Abstract:
With a rapid growth in the global population, the modern world is undergoing a rapid expansion
of residential areas, especially in urban centres. This continuously demands for increased general services
and basic amenities, which are required according to the kind of population associated with the places. The
advent of location-based online social networks (LBSNs) has made it much easier to collect voluminous
data about users in different locations or spatial regions. The problem of mining location types from the
LBSN data is largely unexplored. In this paper, we propose a pattern mining approach, using the geo-socialtemporal data collected from LBSNs, to infer types of different locations. The proposed method first mines
frequent co-located users and user components from an LBSN and then performs a temporal pattern analysis
to finally categorize the locations. Extensive experiments are conducted on two real datasets that demonstrate
the efficacy of the proposed method in terms of mean reciprocal rank (MRR), visualisations, and insights.
The resulting inference mechanism would be very useful in several application domains including urban
planning, billboard placement, tour planning, and geo-social event planning
