BigDataFr recommande: A global, long term look at links to socio-economic trends

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.

Introduction

Human activities have transformed over half of the global land surface [1], a trend that continues to increase and is apparent in satellite imagery. One of the clearest signs is night-time lights as seen from space. Two central datasets are those derived from the Defense Meteorological Satellite Program (DMSP) and its successor, the Visible Infrared Imaging Radiometer Suite (VIIRS). […]

Read more
By Jeremy Proville, Daniel Zavala-Araiza, Gernot Wagner
Source: journals.plos.org