Resembling Population Density Distribution with Massive Mobile Phone Data
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 t...
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doaj-c179ec5abc154a808aa10db265ce98ed2020-11-24T20:46:48ZengUbiquity PressData Science Journal1683-14702018-10-011710.5334/dsj-2018-024682Resembling Population Density Distribution with Massive Mobile Phone DataTeerayut Horanont0Thananut Phiboonbanakit1Santi Phithakkitnukoon2Sirindhorn International Institute of Technology (SIIT), Thammasat University, Pathum ThaniSirindhorn International Institute of Technology (SIIT), Thammasat University, Pathum ThaniDepartment of Computer Engineering, Faculty of Engineering, Chiang Mai University; Excellence Center in Infrastructure Technology and Transportation Engineering (ExCITE), Faculty of Engineering, Chiang Mai UniversityAs 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.https://datascience.codata.org/articles/840mobile phone data analysiscall detail recordsdata representativeness |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Teerayut Horanont Thananut Phiboonbanakit Santi Phithakkitnukoon |
spellingShingle |
Teerayut Horanont Thananut Phiboonbanakit Santi Phithakkitnukoon Resembling Population Density Distribution with Massive Mobile Phone Data Data Science Journal mobile phone data analysis call detail records data representativeness |
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 |
publisher |
Ubiquity Press |
series |
Data Science Journal |
issn |
1683-1470 |
publishDate |
2018-10-01 |
description |
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. |
topic |
mobile phone data analysis call detail records data representativeness |
url |
https://datascience.codata.org/articles/840 |
work_keys_str_mv |
AT teerayuthoranont resemblingpopulationdensitydistributionwithmassivemobilephonedata AT thananutphiboonbanakit resemblingpopulationdensitydistributionwithmassivemobilephonedata AT santiphithakkitnukoon resemblingpopulationdensitydistributionwithmassivemobilephonedata |
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1716811439311683584 |