Area 4: Location Analytics

The widespread use of smart phone has led to the proliferation of location based social networks (LBSNs), such as the Twitter, Foursquare, Instagram and YouTube. These networks enable people from all walks of life to exchange their thoughts, disseminate multimedia information, share travelling experience and connect to each other more conveniently than ever before. These developments also spur the need for many location based services, such as POI recommendation etc., in order to better help users in their daily lives.

Figure 4.1 shows the three main elements in a LBSN: user, location, and location-tagged user generated content. LBSNs not only facilitate the sharing of location-embedded information, but also project new social structures connected by the interdependency from physical locations. In this research, we focus on the comprehensive location analytics research. As shown in Figure 4.2, large-scale live UGC from various LBNS’s is combined to obtain a representative view for:

  • Understanding user's interest and social community
  • Generating location ontology
  • Mining media semantics

Figure 4.1: Illustration of the structure of a location based social network (LBSN)

Figure 4.2: First order and higher order location analytics. Users will check-in, upload photos, and wrrite comments at some location. First order analytics such as user flow, demographics, hotspots, and higher order analytics such as user community, user interest, events, location ontology can be mined from large scale location based user generated live data.


From the derived location analytics, we want to better understand what is happening and who are involved in any events, and at any time and anywhere. From which, various services can be provided or improved to facilitate users’ activities.