  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.
