Abstract
We use a parallelized spatial analytics platform to process the twenty-one year totality of the longest-running time series of night-time lights data—the Defense Meteorological Satellite Program (DMSP) dataset—surpassing the narrower scope of prior studies to assess changes in area lit of countries globally. Doing so allows a retrospective look at the global, long-term relationships between night-time lights and a series of socio-economic indicators. We find the strongest correlations with electricity consumption, CO2 emissions, and GDP, followed by population, CH4 emissions, N2O emissions, poverty (inverse) and F-gas emissions. Relating area lit to electricity consumption shows that while a basic linear model provides a good statistical fit, regional and temporal trends are found to have a significant impact.
Highlights
Human activities have transformed over half of the global land surface [1], a trend that continues to increase and is apparent in satellite imagery
Two central datasets are those derived from the Defense Meteorological Satellite Program (DMSP) and its successor, the Visible Infrared Imaging Radiometer Suite (VIIRS)
Our night-time lights input dataset consists of annual composites of the stable lights band from DMSP-OLS Nighttime Lights Time Series Version 4, spanning 1992–2013 [2]
Summary
Human activities have transformed over half of the global land surface [1], a trend that continues to increase and is apparent in satellite imagery. There is a long literature exploring the imagery provided by these products, and the wide variety of applications they can serve. Perhaps most importantly, they are able to inform our understanding about the relationship between human activities and our environment at a global scale, without relying on national statistics with oft-differing methodologies and motivations by those collecting them. DMSP data are the longest-running time series of night-time lights, dating back to 1992 [2]. Over this period, a great deal of topics has been explored, at various spatial scales. DMSP has been used for everything from generating detailed CO2 emission maps [10,11] to creating innovative
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