Location Based Social Networks (LBSN) like Twitter or Instagram are a good source for user spatio-temporal behavior. These networks collect data from users in such a way that they can be seen as a set of collective and distributed sensors of a geographical area. A low rate sampling of user’s location information can be obtained during large intervals of time that can be used to discover complex patterns, including mobility profiles, points of interest or unusual events. These patterns can be used as the elements of a knowledge base for different applications in different domains like mobility route planning, touristic recommendation systems or city planning. The aim of this paper is twofold, first to analyze the frequent spatio-temporal patterns that users share when living and visiting a city. This behavior is studied by means of frequent itemsets algorithms in order to establish some associations among visits that can be interpreted as interesting routes or spatio-temporal connections. Second, to analyze how the spatio-temporal behavior of a large number of users can be segmented in different profiles. These behavioral profiles are obtained by means of clustering algorithms that show the different patterns of behavior of visitors and citizens. The data analyzed was obtained from the public data feeds of Twitter and Instagram within an area surrounding the cities of Barcelona and Milan for a period of several months. The analysis of these data shows that these kind of algorithms can be successfully applied to data from any city (or general area) to discover useful patterns that can be interpreted on terms of singular places and areas and their temporal relationships.

Peer Reviewed