A review of scientific advancements in datasets derived from big data for monitoring the Sustainable Development Goals
The Sustainable Development Goals (SDGs) suffer from a lack of national data needed for effective monitoring and implementation. Almost half of the SDG indicators are not regularly produced, and available datasets are often out-of-date. New monitoring approaches using big data are advancing rapidly...
Gespeichert in:
Veröffentlicht in: | Sustainability science 2021-09, Vol.16 (5), p.1701-1716 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 1716 |
---|---|
container_issue | 5 |
container_start_page | 1701 |
container_title | Sustainability science |
container_volume | 16 |
creator | Allen, Cameron Smith, Maggie Rabiee, Maryam Dahmm, Hayden |
description | The Sustainable Development Goals (SDGs) suffer from a lack of national data needed for effective monitoring and implementation. Almost half of the SDG indicators are not regularly produced, and available datasets are often out-of-date. New monitoring approaches using big data are advancing rapidly and can complement official statistics to help fill critical data gaps. However, there is poor information-sharing on the latest innovations and research collaborations across different thematic areas, and limited evaluation of strengths and weaknesses for supporting national monitoring. This paper provides a systematic review of the academic literature over the past 5 years relating to the use of big data to support monitoring of the SDGs. It reviews the state-of-the-art research using big data and advanced analytics to produce new datasets, the alignment of these datasets with the official SDG indicators, the main types and sources of big data used, and the analytical methods applied. We developed a set of evaluation criteria and applied it to highlight some of the strengths and limitations of these datasets derived from big data. We find that recent research has developed a considerable range of new datasets that could contribute to monitoring 15 goals, 51 targets, and 69 indicators. Dominant focal areas of research include land and biodiversity, health, water, cities and settlements, and poverty. Satellite and Earth Observation data were the primary sources used, most commonly applied with machine learning methods and cloud computing. However, several challenges remain, including ensuring the relevance of new datasets for monitoring SDG indicators, cost and accessibility considerations, sustainability aspects, and linking global datasets to nationally owned monitoring processes. |
doi_str_mv | 10.1007/s11625-021-00982-3 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2556149618</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2556149618</sourcerecordid><originalsourceid>FETCH-LOGICAL-c346t-de364d31a5b5fb95d3a8a16b0b0ac7264420c65018e64eaa1db15e562dab793</originalsourceid><addsrcrecordid>eNp9UMtOwzAQtBBIlMIPcLLE2eBH7CTHqkBBQuJQ7pYdb4qrJg52GsTfkzYIbpxmVjszqx2Erhm9ZZTmd4kxxSWhnBFKy4ITcYJmrFCcZFTmp79cyXN0kdKWUsXzspihYYEjDB4-cahxqjy0va99hY0bTFtBM84J-xY705sEI3cQ_QAO1zE02PrNcYPrEHETWt-H6NsN7t8Br_epN741dgf4HgbYhe6QhlfB7NIlOqtHgKsfnKP148Pb8om8vK6el4sXUolM9cSBUJkTzEgra1tKJ0xhmLLUUlPlXGUZp5WSlBWgMjCGOcskSMWdsXkp5uhmSu1i-NhD6vU27GM7HtRcSsWyUrFiVPFJVcWQUoRad9E3Jn5pRvWhXT21q8d29bFdLUaTmEypO3wM8S_6H9c3e9N-1w</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2556149618</pqid></control><display><type>article</type><title>A review of scientific advancements in datasets derived from big data for monitoring the Sustainable Development Goals</title><source>SpringerNature Journals</source><creator>Allen, Cameron ; Smith, Maggie ; Rabiee, Maryam ; Dahmm, Hayden</creator><creatorcontrib>Allen, Cameron ; Smith, Maggie ; Rabiee, Maryam ; Dahmm, Hayden</creatorcontrib><description>The Sustainable Development Goals (SDGs) suffer from a lack of national data needed for effective monitoring and implementation. Almost half of the SDG indicators are not regularly produced, and available datasets are often out-of-date. New monitoring approaches using big data are advancing rapidly and can complement official statistics to help fill critical data gaps. However, there is poor information-sharing on the latest innovations and research collaborations across different thematic areas, and limited evaluation of strengths and weaknesses for supporting national monitoring. This paper provides a systematic review of the academic literature over the past 5 years relating to the use of big data to support monitoring of the SDGs. It reviews the state-of-the-art research using big data and advanced analytics to produce new datasets, the alignment of these datasets with the official SDG indicators, the main types and sources of big data used, and the analytical methods applied. We developed a set of evaluation criteria and applied it to highlight some of the strengths and limitations of these datasets derived from big data. We find that recent research has developed a considerable range of new datasets that could contribute to monitoring 15 goals, 51 targets, and 69 indicators. Dominant focal areas of research include land and biodiversity, health, water, cities and settlements, and poverty. Satellite and Earth Observation data were the primary sources used, most commonly applied with machine learning methods and cloud computing. However, several challenges remain, including ensuring the relevance of new datasets for monitoring SDG indicators, cost and accessibility considerations, sustainability aspects, and linking global datasets to nationally owned monitoring processes.</description><identifier>ISSN: 1862-4065</identifier><identifier>EISSN: 1862-4057</identifier><identifier>DOI: 10.1007/s11625-021-00982-3</identifier><language>eng</language><publisher>Tokyo: Springer Japan</publisher><subject>Big Data ; Biodiversity ; Climate Change Management and Policy ; Cloud computing ; Data analysis ; Datasets ; Earth and Environmental Science ; Earth observations (from space) ; Environment ; Environmental Economics ; Environmental Management ; Indicators ; Landscape Ecology ; Learning algorithms ; Literature reviews ; Machine learning ; Monitoring ; Poverty ; Public Health ; Review Article ; Satellite observation ; State-of-the-art reviews ; Sustainability Science Innovation and Capacity Development ; Sustainable Development</subject><ispartof>Sustainability science, 2021-09, Vol.16 (5), p.1701-1716</ispartof><rights>The Author(s), under exclusive licence to Springer Japan KK, part of Springer Nature 2021</rights><rights>The Author(s), under exclusive licence to Springer Japan KK, part of Springer Nature 2021.</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c346t-de364d31a5b5fb95d3a8a16b0b0ac7264420c65018e64eaa1db15e562dab793</citedby><cites>FETCH-LOGICAL-c346t-de364d31a5b5fb95d3a8a16b0b0ac7264420c65018e64eaa1db15e562dab793</cites><orcidid>0000-0001-9954-6684</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11625-021-00982-3$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11625-021-00982-3$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Allen, Cameron</creatorcontrib><creatorcontrib>Smith, Maggie</creatorcontrib><creatorcontrib>Rabiee, Maryam</creatorcontrib><creatorcontrib>Dahmm, Hayden</creatorcontrib><title>A review of scientific advancements in datasets derived from big data for monitoring the Sustainable Development Goals</title><title>Sustainability science</title><addtitle>Sustain Sci</addtitle><description>The Sustainable Development Goals (SDGs) suffer from a lack of national data needed for effective monitoring and implementation. Almost half of the SDG indicators are not regularly produced, and available datasets are often out-of-date. New monitoring approaches using big data are advancing rapidly and can complement official statistics to help fill critical data gaps. However, there is poor information-sharing on the latest innovations and research collaborations across different thematic areas, and limited evaluation of strengths and weaknesses for supporting national monitoring. This paper provides a systematic review of the academic literature over the past 5 years relating to the use of big data to support monitoring of the SDGs. It reviews the state-of-the-art research using big data and advanced analytics to produce new datasets, the alignment of these datasets with the official SDG indicators, the main types and sources of big data used, and the analytical methods applied. We developed a set of evaluation criteria and applied it to highlight some of the strengths and limitations of these datasets derived from big data. We find that recent research has developed a considerable range of new datasets that could contribute to monitoring 15 goals, 51 targets, and 69 indicators. Dominant focal areas of research include land and biodiversity, health, water, cities and settlements, and poverty. Satellite and Earth Observation data were the primary sources used, most commonly applied with machine learning methods and cloud computing. However, several challenges remain, including ensuring the relevance of new datasets for monitoring SDG indicators, cost and accessibility considerations, sustainability aspects, and linking global datasets to nationally owned monitoring processes.</description><subject>Big Data</subject><subject>Biodiversity</subject><subject>Climate Change Management and Policy</subject><subject>Cloud computing</subject><subject>Data analysis</subject><subject>Datasets</subject><subject>Earth and Environmental Science</subject><subject>Earth observations (from space)</subject><subject>Environment</subject><subject>Environmental Economics</subject><subject>Environmental Management</subject><subject>Indicators</subject><subject>Landscape Ecology</subject><subject>Learning algorithms</subject><subject>Literature reviews</subject><subject>Machine learning</subject><subject>Monitoring</subject><subject>Poverty</subject><subject>Public Health</subject><subject>Review Article</subject><subject>Satellite observation</subject><subject>State-of-the-art reviews</subject><subject>Sustainability Science Innovation and Capacity Development</subject><subject>Sustainable Development</subject><issn>1862-4065</issn><issn>1862-4057</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9UMtOwzAQtBBIlMIPcLLE2eBH7CTHqkBBQuJQ7pYdb4qrJg52GsTfkzYIbpxmVjszqx2Erhm9ZZTmd4kxxSWhnBFKy4ITcYJmrFCcZFTmp79cyXN0kdKWUsXzspihYYEjDB4-cahxqjy0va99hY0bTFtBM84J-xY705sEI3cQ_QAO1zE02PrNcYPrEHETWt-H6NsN7t8Br_epN741dgf4HgbYhe6QhlfB7NIlOqtHgKsfnKP148Pb8om8vK6el4sXUolM9cSBUJkTzEgra1tKJ0xhmLLUUlPlXGUZp5WSlBWgMjCGOcskSMWdsXkp5uhmSu1i-NhD6vU27GM7HtRcSsWyUrFiVPFJVcWQUoRad9E3Jn5pRvWhXT21q8d29bFdLUaTmEypO3wM8S_6H9c3e9N-1w</recordid><startdate>20210901</startdate><enddate>20210901</enddate><creator>Allen, Cameron</creator><creator>Smith, Maggie</creator><creator>Rabiee, Maryam</creator><creator>Dahmm, Hayden</creator><general>Springer Japan</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7U6</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X2</scope><scope>7XB</scope><scope>87Z</scope><scope>88I</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FK</scope><scope>8FL</scope><scope>8G5</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>L.-</scope><scope>L6V</scope><scope>LK8</scope><scope>M0C</scope><scope>M0K</scope><scope>M2O</scope><scope>M2P</scope><scope>M7P</scope><scope>M7S</scope><scope>MBDVC</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0001-9954-6684</orcidid></search><sort><creationdate>20210901</creationdate><title>A review of scientific advancements in datasets derived from big data for monitoring the Sustainable Development Goals</title><author>Allen, Cameron ; Smith, Maggie ; Rabiee, Maryam ; Dahmm, Hayden</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c346t-de364d31a5b5fb95d3a8a16b0b0ac7264420c65018e64eaa1db15e562dab793</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Big Data</topic><topic>Biodiversity</topic><topic>Climate Change Management and Policy</topic><topic>Cloud computing</topic><topic>Data analysis</topic><topic>Datasets</topic><topic>Earth and Environmental Science</topic><topic>Earth observations (from space)</topic><topic>Environment</topic><topic>Environmental Economics</topic><topic>Environmental Management</topic><topic>Indicators</topic><topic>Landscape Ecology</topic><topic>Learning algorithms</topic><topic>Literature reviews</topic><topic>Machine learning</topic><topic>Monitoring</topic><topic>Poverty</topic><topic>Public Health</topic><topic>Review Article</topic><topic>Satellite observation</topic><topic>State-of-the-art reviews</topic><topic>Sustainability Science Innovation and Capacity Development</topic><topic>Sustainable Development</topic><toplevel>online_resources</toplevel><creatorcontrib>Allen, Cameron</creatorcontrib><creatorcontrib>Smith, Maggie</creatorcontrib><creatorcontrib>Rabiee, Maryam</creatorcontrib><creatorcontrib>Dahmm, Hayden</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Access via ABI/INFORM (ProQuest)</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>Agricultural Science Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Research Library (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>ABI/INFORM Global</collection><collection>Agricultural Science Database</collection><collection>Research Library</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Research Library (Corporate)</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Sustainability science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Allen, Cameron</au><au>Smith, Maggie</au><au>Rabiee, Maryam</au><au>Dahmm, Hayden</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A review of scientific advancements in datasets derived from big data for monitoring the Sustainable Development Goals</atitle><jtitle>Sustainability science</jtitle><stitle>Sustain Sci</stitle><date>2021-09-01</date><risdate>2021</risdate><volume>16</volume><issue>5</issue><spage>1701</spage><epage>1716</epage><pages>1701-1716</pages><issn>1862-4065</issn><eissn>1862-4057</eissn><abstract>The Sustainable Development Goals (SDGs) suffer from a lack of national data needed for effective monitoring and implementation. Almost half of the SDG indicators are not regularly produced, and available datasets are often out-of-date. New monitoring approaches using big data are advancing rapidly and can complement official statistics to help fill critical data gaps. However, there is poor information-sharing on the latest innovations and research collaborations across different thematic areas, and limited evaluation of strengths and weaknesses for supporting national monitoring. This paper provides a systematic review of the academic literature over the past 5 years relating to the use of big data to support monitoring of the SDGs. It reviews the state-of-the-art research using big data and advanced analytics to produce new datasets, the alignment of these datasets with the official SDG indicators, the main types and sources of big data used, and the analytical methods applied. We developed a set of evaluation criteria and applied it to highlight some of the strengths and limitations of these datasets derived from big data. We find that recent research has developed a considerable range of new datasets that could contribute to monitoring 15 goals, 51 targets, and 69 indicators. Dominant focal areas of research include land and biodiversity, health, water, cities and settlements, and poverty. Satellite and Earth Observation data were the primary sources used, most commonly applied with machine learning methods and cloud computing. However, several challenges remain, including ensuring the relevance of new datasets for monitoring SDG indicators, cost and accessibility considerations, sustainability aspects, and linking global datasets to nationally owned monitoring processes.</abstract><cop>Tokyo</cop><pub>Springer Japan</pub><doi>10.1007/s11625-021-00982-3</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0001-9954-6684</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1862-4065 |
ispartof | Sustainability science, 2021-09, Vol.16 (5), p.1701-1716 |
issn | 1862-4065 1862-4057 |
language | eng |
recordid | cdi_proquest_journals_2556149618 |
source | SpringerNature Journals |
subjects | Big Data Biodiversity Climate Change Management and Policy Cloud computing Data analysis Datasets Earth and Environmental Science Earth observations (from space) Environment Environmental Economics Environmental Management Indicators Landscape Ecology Learning algorithms Literature reviews Machine learning Monitoring Poverty Public Health Review Article Satellite observation State-of-the-art reviews Sustainability Science Innovation and Capacity Development Sustainable Development |
title | A review of scientific advancements in datasets derived from big data for monitoring the Sustainable Development Goals |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T17%3A57%3A42IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20review%20of%20scientific%20advancements%20in%20datasets%20derived%20from%20big%20data%20for%20monitoring%20the%20Sustainable%20Development%20Goals&rft.jtitle=Sustainability%20science&rft.au=Allen,%20Cameron&rft.date=2021-09-01&rft.volume=16&rft.issue=5&rft.spage=1701&rft.epage=1716&rft.pages=1701-1716&rft.issn=1862-4065&rft.eissn=1862-4057&rft_id=info:doi/10.1007/s11625-021-00982-3&rft_dat=%3Cproquest_cross%3E2556149618%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2556149618&rft_id=info:pmid/&rfr_iscdi=true |