Spatiotemporal dynamics of population density in China using nighttime light and geographic weighted regression method

The distribution and dynamic changes of regional or national population data with long time series are very important for regional planning, resource allocation, government decision-making, disaster assessment, ecological protection, and other sustainability research. However, the existing populatio...

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Bibliographic Details
Published in:International journal of digital earth 2023-12, Vol.16 (1), p.2704-2723
Main Authors: Guo, Wei, Liu, Jinke, Zhao, Xuesheng, Hou, Wei, Zhao, Yunxuan, Li, Yongxing, Sun, Wenbin, Fan, Deqin
Format: Article
Language:English
Subjects:
Artificial neural networks
Decision making
Desaturation
Disasters
Distribution
Driving force analysis
Geographically weighted regression
Imaging radiometers
Infrared imaging
Infrared radiometers
Land use
Neural networks
Night
Night-time
Nighttime
Nighttime light
Population density
Population spatialization
Radiometers
Radiometry
Regional planning
Remote sensing
Resource allocation
Spatial distribution
Time series
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Summary:The distribution and dynamic changes of regional or national population data with long time series are very important for regional planning, resource allocation, government decision-making, disaster assessment, ecological protection, and other sustainability research. However, the existing population datasets such as LandScan and WorldPop all provide data from 2000 with limited time series, while GHS-POP only utilizes land use data with limited accuracy. In view of the limited remote sensing images of long time series, it is necessary to combine existing multi-source remote sensing data for population spatialization research. In this research, we developed a nighttime light desaturation index (NTLDI). Through the cross-sensor calibration model based on an autoencoder convolutional neural network, the NTLDI was calibrated with the same period Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS-DNB) data. Then, the geographically weighted regression method is used to determine the population density of China from 1990 to 2020 based on the long time series NTL. Furthermore, the change characteristics and the driving factors of China's population spatial distribution are analyzed. The large-scale, long-term population spatialization results obtained in this study are of great significance in government planning and decision-making, disaster assessment, resource allocation, and other aspects.
ISSN:1753-8947
1753-8955
DOI:10.1080/17538947.2023.2233493