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Smart Profiling of City Areas Based on Web Data

The paper presents a framework for characterizing and profiling city areas from available data provided by online web services and web sites. These data are points of interest (restaurants, services, hotels, schools, churches, shops, wi-fi access points, etc.) disseminated in the city, local news, traffic information, city events, lifestyle and human behaviors. The framework allows selecting the different data sources, preprocessing the data, extracting meaningful features, executing a clustering algorithm to determine the profiles of the single areas of the city, and visualizing the results on the city map. The definition of the areas is based on the construction of a virtual grid of squared cells on the city. We employed the framework for profiling areas of the metropolitan city of Milan, Italy. We tested different cell sizes and employed the k-means clustering algorithm to group similar areas of the city. We highlight how areas belonging to the same cluster, although located in different zones of the city, actually present similar characteristics. Such a framework can be of the utmost importance for several entities. By exploiting the profiles of the city areas, citizens can benefit from tailored services, enterprises can define ad hoc marketing strategies, and local governments can be supported in decision making.