Discovering Spatial Interaction Communities from Mobile Phone Data

Abstract

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 spatial‐proximity 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.

Exploring the Effects of Sampling Locations for Calibrating the Huff Model Using Mobile Phone Location Data

“…Following the approach described above, attracted trips from cell phone towers to each commercial area were extracted. Many previous studies have used mobile phone location data to investigate the spatial interactions in complex urban environment [31,41,42]. There may be some uncertainties in the extraction of origins/destinations from mobile phone data.…”

“…Fortunately, the advent of information and communication technology (ICT) aids the acquisition of human trajectory data by lowering the cost of collecting, storing, processing, and sharing data and information [27,28]. Large volume data (such as GPS tracking data, mobile phone location data, social media check-in data, and so on), give new insights and a better understanding of human mobility and behaviors [29,30]; community detection [31][32][33]; urban activity space and dynamics [34,35]; and spatial interaction and modeling [36,37]. Regarding the calibration of spatial interaction model, most use all sampling locations to calibrate.…”

“…Thus, some researchers view this type of data as a reasonable source to describe human mobility and model spatial interactions [29,31,[33][34][35]37], and many valuable findings regarding human dynamics in the era of big data have been explored from this kind of data. For example, Gao et al [31] propose an alternative modularity function which incorporates a calibrated gravity model to discover the clustering structures of spatial-interaction communities generated by massive mobile phone users.…”

“…Thus, some researchers view this type of data as a reasonable source to describe human mobility and model spatial interactions [29,31,[33][34][35]37], and many valuable findings regarding human dynamics in the era of big data have been explored from this kind of data. For example, Gao et al [31] propose an alternative modularity function which incorporates a calibrated gravity model to discover the clustering structures of spatial-interaction communities generated by massive mobile phone users. Liang et al [41] analyze the collective intra-urban mobility using a modified spatial interaction model, and Simini et al [42] propose a radiation model which predicts mobility patterns in good agreement with observed data when compared with the calibrated gravity model by using different data sources including mobile phone data.…”

Understanding Spatiotemporal Patterns of Human Convergence and Divergence Using Mobile Phone Location Data

“…Human mobility source-sink areas can also be identified based on temporal variations in pick-up and drop-off locations [27]. Mobility networks can also be created from human movements, reflecting the spatial interactions of different urban areas and communities, or areas with close connections can be detected and used to evaluate and optimize urban planning [28,29].…”