The Digital Life of Walkable Streets

TL;DR

This paper investigates using social media data from Flickr and Foursquare to automatically identify safe and walkable streets, offering a cost-effective alternative to traditional survey-based methods and revealing patterns in social media tagging related to walkability.

Contribution

It demonstrates the potential of social media data to assess street walkability and safety, providing a novel, automated approach that complements existing manual scoring methods.

Findings

Unsafe streets are photographed mainly during the day.

Walkable streets are tagged with walkability-related keywords.

Social media data can effectively identify walkable and safe streets.

Abstract

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.