Context (archaeology)

World Wide Web

Mobile computing

Archaeology

Telecommunications

Set (abstract data type)

Data mining

Computer science

Mobile social network

Computer network

Geography

Mobile phone

Data science

Artificial intelligence

Cluster analysis

Polygon (computer graphics)

Programming language

Human–computer interaction

Machine learning

Frame (networking)

IEEE Transactions on Intelligent Transportation Systems

Mobile phones have emerged as an essential part of people's lives. The data produced from them can be utilized to derive the spatio-temporal information of their users' whereabouts. We can obtain a rich data set of human activities, interactions, social relationships, and mobility. Hence, it has been possible to explore these information sources with applications ranging from disaster management to disease epidemiology. In this paper, we have focused on the use of call detail records to explore and interpret patterns embedded in interaction flows of people through their mobile phone calls. To do so, we consider the geographical context of subscribers/celltowers to discover structures of spatio-temporal interactions and communities' patterns in Macau. We have explored the inter and intra-polygon interaction flows. The results suggest that subscribers tend to communicate within a spatial-proximity community. In order to delineate relatively contiguous objects with similar attribute values, we have implemented an efficient hierarchical clustering approach. By identifying key objects and their close associates and exploring their communication patterns, we can detect shared interests and dominant interactions that influence societal patterns. Such insight is useful for resource optimization in network planning, content distribution, and urban planning.

Extracting Significant Mobile Phone Interaction Patterns Based on Community Structures