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

• Published in: International journal of digital earth Vol. 16; no. 1; pp. 2704 - 2723
• Main Authors: Guo, Wei, Liu, Jinke, Zhao, Xuesheng, Hou, Wei, Zhao, Yunxuan, Li, Yongxing, Sun, Wenbin, Fan, Deqin
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 31.12.2023; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Artificial neural networks; China; data collection; 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; regression analysis; Remote sensing; Resource allocation; Spatial distribution; Time series; time series analysis; China

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...