Discovery of spatio-temporal patterns from location based social networks
Document typeConference report
Rights accessRestricted access - publisher's policy
Location Based Social Networks (LBSN) have become an interesting source for mining user behavior. These networks (e.g. Twitter, Instagram or Foursquare) collect spatio-temporal data from users in a way that they can be seen as a set of collective and distributed sensors on a geographical area. Processing this information in different ways could result in patterns useful for several application domains. These patterns include simple or complex user visits to places in a city or groups of users that can be described by a common behavior. The domains of application range from the recommendation of points of interest to visit and route planning for touristic recommender systems to city analysis and planning. This paper presents the analysis of data collected for several months from such LBSN inside the geographical area of two large cities. The goal is to obtain by means of unsupervised data mining methods sets of patterns that describe groups of users in terms of routes, mobility patterns and behavior profiles that can be useful for city analysis and mobility decisions.
CitationBéjar, J. [et al.]. Discovery of spatio-temporal patterns from location based social networks. A: International Conference of the Catalan Association for Artificial Intelligence. "Artificial Intelligence Research and Development. Recent Advances and Applications". Barcelona: IOS Press, 2014, p. 126-135.
|museros13.pdf||Artitcle Principal||809.0Kb||Restricted access|