Improving population mapping using Luojia 1-01 nighttime light image and location-based social media data.

Fine-resolution population mapping, which is vital to urban planning, public health, and disaster management, has gained considerable attention in socioeconomic and environmental studies. Although population distribution has been considered highly correlated with urban facilities, the quantitative relationship between the two has yet to be revealed when considering huge heterogeneity. To address this problem, the present study proposed a novel population mapping method by adopting Luojia 1-01 nighttime light imagery, points of interest (POI), and social media check-in data. A grid-based attraction degree (AD) model was built to quantify the possibility of population concentration in each geographic unit with a comprehensive consideration of the distribution and the popularity of facilities. On the basis of kernel density estimation, 16 attraction indexes were extracted by matching POI and check-in data. Multiple variables were used to train a random forest model, through which fine-scale population mapping was conducted in Zhejiang, China. The comparison between demographic and WorldPop data proved the high accuracy of our approach (R

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Copyright © 2020. Published by Elsevier B.V.

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Declaration of competing interest The authors declare no conflict of interest.