In the age of Big Data, the widespread use of location-awareness technologies has made it possible to collect spatio-temporal interaction data for analyzing flow patterns in both physical space and cyberspace. This research attempts to explore and interpret patterns embedded in the network of phone-call interaction and the network of phone-users' movements, by considering the geographical context of mobile phone cells. We adopt an agglomerative clustering algorithm based on a Newman-Girvan modularity metric and propose an alternative modularity function incorporating a gravity model to discover the clustering structures of spatial-interaction communities using a mobile phone dataset from one week in a city in China. The results verify the distance decay effect and spatial continuity that control the process of partitioning phone-call interaction, which indicates that people tend to communicate within a spatialproximity community. Furthermore, we discover that a high correlation exists between phone-users' movements in physical space and phone-call interaction in cyberspace. Our approach presents a combined qualitative-quantitative framework to identify clusters and interaction patterns, and explains how geographical context influences communities of callers and receivers. The findings of this empirical study are valuable for urban structure studies as well as for the detection of communities in spatial networks.
Furthermore, REF utilizes one week's mobile phone data to analyze the structures of spatial interaction communities of a city in China.
Discovering Spatial Interaction Communities from Mobile Phone Data
Transactions in Gis
Computer Science