Resembling population density distribution with massive mobile phone data

© 2018 The Author(s). 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 densi...

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Main Authors: Teerayut Horanont, Thananut Phiboonbanakit, Santi Phithakkitnukoon
Format: Journal
Published: 2018
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Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85055752940&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/62644
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-626442018-11-29T07:38:04Z Resembling population density distribution with massive mobile phone data Teerayut Horanont Thananut Phiboonbanakit Santi Phithakkitnukoon Computer Science © 2018 The Author(s). 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. 2018-11-29T07:38:04Z 2018-11-29T07:38:04Z 2018-10-03 Journal 16831470 2-s2.0-85055752940 10.5334/dsj-2018-024 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85055752940&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/62644
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Computer Science
spellingShingle Computer Science
Teerayut Horanont
Thananut Phiboonbanakit
Santi Phithakkitnukoon
Resembling population density distribution with massive mobile phone data
description © 2018 The Author(s). 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.
format Journal
author Teerayut Horanont
Thananut Phiboonbanakit
Santi Phithakkitnukoon
author_facet Teerayut Horanont
Thananut Phiboonbanakit
Santi Phithakkitnukoon
author_sort Teerayut Horanont
title Resembling population density distribution with massive mobile phone data
title_short Resembling population density distribution with massive mobile phone data
title_full Resembling population density distribution with massive mobile phone data
title_fullStr Resembling population density distribution with massive mobile phone data
title_full_unstemmed Resembling population density distribution with massive mobile phone data
title_sort resembling population density distribution with massive mobile phone data
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85055752940&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/62644
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