Spatio-Temporal Analysis of Mobile Phone Network based on Self-Organizing Feature Map

Mohammadhossein Ghahramani, Meng Chu Zhou, Yan Qiao, Nai Qi Wu

Research output: Contribution to journalArticlepeer-review

Abstract

Spatio-temporal 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 of the 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 spatio-temporal 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. .

Original languageEnglish (US)
JournalIEEE Internet of Things Journal
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

Keywords

  • Analytical models
  • Call Detail Records (CDR)
  • Clustering algorithms
  • Feature extraction
  • Mobile handsets
  • Mobile phone data analysis
  • Neurons
  • Poles and towers
  • Self-organizing feature map (SOFM).
  • Shape
  • Spatio-temporal analysis

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