TY - GEN
T1 - Spatio-temporal analysis of mobile phone data for interaction recognition
AU - Ghahramani, Mohammadhossein
AU - Zhou, Mengchu
AU - Hon, Chi Tin
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/18
Y1 - 2018/5/18
N2 - Since the last decade mobile phones have changed people's lives. Mobile phone data can be utilized to derive the spatio-temporal data of subscriptions' whereabouts. It has been possible to study the mobility and traffic estimation with applications ranging from disaster management to disease epidemiology. In this work, we have focused on the use of Call Detail Records (CDRs) 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 cell towers to discover structures of spatio-temporal interaction communities in Macau. We have explored the inter and intra-polygon interaction flows. The results suggest that subscriptions tend to communicate within a spatial-proximity community. Understanding such insight is essential for resource optimization in network planning and content distribution.
AB - Since the last decade mobile phones have changed people's lives. Mobile phone data can be utilized to derive the spatio-temporal data of subscriptions' whereabouts. It has been possible to study the mobility and traffic estimation with applications ranging from disaster management to disease epidemiology. In this work, we have focused on the use of Call Detail Records (CDRs) 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 cell towers to discover structures of spatio-temporal interaction communities in Macau. We have explored the inter and intra-polygon interaction flows. The results suggest that subscriptions tend to communicate within a spatial-proximity community. Understanding such insight is essential for resource optimization in network planning and content distribution.
UR - http://www.scopus.com/inward/record.url?scp=85048229520&partnerID=8YFLogxK
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U2 - 10.1109/ICNSC.2018.8361374
DO - 10.1109/ICNSC.2018.8361374
M3 - Conference contribution
AN - SCOPUS:85048229520
T3 - ICNSC 2018 - 15th IEEE International Conference on Networking, Sensing and Control
SP - 1
EP - 6
BT - ICNSC 2018 - 15th IEEE International Conference on Networking, Sensing and Control
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Networking, Sensing and Control, ICNSC 2018
Y2 - 27 March 2018 through 29 March 2018
ER -