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Mining location based social networks to understand the citizen’s check-in patterns


Analyzing and understanding the movement patterns of the citizen’s with in a city, plays an important role in urban and transportation planning. Though many recent research papers focused on mining LBSN services data and performed in-depth analysis of users’ mobility patterns and their impact on their social inter-connections and friends. This paper focuses on understanding the Citizen’s movement patterns of socially interconnected users in friendship networks, by analyzing their spatial-temporal footprints/check-ins. The aim of this paper is to find the impact of structural patterns hidden in the nodes of a friendship network and external environment changes on the check-in patterns of the users. First, we classify each spatial check-in event based on its cause into either self reinforcing behavior or social influence or external stimulus. Then we mine the collective behavior of the all the users during some special events.

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Correspondence to Sonia Khetarpaul.

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Khetarpaul, S. Mining location based social networks to understand the citizen’s check-in patterns. Computing (2021).

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  • Spatial and temporal data
  • Spatial influence
  • Check-in patterns
  • Location based social networks

Mathematics Subject Classification

  • 68T09
  • 91D30