME 2 3
68. Next? 68 4 • Add ambient population • Model Los Angeles, Boston, Providence • Describe commonality and differences 1 CRIME 2 3
69. Structural layout ‘GENERATE’ THE CITY Q: can we formalize the desired qualities of a neighborhood and prototype it?
70. Why simulate/generate a city • Endless discussions between stakeholders • Describe, predict => play • New insights 70 2 1 3 STRUCTURAL LAYOUT 4
71. Why simulate/generate a city • Endless discussions between stakeholders • Describe, predict => play • New insights 71 2 1 3 STRUCTURAL LAYOUT 4
72. Structural layout Design and enhance existing layouts • Learn from thousands of examples • Respecting the existing constraints • Build neighborhoods that work 72 2 1 3 STRUCTURAL LAYOUT 4
73. Constructive Machine Learning Traditional approaches are limited • Model complex relations • Predict structured objects • Hard and soft constraints on the output 73 2 1 3 STRUCTURAL LAYOUT 4
74. Constructive Machine Learning Traditional approaches are limited • Model complex relations • Predict structured objects • Hard and soft constraints on the output 74 2 1 3 STRUCTURAL LAYOUT 4 In collaboration with Andrea Passerini and MIT Media Lab
75. Why does it matter?
76. Data mining to understand • Data mining as an inexpensive way to understand urban mechanisms • Predict social outcome from newly arise data • Deep understanding of city life through multi-modal data 76
77. Limitations • Presence of data • Bias on data and models • Partial view • Domain adaptation (is it?) 77
78. Multi-disciplinariety means discussions We collaborate with • Studio Carlo Ratti (MIT) • Humnetlab Marta Gonzalez (MIT) • Data-pop alliance, World Bank • Criminology researcher • MIT Media Lab Changing places 78
79. Multi-disciplinary investigation into urban life 79 Describe Predict Generate
80. We published CONFERENCES • De Nadai, M., et al. "The Death and Life of Great Italian Cities: A Mobile Phone Data Perspective." WWW, 2016. • De Nadai, M., et al. "Are Safer Looking Neighborhoods More Lively?: A Multimodal Investigation into Urban Life." ACM MM, 2016. JOURNALS • Barlacchi, G., De Nadai M., et al. A multi-source dataset of urban life in the city of Milan and the Province of Trentino. Scientific data, 2 (2015). • Centellegher, S., De Nadai M., et al. "The Mobile Territorial Lab: a multilayered and dynamic view on parents’ daily lives." EPJ Data Science 5.1 (2016). 80
81. The plan for the (next) future 81
82. Thanks!
83. Safety perception: fix sparse votes • Learning safety perception, predict in Rome and Milan • AlexNet CNN trained on Places205* 83 * 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
84. “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
85. Safety perception: aggregation 85 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