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.
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
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
• 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.
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
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
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
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
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
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
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