Ireland Humanitarian Data | Crisis Response Datasets

Ireland - Human Development Indicators

UNDP Human Development Reports Office (HDRO)

Time Period of the Dataset [?]: January 01, 1990-December 31, 2022 ... More

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

This dataset is part of the data series [?]: UNDP Human Development Reports Office - Human Development Indicators

CSV

Ireland - Age and Sex Structures (2015-2030)

WorldPop

Time Period of the Dataset [?]: January 01, 2015-December 31, 2030 ... More

Constrained estimates of total number of people per grid square broken down by gender and age groupings (including 0-1 and by 5-year up to 90+) for Ireland, version v1. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are estimated number of male, female or both in each age group per grid square. The difference between constrained and unconstrained you can read on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained File Descriptions: {iso} {gender} {age group} {year} {type} {resolution}.tif iso Three-letter country code gender m = male, f= female, t = both genders age group 00 = age group 0 to 12 months 01 = age group 1 to 4 years 05 = age group 5 to 9 years 90 = age 90 years and over year Year that the population represents type CN = Constrained , UC= Unconstrained resolution Resolution of the data e.q. 100m = 3 arc (approximately 100m at the equator)

This dataset updates: Every year

GeoTIFF

Ireland - Spatial Distribution of Population (2015-2030)

WorldPop

Time Period of the Dataset [?]: January 01, 2015-December 31, 2030 ... More

Constrained estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution. The difference between constrained and unconstrained you can read on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

This dataset updates: Every year

GeoTIFF

Ireland: Road Surface Data

HeiGIT (Heidelberg Institute for Geoinformation Technology)

Time Period of the Dataset [?]: November 07, 2024-November 07, 2024 ... More

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between **paved** and **unpaved** surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the [paper](http://arxiv.org/abs/2410.19874) Roughly 0.2076 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.0592 and 0.009 (in million kms), corressponding to 28.5262% and 4.3441% respectively of the total road length in the dataset region. 0.1393 million km or 67.1297% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0008 million km of information (corressponding to 0.6061% of total missing information on road surface) It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications. This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications. AI features: pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved." pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved). osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved." combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved." combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved." n_of_predictions_used: Number of predictions used for the feature length estimation. predicted_length: Predicted length based on the DL model’s estimations, in meters. DL_mean_timestamp: Mean timestamp of the predictions used, for comparison. OSM features may have these attributes(Learn what tags mean here): name: Name of the feature, if available in OSM. name:en: Name of the feature in English, if available in OSM. name:* (in local language): Name of the feature in the local official language, where available. highway: Road classification based on OSM tags (e.g., residential, motorway, footway). surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt). smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad). width: Width of the road, where available. lanes: Number of lanes on the road. oneway: Indicates if the road is one-way (yes or no). bridge: Specifies if the feature is a bridge (yes or no). layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels). source: Source of the data, indicating the origin or authority of specific attributes. Urban classification features may have these attributes: continent: The continent where the data point is located (e.g., Europe, Asia). country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States). urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban) urban_area: Name of the urban area or city where the data point is located. osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature. osm_type: Type of OSM element (e.g., node, way, relation). The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer. This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information. We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

This dataset updates: Every six months

This dataset is part of the data series [?]: Heidelberg Institute for Geoinformation Technology - Road Surface Data

Coronavirus (COVID-19) Vaccinations

HDX

Time Period of the Dataset [?]: December 17, 2020-January 11, 2024 ... More

The map and chart below show the number of COVID-19 vaccination doses administered per 100 people within a given population. Note that this does not measure the total number of people that have been vaccinated (which is usually two doses).

6300+ Downloads

This dataset updates: Never

CSV

Total COVID-19 Tests Performed by Country

HDX

Time Period of the Dataset [?]: January 18, 2020-January 11, 2024 ... More

'Our World in Data' is compiling COVID-19 testing data over time for many countries around the world. They are adding further data in the coming days as more details become available for other countries. In some cases figures refer to the number of tests, in other cases to the number of individuals who have been tested. Refer to documentation provided here.

22000+ Downloads

This dataset updates: Every week

XLSX
CSV

Ireland - Education Indicators

UNESCO

Time Period of the Dataset [?]: January 01, 1970-December 31, 2023 ... More

Education indicators for Ireland. Contains data from the UNESCO Institute for Statistics bulk data service covering the following categories: SDG 4 Global and Thematic (made 2024 February), Other Policy Relevant Indicators (made 2024 February), Demographic and Socio-economic (made 2024 February)

300+ Downloads

This dataset updates: Every three months

This dataset is part of the data series [?]: UNESCO - Education Indicators

CSV

Ireland - Internal Displacements (New Displacements) – IDPs

Internal Displacement Monitoring Centre (IDMC)

Time Period of the Dataset [?]: January 01, 2015-December 31, 2023 ... More

Internally displaced persons are defined according to the 1998 Guiding Principles as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border. "Internally displaced persons - IDPs" refers to the number of people living in displacement as of the end of each year. "Internal displacements (New Displacements)" refers to the number of new cases or incidents of displacement recorded, rather than the number of people displaced. This is done because people may have been displaced more than once. Contains data from IDMC's Global Internal Displacement Database.

100+ Downloads

This dataset updates: Every year

This dataset is part of the data series [?]: IDMC - Internally displaced persons

CSV

Ireland: WOF Administrative Subdivisions and Human Settlements

Who's On First

Time Period of the Dataset [?]: August 18, 2015-March 06, 2024 ... More

This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes: - macroregion (admin-1 including region) - region (admin-2 including state, province, department, governorate) - macrocounty (admin-3 including arrondissement) - county (admin-4 including prefecture, sub-prefecture, regency, canton, commune) - localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb) The dataset also contains human settlement points and polygons for: - localities (city, town, and village) - neighbourhoods (borough, macrohood, neighbourhood, microhood) The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names. Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.

10+ Downloads

This dataset updates: Every year

This dataset is part of the data series [?]: WOF - Administrative Subdivisions and Human Settlements

SHP

Ireland: Population Density for 400m H3 Hexagons

Kontur

Time Period of the Dataset [?]: July 10, 2023-November 01, 2023 ... More

Ireland population density for 400m H3 hexagons. Built from Kontur Population: Global Population Density for 400m H3 Hexagons Vector H3 hexagons with population counts at 400m resolution. Fixed up fusion of GHSL, Facebook, Microsoft Buildings, Copernicus Global Land Service Land Cover, Land Information New Zealand, and OpenStreetMap data.

50+ Downloads

This dataset updates: As needed

This dataset is part of the data series [?]: Kontur - Population Density for 400m H3 Hexagons

Geopackage

Ireland: Administrative Division with Aggregated Population

Kontur

Time Period of the Dataset [?]: April 07, 2022-April 07, 2022 ... More

Ireland administrative division with aggregated population. Built from Kontur Population: Global Population Density for 400m H3 Hexagons on top of OpenStreetMap administrative boundaries data. Enriched with HASC codes for regions taken from Wikidata. Global version of boundaries dataset: Kontur Boundaries: Global administrative division with aggregated population

50+ Downloads

This dataset updates: As needed

This dataset is part of the data series [?]: Kontur - Administrative Division with Aggregated Population

Geopackage

Europe: Coronavirus (COVID-19) Subnational Cases

HDX

Time Period of the Dataset [?]: August 02, 2021-April 18, 2025 ... More

This dataset contains the number of confirmed cases, recoveries and deaths by country and subnational region due to the Coronavirus pandemic in Europe. Since the outbreak of the COVID-19 crisis, the Joint Research Centre (JRC) has been supporting the European Commission in multidisciplinary areas to understand the COVID-19 emergency, anticipate its impacts, and support contingency planning. This data provides an overview of the monitoring in the area of the 34 UCPM Participating States plus Switzerland related to sub-national data (admin level 1) on numbers of contagious and fatalities by COVID-19, collected directly from the National Authoritative sources (National monitoring websites, when available). The sub-national granularity of the data allows to have a fit-for-purpose model to early capture the local spread and response to the COVID-19 outbreak. The data is maintained on the JRC COVID-19 Github Repository

3300+ Downloads

This dataset updates: As needed

This dataset is part of the data series [?]: HDX - COVID-19 Subnational Cases

CSV

Facebook Business Activity Trends during COVID19

Data for Good at Meta

Time Period of the Dataset [?]: March 01, 2020-May 15, 2023 ... More

Business Activity Trends During COVID-19 uses the rate that businesses post on Facebook compared to pre-crisis levels to measure how crisis events are affecting different economic sectors each day. Learn more details here: https://dataforgood.facebook.com/dfg/tools/business-activity-trends and https://dataforgood.facebook.com/dfg/resources/business-activity-trends-methodology-paper

500+ Downloads

This dataset updates: As needed

CSV

Small Business Surveys - Aggregated Data

Data for Good at Meta

Time Period of the Dataset [?]: September 01, 2016-March 24, 2023 ... More

More than 200 million businesses use Facebook globally. The goal of Meta’s quarterly Small Business Surveys is to learn about the unique perspectives, challenges and opportunities of small and medium-sized businesses (SMBs). The Future of Business (FoB) Survey is conducted biannually in partnership with the World Bank and the Organisation for Economic Cooperation and Development (OECD) across nearly 100 countries. The target population consists of SMEs that have an active Facebook Business Page and include both newer and longer-standing businesses, spanning across a variety of sectors. Meta also conducts the Global State of Small Business (GSoSB) Survey bi-annually in partnership with various academic partners across approximately 30 countries. Similarly to the FoB Survey, the target population is active Facebook Page Administrators, but also includes the general population of Facebook users. Survey questions for all surveys cover a range of topics depending on the survey wave such as business characteristics, challenges, financials and strategy in addition to custom modules related to regulation, gender inequity, access to finance, digital technologies, reduction in revenues, business closures, international trade, inflation, reduction of employees and challenges/needs of the business. Aggregated country level data for each survey wave is available to the public on HDX and controlled access microdata is available to Data for Good at Meta partners. Please visit https://dataforgood.facebook.com/dfg/tools/future-of-business-survey to apply for access to microdata or contact dataforgood@fb.com for any questions.

18000+ Downloads

This dataset updates: Every three months

CSV
PDF

Facebook Commuting Zones

Data for Good at Meta

Time Period of the Dataset [?]: July 04, 2021-March 15, 2023 ... More

Commuting zones are geographic areas where people live and work and are useful for understanding local economies, as well as how they differ from traditional boundaries. Learn more here: https://dataforgood.facebook.com/dfg/tools/commuting-zones

1800+ Downloads

This dataset updates: As needed

CSV

Movement Range Maps

Data for Good at Meta

Time Period of the Dataset [?]: March 01, 2020-May 24, 2022 ... More

NOTE: We plan to no longer update this dataset after May 22 2022. These data sets are intended to inform researchers and public health experts about how populations are responding to physical distancing measures. In particular, there are two metrics, Change in Movement and Stay Put, that provide a slightly different perspective on movement trends. Change in Movement looks at how much people are moving around and compares it with a baseline period that predates most social distancing measures, while Stay Put looks at the fraction of the population that appear to stay within a small area during an entire day. Full details, including the privacy protections in this data, are available here: https://research.fb.com/blog/2020/06/protecting-privacy-in-facebook-mobility-data-during-the-covid-19-response/

61000+ Downloads

This dataset updates: As needed

TXT
zip

Survey on Gender Equality At Home

Data for Good at Meta

Time Period of the Dataset [?]: September 21, 2020-January 14, 2022 ... More

This data contains aggregated weighted statistics at the regional level by gender for the 2020 Survey on Gender Equality At Home as well as the country and regional level for the 2021 wave. The Survey on Gender Equality at Home generates a global snapshot of women and men’s access to resources, their time spent on unpaid care work, and their attitudes about equality. Researchers and nonprofits interested in access to survey microdata can apply at: https://dataforgood.facebook.com/dfg/tools/survey-on-gender-equality-at-home

5200+ Downloads

This dataset updates: As needed

XLSX

Novel Coronavirus (COVID-19) Cases Data

Johns Hopkins University Center for Systems Science and Engineering

Time Period of the Dataset [?]: January 22, 2020-March 09, 2023 ... More

JHU Has Stopped Collecting Data As Of 03/10/2023 After three years of around-the-clock tracking of COVID-19 data from around the world, Johns Hopkins has discontinued the Coronavirus Resource Center’s operations. The site’s two raw data repositories will remain accessible for information collected from 1/22/20 to 3/10/23 on cases, deaths, vaccines, testing and demographics. 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

304000+ Downloads

This dataset updates: As needed

CSV

Airports in Ireland

OurAirports

Time Period of the Dataset [?]: January 01, 2008-April 18, 2025 ... More

List of airports in Ireland, with latitude and longitude. Unverified community data from http://ourairports.com/countries/IE/

10+ Downloads

This dataset updates: Live

This dataset is part of the data series [?]: Our Airports - Airports

CSV

Ireland: High Resolution Population Density Maps + Demographic Estimates

Data for Good at Meta

Time Period of the Dataset [?]: September 19, 2019-September 27, 2019 ... More

These high-resolution maps estimate not only the number of people living within 30-meter grid tiles, but also provide insights on demographics at unprecedentedly high resolutions. These maps aren’t built using Facebook data and instead rely on combining the power of machine vision AI with satellite imagery and census information.

1000+ Downloads

This dataset updates: As needed

GeoTIFF
CSV