Ukraine

Data Grid Completeness Expand
Affected People
3 Datasets
25%  50%  25% 
Coordination & Context
4 Datasets
40%  20%  40% 
Food Security & Nutrition
1 Datasets
50%  50% 
Geography & Infrastructure
3 Datasets
50%  25%  25% 
Health & Education
2 Datasets
100% 
Population & Socio-economy
2 Datasets
100% 
What is Data Grid Completeness?
Data Grid Completeness defines a set of core data that are essential for preparedness and emergency response. For select countries, the HDX Team and trusted partners evaluate datasets available on HDX and add those meeting the definition of a core data category to the Data Grid Completeness board above. Please help us improve this feature by sending your feedback to hdx@un.org.
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Presence, freshness, and quality of dataset
  • Dataset fully matches criteria and is up-to-date
  • Dataset partially matches criteria and/or is not up-to-date
  • No dataset found matching the criteria
  • Updated 24 June 2021 | Dataset date: January 01, 2021-March 31, 2021
    This dataset contains a list of health facilities in Donetska and Luhanska oblasts.
    600+ Downloads
    This dataset updates: As needed
  • Updated 24 June 2021 | Dataset date: January 01, 2020-January 01, 2020
    This dataset contains a list of education facilities in Donetska and Luhanska oblasts.
    300+ Downloads
    This dataset updates: Never
  • Updated 8 May 2021 | Dataset date: May 19, 2020-May 19, 2020
    Pool fund Extended allocation Details
    60+ Downloads
    This dataset updates: As needed
  • Updated 6 May 2021 | Dataset date: January 01, 2016-December 31, 2020
    This dataset contains events where air- and ground-launched explosive weapons affected health facilities between 2016 and 2020.
    400+ Downloads
    This dataset updates: As needed
  • Updated 4 May 2021 | Dataset date: January 01, 1990-December 31, 2030
    The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities. The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions. The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population. The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.
    300+ Downloads
    This dataset updates: Every year
  • Under the leadership of UNDP and DCO, an inter-agency task team developed the UN framework for the immediate socio-economic response to COVID-19 (adopted in April 2020) to govern its response over 12 to 18 months. To measure the UN’s support to the socio-economic response and recovery, UN entities developed a simple monitoring framework with 18 programmatic indicators (endorsed by the UNSDG in July 2020). Lead entities – based on their mandate and comparative advantage – were nominated to lead the development of methodological notes for each indicator and lead the collection of data at the country level. These lead entities reported through the Office of the Resident Coordinators the collective UN results on a quarterly basis through UN Info. All 2020 data was reported by March 2021. This is the UN development system’s first comprehensive attempt at measuring its collective programming contribution and results. These programmatic indicators enabled the UN system to monitor the progress and achievements of UNCT’s collective actions in socio-economic response. In support of the Secretary-General’s call for a "… single, consolidated dashboard to provide up-to-date visibility on [COVID-19] activities and progress across all pillars” all data was published in real time on the COVID-19 data portal, hosted by DCO. The data is disaggregated by geography (rural/urban), sex, age group and at-risk populations -- to measure system-wide results on the socio-economic response to the pandemic, in order to ensure UNDS accountability and transparency for results.
    200+ Downloads
    This dataset updates: Every three months
  • Updated 11 April 2021 | Dataset date: January 01, 2018-December 31, 2018
    The UNHCR Livelihoods Monitoring Framework takes a program-based approach to monitoring, with the aim of tracking both outputs and the impact of UNHCR dollars spent on programming (either via partners or through direct implementation). The process for developing the indicators began in 2015 with a review of existing tools and approaches. Consultations were held with governments, the private sector, field-based staff and civil society partners to devise a set of common, standardized measures rooted in global good practices. Since 2017, a data collection (survey) has been rolled out globally, and the participating operations conducted a household surveys to a sample of beneficiaries of each livelihoods project implemented by UNHCR and its partner. The dataset consists of baseline and endline data from the same sample beneficiaries, in order to compare before and after the project implementation and thus to measure the impact. More info is available on the official website: https://lis.unhcr.org
    This dataset updates: Never
  • Updated 11 April 2021 | Dataset date: January 01, 2019-December 31, 2019
    The UNHCR Livelihoods Monitoring Framework takes a program-based approach to monitoring, with the aim of tracking both outputs and the impact of UNHCR dollars spent on programming (either via partners or through direct implementation). The process for developing the indicators began in 2015 with a review of existing tools and approaches. Consultations were held with governments, the private sector, field-based staff and civil society partners to devise a set of common, standardized measures rooted in global good practices. Since 2017, a data collection (survey) has been rolled out globally, and the participating operations conducted a household surveys to a sample of beneficiaries of each livelihoods project implemented by UNHCR and its partner. The dataset consists of baseline and endline data from the same sample beneficiaries, in order to compare before and after the project implementation and thus to measure the impact. More info is available on the official website: https://lis.unhcr.org
    This dataset updates: Never
  • Updated 4 March 2021 | Dataset date: November 20, 2020-November 20, 2020
    Data on access constraints, aid workers security, % of affected CERF and CBPF projects combined with the status of Polio vaccination in the HRP countries.
    200+ Downloads
    This dataset updates: As needed
  • Updated 24 November 2020 | Dataset date: January 01, 2000-December 31, 2020
    WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset. Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc-seconds (approximately 1km at the equator) -Unconstrained individual countries 2000-2020: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population count datasets by dividing the number of people in each pixel by the pixel surface area. These are produced using the unconstrained top-down modelling method. -Unconstrained individual countries 2000-2020 UN adjusted: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population UN adjusted count datasets by dividing the number of people in each pixel, adjusted to match the country total from the official United Nations population estimates (UN 2019), by the pixel surface area. These are produced using the unconstrained top-down modelling method. Data for earlier dates is available directly from WorldPop. WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00674
    100+ Downloads
    This dataset updates: Every year
  • Updated 24 November 2020 | Dataset date: January 01, 2000-December 31, 2020
    WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset. Bespoke methods used to produce datasets for specific individual countries are available through the WorldPop Open Population Repository (WOPR) link below. These are 100m resolution gridded population estimates using customized methods ("bottom-up" and/or "top-down") developed for the latest data available from each country. They can also be visualised and explored through the woprVision App. The remaining datasets in the links below are produced using the "top-down" method, with either the unconstrained or constrained top-down disaggregation method used. Please make sure you read the Top-down estimation modelling overview page to decide on which datasets best meet your needs. Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 3 and 30 arc-seconds (approximately 100m and 1km at the equator, respectively): - Unconstrained individual countries 2000-2020 ( 1km resolution ): Consistent 1km resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020. - Unconstrained individual countries 2000-2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020. - Unconstrained individual countries 2000-2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019) -Unconstrained individual countries 2000-2020 UN adjusted ( 1km resolution ): Consistent 1km resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019). -Unconstrained global mosaics 2000-2020 ( 1km resolution ): Mosaiced 1km resolution versions of the "Unconstrained individual countries 2000-2020" datasets. -Constrained individual countries 2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using constrained top-down methods for all countries of the World for 2020. -Constrained individual countries 2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using constrained top-down methods for all countries of the World for 2020 and adjusted to match United Nations national population estimates (UN 2019). Older datasets produced for specific individual countries and continents, using a set of tailored geospatial inputs and differing "top-down" methods and time periods are still available for download here: Individual countries and Whole Continent. Data for earlier dates is available directly from WorldPop. WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645
    200+ Downloads
    This dataset updates: Every year
  • Updated 9 September 2020 | Dataset date: January 01, 2018-December 31, 2018
    This dataset is compiled from two categories of sources: (a) verified security events submitted to Insecurity Insight by 30 Aid in Danger partner agencies; and (b) publicly reported events identified by Insecurity Insight and published in the Aid in Danger Monthly News Brief. Events are categorised by date, country, type of organisation affected and event category, based on standard definitions.
    600+ Downloads
    This dataset updates: As needed
  • Updated 4 September 2020 | Dataset date: August 31, 2020-August 31, 2020
    The new and emerging access constraints that people are currently experiencing because of the COVID-19 outbreak.
    300+ Downloads
    This dataset updates: As needed
  • Updated 2 September 2020 | Dataset date: August 31, 2020-August 31, 2020
    This dataset contains scores for humanitarian access constraints into country, constraints within country, impacts the constraints have led to as well as the mitigation strategies in place to limit the impact. The scores have the following interpretations: 0 = NA, 1 = No or open, 2 = partially open/closed, 3 = Yes or closed
    100+ Downloads
    This dataset updates: Every two weeks
  • Novel Corona Virus (COVID-19) epidemiological data since 22 January 2020. The data is compiled by the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) from various sources including the World Health Organization (WHO), DXY.cn, BNO News, National Health Commission of the People’s Republic of China (NHC), China CDC (CCDC), Hong Kong Department of Health, Macau Government, Taiwan CDC, US CDC, Government of Canada, Australia Government Department of Health, European Centre for Disease Prevention and Control (ECDC), Ministry of Health Singapore (MOH), and others. JHU CCSE maintains the data on the 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository on Github. Fields available in the data include Province/State, Country/Region, Last Update, Confirmed, Suspected, Recovered, Deaths. On 23/03/2020, a new data structure was released. The current resources for the latest time series data are: time_series_covid19_confirmed_global.csv time_series_covid19_deaths_global.csv time_series_covid19_recovered_global.csv ---DEPRECATION WARNING--- The resources below ceased being updated on 22/03/2020 and were removed on 26/03/2020: time_series_19-covid-Confirmed.csv time_series_19-covid-Deaths.csv time_series_19-covid-Recovered.csv
    409000+ Downloads
    This dataset updates: Live
  • UNOSAT code: FL20200626UKR This map illustrates satellite-detected surface waters in Ivano-Frankivska and Ternopilska Oblastof Ukraine as observed from a Sentinel-1 image acquired on 24 June 2020. Within the ana lyzed area of about 642 km2, a total of a bout 35 km2 of lands appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT .
    10+ Downloads
    This dataset updates: Never
  • Updated 29 June 2020 | Dataset date: January 01, 2019-December 31, 2020
    Operational presence of partners who does what where by oblast (admin1) from January to December 2019
    90+ Downloads
    This dataset updates: Every three months
  • Updated 29 June 2020 | Dataset date: January 01, 2000-December 31, 2020
    WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset. A description of the modelling methods used for age and sex structures can be found in Tatem et al and Pezzulo et al. Details of the input population count datasets used can be found here, and age/sex structure proportion datasets here. Both top-down 'unconstrained' and 'constrained' versions of the datasets are available, and the differences between the two methods are outlined here. The datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World structured by male/female and 5-year age classes (plus a <1 year class). These efforts necessarily involved some shortcuts for consistency. The unconstrained datasets are available for each year from 2000 to 2020. The constrained datasets are only available for 2020 at present, given the time periods represented by the building footprint and built settlement datasets used in the mapping. Data for earlier dates is available directly from WorldPop. WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00646
    60+ Downloads
    This dataset updates: Every year
  • Updated 6 May 2020 | Dataset date: January 01, 2020-December 31, 2020
    This data contains the number of people in need, internally displaced persons (IDPs), returnees and refugees for 25 countries.
    1400+ Downloads
    This dataset updates: Every year
  • Updated 6 May 2020 | Dataset date: March 26, 2020-March 26, 2020
    Data on unmitigated(no intervention) COVID-19 scenarios for OCHA HRP countries. Simulation done by Imperial College London.
    1200+ Downloads
    This dataset updates: As needed
  • Updated 30 April 2020 | Dataset date: May 01, 2018-May 01, 2018
    Ukraine education facilities in Donetska and Luhanska
    20+ Downloads
    This dataset updates: As needed
  • Updated 21 March 2020 | Dataset date: January 01, 2007-December 31, 2007
    Contains data from the DHS data portal. There is also a dataset containing Ukraine - National Demographic and Health Data on HDX. The DHS Program Application Programming Interface (API) provides software developers access to aggregated indicator data from The Demographic and Health Surveys (DHS) Program. The API can be used to create various applications to help analyze, visualize, explore and disseminate data on population, health, HIV, and nutrition from more than 90 countries.
    2200+ Downloads
    This dataset updates: Every year
  • Updated 21 March 2020 | Dataset date: January 01, 2007-December 31, 2007
    Contains data from the DHS data portal. There is also a dataset containing Ukraine - Subnational Demographic and Health Data on HDX. The DHS Program Application Programming Interface (API) provides software developers access to aggregated indicator data from The Demographic and Health Surveys (DHS) Program. The API can be used to create various applications to help analyze, visualize, explore and disseminate data on population, health, HIV, and nutrition from more than 90 countries.
    2900+ Downloads
    This dataset updates: Every year
  • Updated 16 March 2020 | Dataset date: December 31, 2018-December 31, 2018
    This dataset contains the affected populations, people in need and targeted populations by sector, region and disaggregated by sex and age. The dataset is produced by the United Nations for the Coordination of Humanitarian Affairs (OCHA) in collaboration with humanitarian partners.
    400+ Downloads
    This dataset updates: Every year
  • Updated 11 October 2019 | Dataset date: January 22, 2018-December 14, 2018
    In 2016, REACH with the support of OFDA conducted the first area based assessment (ABA) in Ukraine focusing on access to basic services for the 100 government controlled cities and villages along the line of contact seperating the government controlled (GCA) and non government controlled areas (NGCA) of the Donetsk and Luhansk oblasts. The assessment found that access to basic services in conflict-affected areas the assessed communities had been severely disrupted due to the disconnection between peripheries of large cities Donbas and the city centers now in non-governmental controlled areas (NGCA). As a result of the seperation between GCA and NGCA, settlements along the line of contact in the GCA have reorganised themselves inward towards government controlled urban centers. The networks of communities that access basic services unit geographies in government controlled areas have adjusted to the reality of a disconnection with non-government controlled cities by accessing all services and markets in reorganizing into new basic service units surrounding cities controlled by the Ukrainian Government. Based on findings from the area based assessment (ABA), this reorganization of basic services units in Donetsk and Luhansk Oblastsbas increases pressure on administrative services, housing, education and health services due to multiple potential factors including: i) the departure of qualified personnel ii) the arrival of conflict-displaced populations iii) and the relocation of of administrative centers. In order to support effective recovery and longer-term development planning it is critical to understand how the conflict has brought new challenges to service delivery in GCA urban centers close to the line of contact, particularly with regards to the new population flows following the effective closure of large urbanized areas in the NGCA. General Objective To understand the gaps and in the provision of basic services (by service providers) and the barriers to accessing basic services (by households) in raions that have been been separated by the contact line in Donetsk and Luhansk oblasts.
    This data is by request only