Nighttime Lights and Population Migration: Revisiting Classic Demographic Perspectives with an Analysis of Recent European Data

Abstract

This study examines whether the Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime lights can be used to predict population migration in small areas in European Union (EU) countries. The analysis uses the most current data measured at the smallest administrative unit in 18 EU countries provided by the European Commission. The ordinary least squares regression model shows that, compared to population size and gross domestic product (GDP), lights data are another useful predictor. The predicting power of lights is similar to population but it is much stronger than GDP per capita. For most countries, regression models with lights can explain 50–90% of variances in small area migrations. The results also show that the annual VIIRS lights (2015–2016) are slightly better predictors for migration population than averaged monthly VIIRS lights (2014–2017), and their differences are more pronounced in high latitude countries. Further, analysis of quadratic models, models with interaction effects and spatial lag, shows the significant effect of lights on migration in the European region. The study concludes that VIIRS nighttime lights hold great potential for studying human migration flow, and further open the door for more widespread application of remote sensing information in studying dynamic demographic processes.

Patterns of exposure to SARS-CoV-2 carriers manifest multiscale association between urban landscape morphology and human activity

“…Our results show that at least for the time and location covered in this study, satellite-based levels of NTL and LPI are statistically significant measures to the likelihood of a given cell within the urban area to host a RLSC. While linkages between NTL and different aspects of human activity were previously investigated in numerous studies 29,30,31,32,33,34,35,36 to the best of our knowledge association between spatial patterns of human activity, and satellite-derived urban landscape heterogeneity, as expressed here by the distribution of RLSC and levels of LPI, respectively, is shown here for the first time.…”

“…Complementary information on the spatial characteristic of the urban landscape and its expression in patterns of human activity is obtained by the nighttime light signal (NTL). The NTL is commonly used in urban studies, and has been associated with socioeconomic dynamics 29 , population density 30 , urbanization 31 , crime analysis 32 , migrations 33 , military conflicts 34 , spread of epidemics 8 identification of commercial areas 35 , tourism 36 among many others, constituting a good predictor of the magnitude of human activity.…”

Prediction of the Old-Age Dependency Ratio in Chinese Cities Using DMSP/OLS Nighttime Light Data

“…Nighttime light indices are important spatio-temporal characteristics that reflect human activities, providing a new perspective to reveal the location of the illumination and the extent of human habitation [ 48 , 49 ]. Previous studies have found the application potential of nighttime light in economic and social development [ 50 ], but there is little research on nighttime light and the ODR.…”

Self-Supervised Material and Texture Representation Learning for Remote Sensing Tasks

“…We use SGD optimizer, batch-size of 128, learning rate of 0.0005, momentum of 0.6, and weight decay of 0.001. For performance, we report the mean average precision of all classes (19).…”

“…Automated understanding of remote sensing imagery has been a long standing goal of the computer vision community. Its broad applicability has driven research and development in construction phase detection [23], infrastructure mapping [36,55,71,100], land use monitoring [41], post natural disaster damage assessment [42,89,97], urban 3D reconstruction [39,57], population migration prediction [19], and climate change tracking [79]. Most of those methods require some degree of annotation effort, which is often expensive and/or time consuming.…”