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Discovering Spatial Interaction Communities from Mobile Phone D ata
Author(s) -
Gao Song,
Liu Yu,
Wang Yaoli,
Ma Xiujun
Publication year - 2013
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/tgis.12042
Subject(s) - cyberspace , modularity (biology) , mobile phone , context (archaeology) , computer science , cluster analysis , phone , spatial contextual awareness , hierarchical clustering , metric (unit) , geography , human–computer interaction , data mining , world wide web , the internet , engineering , artificial intelligence , telecommunications , linguistics , philosophy , genetics , operations management , archaeology , biology
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.

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