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Leveraging Big Data and Analytics to Improve Food, Energy, and Water System Sustainability
With the world population projected to grow significantly over the next few decades, and in the presence of additional stress caused by climate change and urbanization, securing the essential resources of food, energy, and water is one of the most pressing challenges that the world faces today. Ther...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7931953/ https://www.ncbi.nlm.nih.gov/pubmed/33693388 http://dx.doi.org/10.3389/fdata.2020.00013 |
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author | Pitts, Joshua Gopal, Sucharita Ma, Yaxiong Koch, Magaly Boumans, Roelof M. Kaufman, Les |
author_facet | Pitts, Joshua Gopal, Sucharita Ma, Yaxiong Koch, Magaly Boumans, Roelof M. Kaufman, Les |
author_sort | Pitts, Joshua |
collection | PubMed |
description | With the world population projected to grow significantly over the next few decades, and in the presence of additional stress caused by climate change and urbanization, securing the essential resources of food, energy, and water is one of the most pressing challenges that the world faces today. There is an increasing priority placed by the United Nations (UN) and US federal agencies on efforts to ensure the security of these critical resources, understand their interactions, and address common underlying challenges. At the heart of the technological challenge is data science applied to environmental data. The aim of this special publication is the focus on big data science for food, energy, and water systems (FEWSs). We describe a research methodology to frame in the FEWS context, including decision tools to aid policy makers and non-governmental organizations (NGOs) to tackle specific UN Sustainable Development Goals (SDGs). Through this exercise, we aim to improve the “supply chain” of FEWS research, from gathering and analyzing data to decision tools supporting policy makers in addressing FEWS issues in specific contexts. We discuss prior research in each of the segments to highlight shortcomings as well as future research directions. |
format | Online Article Text |
id | pubmed-7931953 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79319532021-03-09 Leveraging Big Data and Analytics to Improve Food, Energy, and Water System Sustainability Pitts, Joshua Gopal, Sucharita Ma, Yaxiong Koch, Magaly Boumans, Roelof M. Kaufman, Les Front Big Data Big Data With the world population projected to grow significantly over the next few decades, and in the presence of additional stress caused by climate change and urbanization, securing the essential resources of food, energy, and water is one of the most pressing challenges that the world faces today. There is an increasing priority placed by the United Nations (UN) and US federal agencies on efforts to ensure the security of these critical resources, understand their interactions, and address common underlying challenges. At the heart of the technological challenge is data science applied to environmental data. The aim of this special publication is the focus on big data science for food, energy, and water systems (FEWSs). We describe a research methodology to frame in the FEWS context, including decision tools to aid policy makers and non-governmental organizations (NGOs) to tackle specific UN Sustainable Development Goals (SDGs). Through this exercise, we aim to improve the “supply chain” of FEWS research, from gathering and analyzing data to decision tools supporting policy makers in addressing FEWS issues in specific contexts. We discuss prior research in each of the segments to highlight shortcomings as well as future research directions. Frontiers Media S.A. 2020-04-28 /pmc/articles/PMC7931953/ /pubmed/33693388 http://dx.doi.org/10.3389/fdata.2020.00013 Text en Copyright © 2020 Pitts, Gopal, Ma, Koch, Boumans and Kaufman. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Big Data Pitts, Joshua Gopal, Sucharita Ma, Yaxiong Koch, Magaly Boumans, Roelof M. Kaufman, Les Leveraging Big Data and Analytics to Improve Food, Energy, and Water System Sustainability |
title | Leveraging Big Data and Analytics to Improve Food, Energy, and Water System Sustainability |
title_full | Leveraging Big Data and Analytics to Improve Food, Energy, and Water System Sustainability |
title_fullStr | Leveraging Big Data and Analytics to Improve Food, Energy, and Water System Sustainability |
title_full_unstemmed | Leveraging Big Data and Analytics to Improve Food, Energy, and Water System Sustainability |
title_short | Leveraging Big Data and Analytics to Improve Food, Energy, and Water System Sustainability |
title_sort | leveraging big data and analytics to improve food, energy, and water system sustainability |
topic | Big Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7931953/ https://www.ncbi.nlm.nih.gov/pubmed/33693388 http://dx.doi.org/10.3389/fdata.2020.00013 |
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