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

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 applications 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. Over 500 thousand points, which represent the photos and the POIs collected from the above-mentioned social media, 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.