Click on the name of the country/territory to navigate to the response from that country/territory.

Angola

Short summary:

The methods used to innovate the indicators were based on surveys carried out by CAPI.

---Added 2022---


Armenia

Short summary:

Armstat takes its first steps: the eHealth system database generates all individual data from all state, municipal and private entities that provide healthcare services. Individual data about patients are registered by identification codes, and the patient has the right to get acquainted with the entire history of his disease, the process of vaccination and treatment by any doctor, any health care subject. The executive agency of the Ministry of Health, to which the management of this system is delegated, actually becomes an agency that has access to all “Big Data” in the field of health and has already expressed a desire to start the procedure for obtaining the status of “Other Producer of Official Statistics” in accordance with the requirements of the RA Law “On Official Statistics”.

Link(s) toward to any detailed information or materials:

https://corporate.armed.am/en/about-system/ehealth-in-armenia

Additional comments, challenges, suggestions:

Lack of knowledge and skills. Lack of new job profiles for statisticians (data scientists, data analysts and data engineers).

---Added 2023---


Austria

Short summary:

e.g. 2.5.2. Common Farmland Bird Index, 5.2.1 and 5.2.2 Violence against women

---Added 2023---


Azerbaijan

Short summary:

Produced data on 31 out of 112 indicators is prepared on the basis of administrative sources. Data for one of these indicators (SDG 15.4.2) is provided by the State Space Agency of the Republic of Azerbaijan.

Link(s) toward to any detailed information or materials:

https://sdg.azstat.org/en/home

---Added 2023---


Bangladesh

Short summary:

We have used satellite image to generate poverty estimate along with other socioeconomic data.

---Added 2022---


Belarus

Short summary:

To measure the progress towards sustainable development at the global level, the use of space technologies and geographic information systems (GIS) are considered as innovative data acquisition tools. A special working group for their implementation has been established at the initiative of Belstat and with the support of the National Coordinator for achieving the SDGs in Belarus.

Within the framework of this working group, Belstat has established cooperation with the State Committee on Property of the Republic of Belarus, which is responsible for space and GIS technologies, the unitary enterprise "Geoinformation Systems" of the National Academy of Sciences of Belarus - the national operator of the Belarusian space system for Earth remote sensing, as well as a number of line ministries and organizations to adapt the international methodologies of SDG indicators with the application of GIS to national conditions.

Taking into account the available potential, 9 indicators of the national list of SDG indicators are currently calculated using GIS technologies. Thus, to calculate indicator 6.6.1 "Change in the extent of water-related ecosystems over time", specialists of the State Committee on Property use data from the Land Information System of the Republic of Belarus (LIS). In the process of creating and maintaining (operating, updating) LIS, Earth remote sensing data obtained through aerial surveys, including unmanned aerial vehicle surveys, space imagery with subsequent processing (desktop interpretation and digitisation) are used.

To calculate indicator 9.1.1 "Proportion of the rural population who live within 2 km of an all-season road", specialists in the field of geo-information systems of the organization subordinated to the Ministry of Transport and Communications of the Republic of Belarus use the specialized QGIS software.

This indicator is produced using data from GIS Road Cadastre, as well as from data on:

  • the length of public motor roads;
  • number of population in rural settlements;
  • boundaries of settlements;
  • number, name and category of settlements.

The road network for this indicator is formed using a geodetic base and is updated on the basis of high resolution satellite images. Boundaries of settlements are updated from open map sources and resources: "Public cadastral map of the Republic of Belarus", "Public land information map of Belarus", "Yandex", "Google", "OpenStreetMap" and others.

To calculate the indicator, a buffer zone of 2 kilometers of public roads is formed in the GIS. Using spatial analysis tools, settlements that do not fall within the buffer zone are selected. Based on the number of people living in rural settlements, the proportion of the rural population living within 2 km of an all-season road is determined.

Indicators 15.1.1 "Forest area as a proportion of total land area", 15.2.1.1 "Forest coverage" are calculated by specialists of the Ministry of Forestry of the Republic of Belarus based on, among other, materials of aerial and/or space surveys of the forest inventory unit.

Processing of data from aerospace surveys and expeditionary field surveys of forests is carried out using specialized software packages (used to generate orthophotomaps, use stereo images) and geoinformation systems (GIS "FORMOD" is used to prepare and process cartographic information).

In order to determine the area of land covered by forests, processed cartographic and attributive data are linked into a unified database on forest resources of the Republic of Belarus.

In addition to the above-mentioned indicators, space technology and GIS are also used in the calculation of the following indicators:

  • 6.5.2 Proportion of transboundary basin area with an operational arrangement for water cooperation;
  • 9.c.1 Proportion of population covered by a mobile network, by technology;
  • 11.3.1 Ratio of land consumption rate to population growth rate;
  • 11.7.1 Average share of the built-up area of cities that is open space for public use for all, by sex, age and persons with disabilities
  • 15.3.1 Proportion of land that is degraded over total land area.

At present the work on the extension of the list of SDG indicators, the production and dissemination of which can be done using GIS, the implementation of geoinformation modules to enable geospatial visualization and analysis of spatially referenced statistical data using geographic coordinates, the creation of a geostatistical data portal is ongoing. For that, the Belstat, with the support of UNFPA, has engaged an expert in the field of GIS.

Link(s) toward to any detailed information or materials:

http://sdgplatform.belstat.gov.by/datasets/6.5.2

http://sdgplatform.belstat.gov.by/datasets/6.6.1

http://sdgplatform.belstat.gov.by/datasets/9.1.1

http://sdgplatform.belstat.gov.by/datasets/9.c.1

http://sdgplatform.belstat.gov.by/datasets/11.3.1

http://sdgplatform.belstat.gov.by/datasets/11.7.1

http://sdgplatform.belstat.gov.by/datasets/15.1.1

http://sdgplatform.belstat.gov.by/datasets/15.2.1.1

http://sdgplatform.belstat.gov.by/datasets/15.3.1

---Updated 2023---


Belgium

Short summary:

We did use data on road congestion published by the Joint Research Center (European Commission) and a GPS system operator, but this has been discontinued. We have removed this indicator from the list.

---Added 2022---


Bosnia and Herzegovina

Additional comments, challenges, suggestions:

Non-traditional data collection methods and innovative data sources are still not used.

---Added 2022---


Brazil

Short summary:

We use monitoring data for calculating the indicators 6.3.2, 6.6.1 (produced by National Water Agency and Sanitation - ANA, partner institution) and 15.1.1 (produced by Brazilian Forest Service - SFB, partner institution). We have been discussing with collaborative institutions the use of satellite imagery and air-pollution stations to produced the indicators 3.9.1 and 11.6.2. For non-statistical indicators we use qualitative information from partner institutions, documents, legislation, etc.

Link(s) toward to any detailed information or materials:

https://odsbrasil.gov.br

---Added 2022---


Bulgaria

Short summary:

In collaboration with the Ministry of environment and waters it is possible to produce data on air pollution indicators by using the existing air-pollution stations, part of the National System for Environmental Monitoring (NSEM).

Link(s) toward to any detailed information or materials:

https://eea.government.bg/en/nsmos/index.html

---Added 2023---


Burundi

Additional comments, challenges, suggestions:

Manque de capacités quant à l’exploitation des sources non traditionnelles

***Please find below translated version***

Additional comments, challenges, suggestions:

Lack of capacity to exploit non-traditional sources

---Added 2023---


Cabo Verde

Short summary:

In the future we intend to use geospatial data to monitor the SDGs

---Added 2023---


Cameroon

Short summary:

For now, none of these modes are used. However, the INS plans to sign partnerships with the private sector, in particular telephone operators and the electricity sector, to use their databases for statistical purposes. This approach should make it possible to have the values of certain indicators in real-time, in particular household consumption expenditure.

Link(s) toward to any detailed information or materials:

Cameroon.opendataforafrica.org and www.ins-cameroun.cm

Additional comments, challenges, suggestions:

The main challenge is to have adequate equipment for the collection of non-traditional data, in particular geospatial data, big data, etc.

---Added 2022---


Canada

Short summary:

We have used some crowdsourcing at the beginning of the pandemic which links to some of the indicators (i.e. NEET, mental health, labour market etc.). But we also use civil society information in each of our infographics and factsheets to highlight the work and data information for these groups.

Some examples where non-traditional data are used include the following: indicator 11.2.1 - Proportion of population that has convenient access to public transport, by sex, age and persons with disabilities, Statistics Canada leverages geospatial information and analysis in addition to Census data to measure and report the indicator. For indicator 11.6.2 - Annual mean levels of fine particulate matter in cities (population weighted), Statistics Canada leverages data from the National Air Pollution Surveillance Program which compiles data from numerous air monitoring stations located across Canada. Indicator 9.c.1 - Proportion of population covered by a mobile network, by technology is measured using data from the Canadian Radio-television and Telecommunications Commission which compiles data obtained from service providers.

---Updated 2023---


Colombia

Short summary:

DANE's approach to data from alternative sources has made it possible to recognize as official the indicators calculated using satellite images, such indicators are 9.1.1, 11.1.1, 11.2.1, 11.3.1, 11.7.1.

Also, based on the D4N strategy, we have carried out these projects:

-Estimation of the census Multidimensional Poverty Index (MPI) using machine learning and satellite images, disaggregated for municipalities and at the block level for municipal capitals.

-Obtaining complementary information for indicators 16.b.1 and 16.7.2 using information from social networks and natural language processing techniques and development of visualization boards with the results of the project. -Development of a statistical information system for education, associated with SDG 4 and which seeks the integration and interoperability of different sources of information for monitoring SDG indicators associated with public policy. Some of the innovative sources and methods implemented are: Satellite images, social media, open street map and SAE, convolutional neural networks, natural language processing methods and web scraping.

Link(s) toward to any detailed information or materials:

https://www.dane.gov.co/index.php/estadisticas-por-tema/estadisticas-experimentales

---Updated 2023---


Costa Rica

Short summary:

Se han trabajado y se cuenta con datos los siguiente indicadores: 6.3.2 Proporción de masas de agua de buena calidad, 6.5.2 Proporción de la superficie de cuencas transfronterizas sujetas a arreglos operacionales para la cooperación en materia de aguas, 11.6.2 Niveles medios anuales de partículas finas en suspensión (por ejemplo, PM2.5 y PM10) en las ciudades (ponderados según la población). Se ha estado analizando la viabilidad y la metología de calculo de los siguientes indicadores: 9.1.1 Proporción de la población rural que vive a menos de 2 km de una carretera transitable todo el año, 11.3.1 Relación entre la tasa de consumo de tierras y la tasa de crecimiento de la población, 11.7.1 Proporción media de la superficie edificada de las ciudades que se dedica a espacios abiertos para uso público de todos, desglosada por sexo, edad y personas con discapacidad, 14.1.1 a) Índice de eutrofización costera; y b) densidad de detritos plásticos

Link(s) toward to any detailed information or materials:

https://inec.cr/estadisticas-fuentes/objetivos-desarrollo-sostenible

Additional comments, challenges, suggestions:

El uso de Fuente de información no tradicionales implica retos para el sistema estadístico nacional, ya que es necesario generar normativa que estandarice el uso de este tipo de fuentes y que cumpla con los criterios establecidos por el código de buenas practicas estadísticas. Otro desafio es la resistencia al uso de estas nuevas fuentes de información, se desconoce en el país indicadores nuevos generados apartir de estas nuevas fuentes de información.

***Please find below translated version***

Short summary:

The following indicators have been worked on and have data: 6.3.2 Proportion of water bodies of good quality, 6.5.2 Proportion of the surface of transboundary basins subject to operational arrangements for cooperation in water matters, 11.6.2 Levels annual averages of fine suspended particles (e.g. PM2.5 and PM10) in cities (population-weighted). The feasibility and calculation methodology of the following indicators have been analyzed: 9.1.1 Proportion of the rural population that lives less than 2 km from a road passable all year round, 11.3.1 Relationship between the rate of land consumption and the population growth rate, 11.7.1 Average proportion of the built area of ​​cities that is dedicated to open spaces for public use by all, disaggregated by sex, age and people with disabilities, 14.1.1 a) Index of coastal eutrophication; and b) density of plastic debris

Link(s) toward to any detailed information or materials:

https://inec.cr/estadisticas-fuentes/objetivos-desarrollo-sostenible

Additional comments, challenges, suggestions:

The use of non-traditional sources of information implies challenges for the national statistical system, since it is necessary to generate regulations that standardize the use of this type of sources and that comply with the criteria established by the code of good statistical practices. Another challenge is the resistance to the use of these new sources of information; new indicators generated from these new sources of information are unknown in the country.

---Updated 2023---


Côte d’Ivoire

Short summary:

Cette collecte de type non traditionnelle a permis disposer de données auprès des ONG en charge des personnes vulnérables.

Additional comments, challenges, suggestions:

cette expérience a permis le problème de stockage de données de ces ONG et les difficultés qu'ils rencontrent pour produire des statistiques sur les personnes vulnérables et les laissées pour compte

***Please find below translated version***

Short summary:

This non-traditional type collection made it possible to obtain data from NGOs in charge of vulnerable people.

Additional comments, challenges, suggestions:

This experience highlighted the data storage problem of these NGOs and the difficulties they encounter in producing statistics on vulnerable people and those left behind.

---Added 2023---


Cuba

Short summary:

La utilización de fuentes de datos no tradicionales ha estado asociada en lo fundamental a indicadores de ODS seleccionados. Mediante  encuestas telefónicas, datos de estaciones meteorológicas, imágenes satelitales, entre otras, se han  completado o complementad indicadores ODS de los objetivos 3 Garantizar una vida sana y promover el bienestar de todos a todas las edades, 6 Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos, 11 Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles, 13 Adoptar medidas urgentes para combatir el cambio climático y sus efectos y 15 Proteger, restablecer y promover el uso sostenible de los ecosistemas terrestres, gestionar sosteniblemente los bosques, luchar contra la desertificación, detener e invertir la degradación de las tierras y detener la pérdida de biodiversidad.  

***Please find below translated version***

Short summary:

The use of non-traditional data sources has been fundamentally associated with selected SDG indicators. Through telephone surveys, data from weather stations, satellite images, among others, SDG indicators have been completed or complemented by objectives 3 Guarantee a healthy life and promote the well-being of all at all ages, 6 Guarantee the availability and sustainable management of water and sanitation for all, 11 Make cities and human settlements inclusive, safe, resilient and sustainable, 13 Take urgent action to combat climate change and its effects and 15 Protect, restore and promote the sustainable use of ecosystems land, sustainably manage forests, fight desertification, halt and reverse land degradation and halt biodiversity loss.

---Added 2023---


Czechia

Short summary:

Geospatial information and Earth observation: Indicator 2.4.1, 6.6.1, 11.2.1, 11.3.1, 11.7.1, 15.3.1, 15.4.2.

---Added 2023---


Denmark

Short summary:

Mobile network coverage figures are collected from Mobile network operators by the Agency of Infrastructure and data.

Link(s) toward to any detailed information or materials:

https://www.dst.dk/da/Statistik/temaer/SDG/globale-verdensmaal/09-industri-innovation-og-infrastruktur/delmaal-c/indikator-1

Additional comments, challenges, suggestions:

The possibility of geographical disaggregation is being currently discussed.

---Added 2023---


Dominican Republic

Short summary:

Monitoring stations to evaluate air quality are used to the indicator 11.6.2. About Objective 15, the country currently reports 15.1.1 and 15.4.2 indicators using non-traditional sources such as satellite images and spatial overlay.

The Ministry of the Environment and Natural Resources manages the air quality monitoring stations. With the measurements, we calculated the indicator 11.6.2.

Link(s) toward to any detailed information or materials:

http://ods.gob.do/Indicador/Index/178 , http://ods.gob.do/Indicador/Index/183?fromMenu=True, http://ods.gob.do/Indicador/Index/141?fromMenu=True

---Updated 2023---


Ecuador

Short summary:

At the moment, at the national level, non-traditional sources of information respond mainly to SDGs related to environmental issues, and correspond to geospatial and remote sensing information. In order to generate the calculation of certain indicators, the use of non-traditional sources has been evaluated and applied within the framework of the National Statistical System. For example, indicators 15.1.1 and 15.1.2, related to surface size and ecosystems, generate their calculation through information that is collected through remote sensing satellite images, where geographic layers are analyzed to obtain numerical records of the surface size, as well as SDG 6 indicators that are produced with information from hydrological stations or indicator 11.6.2 that could be generated solely from information collected by air quality monitoring stations. Finally, it is important to point out that, within the framework of statistical planning, the inventory of information sources is updated annually, in which it is planned to include a greater number of non-traditional sources of information.

At the moment, INEC has estimated three SDG indicators related to environmental issues through Non-Traditional Sources that are being generated by the Environmental Authority, these are: 13.2.1 "Number of countries with nationally determined contributions, long-term strategies, national adaptation plans and adaptation communications reported to the UNFCCC secretariat", 15.1.1 "Forest area as a proportion of total area" and 15.1.2 "Proportion of important sites for terrestrial and freshwater biodiversity included in protected areas by ecosystem type". In turn, in order to identify a battery of indicators categorised as Tier II and Tier III at the national level, which can be estimated through Non-Traditional Information Sources, INEC is working on the review of data and metadata for which custodian agencies have data information.

Link(s) toward to any detailed information or materials:

https://www.ecuadorencifras.gob.ec/objetivos-de-desarrollo-sostenible/

https://www.ecuadorencifras.gob.ec/documentos/web-inec/Sitios/Programa_Nacional_de_Estadistica/Micrositio_PNE_2021_2025/index.html

Additional comments, challenges, suggestions:

In accordance with what was previously described, in current times there has been a considerable increase in the number of non-traditional sources of information, in part thanks to the advancement of technological tools and the development of algorithms for the collection and processing of information.In this sense, the INEC is aware of the evolution of this type of sources and, therefore, considers them within its inventory so that they can be identified for their different purposes, including the calculation of indicators.

Ecuador considers within its work areas the use of non-traditional sources of information that, at the moment, are applicable to the environmental and agricultural fields; therefore, it is of interest to actively participate in the work spaces that the custodial agencies and international organizations are currently executing and plan to develop, for: i) the definition of guidelines and directives to ensure the quality of data from these new sources; and, ii) the generation of mechanisms so that these sources, mainly from monitoring and remote sensing systems, are incorporated into the official information collection. On the other hand, it is a challenge for INEC and other statistical offices to strengthen their institutional capacities in order to take advantage of the benefits provided by non-traditional sources of information, in order to complement the production of official statistics and thus advance in the reporting of SDG indicators at a lower cost.

---Updated 2023---


Egypt

Short summary:

Currently, CAPMAS GIS and SDU teams are work on producing some of goal 11 indicators.

---Added 2023---


Ethiopia

Short summary:

Types of data non-traditional data: poverty related data, agricultural production, economic and social sector data (e.g. manufacturing data, customs, labor force data, education sector, etc.)

partners:

Local: Ministry of agriculture, Customs Authority, Ministry of Education, Ministry of Innovation and Technology, and other MDAs;

International: UNSD, AfDB, and other partner that are engaged with providing technical and financial supports

In collaboration with UNSD with the technical support under the D4N project, there is a need/ will to use the Small Area Estimation (SAE) method to produce disaggregated statistics for poverty and agriculture. However, there is a problem/challenge with the availability and quality of auxiliary data that can be utilized for SAE method. Furthermore, this requires technical capacity to develop suitable and reliable model or SAE method procedures to be applied consistently over time.  

Additional comments, challenges, suggestions:

ESS needs to apply the Small Area Estimation (SAE) method to produce disaggregated statistics for poverty and agriculture. However, there is a problem/challenge with the availability of quality and timely auxiliary data. Furthermore, this requires multi-disciplinary skills and sustainable technical capacity for the use of this SAE procedures to be applied over time.  Therefore, development partners are highly encourage to work together with the ESS so that the country as a whole can benefit for the development of the production of more official statistics.

---Updated 2023---


Fiji

Short summary:

Fiji Bureau of Statistics have access to Tax data from the tax office. This is used to determine relevant SDGs indicators. Using of auxiliary information enables Fiji Bureau of Statistics to assess other SDG indicators not relevant to tax and revenue.

---Added 2023---


Finland

Short summary:

In Finland, the network cooperation, co-operation group and data producer network, are used to strengthen our coverage of data as well as to expand our expertise base. The national SDG database included in late 2022 data for 171 SDG indicators i.e. the data coverage was 69.2%. Of this available data the statistical office and other official statistics producing organizations produce 80%. The remaining 20% of the data which comes from other, wide variety of sources and possess questions about adherence of statistical principles as well as conceptual, methodological and organizational concerns. This data originates from expert estimations (44%), admistrative datasets from ministries (36%), research data from universities and other R&D organisations (13%) and sample surveys from various public sources (7%). Especially qualitative expert data needs have been converted into numerical data and admistrative datasets, research and sample generalized for statistical purposes. For statistical office reliability and validation of information and output data is crucial. The practical quantification methods used to transform this kind of alternative data to output that suit SDG monitoring purpose is many times non-orthodox and ad-hoc by nature.

---Updated 2023---


Germany

Short summary:

For a range of SDG indicators, the calculation is based on geospatial data. For example for the delineation and determination of area under protection (SDG indicators 14.5.1, 15.1.2 and 15.4.1), geospatial data is used and processed using geoinformation software. Also, for the determination of the areas that are above a certain altitude in order to determine mountain ecosystems (SDG indicator 15.4.2) a geospatial data set is used.

Data from air-pollution stations is used to report on particulate matter in cities (indicator 11.6.2).

Link(s) toward to any detailed information or materials:

http://sdg-indicators.de/11-6-2/

http://sdg-indicators.de/14-5-1/

http://sdg-indicators.de/15-1-2/

http://sdg-indicators.de/15-4-1/

http://sdg-indicators.de/15-4-2/

---Added 2022---


Greece

Short summary:

Most of the times, non-traditional data are not considered (yet) a proper source of official statistics. There are many limitations regarding the providers and legal difficulties. At the same time, official statistics should take into consideration the modern era where many traditional data sources may fail to capture all the aspects of the modern economy. That’s why, are following users’ needs ELSTAT sign a memorandum of Cooperation with the National Observatory of Athens regarding the exploitation of  geospatial data.

Link(s) toward to any detailed information or materials:

https://www.statistics.gr/documents/20181/306935/memorandum_GGDOPIF_20.04.2021.pdf/0c4feba1-8684-5046-2a52-6f6bc6687585

---Added 2022---


Guam

Short summary:

The G3 dashboard is updated with data collected from the Guam Green Growth working group.

Link(s) toward to any detailed information or materials:

https://guamgreengrowth.org/dashboard/

Additional comments, challenges, suggestions:

No full automation of data systems. There is also a lack of technical resource capacity.

---Added 2023---


Hungary

Short summary:

Indicators concerning weather data and air pollution data.

---Added 2022---


Ireland

Short summary:

SDG Indicators developed from the publication linked above (Measuring Distances to Everyday Services in Ireland are: SDG 1.4.1 Proportion of population living in households with access to basic services; 9.1.1 Proportion of the rural population who live within 2 km of an all-season road; 11.2.1 Proportion of population that has convenient access to public transport, by sex, age and persons with disabilities3.8.1 Coverage of essential health services; 8.10.1 (a) Number of commercial bank branches per 100,000 adults and (b) number of automated teller machines (ATMs) per 100,000 adults.

SDG Indicator 15.4.2 The “Mountain Green Cover Index” has been developed from satellite imagery (in progress)

Link(s) toward to any detailed information or materials:

Measuring Distances to Everyday Services in Ireland  https://www.cso.ie/en/releasesandpublications/ep/p-mdsi/measuringdistancetoeverydayservicesinireland/                                                                                          

National Landcover Map  https://www.epa.ie/our-services/monitoring--assessment/assessment/irelands-environment/land--soil/current-trends-land-and-soil/

---Added 2022---


Israel

Short summary:

Non-traditional and innovative data will be used for air pollution and water quality.

---Added 2022---


Italy

Short summary:

Non traditional data will be used for energy data, considering the implementing also of the Register of Place

SDGs indicators related to goal 11, 13, 15

Link(s) toward to any detailed information or materials:

https://www.istat.it/en/well-being-and-sustainability/sustainable-development-goals/istat-indicators-for-sustainable-development

---Updated 2023---


Jamaica

Additional comments, challenges, suggestions:

STATIN is examining the possibility of using big data to gather data for some indicators. However, this is constrained by limited resources as we are currently engaged in the Census.

---Added 2022---


Japan

Short summary:

Japan publishes the values of SDG global indicators 15.4.2(a) (Mountain Green Cover Index) and 11.3.1 (Ratio of land consumption rate to population growth rate) using satellite-earth-observation-based land-use/land-cover classification data provided by the Japan Aerospace Exploration Agency (JAXA) and land elevation data provided by the Geospatial Information Authority of Japan.

Link(s) toward to any detailed information or materials:

Validation of SDG Indicator 15.4.2(a) (Mountain Green Cover Index)

 *New indicator names from the March 2023 annual review are not reflected in the report.

https://www.soumu.go.jp/main_content/000763968.pdf

Validation of SDG Indicator 11.3.1 (Ratio of land consumption rate to population growth rate)

https://www.soumu.go.jp/main_content/000837974.pdf

---Updated 2023---


Jordan

Short summary:

As all the SDGs indicators from traditional data sources are available and collected regularly, the integration of SDGs indicators should use non-traditional data collection methods to improve the level of data collected.

The first try to collect data from non-traditional method was directed for indicator 9.1.1. Different efforts were mde to collect data for this indicator. The efforts included data collection using GIS and population data. The data collection faced difficulties to collect information related to the road roughnesses in the rural area which required high budget. The data for this indicator was not completed yet. In case the availability of the needed budgets, data collection can be proceeded to caculated indicator 9.1.1.

Additional comments, challenges, suggestions:

In non-traditional methods the financial issues form the core of obstacles that face data collection. Moreover, the experience of data collection using these sources is not enough and the exchange of this experience is not enough among countries.

---Updated 2023---


Latvia

Additional comments, challenges, suggestions:

The statistics on the use of new data sources are included in the regular development activities and are not allocated separately to SDGs.

---Added 2022---


Luxembourg

Short summary:

Geospatial information is used to establish indicator 6.6.1 Change in the extent of water-related ecosystems over time.

Official sensor networks is used to establish indicator 11.6.2 Annual mean levels of fine particulate matter (e.g. PM2.5 and PM10) in cities

Private-sector data (smart meter on electricity and natural gas) will be used in the future to establish indicators related to energy (SDG 7)

---Added 2023---


Malaysia

Short summary:

DOSM has produced four indicators using spatial data which is:

•SDG 9.1.1: Proportion of the rural population who live within 2 km of an all-season road;

•SDG 11.1.1: Proportion of urban population living in slums, informal settlements or inadequate housing;

•SDG 11.2.1: Proportion of population that has convenient access to public transport, by sex, age and persons with disabilities; and

•SDG 11.7.1: Average share of the built-up area of cities that is open space for public use for all, by sex, age and persons with disabilities.

The spatial data is obtained from PlanMalaysia for SDG 11.1.1, 11.2.1 and 11.7.1.

---Added 2023---


Maldives

Short summary:

Used CDR from the communication company to explore the population movement within the capital city. Also, the data collected by a CSO on Beach cleanups were used to estimate the Plastic debris. 

Additional comments, challenges, suggestions:

For CDR had issues in Data Sharing in the absence of a Data protection Act in the Country. And for CSO data, they do not have data maintained in a standard way and do not have enough technical and human capacity for data management.

---Added 2023---


Mali

Short summary:

Les types de données non traditionnelles utilisées au Mali demeurent les données satellitaires.

Dans le cadre du dénombrement du 5ème Recensement Général de la Population et de l’Habitat (RGPH5) qui s’est déroulé de juin à décembre 2022, le Mali a fait recours à cette méthode pour la collecte des données des zones situées dans le centre et le nord du pays. Toutefois les résultats définitifs de ce recensement restent toujours attendus. Aussi, il faut préciser que dans le cadre de ce dénombrement, trois (03) méthodes de collecte ont été utilisées. Il s’agit de :

- la méthode CAPI ;

- la méthode PAPI (dans les régions du pays en situation d’insécurité mais accessibles) ;

- et la méthode GRID3 (dans les régions du pays en situation d’insécurité non accessibles).

Link(s) toward to any detailed information or materials:

www.https://instat-mali.org/fr

Additional comments, challenges, suggestions:

L'un des défis c'est une meilleure collaboration entre l'INSTAT et l'Institut Géographique du Mali (IGM). Par ailleurs, une initiative est présentement en cours à l'INSTAT avec Statistique Suède (SCB) pour renforcer les capacités d'une équipe Expert SIG en notre sein.

***Please find below translated version***

Short summary:

The non-traditional types of data used in Mali remain satellite data.

As part of the enumeration of the 5th General Population and Housing Census (RGPH5) which took place from June to December 2022, Mali used this method to collect data from areas located in the center and the north of the country. However, the final results of this census are still awaited. Also, it should be noted that as part of this count, three (03) collection methods were used. It is :

- the CAPI method;

- the PAPI method (in regions of the country in a situation of insecurity but accessible);

- and the GRID3 method (in insecure and inaccessible regions of the country).

Link(s) toward to any detailed information or materials:

www.https://instat-mali.org/fr

Additional comments, challenges, suggestions:

One of the challenges is better collaboration between INSTAT and the Geographical Institute of Mali (IGM). Furthermore, an initiative is currently underway at INSTAT with Statistics Sweden (SCB) to strengthen the capacities of a GIS Expert team within us.

---Added 2023---


Malta

Short summary:

The innovative data used includes plastic card usage and mobile data usage.

---Added 2023---


Mauritius

Additional comments, challenges, suggestions:

Thanks to UN Habitat (SDG 11 custodian agencies), national stakeholders have benefitted from technical assistance, and are currently using GIS tools and open source data to produce data for SDG 11 indicators (11.2.1, 11.3.1, and 11.7.1).

Challenges are: (i) Lack of expertise , (ii) Data accessibility from private sectors at national may be a hindrance, and may lead to legal implications and (iii) Cost of non-traditional data

---Added 2023---


Mozambique

Short summary:

The NSO intends to start a process of methodological innovation in dissemination and is focused on geoconferencing using data disaggregated by province. Thus, the indicators whose data are intended to be reported using georeferenced data are: 1.2.1 Proportion of population living below the national poverty line, by sex and age; 2.2.1 Prevalence of stunting (height for age <-2 standard deviation from the median of the World Health Organization (WHO) Child Growth Standards) among children under 5 years of age; 2.2.2 Prevalence of malnutrition (weight for height >+2 or <-2 standard deviation from the median of the WHO Child Growth Standards) among children under 5 years of age, by type (wasting and overweight); 3.1.2 Proportion of births attended by skilled health personnel; 3.2.1 Under‑5 mortality rate; 3.2.2 Neonatal mortality rate; 3.3.2 Tuberculosis incidence per 100,000 population; 3.3.3 Malaria incidence per 1,000 population; 3.7.1 Proportion of women of reproductive age (aged 15–49 years) who have their need for family planning satisfied with modern methods; 3.b.1 Proportion of the target population covered by all vaccines included in their national programme; 4.5.1 Parity indices (female/male, rural/urban, bottom/top wealth quintile and others such as disability status, indigenous peoples and conflict-affected, as data become available) for all education indicators on this list that can be disaggregated; 4.c.1 Proportion of teachers with the minimum required qualifications, by education level; 5.2.1 Proportion of ever-partnered women and girls aged 15 years and older subjected to physical, sexual or psychological violence by a current or former intimate partner in the previous 12 months, by form of violence and by age; 5.b.1 Proportion of individuals who own a mobile telephone, by sex; 6.1.1 Proportion of population using safely managed drinking water services; 6.2.1 Proportion of population using (a) safely managed sanitation services and (b) a hand-washing facility with soap and water; 7.1.1 Proportion of population with access to electricity; 8.5.2 Unemployment rate, by sex, age and persons with disabilities; 8.6.1 Proportion of youth (aged 15–24 years) not in education, employment or training; 8.7.1 Proportion and number of children aged 5–17 years engaged in child labour, by sex and age; 16.9.1 Proportion of children under 5 years of age whose births have been registered with a civil authority, by age

Link(s) toward to any detailed information or materials:

https://mozambique.opendataforafrica.org/

Additional comments, challenges, suggestions:

The need to strengthen technical and institutional capacities at all levels of statistical production

---Added 2023---


Myanmar

Short summary:

We can’t calculate indicators of SDG goal 5 which are non-traditional data. Relating that indicators we have no methodology.

Link(s) toward to any detailed information or materials:

You can get the link of ASEANstat.

Additional comments, challenges, suggestions:

If possible, we want to get training relating those indicators.

---Added 2023---


Nepal

Short summary:

Some kind of sensitization has been initiated for citizen data. Satellite imagery have also been used to some extent. But the NSO is not in a position to purchase high resolution satellite imagery.

Link(s) toward to any detailed information or materials:

Later

---Added 2022---


Netherlands

Short summary:

We use satellite data for the natural capital accounts, which include a number of SDG indicators. Sensor data are used for example for data on fine partulate matter. Citizen data is used for biodiversity related indicator.

Link(s) toward to any detailed information or materials:

https://www.cbs.nl/en-gb/society/nature-and-environment/natural-capital

---Updated 2023---


Nigeria

Additional comments, challenges, suggestions:

Nigeria is willing to explore the use of non-traditional data sources or innovative data collection methods, such as mobile phone data, satellite imagery, and citizen-generated data, SDGs, the impacts of the pandemic, and vulnerabilities of some population groups. kindly assist. Thanks

---Added 2022---


Peru

Short summary:

Los indicadores referidos a contaminación del aire del ODS 11 se elaboran desde las instituciones rectoras como el Servicio Nacional de Meteorología e Hidrología del Perú - SENAMHI y la Dirección General de Salud Ambiental – DIGESA del Ministerio de Salud. Estas instituciones por medio de las estaciones que miden la contaminación del aire y ruido en puntos clave de las ciudades determinan el nivel de contaminación del aire y del ruido.

Link(s) toward to any detailed information or materials:

Link SENAMHI: https://www.senamhi.gob.pe/?p=calidad-del-aire. Link DIGESA: http://www.digesa.minsa.gob.pe/DCOVI/Programa_nacional_vigilancia_calidad_aire.asp#:~:text=La%20DIGESA%20realiza%20la%20Vigilancia,%C2%B0%20010%2D2019%2DMINAM

Additional comments, challenges, suggestions:

Entre los retos se encuentra el manejo de fuente de información alternativo que permita complementar o construir los indicadores de los ODS

***Please find below translated version***

Short summary:

The indicators referring to air pollution of SDG 11 are prepared by the governing institutions such as the National Service of Meteorology and Hydrology of Peru - SENAMHI and the General Directorate of Environmental Health - DIGESA of the Ministry of Health. These institutions, through stations that measure air and noise pollution in key points of the cities, determine the level of air and noise pollution.

Link(s) toward to any detailed information or materials:

Link SENAMHI: https://www.senamhi.gob.pe/?p=calidad-del-aire.

Link PROMPTED: http://www.digesa.minsa.gob.pe/DCOVI/Programa_nacional_vigilancia_calidad_aire.asp#:~:text=La%20DIGESA%20realiza%20la%20Vigilancia,%C2%B0%20010%2D2019%2DMINAM

Additional comments, challenges, suggestions:

Among the challenges is the management of an alternative source of information that allows complementing or constructing the SDG indicators.

---Updated 2023---


Philippines

Short summary:

Using night-time lights for small area estimates of poverty.

---Added 2022---


Poland

Short summary:

Statistics Poland performed an exercise on applying non-traditional data (in this case – data from i.a. remote sensing) for SDG 9 and SDG 11. The exercise was a result of the experimental statistics research work carried out internally in Statistics Poland. It answered the needs connected to the monitoring of the Sustainable Development Goals of the 2030 Agenda. It concerned three indicators on access to all-season roads in rural areas as well as open public spaces and build-up area of cities, namely:

• 9.1.1 Proportion of the rural population who live within 2 km of an all-season road,

• 11.3.1 Ratio of land consumption rate to population growth rate to public space and green areas,

• 11.7.1 Average share of the built-up area of cities that is open space for public use for all, by sex, age and persons with disabilities.

The indicators were chosen based both on the data gap analysis and their relevance (both to global and local sustainable development). The indicators were calculated basing on the methodology proposed by the United Nations using data from several sources (including Sentinel satellite data). They will serve as a tool for local governments, NGOs and other stakeholders for better and more sustainable management of urban development in Poland. The indicators will be made available for public at the beginning of 2024 in a new Statistics Poland's product for experimental SDG statistics (which will be incorporated into Polish NRP - SDG Platform).

---Updated 2023---


Portugal

Short summary:

Under the scope of The Land Use and Land Cover Statistics, based on the Land Use and Land Cover Map (COS) produced by the Directorate-General of Territory (DGT) for Mainland Portugal, Statistics Portugal has calculated the indicator "Evolution of the efficiency of artificial land by inhabitant", which consists of a proxy indicator, as proposed by the Joint Research Centre, of the SDG indicator 11.3.1, and the indicators "Natural and artificial open water surface area" and "Rate of surface variation of open water", which correspond to one of the sub-indicators defined for the monitoring of SDG indicator 6.6.1. COS is a thematic cartography that divides surface area into landscape units. The cartography is obtained by visual image interpretation of ortho-rectified aerial photographs, with a spatial resolution of ≤ 50 cm and four spectral bands (blue, green, red and infrared), and minimum mapping unit (UMC) of 1 ha. The latest COS (2018) consists of four levels of detail that can be grouped into 9 classes of first level of detail:

  • Artificial land;
  • Cropland area;
  • Grassland area;
  • Agro-forestry areas;
  • Forest area;
  • Shrubland area;
  • Open spaces or sparse vegetated areas;
  • Wetlands;
  • Surface water bodies.

The periodicity of COS is not defined, and the production of new editions has varied between three and five years. More recently, DGT has made available an annual Conjunctural Land Cover Map (COSc), based on spatial technologies and Artificial Intelligence, which includes machine learning algorithms and expert knowledge rules to automatically classify multispectral and intra-annual series of Sentinel-2 satellite image data. The production of a COSc annually enables the production of annual statistics on land cover, but the level of detail of the information to be made available needs to be analysed and assessed.

Though our previous answer still stands, we inform that a new version of Portuguese Land Use and Land Cover Map is planned to be published in the short term by Directorate-General of Territory (DGT) with a minimum mapping unit of 0,5ha, which considerably improves accuracy data, supporting measurements closest to reality. For more information, please consult our previous answer.

Link(s) toward to any detailed information or materials:

https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_perfsdg&objetivo=6&indicador=6.6&indicador2=6.6.1&xlang=en; https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_perfsdg&objetivo=11&indicador=11.3&indicador2=11.3.1&xlang=en

---Updated 2023---


Republic of Korea

Short summary:

Statistics Korea and IBS (Institute for Basic Science) signed a MOU (Memorandum of Understanding) in October 2022. It is for research collaboration about producing SDG indicator by using geospatial information and remote sensing. IBS has the AI technology analysing remote sensing data and research skill. So, we expect to contribute the SDG indicators methodology development.

---Added 2022---


Russian Federation

Short summary:

Federal Service for Hydrometeorology and Environmental Monitoring uses such stations to calculate arithmetic mean of the average daily concentrations of fine particulate matter (PM2.5 and PM10) in the atmosphere of individual cities (SDG 11.6.2)

Link(s) toward to any detailed information or materials:

No updates since 2022

Additional comments, challenges, suggestions:

No updates since 2022

---Added 2022---


Saudi Arabia

Short summary:

We have a plan to use big data methods for the calculation of some of the SDG indicators. The plan is to work on: 11.3.1 Ratio of land consumption rate to population growth rate and 13.2.2 Total greenhouse gas emissions per year. For 11.3.1 we plan to use geospatial data, satellite imagery analysis and population statistics to calculate this indicator. For 13.2.2, we plan to use the data from air quality measuring stations in addition to transportation data to calculate this indicator. We are still in the planning stage for the exact methodology and data acquisition.

17.14.1 (Ministry of Economics and Planning), 11.2.1, Royal Commission for Riyadh City.

Link(s) toward to any detailed information or materials:

https://www.stats.gov.sa/sites/default/files/Progress_Towards_the_Sustainable_Development_Goals_2021_EN_0.pdf

Additional comments, challenges, suggestions:

Regional hubs for big data to support SDGs monitoring is a good idea.

---Updated 2023---


Singapore

Short summary:

For SDG 15.1.1 “Forest area as a proportion of total land area”, forest area and total land area of Singapore were determined by using high-resolution satellite imagery with automated land cover mapping and visual correction.

For SDG 12.6.1 “Number of companies publishing sustainability reports”, data on the number of Singapore Exchange (SGX) listed issuers having sustainability reports were extracted from a joint biennial publication by National University of Singapore (NUS) Centre for Governance and Sustainability (CGS) and SGX as appropriate proxy for the SDG indicator.

---Updated 2023---


Slovakia

Short summary:

Air-pollutions station measure of fine particulate matter in cities for SDG 11.6.2

---Added 2023---


Spain

Short summary:

A Working Group on SDG Indicators has been established between INE and the National Geographic Institute to collaborate in the production of SDG indicators based on Earth observations and GIS tools.

Besides two indicators are being developed from geoespatial data.

---Updated 2023---


Sri Lanka

Short summary:

If there is no mechanism to collect data for a survey or face challenges to collect reliable data or facing economic crisis to get resources to conduct a census, because conducting a census is very costly for a country which is undergoing economic crisis. Therefore the NSO can go for administrative data or other non traditional data for compiling indicators.

---Added 2023---


State of Palestine

Short summary:

We are trying to integrate the most possible disaggregation within our surveys and coordinating with the admin data sources to have the best disaggregation

---Added 2023---


Togo

Link(s) toward to any detailed information or materials:

www.inseed.tg

Additional comments, challenges, suggestions:

Besoin urgent en appui à la production des données environnementales.

***Please find below (MS Word) translated version***

Link(s) toward to any detailed information or materials:

www.inseed.tg

Additional comments, challenges, suggestions:

Urgent need to support the production of environmental data.

---Added 2022---


Türkiye

Short summary:

Regarding non-traditional data, we use GIS in the production of official statistics and the following SDG indicators:

SDG 11.3.1- Ratio of land consumption rate to population growth rate;

SDG 11.2.1- Proportion of population that has convenient access to public transport;

SDG 11.7.1- Average share of the built-up area of cities that is open space for public use for all

These indicators are produced by TurkStat GIS Department.

Link(s) toward to any detailed information or materials:


Additional comments, challenges, suggestions:

https://sdg.tuik.gov.tr/en/11/

---Updated 2023---


Uganda

Short summary:

The Current NPHC is utilizing digital approaches for data collection.  The household listing process adopted Geospatial technologies.

Link(s) toward to any detailed information or materials:

https://ubosgis.ubos.org/portal/home/

Additional comments, challenges, suggestions:

The Bureau with support from UNWomen has developed a Citizens Generated Data Toolkit that will guide CSOs in producing quality data that could be used for SDG monitoring.

---Added 2023---


United Arab Emirates

Short summary:

The FCSC joined the steering committee of “Earth Observation for Sustainable Cities and Communities” in 2021 and worked on utilizing different methods to calculate SDG indicators such as using Satellite imagery and earth observation. Indicators that are collected includes: 9.1.1, 11.3.1 and 15.4.2. The Hub captures this holistic approach to address, support, and produce granular data in a timely manner whilst using EO & geospatial technology to implement the SDGs. It further track, monitor, and report progress towards the SDGs whilst disseminating data, promoting awareness and engaging all-society with the SDGs. 

Link(s) toward to any detailed information or materials:

https://sdgsuae-fcsa.opendata.arcgis.com/datasets/FCSA::9-1-1-proportion-of-the-rural-population-who-live-within-2-km-of-an-all-season-road/about https://eo-toolkit-guo-un-habitat.opendata.arcgis.com/pages/use-case-uae

---Added 2022---


United Kingdom of Great Britain and Northern Ireland

Short summary:

We used a combination of traditional and non-traditional data sources to bridge data gaps in our SDG indicators reporting. We collaborated with ONS’s Data Science Campus to use Earth observation data on open waters and the survey data from the British Geological Survey (BGS; a not-for-profit public sector research establishment) on groundwaters to update indicator 6.6.1. Additional work was required to assert the quality of the data compared to traditional sources. For example, our data differs to the data on the Global Surface Water Explorer because we use national official high water mark boundaries. We use open data sources when available (for example the Prindex global open-source dataset from an Open Data Institute (ODI) project (indicator 1.4.2).

We started using published research data from charities and Universities (for example research data on maternal mortality from Oxford University (indicator 3.1.1) and research data from non-profit organisation The Waste and Resources Action Programme (WRAP) (indicator 12.3.1). We used citizen science data collected by the charity the Marine Conservation Society (MCS) during their annual beach litter pick event (the Great British Beach Clean) (indicator 14.1.1) . We did a substantial assessment of the data to determine the best measure to use, given constraints in the data. We wrote a quality and methodology document on our data use and shared findings with the charity. The source code for processing the data is open access and publicly available. All our non-official statistical sources have been quality assessed with the tool we have specifically developed for this purpose (see section above).

Link(s) toward to any detailed information or materials:

https://sdgdata.gov.uk/6-6-1/ https://global-surface-water.appspot.com https://www.bgs.ac.uk https://sdgdata.gov.uk/1-4-2/ https://www.prindex.net https://sdgdata.gov.uk/3-1-1/ https://sdgdata.gov.uk/12-3-1/ https://www.ons.gov.uk/economy/environmentalaccounts/articles/leavingnoonebehindareviewofwhohasbeenmostaffectedbythecoronaviruspandemicintheuk/december2021

https://sdgdata.gov.uk/14-1-1/ https://sdgdata.gov.uk/11-4-1/

Additional comments, challenges, suggestions:

On the website, we list where we get the data from on the page for each indicator with the hyperlink for the data source. The Government’s website explains its commitment to delivering the SDGs. Different Government Departments or devolved administrations are responsible for how the UK delivers the SDGs.

---Updated 2023---


Uruguay

Short summary:

Se utilizan imágenes satelitales para la estimación de algunos indicadores (por ejemplo 11.3.1 y 6.5.2). También se está analizando la posibilidad de usar esa metodología para otros indicadores.

En el caso del indicador 14.3.1 se piensa comenzar a utilizar mediciones realizadas por estaciones hidrológicas, asimismo, se utilizan sensores remotos para medir la calidad del aire.

***Please find below translated version***

Short summary:

Satellite images are used for the estimation of some indicators (for example 11.3.1 and 6.5.2). The possibility of using this methodology for other indicators is also being analyzed.

In the case of indicator 14.3.1, it is planned to begin using measurements made by hydrological stations, and remote sensors will also be used to measure air quality.

---Added 2023---


Viet Nam

Short summary:

Vietnam has not officially used non-traditional data for any specific SDG indicator. Data for all SDG indicators (if available) are from official sources in accordance with the Law on Statistics. Data sources such as Bigdata, satellite imagery,... are all in the research and testing stage.

Citizen-generated data (CGD) has been initially researched and implemented in Vietnam. Vietnam has a Report "Community Data - People's Voice on Progress in Achieving Sustainable Development Goals in Vietnam". This report was produced by the Leaving No One Behind (LNOB) Vietnam Partnership Group. Basically, the report describes the process of collecting data from Citizen to evaluate progress in implementing SDGs at different levels in the community, especially goals 1, 4 , 5, 8, 13 and 16

Additional comments, challenges, suggestions:

The current legal framework is a limitation of the application of new data sources (Statistical Law only stipulates 03 form of data sources to collect official statistical information: Statistical survey; Administrative data; Statistical reporting regime).

Resource limitation: financial; Skill.

Another challenge of new, non-traditional data sources and technologies is the lack of guidance on assessing the quality of these sources.

---Updated 2023---


Zambia

Short summary:

 The office in collaboration with other data producers have collected data on SDG 8.10.2, 9.c.1 using the selected non traditional and innovative methods.

---Added 2022---


Zimbabwe

Short summary:

Although ZIMSTAT management agreed on the use of no-traditional sources to complement data from surveys/censuses, there is no progress so far. Only financial and employment data from the private sector is collected although there is a challenge of poor response rates.

Additional comments, challenges, suggestions:

ZIMSTAT is yet to start using non-traditional sources of data to address the issue of data gaps. The NSDS III document developed to cover 2022- 2025 points out this as an urgent option for ZIMSTAT. The next stage is to partner with organisations willing to build capacity within ZIMSTAT to utilise such data.

Agency is yet to start using non-traditional sources. Need for technical assistance.

---Updated 2023---

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