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
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
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
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
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
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
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
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
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 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.
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 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 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
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.
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
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
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
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
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 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
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