Multi-Level Relationships between Satellite-Derived Nighttime Lighting Signals and Social Media–Derived Human Population Dynamics

Satellite-based measurements of the artificial nighttime light brightness (NTL) have been extensively used for studying urbanization and socioeconomic dynamics in a temporally consistent and spatially explicit manner. The increasing availability of geo-located big data detailing human population dynamics provides a good opportunity to explore the association between anthropogenic nocturnal luminosity and corresponding human activities, especially at fine time/space scales. In this study, we used Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB)–derived nighttime light images and the gridded number of location requests (NLR) from China’s largest social media platform to investigate the quantitative relationship between nighttime light radiances and human population dynamics across China at four levels: the provincial, city, county, and pixel levels. Our results show that the linear relationship between the NTL and NLR might vary with the observation level and magnitude. The dispersion between the two variables likely increases with the observation scale, especially at the pixel level. The effect of spatial autocorrelation and other socioeconomic factors on the relationship should be taken into account for nighttime light-based measurements of human activities. Furthermore, the bivariate relationship between the NTL and NLR was employed to generate a partition of human settlements based on the combined features of nighttime lights and human population dynamics. Cross-regional comparisons of the partitioned results indicate a diverse co-distribution of the NTL and NLR across various types of human settlements, which could be related to the city size/form and urbanization level. Our findings may provide new insights into the multi-level responses of nighttime light signals to human activity and the potential application of nighttime light data in association with geo-located big data for investigating the spatial patterns of human settlement