Walkability assessment of urban areas through social media data mining

Urban Informatics is an area of application for computer science focused on cities and life in urban areas, with cross-disciplinary contributions from geography, urban planning and social sciences. Urbanization, sided by the digital revolution, represents a huge opportunity for this kind of applications aimed at an improvement citizens’ quality of life. One of the potential applications of this kind of systems is represented by forms of “bottom-up” evaluations of the status quo, concerning different aspects of the urban texture and supporting decision-making activities. An example of these aspects is walkability (i.e. how comfortable and safe the urban environment is for walking). The computation of indicators describing the characteristics of areas and their usage by pedestrians can be achieved through the exploitation of data from social media, without requiring ad-hoc infrastructures, surveys or observations. This paper will present the results of different analyses on data about the City of Milano (Italy) acquired from different social media and web sources. Acquired metadata were analysed by means of Artificial Intelligence clustering techniques based on the DBSCAN algorithm, in order to achieve homogeneous areas characterized by different aspects (i.e. actual activity of inhabitants/tourists, presence of services) rather than by a top down administrative procedure. Results supply useful indications about perceived walkability and they can support the activity of public institutions in the design and planning of the city.