%0 Journal Article
%T The Digital Life of Walkable Streets
%A Daniele Quercia
%A Luca Maria Aiello
%A Rossano Schifanella
%A Adam Davies
%J Computer Science
%D 2015
%I arXiv
%R 10.1145/2736277.2741631
%X Walkability has many health, environmental, and economic benefits. That is why web and mobile services have been offering ways of computing walkability scores of individual street segments. Those scores are generally computed from survey data and manual counting (of even trees). However, that is costly, owing to the high time, effort, and financial costs. To partly automate the computation of those scores, we explore the possibility of using the social media data of Flickr and Foursquare to automatically identify safe and walkable streets. We find that unsafe streets tend to be photographed during the day, while walkable streets are tagged with walkability-related keywords. These results open up practical opportunities (for, e.g., room booking services, urban route recommenders, and real-estate sites) and have theoretical implications for researchers who might resort to the use social media data to tackle previously unanswered questions in the area of walkability.
%U http://arxiv.org/abs/1503.02825v1