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Night-time lights: A global, long term look at links to socio-economic trends

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...

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Detalles Bibliográficos
Autores principales: Proville, Jeremy, Zavala-Araiza, Daniel, Wagner, Gernot
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5367807/
https://www.ncbi.nlm.nih.gov/pubmed/28346500
http://dx.doi.org/10.1371/journal.pone.0174610
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author Proville, Jeremy
Zavala-Araiza, Daniel
Wagner, Gernot
author_facet Proville, Jeremy
Zavala-Araiza, Daniel
Wagner, Gernot
author_sort Proville, Jeremy
collection PubMed
description 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, CO(2) emissions, and GDP, followed by population, CH(4) emissions, N(2)O 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.
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spelling pubmed-53678072017-04-06 Night-time lights: A global, long term look at links to socio-economic trends Proville, Jeremy Zavala-Araiza, Daniel Wagner, Gernot PLoS One Research Article 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, CO(2) emissions, and GDP, followed by population, CH(4) emissions, N(2)O 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. Public Library of Science 2017-03-27 /pmc/articles/PMC5367807/ /pubmed/28346500 http://dx.doi.org/10.1371/journal.pone.0174610 Text en © 2017 Proville et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Proville, Jeremy
Zavala-Araiza, Daniel
Wagner, Gernot
Night-time lights: A global, long term look at links to socio-economic trends
title Night-time lights: A global, long term look at links to socio-economic trends
title_full Night-time lights: A global, long term look at links to socio-economic trends
title_fullStr Night-time lights: A global, long term look at links to socio-economic trends
title_full_unstemmed Night-time lights: A global, long term look at links to socio-economic trends
title_short Night-time lights: A global, long term look at links to socio-economic trends
title_sort night-time lights: a global, long term look at links to socio-economic trends
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5367807/
https://www.ncbi.nlm.nih.gov/pubmed/28346500
http://dx.doi.org/10.1371/journal.pone.0174610
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