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.

该论文研究了使用社交媒体数据自动识别安全和适宜步行街道以部分自动化计算步行性的可行性，其结果为提供实用机会以及探索步行性领域尚未解答问题的研究提供了理论基础。