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

Ting Ma

doi:10.3390/rs10071128

Abstract

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.

Highlights

It is well documented that anthropogenic nocturnal lighting data are informative and indicative measures of various human activities at the local, regional, and national scales over both time and space [1,2,3,4,5]
There are significant linear (Figure 3a) and log-linear (Figure 3b,c) relationships between the regional sum of the area-weighted nighttime light brightness (NTL) data derived from Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB) images and the total pixel-level number of location requests (NLR) acquired by social media platforms
Satellite-derived observations of artificial lighting signals at night have been widely regarded as an efficient proxy measure of diverse human activities related to demographics, socioeconomics, and urbanization

Summary

Introduction

It is well documented that anthropogenic nocturnal lighting data are informative and indicative measures of various human activities at the local, regional, and national scales over both time and space [1,2,3,4,5]. Previous studies have usually focused on the regional-scale quantitative relationship between the annual composite of nocturnal luminosity and yearly statistical data Such investigations generally quantified the mean response of the anthropogenic nighttime brightness to inter-regional variations and inter-annual changes in demographic and socioeconomic variables (e.g., human population size, gross domestic product, electric power consumption, urban built-up areas) over space and time (typically using monotonic functions such as linear, log-linear, and power-law models). Remotely sensed nighttime light data currently derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument with the day/night band (DNB), which is located on board the Suomi National Polar-Orbiting Partnership (Suomi-NPP) satellite that generates monthly composite products, can provide timely and spatially explicit information regarding artificial lighting signals at night [20,21,22,23,24,25]. How nighttime light brightness signals timely respond to corresponding demographic and socioeconomic activities, at the pixel level, remains less well understood, largely because of the lack of large-scale synchronous observations of human activities

Objectives

Methods

Results

Conclusion