Showcases data.wa.gov publishers whose data sheds light on progress toward the United Nations' Sustainable Development Goals (SDGs), the theme for Open Data Week 2024.

Series Name: Proportion of population practicing open defecation by urban rural (percent)Series Code: SH_SAN_DEFECTRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 6.2.1: Proportion of population using (a) safely managed sanitation services and (b) a hand-washing facility with soap and waterTarget 6.2: By 2030, achieve access to adequate and equitable sanitation and hygiene for all and end open defecation, paying special attention to the needs of women and girls and those in vulnerable situationsGoal 6: Ensure availability and sustainable management of water and sanitation for allFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

This dataset contains information on what papers and concepts researchers find relevant to map domain specific research output to the 17 Sustainable Development Goals (SDGs).

Sustainable Development Goals are the 17 global challenges set by the United Nations. Within each of the goals specific targets and indicators are mentioned to monitor the progress of reaching those goals by 2030. In an effort to capture how research is contributing to move the needle on those challenges, we earlier have made an initial classification model than enables to quickly identify what research output is related to what SDG. (This Aurora SDG dashboard is the initial outcome as proof of practice.)

In order to validate our current classification model (on soundness/precision and completeness/recall), and receive input for improvement, a survey has been conducted to capture expert knowledge from senior researchers in their research domain related to the SDG. The survey was open to the world, but mainly distributed to researchers from the Aurora Universities Network. The survey was open from October 2019 till January 2020, and captured data from 244 respondents in Europe and North America.

17 surveys were created from a single template, where the content was made specific for each SDG. Content, like a random set of publications, of each survey was ingested by a data provisioning server. That collected research output metadata for each SDG in an earlier stage. It took on average 1 hour for a respondent to complete the survey. The outcome of the survey data can be used for validating current and optimizing future SDG classification models for mapping research output to the SDGs.

The survey contains the following questions (see inside dataset for exact wording):

• Are you familiar with this SDG?
  • Respondents could only proceed if they were familiar with the targets and indicators of this SDG. Goal of this question was to weed out un knowledgeable respondents and to increase the quality of the survey data.
• Suggest research papers that are relevant for this SDG (upload list)
  • This question, to provide a list, was put first to reduce influenced by the other questions. Goal of this question was to measure the completeness/recall of the papers in the result set of our current classification model. (To lower the bar, these lists could be provided by either uploading a file from a reference manager (preferred) in .ris of bibtex format, or by a list of titles. This heterogenous input was processed further on by hand into a uniform format.)
• Select research papers that are relevant for this SDG (radio buttons: accept, reject)
  • A randomly selected set of 100 papers was injected in the survey, out of the full list of thousands of papers in the result set of our current classification model. Goal of this question was to measure the soundness/precision of our current classification model.
• Select and Suggest Keywords related to SDG (checkboxes: accept | text field: suggestions)
  • The survey was injected with the top 100 most frequent keywords that appeared in the metadata of the papers in the result set of the current classification model. respondents could select relevant keywords we found, and add ones in a blank text field. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.
• Suggest SDG related glossaries with relevant keywords (text fields: url)
  • Open text field to add URL to lists with hundreds of relevant keywords related to this SDG. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.
• Select and Suggest Journals fully related to SDG (checkboxes: accept | text field: suggestions)
  • The survey was injected with the top 100 most frequent journals that appeared in the metadata of the papers in the result set of the current classification model. Respondents could select relevant journals we found, and add ones in a blank text field. Goal of this question was to get suggestions for complete journals we can use to increase the recall of relevant papers in a new classification model.
• Suggest improvements for the current queries (text field: suggestions per target)
  • We showed respondents the queries we used in our current classification model next to each of the targets within the goal. Open text fields were presented to change, add, re-order, delete something (keywords, boolean operators, etc. ) in the query to improve it in their opinion. Goal of this question was to get suggestions we can use to increase the recall and precision of relevant papers in a new classification model.

In the dataset root you'll find the following folders and files:

• /00-survey-input/
  • This contains the survey questions for all the individual SDGs. It also contains lists of EIDs categorised to the SDGs we used to make randomized selections from to present to the respondents.
• /01-raw-data/
  • This contains the raw survey output. (Excluding privacy sensitive information for public release.) This data needs to be combined with the data on the provisioning server to make sense.
• /02-aggregated-data/
  • This data is where individual responses are aggregated. Also the survey data is combined with the provisioning server, of all sdg surveys combined, responses are aggregated, and split per question type.
• /03-scripts/
  • This contains scripts to split data, and to add descriptive metadata for text analysis in a later stage.
• /04-processed-data/
  • This is the main final result that can be used for further analysis. Data is split by SDG into subdirectories, in there you'll find files per question type containing the aggregated data of the respondents.
• /images/
  • images of the results used in this README.md.
• LICENSE.md
  • terms and conditions for reusing this data.
• README.md
  • description of the dataset; each subfolders contains a README.md file to futher describe the content of each sub-folder.

In the /04-processed-data/ you'll find in each SDG sub-folder the following files.:

• SDG-survey-questions.pdf
  • This file contains the survey questions

  </li>
  <li><strong>SDG-survey-questions.doc</strong>
  <ul>
    <li>This file contains the survey questions</li>
  </ul>
  </li>
  <li><strong>SDG-survey-respondents-per-sdg.csv</strong>
  <ul>
    <li>Basic information about the survey and responses</li>
  </ul>
  </li>
  <li><strong>SDG-survey-city-heatmap.csv</strong>
  <ul>
    <li>Origin of the respondents per SDG survey</li>
  </ul>
  </li>
  <li><strong>SDG-survey-suggested-publications.txt</strong>
  <ul>
    <li>Formatted list of research papers researchers have uploaded or listed they want to see back in the result-set for this SDG.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-suggested-publications-with-eid-match.csv</strong>
  <ul>
    <li>same as above, only matched with an EID. EIDs are matched my Elsevier's internal fuzzy matching algorithm. Only papers with high confidence are show with a match of an EID, referring to a record in Scopus.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-selected-publications-accepted.csv</strong>
  <ul>
    <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe represent this SDG. (TRUE=accepted)</li>
  </ul>
  </li>
  <li><strong>SDG-survey-selected-publications-rejected.csv</strong>
  <ul>
    <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe not to represent this SDG. (FALSE=rejected)</li>
  </ul>
  </li>
  <li><strong>SDG-survey-selected-keywords.csv</strong>
  <ul>
    <li>Based on our previous result set of papers, we presented researchers the keywords that are in the metadata of those papers, they selected keywords they believe represent this SDG.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-unselected-keywords.csv</strong>
  <ul>
    <li>As "selected-keywords", this is the list of keywords that respondents have not selected to represent this SDG.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-suggested-keywords.csv</strong>
  <ul>
    <li>List of keywords researchers suggest to use to find papers related to this SDG</li>
  </ul>
  </li>
  <li><strong>SDG-survey-glossaries.csv</strong>
  <ul>
    <li>List of glossaries, containing keywords, researchers suggest to use to find papers related to this SDG</li>
  </ul>
  </li>
  <li><strong>SDG-survey-selected-journals.csv</strong>
  <ul>
    <li>Based on our previous result set of papers, we presented researchers the journals that are in the metadata of those papers, they selected journals they believe represent this SDG.</li>
  </ul>
  </li>
  <li><strong>SDG-survey-unselected-journals.csv</strong>
  <ul>
    <li>As "selected-journals", this is the list of journals
  

Series Name: Nationally derived quality of open water bodies(percent)Series Code: EN_WBE_NDQLOPWRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 6.6.1: Change in the extent of water-related ecosystems over timeTarget 6.6: By 2020, protect and restore water-related ecosystems, including mountains, forests, wetlands, rivers, aquifers and lakesGoal 6: Ensure availability and sustainable management of water and sanitation for allFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

This dataset describes the distance and percentage of open and conserved coastline marine areas (with and without bays) that are within the San Diego County. Data is taken from the National Oceanic and Atmospheric Administration MPA (marine protected areas) Inventory.

The SDG Indicator 11.7.1: Urban Public Space, Availability and Access, 2023 Release, part of the SDGI collection, measures the average share of the built-up area of a city that is open space for public use for all. UN SDG 11 is "make cities and human settlements inclusive, safe, resilient and sustainable". Aside from environmental benefits, public space can also help improve public health, bolster commUnity, and encourage economic exchange. As one measure of progress towards SDG 11, the UN has established SDG indicator 11.7.1. The indicator was computed by measuring both the proportion of OpenStreetMap (OSM) public space within a given urban center and the proportion of WorldPop gridded population within 400 meters to Open Public Space (OPS). Cities were delineated using the European Commission Joint Research Centre (JRC) Urban Center Database (GHS-UCDB). The SDG indicator 11.7.1 data set provides estimates of the average share of the built-up area of cities that is open space for public use for all for 8,873 urban centers across 180 countries.

Series Name: Proportion of open water bodies with good ambient water quality (percent)Series Code: EN_H2O_OPAMBQRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 6.3.2: Proportion of bodies of water with good ambient water qualityTarget 6.3: By 2030, improve water quality by reducing pollution, eliminating dumping and minimizing release of hazardous chemicals and materials, halving the proportion of untreated wastewater and substantially increasing recycling and safe reuse globallyGoal 6: Ensure availability and sustainable management of water and sanitation for allFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Since 2016, the Sustainable Development Report (SDR) has provided the most up-to-date data available to track and rank the performance of all UN member states on the SDGs. Eighty years after the creation of the UN system, the report also provides improved and updated measures to track countries' efforts to support UN-based multilateralism. In total, more than 200,000 individual data points are used to produce 200+ country and regional SDG profiles. This year's edition was authored by a group of independent experts at the SDG Transformation Center, an initiative of the SDSN.This year's SDR emphasizes the following eight key message:Global commitment to the SDGs is strong: 190 out of 193 countries have presented national action plans for advancing sustainable development. A decade after the adoption of Agenda 30 and the SDGs, 190 of the 193 UN member states have participated in the Voluntary National Review (VNR) process, presenting their SDG implementation plans and sustainable development priorities to the international community. The European Union and State of Palestine have also presented VNRs. Most UN member states have presented two or more VNRs, and 39 countries volunteered to present one in 2025. Only three UN member states have not taken part in the VNR process: Haiti, Myanmar, and the United States. Additionally, a growing number of regional and local leaders have prepared Voluntary Local Reviews (VLRs) to report on SDG implementation at the subnational level. As of March 2025, 249 VLRs were listed on the dedicated UN websiteEast and South Asia has outperformed all other regions in SDG progress since 2015. This year's SDR introduces a streamlined SDG Index (SDGi), which uses 17 headline indicators to track overall SDG progress. On average, East and South Asia has shown the fastest progress on the SDGs since 2015, driven notably by rapid progress on the socioeconomic targetOther countries that have progressed more rapidly than their peers include the following: Benin (Sub-Saharan Africa), Nepal (East and South Asia), Peru (Latin America and the Caribbean), the United Arab Emirates (Middle East and North Africa), Uzbekistan (Eastern Europe and Central Asia), Costa Rica (OECD), and Saudi Arabia (G20)European countries continue to top the SDG Index. Finland ranks first this year and 19 of the top 20 countries are in Europe. Yet even these countries face significant challenges in achieving at least two goals, including those related to climate and biodiversity. In this year's SDG Index, China (#49) and India (#99) have entered the top 50 and top 100 performers respectivelyOn average globally, the SDGs are far off-track. At the global level, none of the 17 goals are currently on course to be achieved by 2030. Conflicts, structural vulnerabilities, and limited fiscal space impede SDG progress in many parts of the world. But while only 17 percent of the targets are on track to be achieved worldwide, most UN member states have made strong progress on targets related to access to basic services and infrastructure, including mobile broadband use (SDG 9), access to electricity (SDG 7), internet use (SDG 9), under-5 mortality rate (SDG 3), and neonatal mortality (SDG 3)Barbados ranks first and the United States ranks last in UN-based multilateralism. Barbados stands out as the country most committed to UN-based multilateralism, while the United States ranks last in this year's Index of countries' support for UN-based multilateralism (UN-Mi). In early 2025, the United States announced its withdrawal from the Paris Climate Agreement and the World Health Organization (WHO) and formally declared its opposition to the SDGs and the 2030 Agenda. Among G20 countries, Brazil is the most committed to UN-based multilateralism, with Chile leading among OECD countries For many developing countries, a lack of fiscal space is the major obstacle to SDG progress. Roughly half the world's population lives in countries that cannot invest adequately in sustainable development due to debt burdens and a lack of access to affordable, long-term capital. Global public goods are vastly under-financed. UN member states gathering at the 4th International Conference on Financing for Development (FfD4) in Seville, Spain (June 30 – July 3, 2025) have an enormous responsibility, not only to their own citizens but to all of humanitySustainable development offers high returns: capital should flow to the emerging and developing countries on more favourable terms. The Global Financial Architecture (GFA) is broken. Money flows readily to rich countries and not to the emerging and developing economies (EMDEs) that offer higher growth potential and rates of return. At the top of the agenda at FfD4 is the need to reform the GFA so that capital flows in far larger sums to the EMDEs. Part 1 of this report (also published online by the SDSN in May 2025) offers practical recommendations to scale up and align international financing flows to support global public goods and achieve sustainable development.About the AuthorsProf. Jeffrey Sachs, Director, SDSN; Project Director of the SDG IndexJeffrey D. Sachs is a world-renowned professor of economics, leader in sustainable development, senior UN advisor, bestselling author, and syndicated columnist whose monthly newspaper columns appear in more than 100 countries. He is the co-recipient of the 2015 Blue Planet Prize, the leading global prize for environmental leadership, and many other international awards and honors. He has twice been named among Time magazine’s 100 most influential world leaders. He was called by the New York Times, “probably the most important economist in the world,” and by Time magazine, “the world’s best known economist.” A survey by The Economist in 2011 ranked Professor Sachs as amongst the world’s three most influential living economists of the first decade of the 21st century.Professor Sachs serves as the Director of the Center for Sustainable Development at Columbia University. He is University Professor at Columbia University, the university’s highest academic rank. During 2002 to 2016 he served as the Director of the Earth Institute. Sachs is Special Advisor to United Nations Secretary-General António Guterres on the Sustainable Development Goals, and previously advised UN Secretary-General Ban Ki-moon on both the Sustainable Development Goals and Millennium Development Goals and UN Secretary-General Kofi Annan on the Millennium Development Goals.Guillaume Lafortune Director, SDSN Paris; Scientific Co-Director of the SDG IndexGuillaume Lafortune took up his duties as Director of SDSN Paris in January 2021. He joined SDSN in 2017 to coordinate the production of the Sustainable Development Report and other projects on SDG data and statistics.Previously, he has served as an economist at the Organisation for Economic Co-operation and Development (OECD) working on public governance reforms and statistics. He was one of the lead advisors for the production of the 2015 and 2017 flagship statistical report Government at a Glance. He also contributed to analytical work related to public sector efficiency, open government data and citizens’ satisfaction with public services. Earlier, Guillaume worked as an economist at the Ministry of Economic Development in the Government of Quebec (Canada). Guillaume holds a M.Sc in public administration from the National School of Public Administration (ENAP) in Montreal and a B.Sc in international economics from the University of Montreal.Contact: guillaume.lafortune@unsdsn.org Grayson Fuller Manager, SDG Index & Data team, SDSNGrayson Fuller is the lead statistician and senior manager for the SDG Index, and of the team working on SDG data and statistics at SDSN. He is co-author of the Sustainable Development Report, for which he manages the data, coding, and statistical analyses. He also coordinates the production of regional and subnational editions of the SDG Index, in addition to other statistical reports, in collaboration with national governments, NGOs and international organizations such as the WHO, UNDP and the European Commission. Grayson received his Masters degree in Economic Development at Sciences Po Paris. He holds a Bachelors in Romance Languages and Latin American Studies from Harvard University, where he graduated cum laude. Grayson has lived in several Latin American countries and speaks English, Spanish, French, Portuguese and Italian. He enjoys playing the violin, rock-climbing and taking care of his numerous plants in his free time.Contact: grayson.fuller@unsdsn.orgGuilherme Iablonovski GIS Specialist, SDG Index & Data team, SDSNGuilherme Iablonovski is a Geospatial Data Specialist at SDSN, where he conceptualizes and develops new geospatial indicators to measure important aspects of the Sustainable Development Goals. He holds a M.Sc in Urban and Environmental Planning from the Ecole d'Urbanisme de Paris, where his research focused on urban metabolism, environmental sustainability and universal scaling laws. Before joining SDSN, Guilherme worked as a solutions engineer for Esri and as geospatial data scientist for humanitarian organizations such as the World Bank, the Red Cross and UNEP. He also teaches GIS at the Peace Studies Master Programme at Université Paris-Dauphine PSL.Contact: guilherme.iablonovski@unsdsn.org---About the PublishersDublin University Press Dublin University Press is Ireland’s oldest printing and publishing house with its origins in Trinity College Dublin in 1734. The mission of Dublin University Press is to benefit society through scholarly communication, education, research and discourse. To further this goal, the Press operates as an open, innovative and inclusive channel for high quality scholarly publishing with an emphasis on equity, diversity and inclusion and with full support for author copyright retention, open access and open scholarship. As an independent, non-profit,

This dataset is a compilation of a number of publications analysed in details to review the role of citizen science in addressing the European Water Framework Directive and the Global Sustainable Development Goals objectives for freshwater environments. This dataset underlies the publication in Open Research Europe titled "The role of citizen science within WFD and SDGs, and how to incentivize the collaboration with environmental regulators".

City of Melbourne Sustainable Development Goals

This site complements PSA OpenSTAT portal allowing data users to visualize data on maps. Users can also explore and download published data, discover and build web maps and apps, and analyze and combine datasets using maps.This site is under construction and data published here are not official!

The OSDG Community Dataset (OSDG-CD) is a public dataset of thousands of text excerpts, which were validated by over 1,400 OSDG Community Platform (OSDG-CP) citizen scientists from over 140 countries, with respect to the Sustainable Development Goals (SDGs). Dataset Information In support of the global effort to achieve the Sustainable Development Goals (SDGs), OSDG is realising a series of SDG-labelled text datasets. The OSDG Community Dataset (OSDG-CD) is the direct result of the work of more than 1,400 volunteers from over 130 countries who have contributed to our understanding of SDGs via the OSDG Community Platform (OSDG-CP). The dataset contains tens of thousands of text excerpts (henceforth: texts) which were validated by the Community volunteers with respect to SDGs. The data can be used to derive insights into the nature of SDGs using either ontology-based or machine learning approaches. 📘 The file contains 42,065 (+285) text excerpts and a total of 303,643 (+2,329) assigned labels. To learn more about the project, please visit the OSDG website and the official GitHub page. Explore a detailed overview of the OSDG methodology in our recent paper "OSDG 2.0: a multilingual tool for classifying text data by UN Sustainable Development Goals (SDGs)". Source Data The dataset consists of paragraph-length text excerpts derived from publicly available documents, including reports, policy documents and publication abstracts. A significant number of documents (more than 3,000) originate from UN-related sources such as SDG-Pathfinder and SDG Library. These sources often contain documents that already have SDG labels associated with them. Each text is comprised of 3 to 6 sentences and is about 90 words on average. Methodology All the texts are evaluated by volunteers on the OSDG-CP. The platform is an ambitious attempt to bring together researchers, subject-matter experts and SDG advocates from all around the world to create a large and accurate source of textual information on the SDGs. The Community volunteers use the platform to participate in labelling exercises where they validate each text's relevance to SDGs based on their background knowledge. In each exercise, the volunteer is shown a text together with an SDG label associated with it – this usually comes from the source – and asked to either accept or reject the suggested label. There are 3 types of exercises: All volunteers start with the mandatory introductory exercise that consists of 10 pre-selected texts. Each volunteer must complete this exercise before they can access 2 other exercise types. Upon completion, the volunteer reviews the exercise by comparing their answers with the answers of the rest of the Community using aggregated statistics we provide, i.e., the share of those who accepted and rejected the suggested SDG label for each of the 10 texts. This helps the volunteer to get a feel for the platform. SDG-specific exercises where the volunteer validates texts with respect to a single SDG, e.g., SDG 1 No Poverty. All SDGs exercise where the volunteer validates a random sequence of texts where each text can have any SDG as its associated label. After finishing the introductory exercise, the volunteer is free to select either SDG-specific or All SDGs exercises. Each exercise, regardless of its type, consists of 100 texts. Once the exercise is finished, the volunteer can either label more texts or exit the platform. Of course, the volunteer can finish the exercise early. All progress is saved and recorded still. To ensure quality, each text is validated by up to 9 different volunteers and all texts included in the public release of the data have been validated by at least 3 different volunteers. It is worth keeping in mind that all exercises present the volunteers with a binary decision problem, i.e., either accept or reject a suggested label. The volunteers are never asked to select one or more SDGs that a certain text might relate to. The rationale behind this set-up is that asking a volunteer to select from 17 SDGs is extremely inefficient. Currently, all texts are validated against only one associated SDG label. Column Description doi - Digital Object Identifier of the original document text_id - unique text identifier text - text excerpt from the document sdg - the SDG the text is validated against labels_negative - the number of volunteers who rejected the suggested SDG label labels_positive - the number of volunteers who accepted the suggested SDG label agreement - agreement score based on the formula (agreement = \frac{|labels_{positive} - labels_{negative}|}{labels_{positive} + labels_{negative}}) Further Information Do not hesitate to share with us your outputs, be it a research paper, a machine learning model, a blog post, or just an interesting observation. All queries can be directed to community@osdg.ai. This CSV file uses UTF-8 character encoding. For easy access on MS Excel, open the file using Data → From Text/CSV. Ple...

End poverty in all its forms everywhere : Poverty in the Pacific is focused on hardship and lack of economic opportunity and social exclusion. While food and extreme poverty remains relatively low, an estimated one in four Pacific islanders are likely to be living below their country’s basic-needs poverty line (BNPL). Children are especially vulnerable to poverty and inequality because of their dependency on adults for care and protection, and for food. Deprivation and lost opportunities in childhood can have detrimental effects that may persist throughout a child’s life. If a child does not receive adequate nutrition, stunting may result, and intellectual development may be impaired. Poorly nourished children are more vulnerable to disease, tend to perform worse in school, and less likely to be productive adults.

Find more Pacific data on PDH.stat.

Series Name: Nationally derived quantity of open water bodies (million of cubic metres per annum)Series Code: EN_WBE_NDQTOPWRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 6.6.1: Change in the extent of water-related ecosystems over timeTarget 6.6: By 2020, protect and restore water-related ecosystems, including mountains, forests, wetlands, rivers, aquifers and lakesGoal 6: Ensure availability and sustainable management of water and sanitation for allFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Abstract

Sustainable Development Goal (SDG) target 2.1 commits countries to end hunger, ensure access by all people to safe, nutritious and sufficient food all year around. Indicator 2.1.2, "Prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES)", provides internationally-comparable estimates of the proportion of the population facing difficulties in accessing food. More detailed background information is available at http://www.fao.org/in-action/voices-of-the-hungry/fies/en/ .

The FIES-based indicators are compiled using the FIES survey module, containing 8 questions. Two indicators can be computed: 1. The proportion of the population experiencing moderate or severe food insecurity (SDG indicator 2.1.2), 2. The proportion of the population experiencing severe food insecurity. These data were collected by FAO through the Gallup World Poll. General information on the methodology can be found here: https://www.gallup.com/178667/gallup-world-poll-work.aspx. National institutions can also collect FIES data by including the FIES survey module in nationally representative surveys.

Microdata can be used to calculate the indicator 2.1.2 at national level. Instructions for computing this indicator are described in the methodological document available under the "DOCUMENTATION" tab above. Disaggregating results at sub-national level is not encouraged because estimates will suffer from substantial sampling and measurement error.

Geographic coverage

National

Analysis unit

Individuals

Universe

Individuals of 15 years or older.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was drawn proportional to the population and the country was stratified by region and by population size strata. Exclusions: The following provinces were excluded from the sample: Thanh Hoa, Ha Tinh, Quang Binh, Dak Lak, An Giang, Kien Giang. The excluded areas represent approximately 12% of the total population. Design effect: 1.29

Mode of data collection

Face-to-face par [f2f]

Research instrument

The questionnaire is provided as an external resource in the Documentation Section.

Cleaning operations

Statistical validation assesses the quality of the FIES data collected by testing their consistency with the assumptions of the Rasch model. This analysis involves the interpretation of several statistics that reveal 1) items that do not perform well in a given context, 2) cases with highly erratic response patterns, 3) pairs of items that may be redundant, and 4) the proportion of total variance in the population that is accounted for by the measurement model.

Sampling error estimates

The margin of error is estimated as 3.5 .This is calculated around a proportion at the 95% confidence level. The maximum margin of error was calculated assuming a reported percentage of 50% and takes into account the design effect.

Link to this report's codebookAbout the AuthorsProf. Jeffrey SachsDirector, SDSN; Project Director of the SDG IndexJeffrey D. Sachs is a world-renowned professor of economics, leader in sustainable development, senior UN advisor, bestselling author, and syndicated columnist whose monthly newspaper columns appear in more than 100 countries. He is the co-recipient of the 2015 Blue Planet Prize, the leading global prize for environmental leadership, and many other international awards and honors. He has twice been named among Time magazine’s 100 most influential world leaders. He was called by the New York Times, “probably the most important economist in the world,” and by Time magazine, “the world’s best known economist.” A survey by The Economist in 2011 ranked Professor Sachs as amongst the world’s three most influential living economists of the first decade of the 21st century.Professor Sachs serves as the Director of the Center for Sustainable Development at Columbia University. He is University Professor at Columbia University, the university’s highest academic rank. During 2002 to 2016 he served as the Director of the Earth Institute. Sachs is Special Advisor to United Nations Secretary-General António Guterres on the Sustainable Development Goals, and previously advised UN Secretary-General Ban Ki-moon on both the Sustainable Development Goals and Millennium Development Goals and UN Secretary-General Kofi Annan on the Millennium Development Goals.Guillaume LafortuneDirector, SDSN Paris; Scientific Co-Director of the SDG IndexGuillaume Lafortune took up his duties as Director of SDSN Paris in January 2021. He joined SDSN in 2017 to coordinate the production of the Sustainable Development Report and other projects on SDG data and statistics.Previously, he has served as an economist at the Organisation for Economic Co-operation and Development (OECD) working on public governance reforms and statistics. He was one of the lead advisors for the production of the 2015 and 2017 flagship statistical report Government at a Glance. He also contributed to analytical work related to public sector efficiency, open government data and citizens’ satisfaction with public services. Earlier, Guillaume worked as an economist at the Ministry of Economic Development in the Government of Quebec (Canada). Guillaume holds a M.Sc in public administration from the National School of Public Administration (ENAP) in Montreal and a B.Sc in international economics from the University of Montreal.Contact: EmailGrayson FullerSenior Analyst, SDG Index, SDSNGrayson Fuller is the Senior Analyst at SDSN. His role consists of managing the data, coding, and statistical analyses for the SDG Index and Dashboards report. He additionally carries out research related to sustainable development. Grayson received his Masters degree in Economic Development at Sciences Po Paris. He holds a Bachelors in Latin American Studies from Harvard University, where he graduated cum laude. Grayson has lived in several Latin American countries and speaks English, Spanish, French, Portuguese, and Russian. He enjoys playing violin and hails from Atlanta, GA.Contact: EmailEamon DrummSenior Program Officer, SDG Transformation CenterEamon Drumm leads the SDG Transformation Center. He has previously worked on policy coherence for sustainable development at the OECD and the UNESCO World Heritage Centre. He also worked for many years for an energy services company developing energy efficiency programs and smart city software products for cities… Originally trained as an urban planner, he has degrees in public policy and urban planning from Sciences Po Paris, the Sorbonne and the University of Virginia. He is originally from the United States and has been living in France since 2010.Contact: EmailAbout the PublishersDublin University PressDublin University Press is Ireland’s oldest printing and publishing house with its origins in Trinity College Dublin in 1734. The mission of Dublin University Press is to benefit society through scholarly communication, education, research and discourse. To further this goal, the Press operates as an open, innovative and inclusive channel for high quality scholarly publishing with an emphasis on equity, diversity and inclusion and with full support for author copyright retention, open access and open scholarship. As an independent, non-profit, ethical and research-centric publisher, Dublin University Press is committed to fostering the achievement of the United Nations Sustainable Development Goals.Sustainable Development Solutions Network (SDSN)The Sustainable Development Solutions Network (SDSN) has been operating since 2012 under the auspices of the UN Secretary-General. SDSN mobilizes global scientific and technological expertise to promote practical solutions for sustainable development, including the implementation of the Sustainable Development Goals (SDGs) and the Paris Climate Agreement.

The SDG Indicator 9.1.1: The Rural Access Index (RAI), 2023 Release data set, part of the SDGI collection, measures the proportion of the rural population who live within 2 kilometers of an all-season road for a given statistical area. UN SDG 9 is "build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation". Addressing inadequate access to roads, especially in rural areas, is critical to achieving SDG 9. According to the UN Sustainable Transport, Sustainable Development 2021 Interagency Report, sustainable transportation helps to eliminate poverty, promote food security, improve access to key health services, increase trade competitiveness, and bolster human rights. As one measure of progress towards SDG 9, the UN has established SDG indicator 9.1.1. The indicator was computed as the proportion of WorldPop gridded population within 2 kilometers to an OpenStreetMap (OSM) all-season road. The SDG indicator 9.1.1 data set provides estimates for the proportion of the rural population with access to all-season roads for 209 countries and 45,073 subnational Units. The data set is available at both national and level 2 subnational resolutions.

The Freshwater Ecosystems Explorer is a free and easy to use data platform providingnaccurate, up-to-date, high-resolution geospatial data depicting the extent freshwater ecosystems change over time. By helping decision-makers understand dynamic ecosystem changes, the data presented on this open access platform is intended to drive action to protect and restore freshwater ecosystems and enable countries to track progress towards the achievement of Sustainable Development Goal Target 6.6.

Open data is the first step to an informed, transparent, and engaged community. Explore our data and tools, provide feedback on what you would like to see next, and find out about opportunities to get involved.