Discovering Spatial Interaction Communities from Mobile Phone <scp>D</scp>ata

TLDR

Big Data and ubiquitous location‑aware technologies now enable collection of spatio‑temporal interaction data to analyze flow patterns in both physical space and cyberspace. This study explores patterns in phone‑call interaction and user movement networks by incorporating the geographical context of mobile phone cells within a combined qualitative‑quantitative framework. Using an agglomerative clustering algorithm based on Newman‑Girvan modularity and a gravity‑model‑augmented modularity function, the authors identify spatial‑interaction communities from a one‑week mobile phone dataset in a Chinese city. The analysis confirms distance decay and spatial continuity in call partitioning, shows strong correlation between physical movements and virtual interactions, and demonstrates that people tend to communicate within spatially proximate communities, offering insights for urban structure and spatial network community detection.

Abstract

Abstract In the age of B ig D ata, 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 N ewman‐ G irvan 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 C hina. 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.