TY - JOUR
T1 - Spatiotemporal Analysis of Mobile Phone Network Based on Self-Organizing Feature Map
AU - Ghahramani, Mohammadhossein
AU - Zhou, Mengchu
AU - Qiao, Yan
AU - Wu, Naiqi
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61803397; in part by the Science and Technology Development Fund (FDCT) of Macau under Grant 0018/2021/A1, Grant 0083/2021/A2, and Grant 0015/2020/AMJ; and in part by the Ministry of Science and Higher Education of the Russian Federation as part of World-Class Research Center Program: Advanced Digital Technologies under Contract 075-15-2020-903.
Publisher Copyright:
© 2014 IEEE.
PY - 2022/7/1
Y1 - 2022/7/1
N2 - Spatiotemporal analysis ranges from simple univariate descriptive statistics to more complex multivariate analyses. Such an analysis can be used to explore spatial and temporal patterns in different domains, i.e., spatial and temporal information of subscribers in Internet of Things networks. Most spatial and temporal analysis techniques are based on conventional quantitative and traditional data mining approaches, such as the k -means algorithm. Clustering approaches based on artificial neural networks can be more efficient since they can reveal nonlinear patterns. Hence, in this work, we tailor an AI-based spatiotemporal unsupervised model such that the underlying pattern structure of a mobile phone network can be revealed, relative similarity among interactions extracted, and the associated patterns analyzed. The proposed approach is based on an optimized self-organizing feature map. It deals with high-dimensionality concerns and preserves inherent data structures. By identifying the spatial and temporal associations, decision makers can explore dominant interactions that can be used for resource optimization in network planning, content distribution, and urban planning.
AB - Spatiotemporal analysis ranges from simple univariate descriptive statistics to more complex multivariate analyses. Such an analysis can be used to explore spatial and temporal patterns in different domains, i.e., spatial and temporal information of subscribers in Internet of Things networks. Most spatial and temporal analysis techniques are based on conventional quantitative and traditional data mining approaches, such as the k -means algorithm. Clustering approaches based on artificial neural networks can be more efficient since they can reveal nonlinear patterns. Hence, in this work, we tailor an AI-based spatiotemporal unsupervised model such that the underlying pattern structure of a mobile phone network can be revealed, relative similarity among interactions extracted, and the associated patterns analyzed. The proposed approach is based on an optimized self-organizing feature map. It deals with high-dimensionality concerns and preserves inherent data structures. By identifying the spatial and temporal associations, decision makers can explore dominant interactions that can be used for resource optimization in network planning, content distribution, and urban planning.
KW - Call detail records (CDRs)
KW - mobile phone data analysis
KW - self-organizing feature map (SOFM)
KW - spatiotemporal analysis
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U2 - 10.1109/JIOT.2021.3127203
DO - 10.1109/JIOT.2021.3127203
M3 - Article
AN - SCOPUS:85119443304
SN - 2327-4662
VL - 9
SP - 10948
EP - 10960
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 13
ER -