Abstract
The massive growth of GPS equipped smartphones coupled with the increasing importance of Social Media has led to the emergence of new location-based services over LBSNs (Location-based Social Networks) which allow citizens to act as social sensors reporting about their locations. This proactive social reporting might be beneficial for researchers in a wide number of scenarios like the one addressed in this paper: monitoring crowds in the city involving an assembly of individuals in term of size, duration, motivation, cohesion and proximity. We introduce a methodology for crowd-detection that combines social data mining, density-based clustering and outlier detection into a solution that can operate on-the-fly to predict public crowds, i.e. to foresee, in short term, the formation of potential multitudes based on the prior analysis of the region. Twitter is mined to analyze geo-tagged data in New York at New Year’s Eve, so that those predictable public crowds are discovered.
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Ben Kalifa, M., Redondo, R.P.D., Fernández Vilas, A., López Serrano, R., Servia Rodríguez, S. (2014). Is There a Crowd? Experiences in Using Density-Based Clustering and Outlier Detection. In: Prasath, R., O’Reilly, P., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8891. Springer, Cham. https://doi.org/10.1007/978-3-319-13817-6_16
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DOI: https://doi.org/10.1007/978-3-319-13817-6_16
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