TY - JOUR
T1 - Extracting Significant Mobile Phone Interaction Patterns Based on Community Structures
AU - Ghahramani, Mohammadhossein
AU - Zhou, Meng Chu
AU - Hon, Chi Tin
N1 - Funding Information:
Manuscript received January 4, 2018; revised March 29, 2018; accepted May 10, 2018. Date of publication July 25, 2018; date of current version February 28, 2019. This work was supported by Fundo para o Desenvolvi-mento das Ciencias e da Tecnologia under Grant 119/2014/A3. The Associate Editor for this paper was Y. Gao. (Corresponding author: MengChu Zhou.) M. Ghahramani is with the Institute of Systems Engineering, Macau University of Science and Technology, Macau 999078, China (e-mail: ghahremani@ieee.org).
Publisher Copyright:
© 2000-2011 IEEE.
PY - 2019/3
Y1 - 2019/3
N2 - 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.
AB - 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.
KW - Big data
KW - call detail records
KW - community detection
KW - mobile phone data analysis
KW - spatio-temporal analysis
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U2 - 10.1109/TITS.2018.2836800
DO - 10.1109/TITS.2018.2836800
M3 - Article
AN - SCOPUS:85059111401
SN - 1524-9050
VL - 20
SP - 1031
EP - 1041
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 3
M1 - 8419779
ER -