Population Mapping with Multisensor Remote Sensing Images and Point-Of-Interest Data

Xuchao Yang, Tingting Ye, Naizhuo Zhao, Qian Chen, Wenze Yue, Jiaguo Qi, Biao Zeng, Peng Jia

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Abstract

Fine-resolution population distribution mapping is necessary for many purposes, which cannot be met by aggregated census data due to privacy. Many approaches utilize ancillary data that are related to population density, such as nighttime light imagery and land use, to redistribute the population from census to finer-scale units. However, most of the ancillary data used in the previous studies of population modeling are environmental data, which can only provide a limited capacity to aid population redistribution. Social sensing data with geographic information, such as point-of-interest (POI), are emerging as a new type of ancillary data for urban studies. This study, as a nascent attempt, combined POI and multisensor remote sensing data into new ancillary data to aid population redistribution from census to grid cells at a resolution of 250 m in Zhejiang, China. The accuracy of the results was assessed by comparing them with WorldPop. Results showed that our approach redistributed the population with fewer errors than WorldPop, especially at the extremes of population density. The approach developed in this study—incorporating POI with multisensor remotely sensed data in redistributing the population onto finer-scale spatial units—possessed considerable potential in the era of big data, where a substantial volume of social sensing data is increasingly being collected and becoming available.
Original languageEnglish
Article number574
Pages (from-to)1-14
Number of pages14
JournalRemote sensing
Volume11
Issue number5
DOIs
Publication statusPublished - 8 Mar 2019

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remote sensing
census
aid
population density
population modeling
population distribution
imagery
land use

Keywords

  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-GOLD

Cite this

Yang, Xuchao ; Ye, Tingting ; Zhao, Naizhuo ; Chen, Qian ; Yue, Wenze ; Qi, Jiaguo ; Zeng, Biao ; Jia, Peng. / Population Mapping with Multisensor Remote Sensing Images and Point-Of-Interest Data. In: Remote sensing. 2019 ; Vol. 11, No. 5. pp. 1-14.
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abstract = "Fine-resolution population distribution mapping is necessary for many purposes, which cannot be met by aggregated census data due to privacy. Many approaches utilize ancillary data that are related to population density, such as nighttime light imagery and land use, to redistribute the population from census to finer-scale units. However, most of the ancillary data used in the previous studies of population modeling are environmental data, which can only provide a limited capacity to aid population redistribution. Social sensing data with geographic information, such as point-of-interest (POI), are emerging as a new type of ancillary data for urban studies. This study, as a nascent attempt, combined POI and multisensor remote sensing data into new ancillary data to aid population redistribution from census to grid cells at a resolution of 250 m in Zhejiang, China. The accuracy of the results was assessed by comparing them with WorldPop. Results showed that our approach redistributed the population with fewer errors than WorldPop, especially at the extremes of population density. The approach developed in this study—incorporating POI with multisensor remotely sensed data in redistributing the population onto finer-scale spatial units—possessed considerable potential in the era of big data, where a substantial volume of social sensing data is increasingly being collected and becoming available.",
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Yang, X, Ye, T, Zhao, N, Chen, Q, Yue, W, Qi, J, Zeng, B & Jia, P 2019, 'Population Mapping with Multisensor Remote Sensing Images and Point-Of-Interest Data' Remote sensing, vol. 11, no. 5, 574, pp. 1-14. https://doi.org/10.3390/rs11050574

Population Mapping with Multisensor Remote Sensing Images and Point-Of-Interest Data. / Yang, Xuchao; Ye, Tingting; Zhao, Naizhuo; Chen, Qian; Yue, Wenze; Qi, Jiaguo; Zeng, Biao; Jia, Peng.

In: Remote sensing, Vol. 11, No. 5, 574, 08.03.2019, p. 1-14.

Research output: Contribution to journalArticleAcademicpeer-review

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T1 - Population Mapping with Multisensor Remote Sensing Images and Point-Of-Interest Data

AU - Yang, Xuchao

AU - Ye, Tingting

AU - Zhao, Naizhuo

AU - Chen, Qian

AU - Yue, Wenze

AU - Qi, Jiaguo

AU - Zeng, Biao

AU - Jia, Peng

PY - 2019/3/8

Y1 - 2019/3/8

N2 - Fine-resolution population distribution mapping is necessary for many purposes, which cannot be met by aggregated census data due to privacy. Many approaches utilize ancillary data that are related to population density, such as nighttime light imagery and land use, to redistribute the population from census to finer-scale units. However, most of the ancillary data used in the previous studies of population modeling are environmental data, which can only provide a limited capacity to aid population redistribution. Social sensing data with geographic information, such as point-of-interest (POI), are emerging as a new type of ancillary data for urban studies. This study, as a nascent attempt, combined POI and multisensor remote sensing data into new ancillary data to aid population redistribution from census to grid cells at a resolution of 250 m in Zhejiang, China. The accuracy of the results was assessed by comparing them with WorldPop. Results showed that our approach redistributed the population with fewer errors than WorldPop, especially at the extremes of population density. The approach developed in this study—incorporating POI with multisensor remotely sensed data in redistributing the population onto finer-scale spatial units—possessed considerable potential in the era of big data, where a substantial volume of social sensing data is increasingly being collected and becoming available.

AB - Fine-resolution population distribution mapping is necessary for many purposes, which cannot be met by aggregated census data due to privacy. Many approaches utilize ancillary data that are related to population density, such as nighttime light imagery and land use, to redistribute the population from census to finer-scale units. However, most of the ancillary data used in the previous studies of population modeling are environmental data, which can only provide a limited capacity to aid population redistribution. Social sensing data with geographic information, such as point-of-interest (POI), are emerging as a new type of ancillary data for urban studies. This study, as a nascent attempt, combined POI and multisensor remote sensing data into new ancillary data to aid population redistribution from census to grid cells at a resolution of 250 m in Zhejiang, China. The accuracy of the results was assessed by comparing them with WorldPop. Results showed that our approach redistributed the population with fewer errors than WorldPop, especially at the extremes of population density. The approach developed in this study—incorporating POI with multisensor remotely sensed data in redistributing the population onto finer-scale spatial units—possessed considerable potential in the era of big data, where a substantial volume of social sensing data is increasingly being collected and becoming available.

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