Is there a crowd? experiences in using density-based clustering and outlier detection

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.