Mining the Social Media Data for a Bottom-Up Evaluation of Walkability

TL;DR

This paper explores using social media data from Flickr and Foursquare to evaluate walkability in Milan by clustering activity points to identify user-defined walkable areas, offering a bottom-up urban analysis approach.

Contribution

It introduces a novel method of assessing urban walkability through social media data clustering, moving beyond traditional top-down city planning methods.

Findings

Effective clustering of 500,000 social media points revealed walkable urban areas.

Demonstrated the feasibility of using social media data for real-time urban quality assessment.

Provided insights into pedestrian activity patterns in Milan.

Abstract

Urbanization represents a huge opportunity for computer applications enabling cities to be managed more efficiently while, at the same time, improving the life quality of their citizens. One of the potential application of this kind of systems is a bottom-up evaluation of the level of walkability of the city (namely the level of usefulness, comfort, safety and attractiveness of an urban area for walking). This is based on the usage of data from social media for the computation of structured indicators describing the actual usage of areas by pedestrians. This paper will present an experimentation of analysis of data about the city of Milano (Italy) acquired from Flickr and Foursquare. The over 500 thousand points, which represent the photos and the POIs collected from the above mentioned social meda, were clustered through an iterative approach based on the DBSCAN algorithm, in order to achieve homogeneous areas defined by the actual activity of inhabitants and tourists rather than by a top down administrative procedure and to supply useful indications on the level of walkability of the city of Milan.