Resembling Population Density Distribution with Massive Mobile Phone Data

Authors

  • Teerayut Horanont Sirindhorn International Institute of Technology (SIIT), Thammasat University, Pathum Thani
  • Thananut Phiboonbanakit Sirindhorn International Institute of Technology (SIIT), Thammasat University, Pathum Thani
  • Santi Phithakkitnukoon Department of Computer Engineering, Faculty of Engineering, Chiang Mai University; Excellence Center in Infrastructure Technology and Transportation Engineering (ExCITE), Faculty of Engineering, Chiang Mai University https://orcid.org/0000-0002-5716-9363

DOI:

https://doi.org/10.5334/dsj-2018-024

Keywords:

mobile phone data analysis, call detail records, data representativeness

Abstract

As the mobile phone data (CDR data) has gained an increasing interest in research, such as social science, transportation, urban informatics, and big data, this study aims at examining the representativeness of the CDR data in terms of resemblance of the actual population density distribution from three perspectives; operator’s market share, urban-rural user population ratio, and user gender ratio. The results reveal that the representativeness of the data does not scale at the same rate with the operator’s market share, the urban-rural user population ratio of 80:20 can best represent the population density distribution, and an equal mixture of male and female user population can best resemble the population density distribution. This study is the first investigation into the representativeness of the CDR data. The findings provide useful information, which can serve an insightful guideline when dealing with the CDR data.

Author Biographies

Teerayut Horanont, Sirindhorn International Institute of Technology (SIIT), Thammasat University, Pathum Thani

Teerayut Horanont, Assistant Professor, School of Information Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, His research interests include Geographic Information System (GIS), Urban Computing, Indoor Navigation, Geospatial Big Data Platform Development and Analysis, Smart City and Precision Agriculture and Open Source Software and Open Standards Development.

Thananut Phiboonbanakit, Sirindhorn International Institute of Technology (SIIT), Thammasat University, Pathum Thani

Thananut Phiboonbanakit, Ph.D. Student, School of Information Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, His research interests include Geographic Information System (GIS) and Big - Data Analysis.

Santi Phithakkitnukoon, Department of Computer Engineering, Faculty of Engineering, Chiang Mai University; Excellence Center in Infrastructure Technology and Transportation Engineering (ExCITE), Faculty of Engineering, Chiang Mai University

Santi Phithakkitnukoon is an associate professor in the Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Thailand. He received B.S. and M.S. in Electrical Engineering from the Southern Methodist University, Texas, USA, and a Ph.D. in Computer Science and Engineering from the University of North Texas, USA in 2009. He was a Lecturer in Computing at the Open University, UK, a research associate at the Culture Lab, Newcastle University, UK, and a postdoctoral researcher at the SENSEable City Lab, Massachusetts Institute of Technology, USA.

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Published

2018-10-03

Issue

Vol. 17 (2018)

Section

Research Papers

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Copyright (c) 2018 The Author(s)

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