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Article

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

1
Ocean College, Zhejiang University, Zhoushan 316021, China
2
Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI 48824, USA
3
Department of Medicine, McGill University, Montreal, QC H3A 1A1, Canada
4
Department of Land Management, Zhejiang University, Hangzhou 310058, China
5
College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
6
Department of Earth Observation Science, Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, 7500 Enschede, The Netherlands
7
International Initiative on Spatial Lifecourse Epidemiology (ISLE)
*
Authors to whom correspondence should be addressed.
Remote Sens. 2019, 11(5), 574; https://doi.org/10.3390/rs11050574
Received: 6 February 2019 / Revised: 25 February 2019 / Accepted: 3 March 2019 / Published: 8 March 2019
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. View Full-Text
Keywords: point-of-interest; remote sensing; nighttime light; population modeling point-of-interest; remote sensing; nighttime light; population modeling
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MDPI and ACS Style

Yang, X.; Ye, T.; Zhao, N.; Chen, Q.; Yue, W.; Qi, J.; Zeng, B.; Jia, P. Population Mapping with Multisensor Remote Sensing Images and Point-Of-Interest Data. Remote Sens. 2019, 11, 574. https://doi.org/10.3390/rs11050574

AMA Style

Yang X, Ye T, Zhao N, Chen Q, Yue W, Qi J, Zeng B, Jia P. Population Mapping with Multisensor Remote Sensing Images and Point-Of-Interest Data. Remote Sensing. 2019; 11(5):574. https://doi.org/10.3390/rs11050574

Chicago/Turabian Style

Yang, Xuchao, Tingting Ye, Naizhuo Zhao, Qian Chen, Wenze Yue, Jiaguo Qi, Biao Zeng, and Peng Jia. 2019. "Population Mapping with Multisensor Remote Sensing Images and Point-Of-Interest Data" Remote Sensing 11, no. 5: 574. https://doi.org/10.3390/rs11050574

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